Soft Computing and Signal Processing: Proceedings of ICSCSP 2018, Volume 1 [1st ed.] 978-981-13-3599-0, 978-981-13-3600-3

Table of contents :
Front Matter ....Pages i-xxii
A New Method for the Spectral Analysis of Unevenly Sampled Time Series (Steven M. Boswell, Alexandra L. Boghosian)....Pages 1-13
Analysis of Early Detection of Emerging Patterns from Social Media Networks: A Data Mining Techniques Perspective (Yadala Sucharitha, Y. Vijayalata, V. Kamakshi Prasad)....Pages 15-25
Initial Centroids for K-Means Using Nearest Neighbors and Feature Means (Muddana A. Lakshmi, Gera Victor Daniel, D. Srinivasa Rao)....Pages 27-34
Secured Cluster-Based Distributed Fault Diagnosis Routing for MANET (Vani Garikipati, N. Naga Malleswara Rao)....Pages 35-51
A Comparative Analysis of Unequal Clustering-Based Routing Protocol in WSNs (Tanmay Biswas, Sushil Kumar, Tapaswini Singh, Kapil Gupta, Deepika Saxena)....Pages 53-62
YouTube Video Ranking by Aspect-Based Sentiment Analysis on User Feedback (Ganpat Singh Chauhan, Yogesh Kumar Meena)....Pages 63-71
Diet Recommendation to Respiratory Disease Patient Using Decision-Making Approach (Prashant Gaurav, Sanjay Kumar Dubey)....Pages 73-81
Detection of False Positive Situation in Review Mining (Devottam Gaurav, Jay Kant Pratap Singh Yadav, Rohit Kumar Kaliyar, Ayush Goyal)....Pages 83-90
Optimization of Cloud Datacenter Using Heuristic Strategic Approach (Biswajit Nayak, Sanjay Kumar Padhi, Prasant Kumar Pattnaik)....Pages 91-100
Blockchain Technology for Decentralized Data Storage on P2P Network (Akshay Raul, Shwetha Kalyanaraman, Kshitij Yerande, Kailas Devadkar)....Pages 101-110
Comparative Analysis of Clustering Algorithms with Heart Disease Datasets Using Data Mining Weka Tool (Sarangam Kodati, R. Vivekanandam, G. Ravi)....Pages 111-117
A Machine Learning Approach for Web Intrusion Detection: MAMLS Perspective (Rajagopal Smitha, K. S. Hareesha, Poornima Panduranga Kundapur)....Pages 119-133
White Blood Cell Classification Using Convolutional Neural Network (Mayank Sharma, Aishwarya Bhave, Rekh Ram Janghel)....Pages 135-143
Analysis of Mobile Environment for Ensuring Cyber-Security in IoT-Based Digital Forensics (G. Maria Jones, S. Godfrey Winster, S. V. N. Santhosh Kumar)....Pages 145-152
Payment Security Mechanism of Intelligent Mobile Terminal (Seshathiri Dhanasekaran, Baskar Kasi)....Pages 153-164
Hybrid Neuro-fuzzy Method for Data Analysis of Brain Activity Using EEG Signals (Rajalakshmi Krishnamurthi, Mukta Goyal)....Pages 165-173
Gait Recognition Using J48-Based Identification with Knee Joint Movements (Jyoti Rana, Nidhi Arora, Dilendra Hiran)....Pages 175-186
Cyber Intelligence Alternatives to Offset Online Sedition by in-Website Image Analysis Through WebCrawler Cyberforensics (N. Santhoshi, K. Chandra Sekharaiah, K. Madan Mohan, S. Ravi Kumar, B. Malathi)....Pages 187-199
Deep Convolutional Neural Network-Based Diabetic Retinopathy Detection in Digital Fundus Images (S. Saranya Rubini, R. Saai Nithil, A. Kunthavai, Ashish Sharma)....Pages 201-209
A Framework for Semantic Annotation and Mapping of Sensor Data Streams Based on Multiple Linear Regression (K. Vijayaprabakaran, K. Sathiyamurthy)....Pages 211-222
Cyclostationarity Analysis of GPS Signals for Spoofing Detection (R. Lakshmi, S. M. Vaitheeswaran, K. Pargunarajan)....Pages 223-232
Implementation of Fingerprint-Based Authentication System Using Blockchain (Dipti Pawade, Avani Sakhapara, Melvita Andrade, Aishwarya Badgujar, Divya Adepu)....Pages 233-242
NSGLTLBOLE: A Modified Non-dominated Sorting TLBO Technique Using Group Learning and Learning Experience of Others for Multi-objective Test Problems (Jatinder Kaur, Surjeet Singh Chauhan, Pavitdeep Singh)....Pages 243-251
Homomorphic Encryption Scheme for Data Security in Cloud Using Compression Technique (D. K. Chandrashekar, K. C. Srikantaiah, K. R. Venugopal)....Pages 253-260
Efficient Query Clustering Technique and Context Well-Informed Document Clustering (Manukonda Sumathi Rani, Geddati China Babu)....Pages 261-271
Motif Shape Primitives on Fibonacci Weighted Neighborhood Pattern for Age Classification (P. Chandra Sekhar Reddy, P. Vara Prasad Rao, P. Kiran Kumar Reddy, M. Sridhar)....Pages 273-280
A Novel Virtual Tunneling Protocol for Underwater Wireless Sensor Networks (A. M. Viswa Bharathy, V. Chandrasekar)....Pages 281-289
Garbage Monitoring System Using Internet of Things (Arpan Patel, Nehal Patel)....Pages 291-298
Mobile Learning Recommender System Based on Learning Styles (Shivam Saryar, Sucheta V. Kolekar, Radhika M. Pai, M. M. Manohara Pai)....Pages 299-312
Privacy Sustaining Constant Length Ciphertext-Policy Attribute-Based Broadcast Encryption (G. Sravan Kumar, A. Sri Krishna)....Pages 313-324
A Comprehensive Study of Challenges and Issues in Cloud Computing (Shadab Siddiqui, Manuj Darbari, Diwakar Yagyasen)....Pages 325-344
Comparative Analysis of Major Jacobian and Gradient Backpropagation Optimizers of ANN on SVPWM (Neeraj Seth, Ashish Ubrani, Sneha Mane, Faruk A. S. Kazi)....Pages 345-357
Minimization of Energy Consumption in Wireless Sensor Networks by Using a Special Mobile Agent (S. Shanthi, Padmalaya Nayak, Sujatha Dandu)....Pages 359-368
Improved Wisdom of Crowds Heuristic for Solving Sudoku Puzzles (Neeraj Pathak, Rajeev Kumar)....Pages 369-377
An End-to-End Secure and Energy-Aware Routing Mechanism for IoT-Based Modern Health Care System (R. Nidhya, S. Karthik, G. Smilarubavathy)....Pages 379-388
Internet of Things: Present State of the Art, Applications, Protocols and Enabling Technologies (A. Anjaiah, A. Govardhan, M. Vazralu)....Pages 389-398
Implementation of Multithreaded BFS Using Bag Data Structure (Hemalatha Eedi, Mohd Abdul Rasheed)....Pages 399-408
Context-Aware Agents for IoT Services (K. Deeba, RA. K. Saravanaguru)....Pages 409-417
Multicriteria-Based Ranking Framework for Measuring Performance of Cloud Service Providers (K. S. Sendhil Kumar, N. Jaisankar)....Pages 419-427
An Optimized Computer Vision and Image Processing Algorithm for Unmarked Road Edge Detection (Jayalakshmi Annamalai, C. Lakshmikanthan)....Pages 429-437
Performance Analysis of EMTCMOS Technique-Based D Flip-Flop Design at Varied Supply Voltages and Distinct Submicron Technology (Patikineti Sreenivasulu)....Pages 439-448
Error Detection Using Counting Technique in Low-Power VLSI (Kumud Kumar Bhardwaj, T. Swapna Rani)....Pages 449-456
Adaptive Sampling Rate Converter for Wireless Sensor Networks (P. Swetha, S. Srinivasa Rao, P. Chandrasekhar Reddy)....Pages 457-466
Improvement of Signal-to-Noise Ratio for MST Radar Using Weighted Semi-parametric Algorithm (C. Raju, T. Sreenivasulu Reddy)....Pages 467-476
A Robust DCT-SVD Based Video Watermarking Using Zigzag Scanning (K. Meenakshi, K. Swaraja, Padmavathi Kora)....Pages 477-485
Digitization and Parameter Extraction of Preserved Paper Electrocardiogram Records (Rupali Patil, Ramesh Karandikar)....Pages 487-495
Segmentation and Classification of CT Renal Images Using Deep Networks (Anil Kumar Reddy, Sai Vikas, R. Raghunatha Sarma, Gurudat Shenoy, Ravi Kumar)....Pages 497-506
A Novel Traffic Sign Recognition System Combining Viola–Jones Framework and Deep Learning (Ajay Jose, Harish Thodupunoori, Binoy B. Nair)....Pages 507-517
Detection of Cardiac Arrhythmia Using Convolutional Neural Network (Padmavathi Kora, K. Meenakshi, K. Swaraja)....Pages 519-526
Dual-Function Radar-Communication Using Neural Network (K. S. Anjali, G. Prabha)....Pages 527-539
Patient Nonspecific Epilepsy Detection Using EEG (Sandeep Banerjee, Varun Alur, Divya Shah)....Pages 541-548
Power Efficient PUF-Based Random Reseeding True Random Number Generator (Anirudh Siripragada, R. Shiva Prasad, N. Mohankumar)....Pages 549-559
Edge Cut Dual-Band Slot Antenna for Bluetooth/WLAN and WiMAX Applications (J. Rajeshwar Goud, N. V. Koteswara Rao, A. Mallikarjuna Prasad)....Pages 561-570
A Complete End-to-End System for Iris Recognition to Mitigate Replay and Template Attack (Richa Gupta, Priti Sehgal)....Pages 571-582
Tampering Detection in Digital Audio Recording Based on Statistical Reverberation Features (Tejas Bhangale, Rashmika Patole)....Pages 583-591
Acoustic Scene Identification for Audio Authentication (Meenal Narkhede, Rashmika Patole)....Pages 593-602
Retinal Blood Vessel Extraction Using Morphological Operators and Kirsch’s Template (Jyotiprava Dash, Nilamani Bhoi)....Pages 603-611
Wire Load Variation-Based Hardware Trojan Detection Using Machine Learning Techniques (N. Suresh Babu, N. Mohankumar)....Pages 613-623
A Neural Network Approach for Content-Based Image Retrieval Using Moments of Image Transforms (D. Kishore, S. Srinivas Kumar, Ch. Srinivasa Rao)....Pages 625-633
Probe-Fed Wideband Implantable Microstrip Patch Antenna for Biomedical and Telemetry Applications (Komal Jaiswal, Ankit Kumar Patel, Shekhar Yadav, Sweta Singh, Ram Suchit Yadav, Rajeev Singh)....Pages 635-642
Mapping Urban Ecosystem Services Using Synthetic-Aperture Radar (SAR) Images from Satellite Data for Rural Microgrids in India (Prem Raheja, Surmeet Kaur Jhajj, Purva Jhaveri, Jignesh Sisodia)....Pages 643-653
Analysis of Denoising Filters for Source Identification Using PRNU Features (Nadia Siddiqui, Syeda Shira Moin, Saiful Islam)....Pages 655-663
Advanced Protection for Automobiles Using MSP430 (C. Ravi Shankar Reddy, V. Siva Kumar Reddy, T. Vinay Simha Reddy, P. Sanjeeva Reddy)....Pages 665-672
Performance Analysis of KNN Classifier with Various Distance Metrics Method for MRI Images (Karthick Ganesan, Harikumar Rajaguru)....Pages 673-682
Comparison of Low Current Mismatch CMOS Charge Pumps for Analog PLLs Using 180 nm Technology (Alan Saldanha, Vijil Gupta, Vinod Kumar Joshi)....Pages 683-692
Optimized Node Swapping for Efficient Energy Usage in Heterogeneous Network (Satyanarayan K. Padaganur, Jayashree D. Mallapur)....Pages 693-701
Content-Based Video Shot Boundary Detection Using Multiple Haar Transform Features (D. Asha, Y. Madhavee Latha)....Pages 703-713
An Analysis of IPv6 Protocol Implementation for Secure Data Transfer in Cloud Computing (Anitha Patibandla, G. S. Naveen Kumar, Anusha Meneni)....Pages 715-721
Anomaly Detection in Crowd Using Optical Flow and Textural Feature (Pranali Ingole, Vibha Vyas)....Pages 723-732
Automatic Tonic (Shruti) Identification System for Indian Classical Music (Mahesh Y. Pawar, Shrinivas Mahajan)....Pages 733-742
Single-Plane Scene Classification Using DeepConvolution Features (Nikhil Damodaran, V. Sowmya, D. Govind, K. P. Soman)....Pages 743-752
A Novel Methodology for Multiplication of Three n-Bit Binary Numbers (Anirban Mukherjee, Niladri Hore, Vinay Kumar)....Pages 753-759
Speed-Breaker Early Warning System Using 77 GHz Long-Range Automotive Radar (Umarani Deevela, Swapna Raghunath, Srinivasa Rao Katuri)....Pages 761-768
Face Recognition using InvariantFeatureVectorsandEnsemble of Classifiers (A. Vinay, Abhijay Gupta, Harsh Garg, Aprameya Bharadwaj, Arvind Srinivas, K. N. Balasubramanya Murthy et al.)....Pages 769-779
Pattern and Frequency Reconfigurable MSA for Wireless Applications (Deeplaxmi V. Niture, Chandrakant S. Patond, S. P. Mahajan)....Pages 781-791
Feature Fusion and Classification of EEG/EOG Signals (Ayushi Mishra, Vikrant Bhateja, Aparna Gupta, Apoorva Mishra, Suresh Chandra Satapathy)....Pages 793-799
High-Efficiency Video Coding De-blocking Filter: Through Content-Split Block Search Algorithm (Perla Anitha, P. Sudhakara Reddy, M. N. Giri Prasad)....Pages 801-809
Key Frame Extraction Using Content Relative Thresholding Technique for Video Retrieval (K. Mallikharjuna Lingam, V. S. K. Reddy)....Pages 811-820
Back Matter ....Pages 821-823

Citation preview

Advances in Intelligent Systems and Computing 900

900th Volume of AISC · 900th Volume of AISC · 900th Volume of AISC · 900th Volume of AISC · 900th Vo

Jiacun Wang G. Ram Mohana Reddy V. Kamakshi Prasad V. Sivakumar Reddy Editors

Soft Computing and Signal Processing Proceedings of ICSCSP 2018, Volume 1

Advances in Intelligent Systems and Computing Volume 900

Series editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.

Advisory Board Chairman Nikhil R. Pal, Indian Statistical Institute, Kolkata, India e-mail: [email protected] Members Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba e-mail: [email protected] Emilio S. Corchado, University of Salamanca, Salamanca, Spain e-mail: [email protected] Hani Hagras, School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK e-mail: [email protected] László T. Kóczy, Department of Information Technology, Faculty of Engineering Sciences, Győr, Hungary e-mail: [email protected] Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA e-mail: [email protected] Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan e-mail: [email protected] Jie Lu, Faculty of Engineering and Information, University of Technology Sydney, Sydney, NSW, Australia e-mail: [email protected] Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico e-mail: [email protected] Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil e-mail: [email protected] Ngoc Thanh Nguyen, Wrocław University of Technology, Wrocław, Poland e-mail: [email protected] Jun Wang, Department of Mechanical and Automation, The Chinese University of Hong Kong, Shatin, Hong Kong e-mail: [email protected]

More information about this series at http://www.springer.com/series/11156

Jiacun Wang G. Ram Mohana Reddy V. Kamakshi Prasad V. Sivakumar Reddy •

•

Editors

Soft Computing and Signal Processing Proceedings of ICSCSP 2018, Volume 1

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Editors Jiacun Wang Department of Computer Science and Software Engineering Monmouth University West Long Branch, NJ, USA G. Ram Mohana Reddy Department of Information Technology National Institute of Technology Karnataka Surathkal, Mangaluru, Karnataka, India

V. Kamakshi Prasad Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad Hyderabad, Telangana, India V. Sivakumar Reddy Department of Electronics and Communication Engineering Malla Reddy College of Engineering and Technology Secunderabad, Telangana, India

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-13-3599-0 ISBN 978-981-13-3600-3 (eBook) https://doi.org/10.1007/978-981-13-3600-3 Library of Congress Control Number: 2018962132 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organizing Committee

Chief Patron Sri. Ch. Malla Reddy Hon’ble MP, Government of India Founder Chairman, MRGI

Patrons Sri. Ch. Mahendar Reddy, Secretary, MRGI Sri. Ch. Bhadra Reddy, President, MRGI

Conference Chair Dr. V. S. K. Reddy, Principal

Publication Chair Dr. Suresh Chandra Satapathy, Professor, KIIT, Bhubaneswar

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Organizing Committee

Convener Prof. P. Sanjeeva Reddy, Director, ECE and EEE

Organizing Chair Dr. M. Murali Krishna, Dean, Academics

Organizing Secretaries Dr. S. Srinivasa Rao, HOD, ECE Dr. D. Sujatha, HOD, CSE Dr. G. Sharada, HOD, IT

Session Chairs Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr.

C. Suchismita, Professor, NIT Rourkela Ram Murthy Garimella, Professor, IIIT Hyderabad Chandra Sekhar, Professor, Osmania University Mohammed Arifuddin Sohel, Professor, Muffakham Jah CET Samrat Lagnajeet Sabat, Professor, HCU Malla Rama Krishna Murty, Professor, ANITS, Visakhapatnam Mohana Sundaram, Professor, VIT, Vellore Suresh Kumar Nagarajan, Professor, VIT, Vellore

Coordinators Dr. S. Shanthi, Professor, CSE Dr. N. S. Gowri Ganesh, Professor, IT Dr. V. Chandrasekar, Professor, CSE Mr. G. S. Naveen Kumar, Associate Professor, ECE Mr. K. Mallikarjuna Lingam, Associate Professor, ECE Mr. M. Vazralu, Associate Professor, IT

Organizing Committee

Organizing Committee Prof. K. Kailasa Rao, Director, CSE and IT Prof. K. Subhas, Professor and Head, EEE Dr. B. Jyothi, Associate Professor, ECE Dr. Pujari Lakshmi Devi, Professor, ECE Dr. C. Ravishankar Reddy, Professor, ECE Dr. Ajeet Kumar Pandey, Professor, CSE Dr. A. Mummoorthy, Professor, IT Dr. V. M. Senthil Kumar, Professor, ECE Dr. Murugeshan Rajamanickam, Professor, ECE Sri. B. Rajeswar Reddy, Administrative Officer

Web Developer Mr. K. Sudhakar Reddy, Assistant Professor, IT

Proceedings Committee Dr. Sucharitha Manikandan, Associate Professor, ECE Ms. P. Anitha, Associate Professor, ECE Ms. M. Gayatri, Associate Professor, CSE Ms. D. Asha, Assistant Professor, ECE Mr. T. Vinay Simha Reddy, Assistant Professor, ECE Mr. N. Sivakumar, Assistant Professor, CSE

Technical Program Committee Dr. E. Venkateshwar Reddy, Professor, CSE Dr. R. Roopa Chandrika, Professor, IT Dr. A. Mummoorthy, Professor, IT Mr. M. Sandeep, Associate Professor, CSE Mr. M. Ramanjaneyulu, Associate Professor, ECE Mr. K. Murali Krishna, Associate Professor, ECE Mr. N. Ramesh, Associate Professor, EEE Mr. K. Srikanth, Associate Professor, CSE Mr. P. Bikshapathy, Associate Professor, CSE Mr. D. Chandrasekhar Reddy, Associate Professor, CSE

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Mr. M. Sambasivudu, Associate Professor, CSE Mr. M. Jaypal, Associate Professor, CSE Ms. J. Suneetha, Associate Professor, IT

Publicity Committee Ms. D. Radha, Associate Professor, CSE Mr. Ch. Kiran Kumar, Assistant Professor, ECE Ms. P. Swetha, Associate Professor, ECE Mr. R. Chinna Rao, Assistant Professor, ECE Ms. Arthi Jeyakumari, Assistant Professor, CSE Mr. P. Raji Reddy, Assistant Professor, EEE Mr. K. D. K. Ajay, Assistant Professor, ECE Ms. Renju Panicker, Assistant Professor, ECE Mr. K. L. N. Prasad, Assistant Professor, ECE Mr. T. Srinivas, Assistant Professor, ECE Ms. R. Sujatha, Assistant Professor, CSE Mr. A. Yogananda, Assistant Professor, IT

Registration Committee Ms. M. Anusha, Assistant Professor, ECE Mr. K. Suresh, Assistant Professor, ECE Mr. V. Shiva Raja Kumar, Assistant Professor, ECE Ms. B. Srujana, Assistant Professor, ECE Ms. D. Kalpana, Assistant Professor, CSE Mr. S. Vishwanath Reddy, Assistant Professor, CSE Mr. Naresh, Assistant Professor, CSE

Hospitality Committee Mr. A. Syam Prasad, Associate Professor, CSE Mr. G. Ravi, Associate Professor, CSE Mr. P. Srinivas Rao, Associate Professor, IT Mr. M. Venu, Assistant Professor, CSE Ms. Novy Jacob, Assistant Professor, IT Mr. M. Anantha Gupta, Assistant Professor, ECE Mr. G. Sekhar Babu, Assistant Professor, EEE Mr. S. Rakesh, Assistant Professor, EEE

Organizing Committee

Organizing Committee

Mr. B. Mahendar, Assistant Professor, IT Mr. P. Harikrishna, Assistant Professor, IT Ms. W. Nirmala, Assistant Professor, CSE Ms. Shruthi Rani Yadav, Assistant Professor, CSE Ms. V. Alekya, Assistant Professor, CSE Ms. G. Shamini, Assistant Professor, CSE Mr. Naveen, Assistant Professor, CSE

Certificate Committee Mr. M. Sreedhar Reddy, Associate Professor, ECE Ms. S. Rajani, Assistant Professor, ECE Ms. M. Hima Bindu, Assistant Professor, ECE Mr. Manoj Kumar, Assistant Professor, CSE Mr. K. Srinivas, Assistant Professor, CSE Ms. Srilakshmi, Assistant Professor, IT

Decoration Committee Mr. M. Anantha Gupta, Assistant Professor, ECE Ms. N. Saritha, Assistant Professor, ECE Mr. O. Saidulu Reddy, Assistant Professor, EEE Mr. B. Srinivasa Rao, Assistant Professor, EEE Ms. M. Nagma, Assistant Professor, ECE Ms. D. Kavitha, Assistant Professor, ECE Mr. Maheswari, Assistant Professor, ECE Ms. Honey Diana, Assistant Professor, CSE Ms. K. Swetha, Assistant Professor, IT Ms. Sireesha, Assistant Professor, CSE Mr. Y. Dileep Babu, Assistant Professor, CSE

Transportation Committee Mr. Mr. Mr. Mr. Mr. Mr.

V. Kamal, Associate Professor, CSE P. Dileep, Associate Professor, CSE G. Ravi, Associate Professor, CSE M. Arun Kumar, Assistant Professor, ECE E. Mahender Reddy, Assistant Professor, ECE Saleem, Assistant Professor, CSE

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Organizing Committee

International and National Advisory Committee Dr. Heggere Ranganath, Chair of CS, The University of Alabama in Huntsville, USA Dr. Someswar Kesh, Professor, Department of CISA, University of Central Missouri, USA Mr. Alex Wong, Senior Technical Analyst, Diligent Inc., USA Dr. Bhaskar Kura, Professor, University of New Orleans, USA Dr. Ch. Narayana Rao, Scientist, Denver, Colorado, USA Dr. Arun Kulkarni, Professor, University of Texas at Tyler, USA Dr. Sam Ramanujan, Professor, Department of CIS and IT, University of Central Missouri, USA Dr. Richard H. Nader, Associate Vice President, Mississippi State University, USA Prof. Peter Walsh, Head of the Department, Vancouver Film School, Canada Dr. Ram Balalachandar, Professor, University of Windsor, Canada Dr. Asoke K. Nandi, Professor, Department of EEE, University of Liverpool, UK Dr. Vinod Chandran, Professor, Queensland University of Technology, Australia Dr. Amiya Bhaumik, Vice Chancellor, Lincoln University College, Malaysia Prof. Soubarethinasamy, UNIMAS International, Malaysia Dr. Sinin Hamdan, Professor, UNIMAS Dr. Hushairi bin Zen, Professor, ECE, UNIMAS Dr. Bhanu Bhaskara, Professor, Majmaah University, Saudi Arabia Dr. Narayanan, Director, ISITI, CSE, UNIMAS Dr. Koteswararao Kondepu, Research Fellow, Scuola Superiore Sant’Anna, Pisa, Italy Shri. B. H. V. S. Narayana Murthy, Director, RCI, Hyderabad Prof. P. K. Biswas, Head, Department of E & ECE, IIT Kharagpur Dr. M. Ramasubba Reddy, Professor, IIT Madras Prof. N. C. Shiva Prakash, Professor, IISc, Bangalore Dr. B. Lakshmi, Professor, Department of ECE, NIT Warangal Dr. Y. Madhavee Latha, Professor, Department of ECE, MRECW, Hyderabad

Preface

The International Conference on Soft Computing and Signal Processing (ICSCSP 2018) was successfully organized by Malla Reddy College of Engineering and Technology, an UGC autonomous institution, during June 22–23, 2018, at Hyderabad. The objective of this conference was to provide opportunities for the researchers, academicians, and industry persons to interact and exchange the ideas, experience, and gain expertise in the cutting-edge technologies pertaining to soft computing and signal processing. Research papers in the above-mentioned technology areas were received and subjected to a rigorous peer review process with the help of program committee members and external reviewers. ICSCSP 2018 received a total of 574 papers, each paper was reviewed by more than two reviewers, and finally, 156 papers were accepted for publication in two separate volumes in Springer AISC series. We would like to express our sincere thanks to Chief Guest Dr. S. B. Gadgil, Outstanding Scientist, Associate Director, RCI, DRDO, and keynote speakers Mr. Aninda Bose, Senior Editor, Springer Nature; Dr. C. Suchismita, Professor, NIT Rourkela; and Dr. Rishu Gupta, Senior Application Engineer, MathWorks, India. We would like to express our gratitude to all the session chairs, viz., Dr. Ram Murthy Garimella, IIIT Hyderabad; Dr. Chandra Sekhar, Osmania University; Dr. Mohammed Arifuddin Sohel, Muffakham Jah College of Engineering and Technology; Dr. Samrat Lagnajeet Sabat, HCU; Dr. Malla Rama Krishna Murty, ANITS, Visakhapatnam; Dr. Mohana Sundaram, VIT, Vellore; and Dr. Suresh Kumar Nagarajan, VIT, Vellore, for extending their support and cooperation. We are indebted to the program committee members and external reviewers who have produced critical reviews in a short time. We would like to express our special gratitude to Publication Chair Dr. Suresh Chandra Satapathy, KIIT, Bhubaneswar, for his valuable support and encouragement till the successful conclusion of the conference. We express our heartfelt thanks to our Chief Patron Sri. Ch. Malla Reddy, Founder Chairman, MRGI; Patrons Sri. Ch. Mahendar Reddy, Secretary, MRGI;

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Sri. Ch. Bhadra Reddy, President, MRGI; Convener Prof. P. Sanjeeva Reddy, Director, ECE and EEE; and Organizing Chair Dr. M. Murali Krishna, Dean. We would also like to thank the organizing secretaries, viz., Dr. S. Srinivasa Rao, HOD, ECE; Dr. D. Sujatha, HOD, CSE; and Dr. G. Sharada, HOD, IT, for their valuable contributions. Our thanks also to all the coordinators and the organizing committee as well as all the other committee members for their contributions in the successful conduct of the conference. Last but not least, our special thanks to all the authors without whom the conference would not have taken place. Their technical contributions have made our proceedings rich and praiseworthy. West Long Branch, NJ, USA Surathkal, India Hyderabad, India Hyderabad, India

Jiacun Wang G. Ram Mohana Reddy V. Kamakshi Prasad V. Sivakumar Reddy

Contents

A New Method for the Spectral Analysis of Unevenly Sampled Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Steven M. Boswell and Alexandra L. Boghosian

1

Analysis of Early Detection of Emerging Patterns from Social Media Networks: A Data Mining Techniques Perspective . . . . . . . . . . . . . . . . . Yadala Sucharitha, Y. Vijayalata and V. Kamakshi Prasad

15

Initial Centroids for K-Means Using Nearest Neighbors and Feature Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muddana A. Lakshmi, Gera Victor Daniel and D. Srinivasa Rao

27

Secured Cluster-Based Distributed Fault Diagnosis Routing for MANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vani Garikipati and N. Naga Malleswara Rao

35

A Comparative Analysis of Unequal Clustering-Based Routing Protocol in WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tanmay Biswas, Sushil Kumar, Tapaswini Singh, Kapil Gupta and Deepika Saxena

53

YouTube Video Ranking by Aspect-Based Sentiment Analysis on User Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ganpat Singh Chauhan and Yogesh Kumar Meena

63

Diet Recommendation to Respiratory Disease Patient Using Decision-Making Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prashant Gaurav and Sanjay Kumar Dubey

73

Detection of False Positive Situation in Review Mining . . . . . . . . . . . . . Devottam Gaurav, Jay Kant Pratap Singh Yadav, Rohit Kumar Kaliyar and Ayush Goyal

83

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Optimization of Cloud Datacenter Using Heuristic Strategic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biswajit Nayak, Sanjay Kumar Padhi and Prasant Kumar Pattnaik

91

Blockchain Technology for Decentralized Data Storage on P2P Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Akshay Raul, Shwetha Kalyanaraman, Kshitij Yerande and Kailas Devadkar Comparative Analysis of Clustering Algorithms with Heart Disease Datasets Using Data Mining Weka Tool . . . . . . . . . . . . . . . . . . . . . . . . . 111 Sarangam Kodati, R. Vivekanandam and G. Ravi A Machine Learning Approach for Web Intrusion Detection: MAMLS Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Rajagopal Smitha, K. S. Hareesha and Poornima Panduranga Kundapur White Blood Cell Classification Using Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Mayank Sharma, Aishwarya Bhave and Rekh Ram Janghel Analysis of Mobile Environment for Ensuring Cyber-Security in IoT-Based Digital Forensics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 G. Maria Jones, S. Godfrey Winster and S. V. N. Santhosh Kumar Payment Security Mechanism of Intelligent Mobile Terminal . . . . . . . . 153 Seshathiri Dhanasekaran and Baskar Kasi Hybrid Neuro-fuzzy Method for Data Analysis of Brain Activity Using EEG Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Rajalakshmi Krishnamurthi and Mukta Goyal Gait Recognition Using J48-Based Identification with Knee Joint Movements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Jyoti Rana, Nidhi Arora and Dilendra Hiran Cyber Intelligence Alternatives to Offset Online Sedition by in-Website Image Analysis Through WebCrawler Cyberforensics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 N. Santhoshi, K. Chandra Sekharaiah, K. Madan Mohan, S. Ravi Kumar and B. Malathi Deep Convolutional Neural Network-Based Diabetic Retinopathy Detection in Digital Fundus Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 S. Saranya Rubini, R. Saai Nithil, A. Kunthavai and Ashish Sharma A Framework for Semantic Annotation and Mapping of Sensor Data Streams Based on Multiple Linear Regression . . . . . . . . . . . . . . . . . . . . 211 K. Vijayaprabakaran and K. Sathiyamurthy

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Cyclostationarity Analysis of GPS Signals for Spoofing Detection . . . . . 223 R. Lakshmi, S. M. Vaitheeswaran and K. Pargunarajan Implementation of Fingerprint-Based Authentication System Using Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Dipti Pawade, Avani Sakhapara, Melvita Andrade, Aishwarya Badgujar and Divya Adepu NSGLTLBOLE: A Modified Non-dominated Sorting TLBO Technique Using Group Learning and Learning Experience of Others for Multi-objective Test Problems . . . . . . . . . . . . . . . . . . . . . 243 Jatinder Kaur, Surjeet Singh Chauhan and Pavitdeep Singh Homomorphic Encryption Scheme for Data Security in Cloud Using Compression Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 D. K. Chandrashekar, K. C. Srikantaiah and K. R. Venugopal Efficient Query Clustering Technique and Context Well-Informed Document Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Manukonda Sumathi Rani and Geddati China Babu Motif Shape Primitives on Fibonacci Weighted Neighborhood Pattern for Age Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 P. Chandra Sekhar Reddy, P. Vara Prasad Rao, P. Kiran Kumar Reddy and M. Sridhar A Novel Virtual Tunneling Protocol for Underwater Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 A. M. Viswa Bharathy and V. Chandrasekar Garbage Monitoring System Using Internet of Things . . . . . . . . . . . . . . 291 Arpan Patel and Nehal Patel Mobile Learning Recommender System Based on Learning Styles . . . . 299 Shivam Saryar, Sucheta V. Kolekar, Radhika M. Pai and M. M. Manohara Pai Privacy Sustaining Constant Length Ciphertext-Policy Attribute-Based Broadcast Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . 313 G. Sravan Kumar and A. Sri Krishna A Comprehensive Study of Challenges and Issues in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Shadab Siddiqui, Manuj Darbari and Diwakar Yagyasen Comparative Analysis of Major Jacobian and Gradient Backpropagation Optimizers of ANN on SVPWM . . . . . . . . . . . . . . . . . 345 Neeraj Seth, Ashish Ubrani, Sneha Mane and Faruk A. S. Kazi

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Minimization of Energy Consumption in Wireless Sensor Networks by Using a Special Mobile Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 S. Shanthi, Padmalaya Nayak and Sujatha Dandu Improved Wisdom of Crowds Heuristic for Solving Sudoku Puzzles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Neeraj Pathak and Rajeev Kumar An End-to-End Secure and Energy-Aware Routing Mechanism for IoT-Based Modern Health Care System . . . . . . . . . . . . . . . . . . . . . . 379 R. Nidhya, S. Karthik and G. Smilarubavathy Internet of Things: Present State of the Art, Applications, Protocols and Enabling Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 A. Anjaiah, A. Govardhan and M. Vazralu Implementation of Multithreaded BFS Using Bag Data Structure . . . . . 399 Hemalatha Eedi and Mohd Abdul Rasheed Context-Aware Agents for IoT Services . . . . . . . . . . . . . . . . . . . . . . . . . 409 K. Deeba and RA. K. Saravanaguru Multicriteria-Based Ranking Framework for Measuring Performance of Cloud Service Providers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 K. S. Sendhil Kumar and N. Jaisankar An Optimized Computer Vision and Image Processing Algorithm for Unmarked Road Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Jayalakshmi Annamalai and C. Lakshmikanthan Performance Analysis of EMTCMOS Technique-Based D Flip-Flop Design at Varied Supply Voltages and Distinct Submicron Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Patikineti Sreenivasulu Error Detection Using Counting Technique in Low-Power VLSI . . . . . . 449 Kumud Kumar Bhardwaj and T. Swapna Rani Adaptive Sampling Rate Converter for Wireless Sensor Networks . . . . 457 P. Swetha, S. Srinivasa Rao and P. Chandrasekhar Reddy Improvement of Signal-to-Noise Ratio for MST Radar Using Weighted Semi-parametric Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 467 C. Raju and T. Sreenivasulu Reddy A Robust DCT-SVD Based Video Watermarking Using Zigzag Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 K. Meenakshi, K. Swaraja and Padmavathi Kora

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Digitization and Parameter Extraction of Preserved Paper Electrocardiogram Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Rupali Patil and Ramesh Karandikar Segmentation and Classification of CT Renal Images Using Deep Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Anil Kumar Reddy, Sai Vikas, R. Raghunatha Sarma, Gurudat Shenoy and Ravi Kumar A Novel Traffic Sign Recognition System Combining Viola–Jones Framework and Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Ajay Jose, Harish Thodupunoori and Binoy B. Nair Detection of Cardiac Arrhythmia Using Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Padmavathi Kora, K. Meenakshi and K. Swaraja Dual-Function Radar-Communication Using Neural Network . . . . . . . . 527 K. S. Anjali and G. Prabha Patient Nonspecific Epilepsy Detection Using EEG . . . . . . . . . . . . . . . . 541 Sandeep Banerjee, Varun Alur and Divya Shah Power Efficient PUF-Based Random Reseeding True Random Number Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Anirudh Siripragada, R. Shiva Prasad and N. Mohankumar Edge Cut Dual-Band Slot Antenna for Bluetooth/WLAN and WiMAX Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 J. Rajeshwar Goud, N. V. Koteswara Rao and A. Mallikarjuna Prasad A Complete End-to-End System for Iris Recognition to Mitigate Replay and Template Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Richa Gupta and Priti Sehgal Tampering Detection in Digital Audio Recording Based on Statistical Reverberation Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Tejas Bhangale and Rashmika Patole Acoustic Scene Identification for Audio Authentication . . . . . . . . . . . . . 593 Meenal Narkhede and Rashmika Patole Retinal Blood Vessel Extraction Using Morphological Operators and Kirsch’s Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Jyotiprava Dash and Nilamani Bhoi Wire Load Variation-Based Hardware Trojan Detection Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 N. Suresh Babu and N. Mohankumar

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A Neural Network Approach for Content-Based Image Retrieval Using Moments of Image Transforms . . . . . . . . . . . . . . . . . . . . . . . . . . 625 D. Kishore, S. Srinivas Kumar and Ch. Srinivasa Rao Probe-Fed Wideband Implantable Microstrip Patch Antenna for Biomedical and Telemetry Applications . . . . . . . . . . . . . . . . . . . . . . 635 Komal Jaiswal, Ankit Kumar Patel, Shekhar Yadav, Sweta Singh, Ram Suchit Yadav and Rajeev Singh Mapping Urban Ecosystem Services Using Synthetic-Aperture Radar (SAR) Images from Satellite Data for Rural Microgrids in India . . . . . . 643 Prem Raheja, Surmeet Kaur Jhajj, Purva Jhaveri and Jignesh Sisodia Analysis of Denoising Filters for Source Identification Using PRNU Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Nadia Siddiqui, Syeda Shira Moin and Saiful Islam Advanced Protection for Automobiles Using MSP430 . . . . . . . . . . . . . . 665 C. Ravi Shankar Reddy, V. Siva Kumar Reddy, T. Vinay Simha Reddy and P. Sanjeeva Reddy Performance Analysis of KNN Classifier with Various Distance Metrics Method for MRI Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673 Karthick Ganesan and Harikumar Rajaguru Comparison of Low Current Mismatch CMOS Charge Pumps for Analog PLLs Using 180 nm Technology . . . . . . . . . . . . . . . . . . . . . . 683 Alan Saldanha, Vijil Gupta and Vinod Kumar Joshi Optimized Node Swapping for Efficient Energy Usage in Heterogeneous Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693 Satyanarayan K. Padaganur and Jayashree D. Mallapur Content-Based Video Shot Boundary Detection Using Multiple Haar Transform Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 D. Asha and Y. Madhavee Latha An Analysis of IPv6 Protocol Implementation for Secure Data Transfer in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 Anitha Patibandla, G. S. Naveen Kumar and Anusha Meneni Anomaly Detection in Crowd Using Optical Flow and Textural Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 Pranali Ingole and Vibha Vyas Automatic Tonic (Shruti) Identification System for Indian Classical Music . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 Mahesh Y. Pawar and Shrinivas Mahajan

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Single-Plane Scene Classification Using Deep Convolution Features . . . . 743 Nikhil Damodaran, V. Sowmya, D. Govind and K. P. Soman A Novel Methodology for Multiplication of Three n-Bit Binary Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 Anirban Mukherjee, Niladri Hore and Vinay Kumar Speed-Breaker Early Warning System Using 77 GHz Long-Range Automotive Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Umarani Deevela, Swapna Raghunath and Srinivasa Rao Katuri Face Recognition using Invariant Feature Vectors and Ensemble of Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 A. Vinay, Abhijay Gupta, Harsh Garg, Aprameya Bharadwaj, Arvind Srinivas, K. N. Balasubramanya Murthy and S. Natarajan Pattern and Frequency Reconfigurable MSA for Wireless Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781 Deeplaxmi V. Niture, Chandrakant S. Patond and S. P. Mahajan Feature Fusion and Classification of EEG/EOG Signals . . . . . . . . . . . . . 793 Ayushi Mishra, Vikrant Bhateja, Aparna Gupta, Apoorva Mishra and Suresh Chandra Satapathy High-Efficiency Video Coding De-blocking Filter: Through Content-Split Block Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 801 Perla Anitha, P. Sudhakara Reddy and M. N. Giri Prasad Key Frame Extraction Using Content Relative Thresholding Technique for Video Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 K. Mallikharjuna Lingam and V. S. K. Reddy Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821

About the Editors

Jiacun Wang received his Ph.D. in computer science engineering from Nanjing University of Science and Technology (NJUST), China, in 1991. He is currently Professor in the Department of Computer Science and Software Engineering at Monmouth University, West Long Branch, New Jersey. From 2001 to 2004, he was a member of scientific staff at Nortel Networks in Richardson, Texas. Prior to joining Nortel, he was Research Associate at the School of Computer Science, Florida International University (FIU), Miami, and Associate Professor at NJUST. He has published numerous books and research papers and is an associate editor of several international journals. He has also served as program chair, program co-chair, special session chair, and program committee member for several international conferences. He is Secretary of the Organizing and Planning Committee of the IEEE SMC Society and has been a senior member of IEEE since 2000. G. Ram Mohana Reddy completed his BE in electronics and communication engineering at Sri Venkateswara University, Andhra Pradesh, in 1987; his M.Tech. in telecommunication systems engineering at IIT Kharagpur, in 1993; and his Ph.D. in cognitive hearing science from the University of Edinburgh in 2005. He is currently Professor and Head of the IT Department at NITK Surathkal, Mangalore. He has contributed to several projects of national and international importance in areas such as affective human-centered computing, big data and cognitive analytics, cognitive hearing and speech science, cloud computing, social multimedia, and social network analysis. He has published numerous books, research papers, and conference proceedings in these areas and is an active member of a number of international associations, including IEEE and ACM. V. Kamakshi Prasad completed his Ph.D. in speech recognition at IIT Madras and his M.Tech. in computer science and technology at Andhra University in 1992. He has more than 20 years of teaching and research experience. His areas of research and teaching interest include speech recognition and processing, image processing, pattern recognition, ad hoc networks, and computer graphics. He has published several books, chapters, research papers in peer-reviewed journals and xxi

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About the Editors

conference proceedings. He is also an editorial board member of the International Journal of Wireless Networks and Communications and a member of several academic committees. V. Sivakumar Reddy is Professor in the Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology. He completed his BE in electronics and communication engineering from SV University, his M.Tech. in digital systems at JNT University, and his Ph.D. in electronics and communication engineering at IIT Kharagpur. His areas of research interest include computer networks and communication, video processing, multimedia system design, operating systems, TCP/IP networks and protocols. He has published more than 100 papers in peer-reviewed journals and conference proceedings in these areas. He is a member of several academic bodies, such as IETE, IEEE, ISTE, and CSI. He is also Reviewer for several IEEE journals.

A New Method for the Spectral Analysis of Unevenly Sampled Time Series Steven M. Boswell and Alexandra L. Boghosian

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1 Fourier Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Fourier Analysis for Nonuniform Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Fourier Analysis by Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 Synthetic Time Series and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1 Signal Amplitude Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Spectral Leakage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3 Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 The Site 609 Hematite-Stained Grain Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Abstract We present a new method for the spectral analysis of unevenly sampled time series. We apply the properties of inverse sums of matrices and pseudoinverses to a constrained least squares formulation of the spectral analysis problem and demonstrate that this approach yields accurate solutions in the form of Fourier coefficients. The Fourier coefficients relate time, power, and phase in a self-consistent manner, improving upon previous spectral analysis methods for unevenly sampled data. Our spectral solutions satisfy Parseval’s theorem, and the inverse transformations of our spectra reconstruct the original, unevenly sampled time series. This is the first presentation of such a method for unevenly sampled data. Keywords Fourier analysis · Spectral analysis · Nonuniform sampling

S. M. Boswell (B) · A. L. Boghosian Lamont-Doherty Earth Observatory, Palisades, NY, USA e-mail: [email protected] A. L. Boghosian e-mail: [email protected] S. M. Boswell · A. L. Boghosian Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_1

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S. M. Boswell and A. L. Boghosian

1 Introduction The decomposition of a time series into sine and cosine basis functions, known as Fourier analysis, is an immensely valuable research tool for identifying and quantifying the periodic behavior of physical systems. Although Fourier analysis is a mature field of inquiry, there remains a ubiquitous and largely unaddressed problem relevant to many science and engineering disciplines: For nonuniformly sampled time series, existing Fourier analysis methodologies are unable to provide spectral solutions that satisfy fundamental conditions required of Fourier analysis. In this manuscript, we present a new method for the spectral analysis of time series with nonuniform sampling satisfying the Nyquist–Shannon sampling theorem. We briefly review the principles of Fourier analysis, relevant theoretical considerations, and the progress made by prior researchers. We then introduce our method and apply it to synthetic time series, illustrating an improvement in harmonic amplitude detection. Following the synthetic time series demonstrations, we apply our method to the controversial record of hematite-stained quartz and feldspar grain (HSG) counts at DSDP Site 609 [1]. Because our method enables a less biased calculation of both power and null spectra, we are able to demonstrate that pre-interpolation of the HSG time series leads to incorrect estimates of statistical significance for the most pronounced harmonics.

1.1 Fourier Analysis A discrete function y(t) of N (even number) elements with period T o can be represented by the summation of orthogonal cosine and sine functions at integer multiples (n) of the fundamental frequency, f o  1/T o , such that N

2 ao  + an · cos(2π n f o t) + bn · sin(2π n f o t). y(t)  2 n1

(1)

The Fourier coefficients (an , bn ) correspond to the amplitudes of the constituent cosine and sine functions, respectively. In practice, the frequency-domain representation of a time series is often computed by the discrete Fourier transform (DFT). The DFT presumes the orthogonality of summation across the sine and cosine functions, which holds in the case of uniform sampling [2] but not for nonuniform sampling in general. Despite this, there exists a unique solution to the Fourier analysis when the effective Nyquist frequency of a signal, one half of the inverse of the mean sampling interval, is greater than its band limit [3]. This principle, a result of the Nyquist–Shannon sampling theorem, holds true for both uniformly and nonuniformly sampled time series [4, 5]. For an adequately sampled, band-limited function, then, an accurate reconstruction of the original time series, synthesized via Eq. 1 or the inverse discrete Fourier transform (IDFT), demonstrates that the estimate itself is the

A New Method for the Spectral Analysis …

3

correct solution. This realization motivates the pursuit of a more general approach to Fourier analysis.

1.2 Fourier Analysis for Nonuniform Sampling While there have been numerous efforts to circumnavigate the uneven sampling problem (e.g., pre-interpolation, the Lomb–Scargle method, the CLEAN algorithm) [6–13], these techniques fail to satisfy fundamental assumptions and theorems of Fourier analysis. Interpolation of data to equidistant spacing is the most common treatment for nonuniform sampling in spectral analysis. Interpolation is problematic, however, because it may suppress higher frequency components and produce biased statistics [6–8]. Given the biasing effects of interpolation and the availability of least squares methods, Scargle [10] made the case that pre-interpolation was disqualifying on philosophical grounds [10]. We consider this argument to be resonant considering the spectral solution can be guaranteed with adequate sampling [5]. The application of linear least squares methods [14] to spectral analysis precipitated significant advances in the study of unevenly sampled time series, of which the Lomb–Scargle method has become standard [9–12]. The modified Lomb–Scargle periodogram is a direct estimate of the periodogram (modulus of the Fourier coefficients), resulting in the loss of phase information. Thus, despite its value in estimating the spectral density of unevenly sampled data, the Lomb–Scargle spectral estimate cannot be inverted to reconstruct the original time series and verify the correctness of the solution. Although Scargle [11] and Hocke and Kämpfer [15] tried to remedy the non-invertibility of the Lomb–Scargle method by calculating heuristic phase estimates, their estimates do not allow the exact reconstruction of the original time series [11, 15]. In a less widely used approach, Roberts et al. [13] sought to find the spectrum of an unevenly sampled time series by iterative deconvolution of truncation- and sampling-induced distortion in the frequency domain [13]. This “spectral cleaning” approach yields nonunique but approximate spectra that can be inverse transformed into reconstructions of the original time series [13, 16, 17]. A critical problem with the CLEAN algorithm is that the resulting spectra do not always obey Parseval’s theorem, the fundamental tenet of spectral analysis positing that the variance of a time series must be equal to the power of its Fourier Transform (i.e., conservation of energy in the time and frequency domains). Parseval’s theorem holds for both uniformly and nonuniformly sampled time series as long as the sampling set is stable [2, 18].

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S. M. Boswell and A. L. Boghosian

1.3 Fourier Analysis by Least Squares In this manuscript, we seek a Fourier analysis method for nonuniformly sampled time series that overcomes the difficulties presented by earlier methods. We accomplish this by modifying the least squares spectral analysis approach. Fourier analysis is equivalent to solving the interpolation problem: −1  x  AT A AT d

(2)

where d is a vector of N observations in the time series, A is the N by N Fourier matrix whose columns are sines and cosines at integer multiples of the standard harmonic (2π divided by the length of the time series), and x is the vector of N Fourier coefficients from which spectral phase and amplitude are derived. For an evenly sampled periodic time series, the summation properties of sines and cosines ensure that AT A is a full rank matrix with orthogonal columns. Because an orthogonal matrix is always invertible, a spectral solution can be found for any evenly sampled time series via Eq. 2 or, equivalently, the DFT. In the case of nonuniform sampling, however, AT A may be ill-conditioned or even singular (the inverse of singular matrix AT A not existing in the strict sense). In the following section, we apply the well-studied properties of inverse sums of matrices and pseudoinverses to solve for the spectral parameters of unevenly sampled time series.

2 Methodology Parseval’s theorem requires that the Fourier coefficients (x) satisfy: 2σ 2 

N 

xi2

(3)

i1

where σ 2 is the variance of the time series. The factor of 2 in Eq. 3 reflects our consideration of only the positive half of the spectrum. Equations 2 and 3 can be combined as the constrained least squares problem: ⎤ ⎡  T AT A x o ⎦ x  A d ⎣ (4) λ xoT 0 2σ 2 where the Fourier coefficients, x, are a solution to the spectral analysis problem when they are equal to the previous estimate, x o . The Lagrange multiplier, λ, enables the minimization of ||Ax − d|| subject to the equality constraint for Parseval’s theorem [19]. We refer to the left-hand side matrix in Eq. 4, with concatenated AT A and equality constraint, as the constrained matrix.

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In the remainder of this section, we present a strategy for solving for the Fourier coefficients in Eq. 4. Even for time series in which AT A is ill-conditioned or singular, there exists an exact and unique solution to the spectral analysis problem if the sampling density of the signal satisfies the Nyquist–Shannon sampling theorem [5]. We solve for the inverse of the constrained matrix by exploiting the properties of inverse sums of matrices [20] and pseudoinverses. We begin by decomposing the constrained matrix into constituent matrices B and C such that B is invertible. Factoring B−1 gives: ⎤−1   AT A x o ⎦  (B + C)−1  B −1 I + C B −1 −1 . ⎣ T xo 0 ⎡

(5)

Henderson and Searle [20] demonstrated that for an invertible (nonsingular) matrix B + C, its inverse can be written in terms of B and C, even if C is not invertible (singular) [20]. They present the matrix identity: (I + P)−1  I − (I + P)−1 P

(6)

where I is the identity matrix and (I + P) is a nonsingular matrix. Letting P  CB−1 , it follows from Eq. 6 that: 

I + C B −1

−1

−1   I − I + C B −1 C B −1 .

(7)

I + CB−1 is singular when the constrained matrix is singular. By substituting Eq. 7 into Eq. 5 and applying the pseudoinverse, we arrive at the expression used to estimate the inverse of the constrained matrix for unevenly sampled time series: ⎤−1   AT A x o ⎦  B −1 − B −1 I + C B −1 + C B −1 . ⎣ xoT 0 ⎡

(8)

est

We have chosen that B is nonsingular, so C must be singular if B + C is singular. When the pseudoinverse in Eq. 8 (denoted by the superscripted plus sign) operates on a singular matrix, the estimate of the inverse constrained matrix varies as a function of the choice of B and C. We use this property to explore how different choices of B enable us to find a vector of Fourier coefficients that satisfies Parseval’s theorem. The estimate of the inverse of the constrained matrix can be used to solve for the Fourier coefficients via Eq. 4: ⎡ ⎤−1 

T AT A x o x A d ⎦ ⎣ T . λ xo 0 2σ 2 est

(9)

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Fig. 1 Graphic representation of the spectral analysis method described in this manuscript

The formulation of Eqs. 4 and 9 requires an initial estimate of the Fourier coefficients to serve as the equality constraint x o . We calculate this initial estimate of the Fourier coefficients in the following manner:

xo λ



⎡ T

⎤−1 

A A xones ⎦ ⎣ T xones 0

AT d √ 2σ 2

(10)

est

where x ones is an N-length vector of ones. Equation 10 is solely intended as a heuristic to initialize the iterative process in Eq. 4. Once the initial estimate has been obtained via Eq. 10, we begin an iterative process where the equality constraint in Eq. 4 is replaced by the average of the previous Fourier coefficient estimates (Fig. 1). For each iteration, matrix B is recast as a matrix of random numbers and is thus assured to be invertible. The solution of Fourier coefficients is found when Parseval’s theorem is obeyed. This can be verified by reconstructing the original time series. Power, amplitude, and phase spectra can then be calculated straightforwardly from the Fourier coefficients. For optimal performance of the algorithm, multiply copies of the random matrix B by logarithmically spaced scalars (between 1 and 1015 , for instance). If any of the resulting sets of Fourier coefficients satisfy Parseval’s theorem, the solution has been found. Otherwise, use the set of coefficients that most closely satisfies this Parseval relation to calculate the new average of Fourier coefficients and continue the procedure.

3 Synthetic Time Series and Results We show that our method accurately captures the spectral information of a randomly sampled time series with three periodicities. We also demonstrate that our method handles time series reconstruction, spectral leakage, and aliasing as expected.

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Fig. 2 Comparison of the spectral analysis method presented in this manuscript with the DFT. a The unevenly sampled time series, b the amplitude spectra produced by our method, c the linear interpolation of the unevenly sampled time series, d the DFT of the interpolated time series, e a portion of the original time series (blue) and the time series reconstructed by the inverse transformation of our spectral amplitude and phase estimates (black dots)

3.1 Signal Amplitude Detection We define the function y(t)  3 · sin(13 · 2π t) + 5 · sin(27.5 · 2π t) + 10 · sin(46 · 2π t) over a length of 10 s and sample the function at 1000 random intervals. When our method is applied to this unevenly sampled time series (Fig. 2a), the resulting spectrum contains line spectra with amplitudes of 2.55, 4.40, and 9.45 at the expected frequencies of 13, 27.5, and 46 Hz, respectively (Fig. 2b). Besides the small fraction of power distributed across the harmonics as sampling-induced distortion [13], the amplitude spectrum produced by our method has the expected spectral character of the sampled function. We compare the above result to the spectra derived from the interpolation of this time series. We linearly interpolate the unevenly sampled data (Fig. 2c) and calculate the DFT. The resulting amplitude spectrum shows that power is errantly distributed across the frequency domain (Fig. 2d). The amplitudes of the line spectra are greatly diminished, with amplitudes of 2.35, 2.04, and 2.65 at the expected frequencies. Because our method effectively computes a Fourier transform, the inverse transformation of our solution produces a time series that reconstructs the original time

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Fig. 3 Amplitude spectra from Fig. 2b (blue), produced by application of the method presented in this manuscript to the unevenly sampled time series in Fig. 2a, are compared to the Lomb–Scargle spectral estimate (red) of the same time series

series (Fig. 2e). Previous work on the reconstruction of unevenly sampled time series using the Lomb–Scargle method has not been able to demonstrate such a result [11, 15]. Furthermore, our method outperforms Lomb–Scargle in terms of accurately capturing the known spectral character of unevenly sampled time series. As an example, we present the amplitude spectra from Fig. 2b in comparison with the Lomb–Scargle estimate for the same time series (Fig. 3).

3.2 Spectral Leakage Sampling theory addresses the treatment of time series as finite partitions of infinite series. This partitioning, or truncation, in the time domain is equal to convolution in the frequency domain. The smearing of power into adjacent frequency bands due to convolution is termed spectral leakage. Consider an evenly sampled sinusoid that has been truncated by a gate (boxcar) function at two different sets of endpoints (Fig. 4a). In the first case, the sinusoid is truncated one datum short of a full cycle. When repetitions of this partition are concatenated to recreate the infinite series, a single sinusoid can interpolate this infinite series. In the second case, consider that the sinusoid is truncated at ¾ of a cycle. If repetitions of this partition are concatenated, a discontinuity is introduced (the dotted red line in Fig. 4a). A single sinusoid cannot interpolate this time series. Conceptually, power in the leaked spectrum must be transferred to other frequency bands in order to capture the abrupt transition at the discontinuity. We compute the amplitude spectra for both cases of this evenly sampled time series using the DFT. In the first case, the DFT produces the line spectrum as expected (Fig. 4b). In the second case, where truncation has introduced a discontinuity, the DFT produces a spectrum where power has been smeared to adjacent bands (Fig. 4c). Next, we consider the same time series but resample them randomly and apply our method. In both cases, the randomly sampled sinusoids are of the same length as

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Fig. 4 a Black sinusoid is truncated at the end of a full cycle such that spectral leakage is not expected in its spectrum. The dashed red sinusoid is truncated at three-quarters of a cycle, introducing a discontinuity (dotted red) and spectral leakage. b The black sinusoid was sampled evenly and unevenly. In the evenly sampled case, the DFT (green) produces an unbiased line spectrum. Our method (blue) produces a similar line spectrum for the unevenly sampled case. c The dashed red sinusoid was also sampled evenly and unevenly. For the evenly sampled time series, the DFT (green) produces a spectrum with the expected leakage. For the unevenly sampled series, our method (blue) yields a spectrum with similar leakage

their evenly sampled counterparts. Our method produces spectra that have a similar form as the spectra from the evenly sampled series (Fig. 4b, c).

3.3 Aliasing For evenly sampled time series, aliasing, or the misidentification of frequency components in a spectrum, occurs when an existing frequency component is greater than the Nyquist frequency. Our method handles aliasing as expected when applied to an evenly sampled series since it is equivalent to the discrete Fourier transform. The alias-free frequency band is larger for unevenly sampled time series than evenly

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Fig. 5 Comparison of the spectral analysis method presented in this manuscript with the DFT when applied to a time series with a single periodic component (30 Hz) greater than the Nyquist frequency (25 Hz). a The linear interpolation of the unevenly sampled time series, b the DFT of the interpolated time series with a principal alias at 20 Hz, c the unevenly sampled time series, d the amplitude spectra produced by our method

sampled ones, however. This rectification of aliasing effects has been cited as an advantage of analyzing unevenly sampled data [12]. We demonstrate the inadequacy of classical spectral analysis when applied to unevenly sampled signals with components greater than the Nyquist frequency by applying the standard treatment of linear interpolation and the DFT to one such time series with a single frequency component. We randomly sample the function y(t)  3 · sin(30 · 2π t) at 100 points such that the average time between the steps is 0.02 s. Then, this time series is linearly interpolated (Fig. 5a). For this interpolated time series, the Nyquist frequency is 25 Hz. Accordingly, we expect that the signal’s 30 Hz harmonic to be aliased at 20 Hz in the spectrum. When the DFT is applied to this interpolated time series, however, the principal alias is indistinguishable from the noise floor (Fig. 5b). We apply our method to the original, unevenly sampled time series (Fig. 5c), analyzing for frequencies between 0 and 50 Hz. Our method correctly identifies a line spectrum at the expected frequency of 30 Hz with an amplitude of 3 (Fig. 5d). The inverse transform of our method’s spectral estimate reconstructs the original time series with accuracy.

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4 The Site 609 Hematite-Stained Grain Record Bond et al. [21] reported an approximately 1500-year cyclicity in the abundance of hematite-stained quartz and feldspar grains (HSG) from Central Atlantic DSDP core Site 609 during the last glacial period [21]. The HSG are interpreted to record intervals of Laurentide Ice Sheet melting and ice rafting due to the presence of HSG-bearing source rocks in the Gulf of St. Lawrence. The proposed 1500-year climate cycle sparked considerable debate as to the nature and timing of millennial-scale climate variability. In the intervening years, however, researchers began to demonstrate that the 1500-year HSG cycle was either due to a spurious interpretation of “cycle” midpoint averaging or introduced via peculiarities in the core chronology [1]. Here, we use the HSG record to demonstrate how the analyst’s methodological choices can result in divergent findings with regards to the significance of periodicities in a given time series. Our point in doing so is to use a widely appreciated but controversial record to highlight the pitfalls in conventional workarounds to the spectral analysis of unevenly sampled time series. We compute the power spectra of the Site 609 HSG series (Fig. 6a) using both the method presented in this manuscript (Fig. 6b) and the conventional approach where linear interpolation precedes the DFT (Fig. 6c). We perform the spectral analyses on the entire HSG record (ca. 14–70 ka BP) with its updated timescale [1]. We remove a linear trend from the time series to satisfy requisite assumptions of stationarity and apply a Hamming window to the observations to minimize spectral leakage. AR(1) null spectra are calculated to test the statistical significance of peaks in both spectral estimates (Fig. 6). As expected, the spectrum of the interpolated time series is redshifted. The first lag autocorrelation parameters for the non-interpolated and linearly interpolated time series are 0.50 and 0.67, respectively. Consequently, the null spectra and confidence intervals are different for each spectrum. The significance levels of pronounced harmonics also differ substantially (Fig. 6). Clearly, the spectral method presented in this manuscript, with solution verification in the time domain, is preferable to misestimating the significance of spectral peaks due to unnecessary pre-interpolation.

5 Conclusion We present a framework for the spectral analysis of unevenly sampled time series, utilizing the properties of inverse sums of matrices and pseudoinverses to solve a constrained least squares formulation of Fourier analysis. Our method finds spectral solutions that both satisfy Parseval’s theorem and reconstruct the original time series. Because the Nyquist–Shannon sampling theorem holds true for unevenly sampled time series, the time series reconstruction is proof that the estimated spectral solution is correct. Our method does not require pre-interpolation of a time series to uniform sampling, and we therefore avoid introducing the corresponding bias into the spectral

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Fig. 6 a Record of hematite-stained quartz and feldspar grains (HSG) from Central Atlantic DSDP core Site 609 during the last glacial period [1]. b The power spectra of the HSG time series produced by our method (blue) with corresponding AR(1) null spectra (black) and 99% significance levels (dashes, α  0.01). c The power spectra produced by the DFT (green) of the linearly interpolated HSG record with corresponding AR(1) null spectra (black) and significance levels (dashes). Both power spectra are smoothed (m  5)

estimate. By comparing the spectra produced by our method to that from the conventional linear interpolation and DFT treatment, we demonstrate that the conventional approach misestimates the statistical significance of pronounced peaks in a widely studied time series of climatic significance. Acknowledgements The authors are grateful to Douglas G. Martinson for both frequent discussions and reviewing drafts of this paper. We further acknowledge the three anonymous reviewers whose constructive feedback benefitted our work. SB is supported by the Chateaubriand Fellowship from the Office for Science and Technology of the Embassy of France in the USA. AB is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-16-44869. Any opinions, findings, and conclusions or recommendations expressed in this manuscript are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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References S.P. Obrochta, H. Miyahara, Y. Yokoyama, T.J. Crowley, A re-examination of evidence for the North Atlantic “1500-year cycle” at Site 609. Quat. Sci. Rev. 55, 23–33 (2012) G.P. Tolstov, Fourier Series, trans. by R. Silverman (Prentice-Hall Inc., Englewood Cliffs, NJ, 1962) C.E. Shannon, Communication in the presence of noise. Proc. IEEE 70, 10–21 (1949) F.J. Beutler, Error-free recovery of signals from irregularly spaced samples. Siam Rev. 8, 328–355 (1966) F.A. Marvasti, Nonuniform Sampling: Theory and Practice (Springer, New York, USA, 2001) L. Horowitz, The effects of spline interpolation on power spectral density. IEEE Trans. Acoust. Speech Sig. Process. 22, 22–27 (1974) M. Schulz, K. Stattegger, SPECTRUM: spectral analysis of unevenly spaced paleoclimatic time series. Comput. Geosci. 23, 929–945 (1997) G. Hernandez, Time series, periodograms, and significance. J. Geophys. Res. A. 104, 10355–10368 (1999) N.R. Lomb, Least squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 447–462 (1976) J.D. Scargle, Studies in astronomical time series analysis. II-statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853 (1982) J.D. Scargle, Studies in astronomical time series analysis. III-Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data. Astrophys. J. 343, 874–887 (1989) W.H, Press, Numerical Recipes 3rd Edition: The Art of Scientific Computing (Cambridge University Press, New York, 2007) D.H. Roberts, J. Lehár, J.W. Dreher, Time series analysis with clean. I. Derivation of a spectrum. Astron. J. 93, 968–989 (1987) P. Vaníˇcek, Further development and properties of the spectral analysis by least-squares. Astrophys. Space Sci. 12, 10–33 (1971) K. Hocke, N. Kämpfer, Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram. Atmos. Chem. Phys. 9, 4197–4206 (2009) S. Baisch, G.H. Bokelmann, Spectral analysis with incomplete time series: an example from seismology. Comput. Geosci. 25, 739–750 (1999) D. Heslop, M.J. Dekkers, Spectral analysis of unevenly spaced climatic time series using CLEAN: signal recovery and derivation of significance levels using a Monte Carlo simulation. Phys. Earth Planet. Inter. 130, 103–116 (2002) F.A. Marvasti, L. Chuande, Parseval relationship of nonuniform samples of one- and twodimensional signals. IEEE Trans. Acoust. Speech Sig. Process. 38, 1061–1063 (1990) D.P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods (Athena Scientific, Belmont, Massachusetts, USA, 1996) H.V. Henderson, S.R. Searle, On deriving the inverse of a sum of matrices. Siam Rev. 23, 53–60 (1981) G.C. Bond, W. Showers, M. Elliot, M. Evans, R. Lotti, I. Hajdas, G. Bonani, S. Johnson, The North Atlantic’s 1–2 Kyr climate rhythm: relation to Heinrich events, Dansgaard/Oeschger cycles and the little ice age, in Mechanisms of Global Climate Change at Millennial Time Scales. Geophysical Monograph, vol. 112, ed. by P.U. Clark, R.S. Webb, L.D. Keigwin (1999), pp. 35–58

Analysis of Early Detection of Emerging Patterns from Social Media Networks: A Data Mining Techniques Perspective Yadala Sucharitha, Y. Vijayalata and V. Kamakshi Prasad

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Importance of Detecting Emerging Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Challenges and Issues Involved in Detection Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Analysis of Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract At present, social media networking sites like Twitter, Flickr, Facebook, YouTube, Instagram are offering a rich assistance for disparate information. Many people are used to extracting and penetrating information in Social Media Networks (SMNs). Detecting emerging patterns from the huge number of messages and tweets around the social networking blogs is crucial for information breeding and marking trends, especially early identification of the emerging patterns can intensively promote real-time intelligent systems. However, at present, we have many methods for discovering emerging patterns which are proposed by various researchers on long range, but they are not producing effective results. In this article, we provide a wide review of different approaches for discovering emerging trends (textual, audio, and video) in SMNs proposed by various researchers in data mining techniques perspective. In this paper, we also discuss the challenges and issues involved in discovering emerging patterns in social media blogs. Y. Sucharitha (B) CMR Institute of Technology, Hyderabad, TS, India e-mail: [email protected] Y. Sucharitha JNTUH, Hyderabad, TS, India Y. Vijayalata Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad, TS, India e-mail: [email protected] V. Kamakshi Prasad JNTUH College of Engineering, Hyderabad, TS, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_2

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Keywords Classification · Emerging trends · Clustering · Social media networks Detection

1 Introduction With the fast growing of social media and micro-blogging networks (Facebook, Twitter, YouTube, etc.) have been the most significant to extract information. People can post their current ideas and views, make comments on the breaking news and events, share the exciting messages and interesting videos in micro-blogging networks (MBNs). Important and abundant information is distributed in the form of commenting, tweeting, and re-tweeting to generate different topics or subjects by online users. The topic is nothing but something that happens at a specific place and time, with all essential preconditions and inevitable consequences. Discovering subjects from series of tweet lists is significant for information extension. Emerging contents generally refers to the subject that can pull in enormous attention in a less time, and the affiliated debates prepossess public judgments much remarkable than they do on general trends. Commonly, emerging contents are obsessed with the subject forced by emerging acts such as accidents in public places, a natural disaster like tsunami, earthquakes, hurricanes, wildfires, cyclones, and public meeting of political leaders. Due to the significance and blow of these emerging contents, people waiting to know the emerging news as possible to project critical control plans, extract business trends, and find useful information. The early discovery could also help the real-time intelligent systems strength fully, such as a real-time personalization, ad targeting, and business strategy. Only a few numbers of people know about emerging topics before they attract a huge number of users in the current MBNs. In the detection of emerging topics, most of them used the method based on keywords and extensions with traditional properties, including time–frequency, feature, and term diffusion. Above-mentioned traditional approaches are effective for detecting patterns when data is in a textual timeline, or joining count of the SMN users increased in a huge scale; otherwise, these are not suitable for early discovery of emerging contents in less timely. A social network is represented in a graph meso-level in Fig. 1. In that picture, nodes are represented as actors and edges consist of relationships among these actors. A SMN Web site over the Internet offers the tremendous capability of connection and communication between users who are located around the globe, interacting with them in various ways. User can share the information with different people including relatives, colleagues and others in social networks like twitter and they also allow making new profiles, liking, disliking, updating and sharing public and private information in twitter. People typically pick the events as per their choice. Data posted to SMN platforms in time and secure information has great value in hard and troubled situations for making decisions. Many charity and helpful organizations perceive the value of the data posted via SWN and are occupied with discovering

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Fig. 1 Meso-level social network diagram

approaches to rapidly and effectively find and compose data that is of most use to them. In emergence topics [1], the collection of social tweets holds useful information which may be helpful for setting up standard strategies that can add to a solution of early knowledge of the situation. Decision making is basically focused on the data need in a realistic manner for understanding the situation and a settled and appropriate standard working method the general population can take after for speedy reaction to emergent events. These days [2], individuals are increasingly used to getting news from SMNs and Web sites. Even so, due to the irresistible data, it takes much time to look for the data related to the developing points from that micro-blogs. Moreover, existing modern and scholastic works are created upon handling information from the single media source, while people like to get exhaustive information from different media sources on one stop, for example, published news, pictures, videos, and the public comments on a specific topic. Social media blogs like Flickr and Twitter [5] give a platform that empowers people to represent or share their ideas, transfer their thoughts, communalize their realities, and circulate updates to followers using different mediums like Web or other applications. Significantly [6], SMNs give an abundance of ongoing information, which, if utilized adequately, can give bit of knowledge about what is occurring across the globe. Specifically, in crisis situations like natural hazards, wildfires, information reached to all users through Web blogging sites has been appeared to be valuable.

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Fig. 2 Architecture of emerging topics detection process

2 Importance of Detecting Emerging Trends It is essential [3] to gather related data of emergent event as early as possible; this can be useful for the guide to complete the rescue and alleviation movement administration during crisis situations. It can also be useful in giving awareness to the people in the current situation. In any case, it is not achievable for the news center to have their journalists to cover the whole influenced area for collecting information about the progressing event for the whole time period. For this situation, they can depend on the information shared by the population on the SMN sites for getting refreshes about the event. Discovering emerging trends using the micro-blogging sites is one of the most recent areas of research. Social media is a far-reaching and changed wellspring of social discussion and sharing information. A social media moves on overrunning our daily life; it has turned into a stage where users can communicate or share ideas and post their messages with huge numbers of people. The architecture of emerging trends detecting process is shown in Fig. 2.

3 Challenges and Issues Involved in Detection Process Social media information usage is not limited to one field, and it extends to all areas like medical, engineering, and science; utilizing the information on social media news is two-edged knife. On the first one, it is easy to access; cost is very low and fast circulation of information among a large number of groups. On the other side, information in SMN is less quality, i.e., fake news circulation. It is one of the major challenging tasks to detect the fake news on SMNs. Unitizing the

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information from different [1] places is a major challenging task to establish the data for decision making. For emerging event, one of the key challenges run across in social information extraction is the event validation by back-to-back social streams in the light of their spatial and temporal variables. Identifying sub-events and their related records over social media [3] is a challenging task as the data given by social media is incomplete, invalid, unstructured, and heterogeneous. Social media means wide, open access, and anyone can post anything irrespective of the status of information (true or false); due to this situation [4], there is the chance to spread wrong messages around the globe which is called rumors. These can create confusion among the users. This is one of the challenges on social media to protect from false information.

4 Analysis of Related Work The tremendous volume of data is generated by users on social media networks like Twitter, Facebook, YouTube, and Flicker in recent days. On these micro-blogging sites, daily huge amount of data is shared and distributed among different user groups. So it is a good platform which used for timely detecting and early prediction of emerging trends which are occurring in the real world. So much research is going on this filed, and many researchers proposed different approaches for detecting emerging topics on social media. This section can give an overview of those recent works done. Lee et al. [1] proposed data clustering slicing model to detect the emerging events on social media. First, they gathered the messages from Web blogging sites regarding crisis then applied the ontology learning approach to get a fast response for an occurrence of emerging events. Authors first developed the system for early warning of emerging events; then, they extended the model for event identification in an early stage by using dynamic ontology learning engineering on the bases of social media. Bao et al. [2] developed a news detection model called Multimedia News Digger (MeDigger) which not just successfully recognizes emerging topics from social streams yet in addition gives the relating data in various modalities. Input is taken from Google News, Flickr, and Twitter form the period of 03/04/2011 to 03/31/2011, and results are detected in the form of crisis events. They used a co-clustering approach which is used for detection, and it has a more exact identification of comparing with existing methods on SMNs. However, because of the staggering data, it costs much time to quest data related to the crisis events form those social media sites. Me-Digger gets a gigantic accomplishment as it progressively demonstrates the most well-known news voted by users on the front page. Abihik and Toshniwal [3] concentrate on the sub-event identification of the social networks data exploration framework. This method proposed with two stages; in the initial step, clusters are framed by taking diverse features of online networking records separately. In the second step, the clustering solutions acquired in the initial step are joined to give a single clustering result. Each of these groups acquired in definite clustering result represents a sub-event. Datasets are collected from YouTube

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Fig. 3 Different micro-blogging sites available on internet

of the period of 2009–11 on red river floods. This work is mainly discussed on acquiring the better understanding of natural calamities using information available from micro-blogging sites. Safeguard and alleviation exercises in crisis circumstances can be upgraded by detecting sub-events on a specific occasion. Authors used MATLAB for implementation of a clustering algorithm. This method is trained only for the particular type of crisis events, and it cannot perform for different natural disasters. Figure 3 is showing different types of SWNs available over Internet. Sampsom et al. [4] demonstrated a method called hashtag and Web linkage which is the conversation-based approach to the collecting of rumors information with accurate ground reality. In social media, so much information is sharing with different groups and sometimes false data is also flowing on social media blogs. For this study, researchers collected 280 group tweets for various news created by a set of keywords and they used classification technique without pruning step for detecting rumors on SMNs. Manaskasemsak et al. [5] discussed a new model to identify crisis events from Twitter data. They created a tweet graph which denotes collection of tweets with the same kind and used Markov clustering algorithm on the twit graph to collect similar tweets. Based on the assumption that a topic represents as a set of related tweets and all events are evaluated and which event has more number of similar tweets that considered as an emerging event and for this experiment they taken data from Twitter. Results are showing up to 80% accuracy to detect the emerging topic. McCreadie et al. [6] proposed a model called emerging analysis identification and management system (EAIMS) for identifying the crisis events management which is happening in daily life on social media. They used machine learning algorithm to detect emerging topics like natural disasters, wildfires and track its details to take better decisions in crisis movement. Phol et al. [7] discussed a method called selforganizing map (SOM) clustering approach for automatic sub-event identification on social media data; it may be images, text, or videos. Data used for this is collected from Flickr and YouTube in emerging time. Cataldi et al. [8] introduced a novel

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Fig. 4 Usage of social networks daily, weekly, and less often

event detection approach on Twitter to extract the real events which occur in a crisis situation. First, they extract the tweets of subjects based on its importance and analyze the relationships among them. They summarized the events based on keyword life cycle leveraging a novel aging theory which is intended to extract events that often occur in precise time intervals and they rarely occur in past. Using page rank algorithm they have given rank to events which are detected based on their similarity tweets. They used the Twitter data stream for this experiment. Some of the related works are discussed in Table 1. It consists of mainly five parts which are objectives of the paper, datasets used for experiment, type of methodology used, and result of the experiment. The last is explained about recommendations which are done by the researcher, and first column is referred as references number. Statistics show in Fig. 4 that users can use social media daily more than once and they share, comment, and communicate a lot of tweets and messages between them, and WhatsApp got the high rating as usage perspective.

5 Conclusion and Future Work Early discovering of the emerging trends is essential in different applications. However, this process is exceptionally complex because a lot of information is distributed in form of tweets, messages, images, and videos through Internet. Here the spreading information may be correct or incorrect (rumor). In this article, the most recent submerged data mining strategies have been reviewed and a comparative study has been examined and summarized. In this article, we did analysis on data mining point of view only, but other techniques are also available to identify emerging topics. In future work, further improvements needed in like emerging events detection with location sensibility and over SWNs and usage of clustering methods producing effective results for grouping tweets and messages into events.

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Table 1 Comparison of previous works done by various researchers in the past Ref. No. Objective of Datasets used Methodology Results Recommendations the article for model [9] They Dataset for Using NB Both textual Add spatial and proposed a experiment classifier and temporal social features to framework gathered from algorithm, features are get better for Twitter Data they sampled important for performance, and comparison stream1,698,517 85,000 disruptive compare this with of two tweets. Event event-related events other existing features that selected is tweets of detection, but event detection could be Formula 1 dataset which performance algorithms helpful for Motor Racing we used to results show detecting Grand Prix, test, train, that disruptive which held on and evaluate combination events like 1–4 November, clustering of optimal terror attacks, 2013, Abu technique textual climate Dhabi features with change temporal updates on features is social media producing networks: better results textual and than others temporal [10]

Detecting penetrable targets like terrorism acts through social networks before and after radicalization process using indicators

Usernames (112) of Twitter and tweets (17,000) from ISIS; build their own dataset

Data preprocess and extracting behavioral patterns using data mining techniques from SWNs

They observed that in early hours unexpected rush on tweets and the slight decrease in Thursdays. They noticed that all tweets are in Arabic, results proving that tweets are translated into other languages

We have to study on relationships of users and have to compare the datasets with other which are available in Twitter accounts as a name of ISIS

[11]

Discovering the communities in social networks based on their preference

The dataset has 50 nodes with 74 various interests

ISCD algorithm (similarity community detector)

Highaccuracy communities are detected with related interest

Compared to the previous models, ISCD produces better results

(continued)

Analysis of Early Detection of Emerging Patterns … Table 1 (continued) Ref. No. Objective of the article [12] Propose a strategy for tracking public health condition trends with social media networks like Twitter [13] Identifies a novel approach for timely warning system of natural calamities like earthquakes and tsunamis [14] Propose a model called text mining to discover and predict the suspicious activities in social media [15] Accurately discovering and collecting crisis topics with the help of real-time messages circulating through SM [16]

Detection of real urban crisis events like fires, storms, and traffic jams based on SMNs

Datasets used for model Form Twitter, 1.6 million health-related tweets are gathered in between 05.2009 and 10.2010 They collected 64,878 tweets and gathered up to 1.9 million entities from 330,000 different users

23

Methodology Results

Recommendations

FP-growth algorithm to find frequent word pattern sets

They want to run this framework on the larger scale to enhance the results

Detecting the emergent health trends, via social media and it is discovering seasonal diseases also Decision tree Producing with crossgood results validation compared to using Weka INGV and Tool detecting events faster than that meanly timely

Implement the framework in night hours because tweet sensitivity is low at night

Suspicious messages on Twitter, YouTube, Flickr, Facebook, etc.

Text corpus, corpus processing, and classification process

Detected and Improve the predicted system execution targeted work time like bomb, explode, and terrorist group like…

52,195,773 messages collected from Twitter stream in between 06.01.2011 and 11.03.2011

Classification Discovering algorithm emerging events early in a crisis situation to enhance the decision management

Accurately predicting and spreading awareness in SWNs

Collected data from Weibo

Tuning and GIS-based visualization

Efficient results achieved by the proposed method

Discovered urban emerging events through microblogging sites

(continued)

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Table 1 (continued) Ref. No. Objective of the article [17] Propose a model for the discovery of crisis trends from anomalies computing through the method

[18]

[19]

Generated model for predicting accurate and early disaster warnings to people using SMNs using ML methods Propose a model for timely identification of rumors trending on social media

Datasets used for model Data is gathered from Twitter API, YouTube, and NASA. Dataset is generated over 20 days from 100 users

Methodology Results

Recommendations

Probability model, SDNML change point detection algorithm

Identifying emerging trends based on re-tweeting and reply in SMNS. They detect new trends as early as text anomaly approaches

It is conducted on offline, but the model is executing in the real social stream to get better results

Analysis of the Twitter datasets of 30–Nov, 1–2 Dec 2015. In the time of heavy rainfall in Chennai

Supervised learning methods called NB and SSVM

SSVM is producing accurate results compared to NB classifier up to 90%

Multi-window model can be implemented on distributed data to get 100% accurate results

Dataset contains Sliding 109 trends window between 2006 mechanism and 2009

Early discovery of rumor signals and distinguishing false rumors trending in social media

When false events are identified, immediately stop them

References 1. C.-H. Lee, C.-H. Wu, H.-C. Yang, W.-S. Wen, C.-Y. Chiang, Exploiting online social data in ontology learning for event tracking and emerging response, in IEEE International Conference on Advances in Social Networks Analysis and Mining (2013) 2. B.-K. Bao, W. Min, J. Sang, C, Xu, Multimedia news digger on emerging topics from social streams, in ACM International Conference Japan (2012) 3. D. Abhik, D. Toshniwal, Sub-event detection during natural hazards using features of social media data, in ACM International Conference, Brazil (2013) 4. J. Sampson, F. Morstatter, L. Wu, H. Liu, Leveraging the implicit structure within social media for emergent rumor detection, in CIKM’16, USA (2016) 5. B. Manaskasemsak, B. Chinthanet, A. Rungsawang, Graph clustering-based emerging event detection from twitter data stream, in ICNCC’16, Kyoto, Japan (2016) 6. R. McCreadie, C. Macdonald, I. Ounis. EAIMS: emerging analysis identification and management system, in SIGIR’16, Pisa, Italy (2016) 7. D. Pohl, A. Bouchachia, H. Hellwagner, Automatic sub-event detection in emerging management using social media (ACM, Lyon, France 2012)

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8. M. Cataldi, L. Di Caro, C. Schifanella, Emerging topic detection on twitter based on temporal and social terms evaluation: in MDMKDD’10 (ACM, J Washington, DC, USA, 2010) 9. N. Alsaedi, P. Burnap, Feature extraction and analysis for identifying disruptive events from social media, in IEEE International Conference on Advances in Social Networks Analysis and Mining (2015) 10. R. Cabrera, M. Barhamgi, D. Camacho, Extracting radicalisation behavioural patterns from social network data, in IEEE 28th International Workshop on Database and Expert Systems Applications (2017) 11. M. Ba-Hutair, Z. Al Aghbari, I. Kamel, On detecting communities in social networks with interests, in IEEE 12th International Conference on Innovations in Information Technology (IIT) (2016) 12. J. Parker I, Y. Weil, A. Ya, O. Friederl, N. Goharianl, A framework for detecting public health trends with twitter, in ASONAM’J3 (Niagara, Ontario, Canada, 2013) 13. M. Avvenuti, S. Cresci, M.N. La Polla, A.M. Maurizio, Earthquake emerging management by social sensing, in Second IEEE International Workshop on Social and Community Intelligence (2014) 14. A. Salim, E. Omar, Cybercrime profiling: text mining techniques to detect and predict criminal activities in micro blog posts (IEEE, 2015) 15. C.-H. Lee, H.-C. Yang. T.-F. Chien, W.-S. Wen, Novel approach for event detection by mining spatio-temporal information on microblogs, in International Conference on Advances in Social Networks Analysis and Mining (2011) 16. X. Zheng, Z. Hui, L. Yunhuai, M. Lin, Crowd sensing of urban emerging events based on social meida big data, in IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications (2014) 17. T. Takahashi, R. Tomioka, K. Yamanishi, Discovering emerging topics in social streams via link-anomaly detection. IEEE Trans. Knowl. Data Eng. 26(2), 120–130 (2014) 18. B. Anbalagan, C. Valliyammai, ChennaiFloods: leveraging human and machine learning for crisis mapping during disasters using social media, in IEEE 23rd International Conference on High Performance Computing Workshop (2016) 19. S. Wang, I. Moise, D. Helbing, T. Terano, Early signals of trending rumor event in streaming social media, in IEEE 41st Annual Conference (ACSA, 2017)

Initial Centroids for K-Means Using Nearest Neighbors and Feature Means Muddana A. Lakshmi, Gera Victor Daniel and D. Srinivasa Rao

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 28 29 30 33 33

Abstract K-means is a popularly used clustering algorithm. Results of k-means clustering algorithm are sensitive to initial centroids chosen that give different clustering results for different runs. The algorithm converges to local optima based on the initial centroids chosen and does not guarantee reaching the global optima. This paper proposes an algorithm for choosing the initial centroids where each initial centroid is determined using the feature means and eliminating its nearest neighbors for choosing the next centroid. Keywords Centroids · Clustering · K-means algorithm · Cost function Nearest neighbors

1 Introduction Clustering method is popularly used knowledge discovery technique. It finds its applications in many areas like data mining, image classification, document retrieval, M. A. Lakshmi · G. Victor Daniel (B) · D. Srinivasa Rao Gandhi Institute of Technology and Management, Hyderabad, India e-mail: [email protected] M. A. Lakshmi e-mail: [email protected] D. Srinivasa Rao e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_3

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computer networks, biological sciences, finance, manufacturing. K-means is the popular hard clustering method that partitions the given dataset into disjoint clusters for continuous data [1–3]. But the k-means algorithm has certain limitations like (i) Assumes that number of clusters is known beforehand, which may not hold good in real applications. However, the value of k can be validated using Elbow method. (ii) k-means takes initial centroids and iterates until convergence condition is reached. But the results of clustering depend on the initial centroids taken. (iii) The k-means clustering converges to local optima depending on initial centroids chosen and order of the instances and global optima may not be guaranteed. As k-means algorithm is sensitive to initial centroids chosen, different runs of the algorithm yield different clustering results. Standard methods of selecting initial centroids are (i) choosing the initial centroids randomly (ii) using k-means++ algorithm.

2 Literature Review Several methods have been proposed in the literature to choose the initial centroids for k-means algorithm. These methods may be broadly classified into • Hybrid methods • Only initialization methods. Determining the initial centroids by applying one of the clustering algorithms and then using the clustering algorithm for actual clustering is called hybrid method. But this method again suffers from the same problems as k-means. So, algorithms are required, to find the initial centers, which are different from the clustering algorithms. Khan and Ahmed [4] proposed method in which initial centers are determined by applying k-means algorithm over each attribute, assuming that each attribute values are normally distributed, the normal curve is then divided into k partitions and initial centers are midpoints of these partitions. MaxMin method [5, 6] proposes a mechanism to choose first centroid at random and ith centroid is chosen such that it has greatest minimum distance to the previously selected centers. Ali Ridho and Kioki proposed Pillar method in which initial centroids are determined using the farthest accumulated distance between them. The method also avoids outliers by choosing only those that have minimum number of neighbors [7, 8]. A number of genetic algorithm-based methods for k-means are proposed. Genetic K-means algorithm (GKA) [9] combines genetic algorithm with classical gradient descent algorithm that converges to global optima. Yi Lu, Shiyong Fotouhi, Deng, Susan proposed FGKA [10] (Fast Genetic K-means Clustering Algorithm) algorithm which always converge to global optimum and runs much faster than GKA. Shboul and Myaeng [11] proposed GAIK (Genetic Algorithm Initializes KM) algorithm combines K-means and Genetic K-means algorithm, where GKA is executed first to give initial values to K-means. This hybrid system minimizes the number

Initial Centroids for K-Means Using Nearest Neighbors …

29

of iterations needed to converge to local minima. KIGA (K-means Initializes Genetic Algorithm) first uses K-means to initialize the genetic algorithm avoids the problem of blind search, the processing time increases. Ujjwal Maulik, Sanghamitra Bandopadhyaya algorithm [12] uses chromosomes, each consists of k randomly chosen points of the dataset which are subject to cross over, mutation and sum of the squared distance from the centers are used as fitness function. Clusters are formed using the centers encoded in the chromosomes, then the centers are replaced by the mean of the points of the cluster.

3 Proposed Method K-means clustering algorithm requires initial centroids and k, the number of clusters as input. Our paper focuses on choosing the initial centroids and not on choosing the value of K, the number of clusters. However, Elbow method is used to select the value of K which is used as input to the k-means algorithm. Standard methods for choosing initial centroids include (i) Randomly selecting the k initial centroids: Because of random selection of centroids, the algorithm converges to different local optima. Hence, the clustering results are different for various runs. (ii) Using k-means++ algorithm to choose the initial k centroids: In this approach, the first centroid is selected at random, to generate the remaining (k − 1) initial centroids. So, the initial centroids thus produced using k-means++ also depends on the first initial centroid chosen. Hence, the clustering results produced by k-means using these centroids, are different for different runs of the algorithm. The proposed algorithm addresses the problem of producing different clustering results. The proposed approach uses two key concepts, one is the feature means and second is the nearest neighbors. First a tuple is formed using feature means and then choose the instance that is closest to the means tuple as one initial centroid. Then the neighbors of this centroid are eliminated to choose the next centroid which is again the instance closest to the means of the features of the remaining instances. This process is repeated until k initial centroids are chosen. This approach produces consistent clustering results for different runs of k-means clustering algorithm which is very close to the clustering results produced by other methods. Algorithm Input: Dataset X with d number of features and N number of instances K is number of clusters Output: K Initial centroids to be used for clustering

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1. Determine the feature means     N of the dataset X N N X i1 , N1 i1 X i2 , . . . , N1 i1 X id , Ck  N1 i1

2. Find the closest instance, in the dataset, Ck to Ck using Euclidian distance. 3. Delete N/K nearest neighbors of the centroid Ck from the dataset X 4. Set N  N−(N/K) Repeat steps 1 to 4, K times until the K initial centroids are obtained. Time complexity O(N*d) to determine the means O(N2 ) for Nearest Neighbors Total Time complexity to find initial centroids is O(K * N 2 ) where N is decreasing by N/K in each iteration. The above algorithm produces the best clustering results that are very close to the results produced by other methods and in single run of the k-means without the need of running several times.

4 Results Discussion The proposed algorithm is tested and compared with the standard methods of choosing initial centroids on the following datasets. The datasets are chosen that has different dataset sizes, number of features and number of clusters (Table 1). The following cost function is used for measuring the performance of the algorithms. SSE 

k  

Dist2 (m i − x)

i1 x∈ct

where x is data point in cluster C i and mi is the centroid of cluster C i . The k-means algorithm was run for the three methods, the proposed and the two standard methods of choosing initial centroids, on the above three datasets. Coding was done in Graph Laboratory. (i) k-means clustering using random initial centroids: K initial centroids are chosen at random and k-means is applied on these centroids. When k-means is

Table 1 Datasets for testing the algorithms Dataset N (No. of instances) Iris Glass Wine

150 214 178

d (No. of dimensions) K (No. of clusters) 4 9 13

3 7 3

Initial Centroids for K-Means Using Nearest Neighbors …

31

Table 2 SSE of k-means clustering algorithm using random initial centroid Seed Iris dataset Glass dataset Wine dataset 0

78.941

371.721

1

78.941

551.543

2,370,169.652 2,370,169.652

100

145.279

357.489

2,370,169.652

200

79.963

357.456

2,633,555.372

500

78.945

348.569

2,370,689.652

800

78.945

553.676

2,305,012.204

1000

79.011

350.498

2,370,689.652

2000

78.945

367.599

23,516,815.946

3000

142.859

373.724

2,634,681.827

5000

78.941

315.718

2,370,689.652

Table 3 SSE of k-means clustering algorithm using the initial centroids produced by k-means++ Seed Iris dataset Glass dataset Wine dataset 0

78.941

338.987

2,633,555.372

1

142.859

404.555

2,633,555.372

2

78.945

358.74

2,370,689.652

3

78.941

324.893

2,370,689.652

4

78.945

409.789

2,370,689.652

5

78.941

338.745

2,642,814.606

6

78.945

338.987

2,629,315.232

7

78.941

338.745

2,632,871.484

8

78.945

365.646

2,370,689.652

9

78.945

338.745

2,370,689.652

run with different seed values, different clustering results are produced. Table 2 shows SSE values for the three datasets. As is shown below, clustering results, SSE values vary for different runs of the algorithm. (ii) k-means with initial centroids chosen using k-means++: k-means algorithm is run by taking the centroids produced by k-means++ algorithm as initial centroids. Since k-means++ also uses first centroid at random to choose the remaining initial centroids, the clustering results produced are different for different runs of k-means algorithm (Table 3). (iii) k-means using the proposed method of obtaining initial centroids: The proposed method choose the first initial centroid as the instance that is closest to the tuple containing feature means. The neighbors of this centroid are eliminated from the dataset and again choose the instance that is closest to the tuple containing feature means of the reduced dataset. This process is continued until all the K initial centroids are obtained. The proposed method is tested on the above three datasets, and SSE values are tabulated below. The values are very

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Table 4 SSE of k-means clustering algorithm using the proposed method for initial centroids Iris dataset Glass dataset Wine dataset 78.945

336.292

2,370,689.652

Fig. 1 Performance comparison of the three algorithms on IRIS dataset

Fig. 2 Performance comparison of the three algorithms on GLASS dataset

near to the best values obtained by other two methods at various iterations. The best values are highlighted in Table 4. Our approach produces good clustering results which is consistent for multiple runs of the clustering algorithm and can be completed fast. Following graphs shows performance comparison of the proposed method with standard initial centroids selection methods on the three datasets (Figs. 1, 2 and 3).

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Fig. 3 Performance comparison of the three algorithms on YEAST dataset

5 Conclusion The results of k-means algorithm are sensitive to initial centroids chosen. It converges to local optima based on the initial centroids selected. The proposed algorithm chooses the initial centroids using the feature means and eliminating its neighbors in choosing the next initial centroid. The proposed method produces consistent clustering results that are close to the best results produced by the standard methods of initial centroids selection like k-means++, random initial centers.

References 1. P.S. Bradley, U.M. Fayyad, Refining initial points for K-means clustering, in Proceedings of the 15th International Conference on Machine Learning (ICML98), ed. by J. Shavlik (Morgan Kaufmann, San Francisco, 1998), pp. 91–99 2. M. Meila, D. Heckerman, An experimental comparison of several clustering and initialization methods, in UAI’98 Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence 3. J.M. Penã, J.A. Lozano, P. Larrañaga, An empirical comparison of four initialization methods for the K-means algorithm (1999) 4. S.S. Khan, A. Ahmad, Cluster center initialization algorithm for K-means clustering. Pattern Recogn. Lett. 25, 1293–1302 (2004) 5. T. Gonzalez, Clustering to minimize the maximum inter cluster. Theor. Comput. Sci. 38(2–3), 293–306 (1985) 6. I. Kotsavounidis, C.-C.J. Kuo, Z. Zhang, A new initialization technique for generalized Llioyd iteration. IEEE Sig. Process. Lett. 1(10), 144–146 (1994) 7. A.R. Barakbah, Y. Kiyoki, A pillar algorithm for K-means optimization for initial centroid designation, in IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (2009) 8. B.B. Bhusare, S.M. Bansode, Centroids initialization for K-means clustering using improved pillar algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3(4), 1317–1322 (2014) 9. K. Krishna, M. Narasimha Murty, Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(3), 433–439 (1999)

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10. Y. Lu, S. Lu, F. Fotouhi, Y. Deng, S.J. Brown, FGKA: a fast-genetic K-means clustering algorithm, in Published 2004 in SAC (2004) 11. B.A. Shboul, S.-H. Myaeng, Initializing K-means using genetic algorithms. Int. J. Comput. Inf. Eng. 12. U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering technique. J. Pattern Recogn. Soc. 33, 1455–1465 (2000)

Secured Cluster-Based Distributed Fault Diagnosis Routing for MANET Vani Garikipati and N. Naga Malleswara Rao

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security Fault Diagnosis Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Pseudo Code for Fault Diagnosis Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Secured Cluster-Based Distributed Fault Diagnosis Routing (SCDFDR) Model . . . . . . 4.1 Cluster Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Secure Cluster Routing Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Fault Diagnosis Route Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Secure Cluster Route Reply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Secure Cluster Data Forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Performance Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 37 38 38 39 40 41 42 43 44 45 45 46 49 50

Abstract Mobile ad hoc network (MANET) is becoming a hot communication environment, and these days, it is more prominent due to its mobile and dynamic deployment nature. However, due to the autonomous and dynamic nature of network topology, it composes them more open to the different kind of attacks. The major problem statement is to assure secure network services to ensure secure communication for different communication areas. In order to overcome this issue, a secured cluster-based distributed fault diagnosis routing for MANET with key distribution and fault diagnosis model is proposed. In the proposed model, a cluster is created depending on the secured cluster function and fault diagnosis function. Cluster-based distribution distributes the aggregated data on each cluster and distributes to the corresponding data center. The node having maximum energy rate value is taken as the V. Garikipati (B) Acharya Nagarjuna University, Guntur, India e-mail: [email protected] N. Naga Malleswara Rao RVR & JC College of Engineering, Guntur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_4

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cluster head, and we introduced pseudonymity for secured routing. We simulated the proposed model using NS2 to determine the efficiency. Keywords Clustering · Diffie–Hellman key · MANET · NS2

1 Introduction Mobile ad hoc network is formed arbitrarily by a set of sensor devices with limited communication resources. In MANETs mobiles devices are autonomous. It organizes the self-communication process, due to the lack of communication features, the mobile devices organize the communication with other autonomous devices. While interacting with autonomous devices, ensuring node trusties is always a challenging factor. By considering this problem level, there are many secure communication routing protocols [1] presented to organize security communication. A flat network-based MANETs routing protocol such as proactive and reactive routing protocols was not predefined with security patterns. This kind of unsecured routing process is vulnerable to the network by various network attacks. However, some of the selfish nodes impact data communication interruption by forging and modifying routing packets, and in addition, the Denial-of-Service (DoS) attacks produce fake routing packets to divert the current routing. To overcome such issues, the clustering schemes [2] organized the network into different group of clusters by computing the node trust, deploying trustworthy nodes in a cluster, and electing the cluster head based on the higher trust rate values, but since these kinds of mechanisms are controlled by cluster heads, if any of the cluster head is compromised it will impact on the entire cluster. Chatterjee et al. [3] proposed a secure trusted auction-oriented clustering-based routing protocol (STACRP) to ensure a trusted environment for MANET. STACRP detects the compromised nodes and organizes cooperative communication between nodes to achieve better throughput and low routing overhead. Park et al. [4] proposed an ID-based anonymous cluster-based security framework for MANET to protect node information and to ensure node privacy. Moreover, this framework combines a pseudonym and threshold signature scheme without a pairing process to ensure node privacy. According to this proposed protocol, the nodes maintain the ID-based anonymity to protect the actual node information, which fulfills the node privacy in a dynamic environment without a trusted entity. This mechanism needs more computation process to produce ID-based anonymous. Liu et al. [5] proposed a trust management for mobile ad hoc networks. The active trust avoids black holes through the active creation of a number of detection routes to detect and obtain nodal trust and thus improves the data route security. The generation and distribution of detection routes in the active trust scheme efficiently utilize the energy in non-hotspots to create as many detection routes needed to achieve the desired security and energy efficiency. This technique requires number of detection routes to maintain the trust, this process requires more trust routing packets which

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increases route overhead. Sureshkumar and Bhavani [6] proposed residual energybased reliable multipath routing scheme (RERMRS) to increase network lifetime based on fault-tolerable routes. These routes are established as multiple routes by selecting path reliability parameters and expected transmission period metric. However, these techniques were limited for small networks and these techniques were dealt with the limited set of a transaction. In addition, when the network size changes these techniques fail to achieve better security and energy efficiency. Karlof and Wagner [7] analyzed the various MANET attacks by considering the implementation of link layer encryption and authentication mechanism. But this kind of approach uses more computational process to mitigate DoS attacks. In this research, we analyze MANET attacks by designing an efficient countermeasure process by designing a secured cluster-based distributed fault diagnosis routing (SCDFDR) protocol. After reviewing various reviewers’ and authors’ contribution, we propose a secured cluster-based distributed fault diagnosis routing (SCDFDR) protocol. In this model, we came up with an idea of fault diagnosis model. The main aim of this model is to identify the untrusted nodes based on the node characteristics and key distribution management process. In this paper, we propose secured cluster protocol in MANET to manage secured correspondence in a mobile portable atmosphere. The proposed protocol organizes cluster based on individual node trust and reliability. We formulate cluster functions and fault diagnosis function to find out multi-dimension clustering with featured security to improve communication efficiency.

2 Network Model In the proposed model, each node stores a set of the pseudonym. The network does not renovate pseudonyms to real identities on any node. The network produces unique link IDs based on pseudonyms using pairing on Diffie–Hellman key authentication. All of the nodes in MANETs are equipped with the same wireless communication interface, such as IEEE 802.11 g. The nodes are deployed into the network with the same communication interface range. Nodes broadcast packet Pi by configuring initial trust value (Ti ) field. The cluster formation function f (Ci ) in Eq. 2.1 signifies these values to determine the trusted cluster. f (Ci )  {Ni , Pi , Ti }.

(2.1)

According to cluster formation function, the network is divided into the different zones. The zone formation divides the network area into different portions where x denote the number of zones. Once the zones were characterized, each zone organizes the nodes within the zone range (x and y range) by computing the cluster formation function.

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 (x) 

logx X,

(2.2)

logx Y, x ≥ 1.

Equation 2.2 determines the network area range (x and y), and the above equation performs the logarithmic operation to compute x and y range of each node.

3 Security Fault Diagnosis Model The security fault diagnosis model computes the node security level by using node communication fault probability function S(q). The fault probability function computes the node’s interactivity rate and energy rate on the different time interval. Lets assume if a node p has a group of neighbor nodes, the maximum probability of interaction is P(q)  log(x,y) Ni , the maximum energy probability rate E(q) is shown in Eq. 3.1. The fault probability function S(q) considers a list of interaction probability rate and energy rate to determine probability rate at different hop level. n E(q) 

N Ei .

(3.1)

i1

S(q)  {P(q), E(q)}

(3.2)

3.1 Pseudo Code for Fault Diagnosis Model Broadcast (node p, f ault diagnosis q, T T L t) 1: search fault at node p; 2: if node p do not hit q 3: t  t + 1; 4: if t 3 million people die due to respiratory disease. The major factors which are responsible for causing these diseases are smoking cigarette, air pollution, allergens, occupational risks and many others [1]. Doctors can only give medicine to the patients, but the medicine is not the only thing that plays an important role in curing the disease and making the patients fit again. The diet also plays an important role in making it happen. Research work was done on the recommendation of diet by many different techniques in previous works. A personalized diet recommendation system for cancer patients was developed in which the patients will get the diet chart on daily basis; there were certain doses in the database which will give the person a random dish in order to fulfil the vitamins contained in the food [2]. Food recommendation system for diabetic patients was developed in which the diet was considered on the basis of normal food, the food that the patient will have certainly in a day and the food which will be avoided by the patient of diabetics [3]. In order to develop an expert system for and many diet recommendation which was developed there, the doctor and the experts used to give the advice to the patient [4]. The disease which we have taken under consideration is chronic obstructive pulmonary disease (COPD), asthma, occupational lung diseases. During the treatment, the patient is not allowed to smoke, work using mask so that they will inhale pure oxygen. Many who are suffering from COPD experiences a difficulty in lung function, it increases day by day, and when exacerbation is found, it is important to pinpoint when the decline in the continuous found worse [5]. Asthma is a chronic disease, and several studies were performed to show that it is rising worldwide [6]. The number of factors such as obstructive spirometry, airway hyper-responsiveness, atopy has been intended as indicators of childhood asthma [7]. Occupational lung diseases are one of the most common diseases which are suffered by people who work in coal industries and silicosis industries; the tiny dust particles enter inside lungs and damage the lungs which leads to respiration problem and after that it leads to heart disease [8]. Respiration is most important due to which many other diseases occur like heart attack, lung cancer.

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2 Methodology Decision-making process is important; it does not mean that it should be right or wrong but rather than choosing from the options provided at a correct level. In this paper, analytic hierarchy process (AHP) technique is used for selecting best diet and the validation is performed by the entropy method. Before meeting the dietitian for the diet and knowing what is the importance of the diet, no one have idea about effective diet [9]. It is used in the ranking of the decision making on the basis of the factors responsible. It consists of three types of operations, including hierarchy construction, priority analysis and consistency verification. Step 1: Find the factor in which you want to make the decision as X. Step 2: B as the option which is you want to know the ranking based on the factor. Step 3: if b has importance and the 1/b for the corresponding as per the denoted in Table 2. Step 4: Compute matrices using Saaty’s fundamental scale shown in Table 1. Step 5: Compute nth root of the product and their sum. Step 6: Normalize nth root of the products determined and obtain the assigned weights. Step 7: Determine the consistency index (C I) from Saaty’s random consistency index table (Table 3).

Table 1 Judgement matrix of AHP X Bi

Bj

Bn

Bi

1

bij

bin

Bj

1/bij

1

bjn

Bn

1/bin

1/ajn

1

Table 2 Saaty’s random consistency index [10] Importance of element Meaning 1

Equally important

3

Slightly important

5

Highly important

7

Extremely important

9

Absolutely important

2, 4, 6, 8

Intermediate important

Table 3 Scale for evaluation [10] N 1 2 CI

0.0

0.0

3

4

5

6

0.58

0.90

1.12

1.24

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Step 8: Calculate and check consistency ratio (CR) which should be less than 0.10 or 10%. Step 9: Calculate the reliability index (RI) of the alternatives. Step 10: Calculate the final rankings of the alternatives.

2.1 Entropy Method It is the method of finding out the weight of each coordinate for multi-attribute decision-making (MCDM) issue. Entropy is one of the best ways to find out the weights. This method is a mix technique of qualitative analysis and quantitative investigation [10]. The steps are as follows for validating entropy method. Step 1: Calculate the eigenvector as we have calculated in AHP. Step 2: Calculate the constant (β) n is the no of diets.   where  1/ ln(n) Step 3: Find the vector (V) by β (li j ∗ ln li j for all j (j  1 to k) and (i  1 to n). Step 4: Calculate the  E  (1 + v). Step 5: W j  E j / E j ( j  1).   li j ∗ W j for all i, j  1 to k. Step 6: Calculate probability vector R3Ii  Step 7: Place the ranking based on the highest to lowest probability vector.

3 Experimental Work The elements which we need in order to make the patient recover fast are vitamin E, vitamin C, vitamin D and protein. Their importance is mentioned Table 4. The diet chart which is designed on the basis of the element by consulting dietitians/doctors is as mentioned in Table 5. The patient suffering from respiratory disease can recover fast if they are having vitamin B, vitamin D, vitamin E and protein. For the recommendation, we have prepared three diet plans involving all the elements which are responsible for the fast recovery as shown in Fig. 1 (Tables 6, 7, 8, 9, 10 and 11). We experimented and we found that the Diet 1 has 0.58836092 which is highest as compared to other diets. We found the result after applying AHP method as shown in the Fig. 2.

4 Validation of Experiment As the steps mentionsed above we found β  1/ln(3)  0.91023, and it is found that the Diet 1 is the best fit for the patient’s recovery as mentioned in Table 12 and Fig. 3.

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Table 4 Elements and their importance in the diet Elements Importance in diet Vitamin E

It is damage caused by substances called free radicals, which can harm cells, tissues and organs and keep the immune system strong against viruses and bacteria [11]

Vitamin C

Antibody production and complement activity, detoxification of histamine, immune response to vaccination, production of interferons [12]

Vitamin D

It helps in recovery of bone fractures and improves your lung functions [13]

Protein

It plays an important role in recovery of respiratory disease as it helps in building and repairing of tissue. It is an important building block of bones, muscles, cartilage, skin and blood [14]

Table 5 Diet chart Diet list

Elements of the diet

Diet 1 (D1)

Breakfast: egg, bread, juice Lunch: mix pulses, rice/roti, mix veg, chicken breast Dinner: milk, chicken/veg salads

Diet 2 (D2)

Breakfast: sandwich, milk, fruit salads Lunch: panier/fish, pulse, rice and roti Dinner: chicken/veg soup, salads, mix veg, roti

Diet 3 (D3)

Breakfast: cornflakes/ oats, milk, juice Lunch: chicken/ sweet potato, black beans salads Dinner: rice, milk, roti, panier vegetable

Table 6 Vitamin C with respect to D1, D2, D3 Vitamin C D1 D2 D1 D2 D3

1 3 0.2

0.33 1 0.14

λavg(max)  3.0537, CI  0.0268, CR  0.0463

D3

Eigenvector (w)

5 7 1

0.2784 0.6500 0.0715

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Table 7 Vitamin D with respect to D1, D2, D3 Vitamin D D1 D2 D1 D2 D3

1 0.55 0.33

2 1 0.5

D3

Eigenvector (w)

3 2 1

0.5348 0.3038 0.1614

D3

Eigenvector (w)

5 3 1

0.6378 0.2577 0.1045

D3

Eigenvector (w)

4 0.33 1

0.6920 0.0900 0.2180

λavg(max)  3.0356, CI  0.0178, CR  0.0307 Table 8 Vitamin E with respect to D1, D2, D3 Vitamin E D1 D2 D1 D2 D3

1 0.33 0.2

3 1 0.33

λavg(max)  3.0330, CI  0.0165, CR  0.0285 Table 9 Protein with respect to D1, D2, D3 Protein D1 D2 D1 D2 D3

1 0.16 0.25

6 1 3

λavg(max)  3.0340, CI  0.0170, CR  0.0293 Table 10 Elements with respect to their importance Element Vitamin C Vitamin D Vitamin E Vitamin C Vitamin D Vitamin E Protein

1 8 5 7

0.125 1 0.165 0.5

0.2 6 1 5

Protein

Eigenvector (w)

0.143 2 0.2 1

0.0404 0.5168 0.1052 0.3377

λavg(max)  4.2504, CI  0.0835, CR  0.0927 Table 11 Ranking table using AHP Vitamin C Vitamin D D1 D2 D3

0.2784 0.6500 0.0715

0.5348 0.3038 0.1614

Vitamin E

Protein

Priority

Rank

0.6378 0.2577 0.1045

0.6920 0.0900 0.2180

0.58836092 0.240740535 0.170898545

1 2 3

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Fig. 1 AHP hierarchal model for the recommendation

Priority of diet

0.8 0.6 0.4

Rank

0.2 0 1

2 Diet

3

Fig. 2 Ranking got by AHP

5 Result Discuss In Table 13, AHP method is used to predict the diet for the patient and the validation of the method was done by the entropy method which is shown in Table 13. This table describes that Diet 1 has highest rank among all. In Fig. 3, Diet 1 has more weights than that of two others. If we compare both the results with entropy and AHP methods, the result we found was the same. So, we

Table 12 Validation with entropy D1

D2

D3

E

Vitamin C Vitamin D Vitamin E Protein Priority

0.2784 0.5348 0.6378 0.6920 0.534197247

0.6500 0.3038 0.2577 0.0900 0.327432738

0.0715 0.1614 0.1045 0.2180 0.138370014

0.249354268 0.097917069 0.205978158 0.268597185

Rank

1

2

3

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After Validation Diet 3 Diet 2 Diet 1 0

0.1

0.2

0.3

0.4

Entropy

AHP

0.5

0.6

0.7

Fig. 3 Final observation after the validation Table 13 Comparison with the AHP and entropy

Final rank

AHP

Entropy

Rank

Diet 1 Diet 2 Diet 3

0.58836092 0.240740535 0.170898545

0.534197247 0.327432738 0.138370014

1 2 3

conclude that the Diet 1 is perfect for the patient and if the patient will take the Diet 1 he will recover fast as compared with others.

6 Conclusion and Future Scope The paper presented the diet recommendation approach by using AHP method. The recommendation is then validated by entropy method. Both methods showed that Diet 1 is better than the other two diets. The input parameters for the diet were taken after consulting the dietitian/doctors by keeping in mind the respiratory disease. The result presented in the paper will be helpful for the patients who are suffering from respiratory disease. By using the approach presented in the paper, they can select the effective diet for themselves in an easier manner. The paper will also help the doctors to recommend the diet to the patients suffering from respiratory disease. In future, the present diet recommendation approach will be applied by considering more number of diets and in different aspects.

References 1. http://www.who.int/respiratory/en/ 2. W. Husain, Application of data mining techniques in a personalized diet recommendation system for cancer patients, in IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER 2011) (Penang, 2011) 3. M. Phanich, P. Phathrajarin, P. Suphakant, Food recommendation system using clustering analysis for diabetic patients. 2010 International Conference on Information Science and Applications (ICISA) (IEEE, 2010)

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4. G. Kovásznai, Developing an expert system for diet recommendation, in 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (Timi¸soara, Romania, 2011) 5. R. Rodriguez-Roisin, Toward a consensus definition for COPD exacerbations. Chest 117(5), 398S–401S (2000) 6. M.B. Bracken, K. Belanger, W.O. Cookson, E. Triche, I.D.C. Christi, B.P. Leaderer, Genetic and perinatal risk factors for asthma onset and severity: a review and theoretical analysis. Epidemiol. Rev. 24, 176–189 (2002) 7. J.K. Peat, C.M. Salome, C.S. Sedgwick, J. Kerrebijn, A.J. Woolcock, A prospective study of bronchial hyper responsiveness and respiratory symptoms in a population of Australian school children. Clin Exp. Allergy 19, 299–306 (1989) 8. A. Sirajuddin, J.P. Kanne, Occupational lung disease. J. Thorac. Imaging 24(4), 310–320 (2009) 9. R. Shukla Kumar, D. Garg, A. Agarwal, An integrated approach of fuzzy AHP and fuzzy TOPSIS in modeling supply chain coordination. Prod. Manuf. Res. 2(1), 415–437 (2014) 10. S.K. Divyaa, N. Yadav, S.K. Dubey, Usability evaluation of mobile phones by using AHPentropy approach, in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (IEEE, 2016) 11. https://medlineplus.gov/ency/article/002406.htm 12. http://www.onegreenplanet.org/natural-health/the-importance-of-b-vitamins-for-your-healthand-the-best-plant-based-sources-to-eat 13. https://www.abundanceandhealth.co.uk/en/blog/64-the-6-important-roles-of-vitamin 14. http://www.mdmag.com/journals/internal-medicine-world-report/2007/2007-08/2007-08_47

Detection of False Positive Situation in Review Mining Devottam Gaurav, Jay Kant Pratap Singh Yadav, Rohit Kumar Kaliyar and Ayush Goyal

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . False Positive Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposal for FESCR Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Datasets Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Classification Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 FESCR Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Datasets and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Datasets Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Effect of Machine Learning on the Performance of Classification . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

84 85 85 85 86 86 87 87 88 88 88 88 89 89 90

Abstract As the Internet is evolving at a steeper rate, reviews related to a product have become a vital data which help users to make informed decisions. Users are totally dependent upon those reviews given by customers with the experience they felt and makers depend on these user-generated reviews to apprehend the sentiments of users related to a product. Henceforth, it is mandatory for both makers and users to create a portal where customers can peruse all the reviews in a comprehensive D. Gaurav (B) · R. K. Kaliyar Bennett University, Greater Noida, India e-mail: [email protected] R. K. Kaliyar e-mail: [email protected] J. K. P. S. Yadav Ajay Kumar Garg Engineering College, Ghaziabad, India e-mail: [email protected] A. Goyal Texas A&M University, Kingsville, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_8

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manner in a less amount of time. Considering this, a predictive model is developed that detects false positive reviews from original reviews and ratings are calculated to judge how these fake reviews create confusion in the mind of customers. Keywords False positive situation · Naive Bayes · k-NN · Decision tree

1 Introduction Electronic users offer a dynamic stage to their users so that customers can give reviews related to the product or facility which users have accomplished there in. With the growth in the utilization of Web, the Internet retailing industry is rising gradually. The method of buying the product has been changed to online retail from traditional retail. Traditional retail comprises buying the products in an offline way; the users can re-examine the products before taking off. Moreover, the electronic suppliers give an opportunity to assess the products in a limited amount. Due to these, online users must rely upon numerous other information. This information may be referred as word-of-mouth publicity or user-generated reviews. Apart from the comments posted by customers, certain supplementary data related to the products are accompanied with the help of sellers. Sellers give every single description of their product. In respect to these descriptions, reviews generated by user, still give more attention toward these descriptions which are accompanied with the help of sellers. In reviews generated by users, details are provided in relation to the product as they start gaining experience. These reviews generated by users have an orientation more toward customers after having a comparison with the outdated data. For new users, the data generated by users are more reliable than traditional data. But a particular product may consist of enormous reviews which turn out to be a difficult situation for other users to go through all these reviews. Due to these, informed decision can’t be formed. On the other hand, if users read only few reviews and decide to purchase an item, he/she may turn into a casualty of one-sided conclusion. In these manners, the reviews generated by users are written in the form of summary, which reflects their sentiments related to that item. Makers may in turn utilize these reviews generated by users to notify the most recent pattern as well as the user’s sentiments for that product. Due to enormous reviews, it turns out to be a difficult situation for makers in going through all the reviews and settle on administrative choices related to their business. Therefore, both users and makers must have portal where reviews are read by customers in the form of summary in a minimal period so that an informed decision can be formed [1]. These generated summaries differ from summarized text because only features are being extracted that are discussed by users and their opinions. We are additionally keen on seeing if there are some positive opinions, i.e., users love the specific feature

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85

Badly Mike Problem!!! Bad calling experience.

Fig. 1 False positive situations

of product, and negative, i.e., users loathe the specific feature of product in a vice versa manner. These summaries are termed as feature-based summary [2]. This summary follows certain steps as: (i) Users first identify the features related to the product and reviews are given by them. (ii) The orientation of sentence related to the specific feature of product is judged that whether it is in positive/negative form. (iii) Finally, the summary based on that feature is formed with extracted data.

2 False Positive Situation Flipkart utilizes a 1–5 rating scale to rate all the products, paying little respect to their category. To comprehend the user’s thinking related to the product, rating is communicated in the form of stars [3]. The stars tell about the fulfillment of users and how they can enhance their products with the end goal to make it more reliable in the market? Figure 1 demonstrates a circumstance where a specific user has given the rating for the product as 5 and in the comment part he expresses that “Badly Mike Problem”. These two conditions negate each other and besides it becomes difficult for the users who are in search of buying the product. These lead to confusion in the mind of customers. These situations are commonly called as false positive situations.

3 Proposal for FESCR Method With on seeing the whole review and rating, neither rating of product is lucid nor product features’ rating is lucid. These assist the users in making a purchase for the feature related item. In this proposal, the main focus is on recognizing the feature for product with FESCR (Feature Extraction System for Customer Reviews) method. The feature can be classified into positive and negative features. After the identification of features, false positive situation is known, by making a comparison between original test label data and predicted label data; if not equal.

3.1 Datasets Collection The reviews given by a specific user for a specific product can be mined from various E-Commerce Web sites such as Flipkart, Amazon, Snapdeal, and so forth, with an API. All the raw data are put away either in a database or in any file like CSV. This appears in Fig. 2.

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Pre-processing Perform Tokenization

Crawling of Reviews

Clean Stop Words Perform Stemming

Review Database

70% Train Data

Construct Classifier Model 30% Test Data

Classify text as positive/negative Obtain trained data using classifier for train data

Feature Selection Evaluate classifier with the test data Extract False Positive Reviews from test data Obtain Average Rating of Customer Reviews

Fig. 2 Architecture of review mining

3.2 Preprocessing It involves the process of formulating the data and cleans the dataset in a way to classify the data. Tokenization: This process involves the chopping of data into pieces which are taken as a sequence of characters as input and at the same time eliminating all irrelevant characters like punctuation marks are eliminated. These pieces are referred as tokens. Stop Word Removal: Some of the most frequently used stop words are “a”, “of”, “the”, “I”, “it”, “you”, etc., and these are generally regarded as “functional words” which do not carry any meaning. Hence, it is practical to remove these words. Stemming: Here, words that are derived are finally reduced in their root form or in stem form. Example may include like “developed”, “development”, “developing”, etc.

3.3 Feature Selection Here, in this process, classifiers are made more efficient with the reduction in the length of data to be examined along with the identification of appropriate features required for classification. Moreover, at this stage, refined features are taken as input to the learning/classification process.

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3.4 Classification Process In this step, a model is being prepared to make predictions using the trained data. The accuracy of the model can be estimated with the help of predictions done for all data which are there in the test set. This can be helpful in comparing the test set’s class values with predicted data. As a result, accuracy may range between 0 and 100%.

4 FESCR Algorithm

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5 Datasets and Results 5.1 Datasets Used To carry out the experiments accurately on the proposed method, the datasets have been extracted from the Flipkart. After extraction as described in Step 1, the total length is of 5232. These datasets are called as mobile datasets. After performing the preprocessing on datasets, the datasets are further split in the ratio of 70:30 which means 70% of the datasets falls in the training category and 30% of the datasets falls in the test category, respectively. The clean data are finally being labeled as positive/negative. The length of positive and negative is of 3662 and 1570, respectively.

5.2 Evaluation Metrics For classifying the data accurately, some metrics are used for evaluation purpose which is given in Table 1. These compare with the class labels allotted to documents by a classifier along with the classes that belong to that products.

5.3 Results and Discussion Among various machine learning algorithms, the most commonly used classifier for classification of sentiment is NB classifier. Here for testing purpose, NB [4], k-NN [5], and DT [6] classifiers are utilized for classification of reviews into positive and negative; but NB performs better than others with suitable feature selection method. All experiments have been carried out with the help of these classifiers in Pycharm. Negative word like no, not, never, didn’t, don’t, can’t inverts the sentence’s polarity which is vital to deal with classification of sentiments. It is finished by linking the first word after the negative word that ought not to be a stop word. For instance, “this is not a good movie”, polarity of word “good” is reversed with “not,” and it progresses toward becoming “not good” with negation. Table 2 shows the performance metrics used for evaluation of binary classifiers like Precision, Recall, F-Measure, and Accuracy [7].

Table 1 Contingency tables

Classified label

Correct label Positive

Negative

Positive

True positive (TP)

False positive (FP)

Negative

False negative (FN) True negative (TN)

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Table 2 Performance measures for binary classification Measure Classification type Formula

Assessment of process

Precision

Binary

Precision 

Recall

Binary

Recall 

F-measure

Binary

F - Measure  (Precision∗Recall) 2 ∗ (Precision+Recall)

Accuracy

Binary

Accuracy 

Table 3 Contingency tables Classifier Accuracy (%) NB k-NN DT

85.49 65.03 69.38

TP TP+FP

TP TP+FN

(TP+TN) (TP+FP+FN+TN)

∗ 100

Overall viability related to a classifier Class agreement of the labeled data with the positive labels given by the classifier Usefulness of a classifier to recognize the positive labeled data Association between positive labeled data and those given by the classifier

Precision (%)

Recall (%)

F-measure (%)

93.05 75.36 68

90.32 82.37 69

91.67 78.71 65

5.4 Effect of Machine Learning on the Performance of Classification It is seen from the analyses that NB classifier improves its performance subsequently after the elimination of extraneous features. This happens because irrelevant features such as false reviews worsen the NB classifiers’ performance. Table 3 illustrates the Accuracy, Precision, Recall, and F-Measure outcomes accomplished by the NB, kNN and DT classifiers, with the above formula shown in Table 2. These outcomes show that the NB classifier outperforms the rest classifiers in all terms because higher is the precision, lower is the false positive with an error rate of 14.50%.

6 Conclusions There are many users who purchase products through E-commerce Web sites are unable to know whether the customers are satisfied by the services provided by the firm. It needs to develop a system where various customers give reviews about the product with the experience they felt. These in turn help the E-commerce enterprises and manufacturers to improve their services and make their product more reliable in

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the market. Thus, it proves to be an excellent chance in order to solve the problem related to a customer. The electronic sites give us a way to form a relationship more closely among the customers for a specific product. These reviews may even exist in thousands or in hundreds which becomes a troublesome for users to go through all the reviews. Hence, an informed decision can’t be formed. So, summaries of those reviews need to be present in a feature-based format for that product.

References 1. Y. Chen, J. Xie, Online consumer review: word-of-mouth as a new element of marketing communication mix. Manage. Sci. 54, 477–491 (2008) 2. A. Kangale, S.K. Kumar, M.A. Naeem, M. Williams, M.K. Tiwari, Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary. Int. J. Syst. Sci. 47(13), 3272–3286 (2016) 3. M. Hu, B. Liu, Mining and summarizing customer reviews, in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2004), pp. 168–177 4. B. Pang, L. Lee, S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10 (Association for Computational Linguistics, 2002), pp. 79–86 5. Wikipedia, k-nearest neighbor algorithm, http://en.wikipedia.org/wiki/K-nearest_neighbor_ algorithm 6. X. Niuniu, L. Yuxun, Review of decision trees (IEEE, 2010) 7. A. Angelpreethi, S.B.R. Kumar, An enhanced architecture for feature based opinion mining from product reviews, in 2017 World Congress on Computing and Communication Technologies (WCCCT) (IEEE, 2017), pp. 89–92

Optimization of Cloud Datacenter Using Heuristic Strategic Approach Biswajit Nayak, Sanjay Kumar Padhi and Prasant Kumar Pattnaik

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Scheduling Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The Role of Cloud in Improving Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Task scheduling is extremely challenging as it is very difficult to utilize resources in the best possible manner with low response time and high throughput. Task scheduling can be designed on the basis of different criteria under several rules and regulations. This is simply nothing but an agreement between cloud users and cloud providers. Task scheduling has attracted a lot of attention. It is very challenging due to the heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. The scheduling algorithm has been emphasized not only on appropriate resource utilization but also on efficient resource utilization. The proposed algorithm performance is estimated based on load balancing of tasks over the nodes and makespan time. Scheduling algorithm is used to enhance the performance of the system by maximizing the CPU utilization, reducing the turnaround time, and maximizing throughput. Tasks are statically scheduled based on which different available resources are allocated at compile time or dynamically. The prime objective for B. Nayak (B) · S. K. Padhi Computer Science & Engineering, Biju Patnaik Technical University, Rourkela, Odisha, India e-mail: [email protected] S. K. Padhi e-mail: [email protected] P. K. Pattnaik School of Computer Science & Engineering, Kalinga Institute of Industrial Technology University, Bhubaneswar, Odisha, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_9

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scheduling of task approach in the cloud is to minimize the task completion time, task waiting time and makespan. And also to optimize the utilization of resources. Keywords Computing · Datacentre · Task scheduling · Algorithm · Makespan

1 Introduction Cloud is a technology that let us to access a shared pool of resources on-demand. The access to the cloud for facilitating ubiquitous, voluminous, on-demand access to the pool of resources can be effortlessly provided with minimum interaction [1]. Cloud technology makes several services of a dynamic type like every service or IT services through the Internet. Task scheduling plays a major role in defining performance and reliability. In task level of scheduling, the task is sent to the datacenter for execution within a stipulated time period. Users send requests to the datacenter for computing jobs, named task. The task is a small piece of work that should be executed within a given period of time. Job scheduler assigns the tasks to the cloud provider for resources. Figure 1 shows how the jobs are executed in the datacenter [1, 2]. The datacenter is responsible for classification of the task according to the requested service and service-level agreement (SLA), and a task may be in the form of inserting data, processing the entered data, accessing, or processing software or storage functions. These tasks are assigned to the available resources; then, the resource performs the task assigned and returns the result back to the user. The above task assigned by using a task scheduling algorithm. As in the diagram, the jobs are assigned to the cloud scheduler. The scheduler examines the status regarding resource availability, and depending upon the status, all the tasks are allocated to the various resources. Cloud technology provides multiple virtual machines (VMs); hence, scheduler assigns multiple tasks to different VMs. Cloud scheduler uses various scheduling algorithm, and the responsibility of the algorithm is to increase the throughput, turnaround time, and system performance [3, 4].

VM

VM VMM

Jobs

Job Queue

H/W Job Scheduler

: VM

VM VMM H/W

Fig. 1 Structure of job scheduling technique

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Scheduling process of cloud datacenters in the cloud computing environment can be characterized through numerous essential factors as given below: • Computing entity: Virtual machine system/method provides the facility for the computing entry. The virtual machine of cloud computing provides the facility for the computing with the help of application software or software, operating system, etc., through which it is possible to execute in a fraction of second. • Job scheduler: The main activity of the cloud computing scheduler is job scheduling whose prime responsibility is to specify the execution order of jobs. • Job queue: Job queue is a place where the jobs are waiting for their execution. Then, the jobs are assigned to a resource/machine when the resources are available. • Job arriving process: Job arriving process provides the facility that the jobs arrive into the scheduling system.

2 Scheduling Characteristics Task allocation to various resources is known as scheduling. Scheduling is the process to schedule tasks with efficient throughput, appropriate resource utilization, and improved performance of the system. Cloud computing came to the market as a distributed concept of computing which added a new technology in the area of distributed technology system. The cloud technology puts its footprint in the technology because it can provide a huge amount of available storage space with very minimal cost, along with the quick response time. Not only the technology but also the users are increasing exponentially day by day, so it is a daunting task for technology providers to provide such kind of facility, so to provide such type of facility, it requires some prototype for scheduling task [5, 6]. Initially, the jobs or tasks are submitted over the cloud, and the cloud asks the cloud information service. If the resource required is available, then it allocates the same depending upon the task scheduling algorithm. And the scheduling of tasks ensures efficient use of resources, load distribution around nodes, and effective execution time.

3 The Role of Cloud in Improving Healthcare Like other industry, health care also concerns with high availability which is must and also some regulatory compliance issues like security as well as privacy. It also focuses on important data movement across borders and ownership. Most of the organization in the area of health care are implementing cloud-based solutions or operating. These technologies are limited, but due to the availability of some tool, the clinical healthcare system is growing rapidly. Figure 2 shows the complete procedure for job allocation [7–9].

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Submit Job

Cloud Information Repository Scheduler

Resource Information Allocate

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Input Data

Fig. 2 Job allocation process

Use of cloud computing probably is not a solution to the entire problem. However, it is great amending which will enhance the efforts and results in an improved healthcare system. Cloud technology reduces and even removes the burden of infrastructure management by providing access to all type of resources and services. This provides an environment that minimizes the expenditure and provides an easy way to adopt required technology. It is not the thing that all the providers take time for adopting new technologies. All the health organization is looking for digitization because to increase the quality of the patient care or health information system. The resources should be assigned in such a way that it should satisfy the user’s request and also enhance the performance of the system [10–13]. There are several parameters used to evaluate the scheduling of tasks, such as: • Makespan: It is defined as the time required for completing all the tasks. One of the major characteristics of a good task scheduling algorithm is diminished makespan. Makespan CTmax  max{CTi } where CT is the completion time. CTi is the completion time of “ith” task. • Deadline: The time required for submitting a task till execution or completion of the task is known as the deadline. Deadline is a constraint; if a task cannot be completed within it, then the scheduling algorithm is treated as week task scheduling algorithm; otherwise, it is treated as good. • Execution Time (ET): Execution time can be defined as the time required for executing the given task. The main aim of the task scheduling algorithm is to keep execution time minimum. • Completion Time (CT): It is the time required for entire job execution. It is different from ET because it includes the ET along with delay. • Performance: Performance is nothing but the efficiency on which services are provided to the users based on their requirement. Efficiency indicates the quality of scheduling algorithm. Efficiency is depending on the requirement by the user and also a cloud service provider.

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• Load balancing: Load balancing is another factor for increasing efficiency. It is always required to distribute the entire load over the cloud network in such a way that no nodes or link in the network remains overflow or underflow.

4 Algorithms The proposed research is the static heuristics. Some of the basic scheduling algorithms are (a) FCFS and (b) RR methods. It assumes that all tasks appear at the same instance of time and also independent of status and availability of the resource. First come first serve assigns the task whenever the resources are available. It assigns the task using arrival time; hence, complexity is less. Round robin method assigns the task to the resource based on arrival time, but a task can use the resource for certain amount of time known as time quantum; then, the task pre-empted, queued, and wait for next execution if it is required. Some heuristic method like the opportunistic load balancing scheduling method tries to schedule the task on the basis of their completion time for the next available resources or machines. It allocates resources in such a way that all the machines are busy simultaneously which leads to the poor makespan. Few more algorithm like minimum completion time algorithm schedules the task on the basis of expected MCT (minimum execution time). It is always not mandatory that on the same machine, the task will have MCT. The above-discussed things may be eradicated using the proposed scheduling algorithm [14–16]. Min–Min Min–Min heuristic determines the task that completes the task with the minimal time period from all the tasks and assigns to the appropriate machine. This process goes on till the completion of all the tasks; hence, the makespan increases as the completion time increases. This approach allocates smaller tasks on faster, and large tasks have to wait for smaller ones for execution. The Min–Min algorithm enhances the system’s overall throughput. Step 1: Start. Step 2: Determine the minimum completion time of each task over all machines. Step 3: Find the minimum completion time overall tasks. Step 4: Depending upon completion time, assign a task to the machine. Step 5: Repeat the Step 3 and Step 4 till all the tasks are scheduled. Step 6: Stop. Max–Min A Max–Min heuristic algorithm has been discussed in the literature. Some of the algorithms give an exact solution, but they provide poor performance with large search spaces. However, a number of authors have worked to improve and enhance the performance of these task scheduling algorithms. There is some similarity between Max–Min and Min–Min, but the difference is that first it chooses the longest task and

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assigns to the best available machine with MCT out of all machines. The makespan and throughput increased in case of Max–Min as compared to Min–Min. Step 1: Start. Step 2: Determine maximum completion time of each task over all machines. Step 3: Find the minimum completion time overall tasks. Step 4: Depending upon maximum completion time, assign a task to the machine. Step 5: Repeat the Step 3 and Step 4 till all the tasks are scheduled. Step 6: Stop. Sufferage Sufferage algorithm maps a resource to a task that would suffer most in terms of expected completion time according to its sufferage value. It first computes the completion time for each task on each resource. Second, the two consecutive minimum completion time for each task is found. The difference between these two values is defined as the suffrage value. Third, the task with maximum suffrage value is assigned to a resource with minimum completion time. Then, the completion times for resources are updated, and the above steps are repeated until the set of tasks becomes empty. This strategy works perfectly, but it has one shortcoming in case more than one task has the same maximum suffrage value. It simply selects the first task without taking into account the other tasks which may cause a starvation problem. Step 1: Start. Step 2: Determine difference between its minimum completion time and minimum completion time over all machines. Step 3: Determine the minimum suffrage. Step 4: Depending upon minimum completion time, assign a task to the machine. Step 5: Repeat the Step 3 and Step 4 till all the tasks are scheduled. Step 6: Stop.

5 Performance Analysis In this section, the algorithm is tested according to the criteria. When the user starts to execute the applications, it is forwarded to the cloud datacenter via a broker where the scheduling is carried out. The process of scheduling is carried out as explained in Fig. 3. Table 1 considers a system with four resources and five tasks to investigate the proper scheduling. The diagrams (Figs. 4, 5, and 6) shows the scheduling of different algorithms mentioned in this article. Each diagram specifies the available resources in “x-axis” and time required to complete the task can be observed in “y-axis.”

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Host VM1 Cloudlet(s)

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Fig. 3 Datacenter performance analysis Table 1 Resources for allocation Resource\task T1 T2 M1 M2 M3 M4

140 100 120 110

20 100 80 90

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T4

T5

60 70 50 75

30 90 100 25

50 80 90 35

Fig. 4 Min–Min scheduling

The above three figure shows the investigation of proper scheduling. Figure 4 shows the investigation of the Min–Min scheduling. Figure 5 shows the investigation of the Max–Min scheduling. Figure 6 shows the investigation of the sufferage scheduling. Table 2 shows the different characteristics of different task scheduling methods along with their benefits and drawback.

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Fig. 5 Max–Min scheduling

Fig. 6 Sufferage scheduling

6 Conclusion The different algorithms are tested for their suitability, feasibility, adaptability in the context of the cloud scenario so that they can facilitate cloud providers to provide a better quality of services. The algorithms are described with an innovative idea and exposed to rigorous testing using various benchmark datasets, and its performance is evaluated in terms of total makespan and expected completion time. The heuristic algorithms are proposed and implemented to satisfy the hard constraints. The experimental evaluation confirms the feasibility of the algorithm in satisfying the constraints. The scheduling algorithm has been proposed which focuses on the appropriate and efficient utilization of the resources. Results show the improvement in performance. The complexity of the approach is analyzed, and it is experimentally observed that the proposed algorithm can further be improved by increasing number of metrics that may result in good performance that can be deployed.

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Table 2 Task scheduling method and their characteristics Technique/model Factor considered Benefit Drawback “Min–Min”

Makespan, expected completion time

“Max–Min”

Makespan, expected completion time

“Sufferage”

Minimum completion time, reliability

Makespan is Deprived load better than balancing and another algorithm quality of service (QOS) features are not considered Makespan is Deprived load better than balancing and another algorithm quality of service (QOS) features are not considered Makespan is Sufferage value better than other is used for algorithm and scheduling also load balancing

Remarks Two phases with smallest overall minimum completion time

Two phases with smallest overall minimum completion time

Also executes a task that suffers the most

References 1. P.K. Suri, S. Rani, Design of task scheduling model for cloud applications in multi cloud environment, in ICICCT 2017 (CCIS 750, 2017), pp. 11–24. https://doi.org/10.1007/978-98110-6544-6_2 2. D.W. Brinkerhoff, Accountability and health systems: toward conceptual clarity and policy relevance. Health Policy Plan. 19(6), 371–379 (© Oxford University Press 2004), https://doi. org/10.1093/heapol/czh052 3. T. Mathew, K.C. Sekaran, J. Jose, Study and analysis of various task scheduling algorithms in the cloud computing environment, in International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, 2014), pp. 658–664. 978-1-4799-30807/14/$31.00_c 2014 4. P. Banga, S.P. Rana, Heuristic based independent task scheduling techniques in cloud computing: a review. Int. J. Comput. Appl. 166(1), 0975–8887 (2017) 5. B. Nayak, S.K. Padhi, P.K. Pattnaik, Impact of cloud accountability on clinical architecture and acceptance of health care system, in 6th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA-2017) (Springer, 2018), pp. 149–157. https:// doi.org/10.1007/978-981-10-7563-6_16 6. B. Nayak, S.K. Padhi, P.K. Pattnaik, Understanding the mass storage and bringing accountability, in National Conference on Recent Trends in Soft Computing and It’s Applications (RTSCA) (2017), pp. 28–35. ISSN 2319-6734 7. S.A. Hamad, F.A. Omara, Genetic-based task scheduling algorithm in cloud computing environment (IJACSA). Int. J. Adv. Comput. Sci. Appl. 7(4), 550–556 (2016) 8. S. Singh, M. Kalra, Task scheduling optimization of independent tasks in cloud computing using enhanced genetic algorithm. Int. J. Appl. Innov. Eng. Manage. (IJAIEM) 3(7), 286–291 (2014). ISSN 2319-4847 9. N.M. Reda, An improved sufferage meta-task scheduling algorithm in grid computing systems. Int. J. Adv. Res. 3(10), 123–129 (2015). ISSN 2320-5407 10. E.K. Tabak, B.B. Cambazoglu, C. Aykanat, Improving the performance of independent task assignment heuristics minmin, maxmin and sufferage. IEEE Transa. Parallel Distrib. Syst. 25(5), 1244–1256 (2014)

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11. E. Kumari, A. Monika, Review on task scheduling algorithms in cloud computing. Int. J. Sci. Environ. Technol. 4(2), 433–439 (2015). ISSN 2278-3687 (O) 12. R.M. Singh, S. Paul, A. Kumar, Task scheduling in cloud computing: review. Int. J. Comput. Sci. Inf. Technol. 5(6), 7940–7944 (2014) 13. N.S. Jain, Task scheduling in cloud computing using genetic algorithm. Int. J. Comput. Sci. Eng. Inf. Technol. Res. (IJCSEITR) 6(4), 9–22 (2016). SSN(P): 2249-6831; ISSN(E): 2249-7943 14. P. Savitha, J.G. Reddy, A review work on task scheduling in cloud computing using genetic algorithm. Int. J. Sci. Technol. Res. 2(8), 241–245 (2013) 15. R.K. Devi, K.V. Devi, S. Arumugam, Dynamic batch mode cost-efficient independent task scheduling scheme in cloud computing. Int. J. Adv. Soft Comput. Appl. 8(2) (2016). ISSN 2074-8523 16. B. Nayak, S.K. Padhi, P.K. Pattnaik, Scheduling issues and analysis under distributed computing environment. J. Adv. Res. Dyn. Control Syst. 10(02), 1475–1479 (2018). ISSN 1943-023X

Blockchain Technology for Decentralized Data Storage on P2P Network Akshay Raul, Shwetha Kalyanaraman, Kshitij Yerande and Kailas Devadkar

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Blockchain Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Data Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Experimental Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Communication Through Metadata Server on the Internet . . . . . . . . . . . . . . . . . . 5.2 Mesh Network in a LAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Sending Files Using Blockchain Metadata Server as a Mediator . . . . . . . . . . . . . 6.2 Sending Files Directly to Peers and Storing Metadata on Blockchain Server . . . 6.3 AES File Encryption Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Mining Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Blockchain is a rapidly growing concept that can be used to store a burgeoning list of records in blocks. There is an inundation of data daily where storage becomes a cardinal problem. To maintain the list of records and by not compromising on storage space, blockchain can be adopted by storing data on a peer-to-peer network. Unutilized storage from another peer can be used to store data. Data stored on another device will be subjected to high security by encryption to abstract data from device owner. This paper uses the concept of blockchain to A. Raul (B) · S. Kalyanaraman · K. Yerande · K. Devadkar Department of Information Technology, Sardar Patel Institute of Technology, Andheri, Mumbai, India e-mail: [email protected] S. Kalyanaraman e-mail: [email protected] K. Yerande e-mail: [email protected] K. Devadkar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_10

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realize the potential of the decentralized Internet. Blockchain makes transaction logs transparent and leaves no room for malpractices. Data distribution over millions of devices will resolve problems such as bandwidth stealing and throttling; additionally, it will lower the latency of data retrieved as the peer will be in proximity to the user. Using blockchain P2P system, it opens a whole new world of possibilities for a new Internet. Keywords Blockchain · Decentralization · Ledger · Peer-to-peer network

1 Introduction To make effective use of free space, distribution of an organization’s data onto millions of mobile devices can achieve decentralized Internet. Blockchain has become a new technological solution, and using P2P data storage will achieve the transparency for data storage. To implement such a system, three major components play a vital role in bootstrapping the system: encryption, compression and blockchain ledger. Decentralized Internet uses blockchain which has a common ledger that can be viewed by all the participating entities which deal with the same transactions. This paper aims at using blockchain ledger to create P2P data distribution system which will maintain complete transparency and keep user’s data secure. The architecture of such a system is two tiers. The upper tier is the cloud controller, which is metadata holder and records transactions (transactions here mean changes in data), controls distribution to millions of devices and has logs where devices are being used for distribution. The lower tier nodes in the network, which is a mobile device or any device capable of storing and connecting itself to the World Wide Web. Encryption of data will be based on AES algorithm. The devices will be able to distribute data to peer directly in mesh network and via the controller in tree network. The device will run the lower tier logic as a background service using minimal resources and utilizing bandwidth as and when feasible. Each device will have to update its data as per the latest timestamp of that data on the controller. This system enables true data democracy, equal power to all peers and no central authority holding the data.

1.1 Blockchain Introduction The blockchain is a distributed public ledger that assists in keeping data transparent across various verticals. A blockchain is a set of records that are linked to each other using hashing. Every block has two hash values. One is the previous block’s hash value to which it is linked and the other being the block’s own hash value. Any illegal tampering of data could be easily noticed by all the nodes connected to the distributed network. It is the popular notion of the use of blockchain for digital currencies [1],

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Fig. 1 Basic blockchain structure

and it has extended far beyond that by removing the need for a third party in any transaction as shown in Fig. 1.

2 Data Distribution Designing a distributed platform for anonymized data set trading without any centralized trusted third party could be one way of distributing data [2]. The platform consists of peers and consensus-based blockchain mechanism, and each peer acts as a data broker, data receiver or verifier for blockchain in a data transfer transaction. It implements a prototype system of the platform using an open-source blockchain mechanism, Hyperledger Fabric, and provides evaluation results of the prototype system. Another way could be to view blockchain as a novel technology, and we should think it as an innovation for managing digital society, which provides fundamental principles to support democratically distributed applications. Based on the design and application of the energy Internet [2], this paper analyses the core architecture of the initial blockchain technology in Fig. 2 and combines the security of the public chain with the efficiency of the private chain, solving the poor efficiency problem of the initial blockchain by using the high efficiency of private chain and achieving decentralized supervision, and providing a credible, safe and efficient performance of the energy Internet in the storage of its massive data, as well as a huge business system.

3 Proposed Scheme The proposed scheme consists of the following components: Data Owner (Peer): The data owner is a peer in the network who wants to store his data on another node. Data Receiver (Peer): Data receiver stores the data on his device.

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Fig. 2 Blockchain architecture of the proposed methodology

Ledger: Every node stores the ledger which contains transaction records in the form of blocks contained in the blockchain. All the nodes update ledger when a new block is added. Node, when entering the network, connects to all the nodes via WebSocket. The node creates its set of private and public keys and broadcasts its information such as its IP address, port number and the public keys, to other nodes in the network. Whenever a node wants to store data to another node, it will encrypt the data and send a request to random nodes selected in the network to store the data [3, 4]. The distribution algorithm used is super-peer selection algorithm is shown in Fig. 3 [5]. The algorithm uses mobility, free memory size and network bandwidth as parameters for selection. The selection of peer is done by calculating profile index of each peer, and peer with highest profile index will be chosen as super-peer which will act as a data receiver. The data owner will now initiate a transaction and broadcast it for validation. All the files present in the initiated transaction by data owner for storage on another node are sent and uploaded to the server where they are encrypted using AES encryption with a private key unique to each user. The random nodes will compute the hash, SHA-256 of the encrypted data which will be sent to the user which will be used to prove that the user is a valid user. Blockchain will hold the metadata of the transaction in a ledger. Each block created will hold the sender device ID and receiver device ID, timestamp, file ID and previous hash value of the block. The user will apply distribution algorithm which will select the most suitable node for storage based on the super-peer selection. The data owner whenever wants to retrieve the file will send a request to the receiver, and the receiver node will return the file to the data owner.

Blockchain Technology for Decentralized Data Storage … Fig. 3 Flowchart

The selection of peer will take place according to this formula:     currentStorage (upTime−downTime)   MaxStorage +  downTime profile index  3

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4 Mining To verify and validate the blockchain [6, 7], it involves the use of miners who check for the validity of the blockchain and give their consent a member in the chain. Each peer maintains a copy of the distributed ledger. Consensus on the order of blocks and transactions in the blockchain is delegated to orderers who provide an ordering service without holding distributed ledger state [8]. If they spot an invalid block in the blockchain, they can flag it for an illegal transaction and broadcast that to all peers. If 51% or more peers agree to penalize a defaulter, the peers can vote to remove the peer from the network. Difficulty in mining is because the SHA-256 hash of a block’s header is lower than or equal to the target for the block to be accepted by the system. Mining in this system is a way for peers to pay for the system used by dedicating their energy resources of battery and hardware to maintaining a free democratic storage system. The server decides which peer should mine a blockchain. Mining Process: 1. If a peer agrees to be a miner, he is assigned a blockchain by the server to validate. 2. Each block of a transaction has an increasing hash, so the miner must spend some amount of energy guessing the root block. 3. The miner then must analyse for upload and download transaction for a peer if the file hash has changed after the file has been uploaded. 4. If the miner detects a change in the hash, he reports this incident to all peers. 5. The rating of the store peer is decreased, and the peers may decrease their consensus. 6. The rating of the miner increases by 1% of current rating, and he is awarded 1% more storage space.

5 Experimental Analysis The experiment is performed using a cluster of mobile nodes running Android operating system. This paper has two approaches to the proposed scheme.

5.1 Communication Through Metadata Server on the Internet In this method, it is proposed that due to lack of P2P protocols and feasibility of peer-to-peer communication on mobiles, we use a coordinator metadata server which serves a node in the network as well as point of communication between peers of different networks. The server serves as storage of updated blockchain server and broadcasts ledger to all peers in all the networks. The topology here is tree topology.

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For each tree, and thus, for each chunk, the source node sends the chunk to its children in series; the same holds for each peer node of the tree, excluding the leaves [9]. Steps of entire communication: 1. Peer connects to the server using WebSocket. 2. After connection, the peer sends his IP address, storage data and the AppID. The AppID is permanently stored on the blockchain server and can be viewed by all peers. 3. The connection created is a live connection with the server, until the mobile goes offline or switches off. 4. If a peer wants to upload a file, it sends the server the filename, file size, file type, file mime, current storage, followed by the file itself. 5. The server then analyses the all connected peers for their storage information, a rating given by consensus and percentage online. 6. Once analysed, it writes transaction to a block in the sender blockchain. 7. Once transaction is written, the file is sent to storage peers. 8. The storage peer on receiving and storing file sends a success acknowledgment. 9. The server destroys all objects related to the file and broadcasts the ledger.

5.2 Mesh Network in a LAN In this approach, all peers are connected to each other and form a true mesh of devices. This approach is intended to implement in a private LAN environment which exists in the corporate world. This method employs transparency of communication in a company and embodies trust between peers. Here a central metadata server which is always live and stores the most updated ledger in the network from which all peers get the ledger or is broadcasted from in case the other node goes offline then the server will send the most updated ledger to the requesting node. Each android device hosts an HTTP server to send and receive files from peers, while each device is connected to the metadata server using WebSocket. Steps of entire communication: 1. Peer connects to the server using WebSocket. 2. After connection, the peer sends its IP address, storage data and the AppID. The AppID is permanently stored on the blockchain server and can be viewed by all. 3. The connection created is a live connection with the server, until the mobile goes offline or switches off. 4. If a peer wants to upload a file, it sends the server the filename, file size, file type, file mime, current storage, followed by the file itself. 5. The server then analyses all connected peers for their storage information, rating given by consensus and percentage online. 6. Once analysed, it writes transaction to a block in the sender blockchain.

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7. Once transaction is written, the server sends storage peers, the IP address of the peer, who wants to upload the file and a key. 8. The storage peer on receiving key and IP address sends a POST request with the key to obtain the file. 9. Send peer checks for validity of the key, and send the file if the key is valid. 10. On receiving, the storage peer sends a success acknowledgment to the blockchain server and the sending peer.

6 Results Devices tested on: Moto G5 Plus, Moto G5S Plus, Lenovo K9. The bandwidth was 2 Mbps, and average file size uploaded was 12 MB. To test the system, each peer was made to upload a file every hour for a day. Table 1 shows tree network results, and Table 2 shows mesh network results.

6.1 Sending Files Using Blockchain Metadata Server as a Mediator See Table 1.

Table 1 Tree network results Criteria

Time (s)

Time taken to send file (12 Mb)

94

Time taken to receive file Server latency

124 2.4

Table 2 Mesh network results Criteria

Time (s)

Time taken to send file (12 Mb)

45

Receiving file time

47

Latency

1.9

Average bandwidth

160 Kbps

Blockchain Technology for Decentralized Data Storage … Table 3 Encryption time Device Video file (100 Mb) (s) Moto G5 Plus Moto G5S Plus Lenovo K9

5.6 4.4 6.3

109

Audio file (10 Mb) (s) Documents (20 Mb) (s) 0.875 0.82 0.93

0.95 0.93 1.02

6.2 Sending Files Directly to Peers and Storing Metadata on Blockchain Server The results proved to be highly supportive of the system as the transparency and trust between peers grew over time. In the second method, where a mesh network was implemented, the bandwidth utilization spread across network, decreased congestion and increased peer reachability.

6.3 AES File Encryption Time Table 3 shows AES encryption time below.

6.4 Mining Rate Mining rate is the rate at which illegitimate transactions are mined for a certain length of a blockchain. Mining rate depends on the device used and length of the chain. Faster a device can mine a blockchain, faster the defaulters are known in the network. The rate for the three devices is as follows: 1. Device 1—Moto G5 Plus: 2105 records/s 2. Device 2—Moto G5S Plus: 2495 records/s 3. Device 3—Lenovo K9: 1893 records/s

7 Conclusion A peer-to-peer data distribution framework is presented using blockchain. The fundamental objective is to provide decentralized and secure data distribution using blockchain. It adds smart contracts between the peers in a decentralized manner where transparency is maintained between peers and all the peers are treated equally, thus distributing power to all. Each peer has an equal vote in the creation of transaction

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and maintains ledger; thus, everything works on consensus and prevents malicious activities because of transparency. The blockchain is maintained on a server, and each peer keeps a ledger with it which is updated when a new transaction is created and added in blockchain. The addition of block in a blockchain depends on the consensus given by the peers in the network, and then only final data transfer between peers takes place. This paper successfully implements and analyses this system on a small scale and lays down foundation for this system to be researched for large scale.

References 1. L.J. Wu1, K. Meng, S. Xu, S.Q. Li1, M. Ding, Y.F. Suo, Democratic centralism: a hybrid blockchain architecture and its applications in energy internet, in 2017 IEEE International Conference on Energy Internet (2017) 2. S. Kiyomoto, On blockchain based anonymized dataset distribution platform, in SERA 2017, London UK, 7–9 June 2017 3. M. Fukumitsu, S. Hasegawat, J. Iwazaki, M. Sakai, D. Takahashi, A proposal of a secure P2P-type storage scheme by using the secret sharing and the blockchain, in 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (2017) 4. N. Chalaemwongwan, W. Kurutach, State of the art and challenges facing consensus protocols on blockchain, in 2018 International Conference on Information Networking (ICOIN), 10–12 Jan 2018 5. J. Supriya, P. Jamdade, K. Mohini, A. Kulkarni, Resource sharing with mobile nodes. Int. J. Sci. Res. Publ. 4(2), 1 (2014). ISSN 2250–3153 6. C. Catalini, J. Gans, Simple economics of the blockchain, in MIT Sloan School Working Paper 5191–16, 23 Nov 2016 7. D. Patel, J. Bothra, V. Patel, Blockchain exhumed, in Asia Security and Privacy (ISEASP), 2017 ISEA, 29 Jan–1 Feb 2017 8. M. Vukolic, Rethinking permissioned blockchains, in BCC’17 Proceedings of the ACM Workshop on Blockchain, Cryptocurrencies and Contracts, 02 Apr 2017 9. G. Bianchi, N.B. Melazzi, L. Bracciale, F.L. Piccolo, S. Salsano, Member, IEEE, Streamline: an optimal distribution algorithm for peer-to-peer real-time streaming. IEEE Trans. Parallel and Distrib. Syst. 21(6), 857–871 (2010)

Comparative Analysis of Clustering Algorithms with Heart Disease Datasets Using Data Mining Weka Tool Sarangam Kodati, R. Vivekanandam and G. Ravi

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weka Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heart Disease Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different Types of Clustering Algorithms in Weka Tool . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Simple K-Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 OPTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Filtered Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Farthest First . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Simple K-Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 OPTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Filtered Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Farthest First . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The heart is the important organ on the human (men or women) body. Life is totally dependent over efficient working of the heart. What if a heart undergoes a disorder, cardiovascular diseases are the most difficult disease for reducing the patient count. According to consequence with a survey conducted by path of WHO, in relation to 17 million peoples die around the world appropriate to consequence with cardiovascular diseases, i.e., 29.20% among all caused death, most of developing countries. Thus, there is a require in relation to getting rid regarding that difficult S. Kodati (B) Department of Computer Science and Engineering, SSSUTMS, Sehore, Bhopal, MP, India e-mail: [email protected] R. Vivekanandam Department of CSE, Muthayammal Engineering College, Namakkal, India e-mail: [email protected] G. Ravi Department of CSE, MRCET, Hydarabad, Telangana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_11

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task CVD the usage of advanced data mining techniques, among discipline according to discover the knowledge of heart disease. One of the fundamental data mining techniques is clustering which is used for analyzing data from diverse perspectives and summarizing them into beneficial information. Clustering is the assignment of concerning objects of a group referred to as clusters. This paper discusses different varieties of unsupervised clustering algorithms like farthest first, filtered cluster hierarchical cluster, OPTICS, simple k-means approach. The algorithms un supervised are used to comparison its performance analysis through Time is taken to assemble the clusters, the cluster differentiated by its true fine and real negative values. Our main intention is to show the comparison of cluster algorithms which are evaluated in Weka tool and find out which set regarding the algorithms may be most appropriate for the heart disease dataset. Keywords Data mining · Heart disease dataset · Clustering algorithms Weka tool

1 Introduction Data mining is involved with the approach concerning computationally extracting unknown knowledge from huge units of data. Extraction is associated with understanding huge data units and imparting selection-making effects because the prognosis and treatment concerning diseases are very important [1]. Data mining can be used to extract knowledge by examining and predicting various types of heart diseases. Healthcare data mining has large potential in accordance with the hidden styles in the data units, namely the medical area. Various data mining approaches are available including their suitability established on the medical healthcare data. Data mining capabilities among health care may have an amazing potential and effectiveness. It automates the system through finding predictive information of huge databases. Heart disease prediction performs an important role in data mining. Finding heart disease requires the overall performance analysis of a number of tests on the patient. However, the use of data mining approaches can decrease the number of tests. This reduced test engages performs a significant role in performance and time. Healthcare data mining is an important undertaking because that allows doctors to consult which attributes are more important for diagnosis such as age, weight, symptoms. This will help the doctors diagnose the disease more efficiently. Knowledge discovery in databases is the method of discovering beneficial data and patterns within data. Knowledge discovery of databases can be accomplished by the usage of data mining. It uses algorithms to eliminate the data and then patterns are derived by the kdd process [2].

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2 Weka Tool Weka tool is a most powerful data mining tool [3]. It is also an open-source tool. It has features like preprocessing filters, selection, classification and regression, clustering, association discovery, visualization. Different types of clustering algorithms are compared by the usage of heart disease dataset in Weka tool.

3 Heart Disease Dataset The dataset is available in UCI Machine Learning Repository. We are using a heart disease dataset. The dataset carries 303 samples and 14 input features as well as 1 output feature. The output feature is the decision class which has value 1 for good credit and 2 for bad credit. The dataset one (1) contains seven hundred (700) instances shown as credit while three hundred (300) instances as bad credit. The dataset consists of options expressed on nominal, ordinal, or interval scales. A listing of all these features [4].

4 Different Types of Clustering Algorithms in Weka Tool 4.1 Simple K-Means K-means [5] is one of the best unsupervised getting to know algorithms as they clear up the clustering problem. It classifies a given dataset through a certain quantity regarding clusters constant a priority. The main concept is according to define k centroids, one because regarding each cluster. This technique is iterated till there is no change in gravity centers. The algorithm works cluster, first place the point k into space represented by the object are clustered have initial group centroids. Each object to a group has nearest centroids. While all objects are assigned, then recalculate the position of the K centroids. This type of cluster is tighter than other clusters.

4.2 Hierarchical Clustering Hierarchical cluster [6] divides the clusters in a sequential manner with nested portions. It consists of the agglomerative approach and divisive approach. (i) Agglomerative: This is a “bottom-up” method, every analysis starts its individual cluster, and similar clusters integrated collectively move over the hierarchy until every data from at intervals one cluster. (ii) Divisive: This is a “top-down” approach, and this hierarchical clustering having all its objects into one cluster then split the cluster into

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the cluster. In its splitting process needs minimum relation for the different cluster and maximum relation in the same cluster.

4.3 OPTICS Ordering points to identify the clustering structure is after of the clustering structure in accordance with an ordering concerning a dataset respect to its solidity based clustering is offered optimization primarily based partitioning algorithms commonly constitute clusters by using a prototype Objects area unit allotted to the cluster represented by the approach of the approach with reference to the most similar prototype. Control approach is used according to optimize the whole cluster such that, e.g., the standard or squared distances regarding objects after reduced. The ordering points to pick out the clustering structure algorithm generates the augmented cluster ordering consisting concerning ordering the points, reach ability values or core values.

4.4 Filtered Cluster The filtered cluster algorithm is primarily based completely on storing the multidimensional data points within a tree. The process regarding the tree is like a binary tree method, as represents a hierarchical subdivision on its data point set’s bounding box the usage of theirs axis after which splitting is aligned by way on hyper planes. Each node on the tree is related with a closed field, referred to as the cell. The root’s cell is the bounding box about the dataset. If the cell consists of at most one point considering its declared according to remain a leaf. Then the finding points concerning the cell are divided according to one side or the ignoble concerning this hyper plane. The resulting is the children of the original cell; this leads to a binary tree structure.

4.5 Farthest First Farthest first finds its variant of K-means [7]; each cluster center point furthermost from the existing cluster center is placed by the K-mean, and this point must be positioned within the data area. So that it greatly speeds up the clustering in most cases, but it needs less move and adjustment for their fast performance. It uses heuristic approach for finding its points. It’s arbitrary point is p1, pick an another point p2 far from p1, pick pi to maximize the distance to the nearest of all centroid, the maximize the min{dist(pi, p1), dist(pi, p2),…}. After all K representatives are chosen, then we define the partition of data area D: cluster Cj consists of all points closer to pj than to any other representative.

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5 Results 5.1 Simple K-Means This simple K-means is tested with heart disease dataset in Weka tool; it produces two different clustered instances, and time used to construct is (complete training data) 0.05 s.

5.2 Hierarchical Clustering This clustered algorithm is tested with heart disease dataset in Weka tool; it produces two different clustered instances; clusters 0: tested negative and cluster 1: tested positive, and time used to construct is (complete training data) 0.23 s.

5.3 OPTICS This OPTICS is tested with heart disease dataset in Weka tool; it produces two different clustered instances, and time used to construct is (complete training data) 0.22 s.

5.4 Filtered Cluster This filtered cluster algorithm is tested with heart disease dataset in Weka tool; it produces two different clustered instances; clusters 0: tested negative and cluster 1: tested positive, and time used to construct is (complete training data) 0.02 s.

5.5 Farthest First This farthest first algorithm is tested with heart disease dataset in Weka tool; it produces two different clustered instances; clusters 0: tested negative and cluster 1: tested positive, and time used to construct is (complete training data) 0.02 s (Fig. 1; Table 1).

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Fig. 1 Weka tool clustering algorithms are used to compare its performance analysis Table 1 Weka tool clustering algorithms are used to compare its performance analysis Clustering Time taken (s) Clustered Clustered Number of algorithm used instances (0) instances (1) clusters Simple K-means

0.05

175

128

303

Hierarchical clustering

0.23

302

1

303

OPTICS Filtered cluster Farthest first

0.22 0.02 0.02

303 175 211

– 125 92

303 303 303

6 Conclusions There are different types of data mining clustering methods used for finding heart disease among patients. They are farthest first, filtered cluster, hierarchical cluster, OPTICS. These techniques are compared by using Weka tool data mining with the heart disease dataset which produces the results as tested positive and tested negative for the affected and not affected by the heart disease. It is the simplest tool for classifying the data of various types of a cluster. It is the primary model for providing the graphical user interface regarding the user while performing the clustering we used the promise data repository. It’s providing the previous project data for analysis. With the assistance of figures, we square measure showing the working of various algorithms used in Weka tool also time taken to form the cluster. Every algorithm has their own importance and we use to them of the behavior regarding the data, but

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about the basis fundamental analysis of this paper, we found that off first clustering algorithm requires minimum time performed to form the cluster and also it is the simplest k-means algorithm as compared to the other algorithms. In the future work, it can incorporate other medical attributes. To mine large amount of unstructured data, the text mining can be used in the available healthcare industry database.

References 1. J. Han, M. Kamber, Data Mining Concepts and Techniques, 2nd edn. (Morgan Kaufmann Publishers, Elsevier) (2006) 2. M. Holsheimer, A. Siebes, Data mining: the search for knowledge in databases. In CWI Report CSR9406, Amsterdam, The Netherlands, 1994 3. G. Holmes, A. Donkin, I.H. Witten, WEKA: a machine learning workbench, in Proceedings of the Second Australian and New Zealand Conference on Intelligent Information Systems (1994), pp. 357–361. http://www.cs.waikato.ac.nz/~ml/ 4. http://archive.ics.uci.edu/ml/datasets/heart+disease 5. M. Pramod Kumar et al., Simultaneous pattern and data clustering using modified K-means algorithm. Int. J. Comput. Sci. Eng. 02(06), 2003–2008 (2010) 6. P. Vijaya, M.N. Murthy, D.K. Subramanian, Leaders–subleaders: an efficient hierarchical clustering algorithm for large data sets. Pattern Recogn. Lett. 25, 505–513 (2004) 7. B. Rama, A survey on clustering current status and challenging issues. Int. J. Comput. Sci. Eng. (IJCSE) 02(09), 2976–2980 (2010)

A Machine Learning Approach for Web Intrusion Detection: MAMLS Perspective Rajagopal Smitha, K. S. Hareesha and Poornima Panduranga Kundapur

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Intensity of Web Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Related Work on HTTP CSIC 2010 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Semantics of Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Tuning of Hyper-Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Open Web Applications Security Project (OWASP), an open-source community committed to serve application developers and security professionals has always accentuated on the dire consequences of web application vulnerabilities like SQLI, XSS, LDAP, and Buffer overflow attacks frequently occurring on the web application threat landscape. Since these attacks are difficult to comprehend, machine learning algorithms are often applied to this problem context for decoding anomalous patterns. This work explores the performance of algorithms like decision forest, neural networks, support vector machine, and logistic regression. Their performance has been evaluated using standard performance metrics. HTTP CSIC 2010, a web intrusion detection dataset is used in this study. Experimental results indicate that SVM and LR have been superior in their performance than their counterparts. Predictive workflows have been created using Microsoft Azure Machine Learning Studio (MAMLS), a scalable machine learning platform which facilitates an integrated development environment to data scientists.

R. Smitha (B) · K. S. Hareesha · P. P. Kundapur Department of Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_12

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Keywords HTTP CSIC 2010 · Azure machine learning · Logistic regression Support vector machine

1 Introduction As and when web attacks become more complex and capricious, research community is compelled to develop intrusion detection systems capable of combatting web intrusions [1, 2]. Typically, web intrusions are quite difficult to detect due to their polymorphic nature. In view of such convolutions, application of machine learning algorithms is preferred [3]. Data scientists have various tools like Weka, Vowpal Wabbit, Rattle, H2 O, and others to choose from [4] in order to build predictive models, but this study is centered on Microsoft Azure Machine Learning Studio (MAMLS) for the creation of predictive workflows, thereafter comparing the performance of different machine learning algorithms. The reason behind choosing MAMLS is to assert the fact that this studio has immense potential to impart cognizance on the usage of algorithms to make data science more fascinating. Most of the intrusion detection approaches are situated on a traditional perspective, wherein modern attack vectors are not contemplated [5, 6]. Therefore, this work aims to perceive intrusion detection from a web application viewpoint by considering HTTP CSIC 2010 dataset which includes attack traces pertaining to SQL injections, Cross site scripting, Xpath attacks, and buffer overflows [7]. The rationale behind choosing disparate machine learning algorithms can be attributed to the “No free lunch” theorem (proposed by David Wolpart, an American scientist and mathematician at Santa Fe Institute that promotes multidisciplinary research) of machine learning which suggests that one algorithm cannot suffice for a current problem. It is always logical to employ multiple algorithms for the same problem and analyze their performance [8, 9]. In this study, four conventional algorithms and their variants from four diverse realms are investigated. • • • •

Logistic regression from the class of regression methods. SVM, a highly acclaimed method for supervised learning. Neural networks, an efficient training algorithm inspired from biological neurons. Decision Tree, a popular tree-based classifier.

All the above-mentioned algorithms are available as modules on MAMLS [10]. Other prominent algorithms available on MAMLS are decision jungle, Bayes point machine, average perceptron, and boosted decision trees. Quite noticeably, the empirical results obtained from them have not been drastically different from their base counterparts but still all the results are reported for the sake of comparison as a proof of concept to “no free lunch theorem”.

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2 Intensity of Web Attacks As reported by positive technologies, (a company quite popular for offering vulnerability assessment and threat analysis solutions) in its whitepaper, SQLi and XSS are the most commonly occurring attacks in Q1 2017. Mainly executed to steal credentials, web attacks often intend to ruin user workstations with advanced types of malware which is known to be contagious [11]. Imperva, (one of the pioneers in the development of integrated security platforms for enterprises) conducted an analysis of web attack trends and reported the key findings in its whitepaper. As per the report (based on data collected from inspecting 198 web applications during January–June 2015), content management systems of enterprises were attacked through SQLi and XSS and is likely to only increase in the coming years [12]. Teresa Meek, an ardent blogger and a former journalist with Miami Herald and Newsday threw light on various types of web attacks and how it tarnishes the reputation, damages the resources and eventually destroys the overall business of an enterprise [13]. Acunetix (a leading tool used by Fortune 500 companies for detecting web attacks) explained Http Parameter Pollution (HPP) in detail by throwing light on hardcoded HTTP parameters, manipulation of WAF rules by hackers and the possible consequences of exploiting variables not handled properly by the web development teams [14]. CISCO listed the ramifications of a successful SQLi which includes authentication bypass, remote command execution, and information disclosure [15]. DB networks (an Information Security company which pioneered in 2009 with its database security equipments, software and services headquartered in California) opined that traditional perimeter security is no longer sufficient to tackle ongoing threats and insisted on adopting machine learning to create a behavioral model which can detect the purposed deviations and assign risk levels to them, thereby allowing the security professionals to focus on unusual events [16]. As per the list of top web application vulnerabilities by OWASP, SQL injections, broken authentication session management and sensitive data exposure occupy the first three slots which is a clear indication of the fact that web application threats will only magnify in the upcoming years and show no sign of diminishing [17] in Fig. 1.

3 Related Work on HTTP CSIC 2010 Dataset Kozik et al. proposed a method for detecting web application vulnerabilities. They compared the performance of three algorithms, namely J48, Adaboost, and Naive Bayes. Their findings showed that J48 exhibited an accuracy of 95.97%, but accuracy cannot be the only performance metric to prove the competence of any algorithm. Therefore, the current work considers other performance metrics like Precision, Recall, and F1-score [18]. Nguyen suggested that intrusion detection cannot be restricted to only traditional context, wherein most of the studies have religiously employed KDD-cup 99 dataset to validate their works. They classified the instances

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Fig. 1 OWASP 2017

of HTTP CSIC 2010 dataset using four algorithms like C45, CART, Random Tree, and Random Forest. Their results showed an average detection rate of 93.65%, but still there is ample scope to improve accuracy [19]. Eiei Han used HTTP CSIC 2010 dataset and compared algorithms random forest and K-means ID3 for classification and achieved 90% accuracy with respect to random forest [20]. Zhang et al. experimented with HTTP CSIC 2010 dataset by using Bayesian inference. They partitioned the dataset into four groups each consisting of one lakh samples. Their performance scores were more inclined toward TP, FP, TN, and FN. There was no specific analysis done with respect to standard performance metrics. They confined their study only to one lakh samples, unlike the current study which has taken into account 223,585 samples [21]. Atienza et al. presented a method using neural projection architecture to differentiate between normal and anomalous traffic of HTTP compiled in HTTP CSIC 2010 dataset. They applied dimensionality reduction based on neural networks which resulted in the most impactful projections of HTTP traffic. Although this work was not directly aimed at a typical classification task unlike the present work, it made some interesting revelations on the existing features of the HTTP CSIC 2010 dataset pertaining to host, contentLength, payload, cookies, etc. [22].

4 Dataset Since web application vulnerabilities are the starting point of this study, HTTP CSIC 2010 dataset appeared to be relevant to this context. It is a modern web intrusion

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detection dataset introduced by Spanish National Research Council which includes two classes: normal and anomalous. Tools like Paros proxy and W3AF were used to generate attack traces [23]. This dataset was formed as a result of enormous traffic directed to an e-commerce site. Since the web application was developed in Spanish, there are many regional characters observable as values of different features [23]. Seventeen features are found in this dataset. This study has considered the entire dataset with 223,585 samples. Random split of 70% (156,509) and 30% (67,075) is followed for training and testing, respectively. Tenfold cross-validation has been applied during the classification process.

5 Preprocessing MAMLS provides a clean missing data module which performs the cleaning operation. Features in the dataset like: protocol, pragma, cacheControl, useragent, acceptEncoding, acceptCharset, connection, and acceptLanguage were found to be more redundant and did not contribute much toward classification process thus dropped [23]. Categorical attribute-like method took three possible values: GET, PUT, and POST. Each possible value was replaced by a numeric value.

6 Feature Selection Filter-based selection and Fisher discriminant analysis are the most commonly used modules for feature engineering on MAMLS [24]. It is worthwhile to mention that these readily available modules when applied directly did not yield promising results for the current problem. An intriguing feature of MAMLS is that it allows data scientists to add custom modules onto their workflows to improve classification performance [24]. Moreover, HTTP CSIC 2010 dataset is not a quintessential dataset but consists of many special characters due to its Spanish origin. Therefore, minimum redundancy maximum relevance (mRMR) generic feature selection module implemented in Python has been incorporated into the workflows [25]. A filter-based approach called mRMR is particularly helpful for the selection of appropriate features. It follows the concept of highest relevance and least redundancy among features in the dataset [26]. This method of feature selection is used to determine the representative capability of the reduced feature set instead of using all the existing features which is not advisable thus leading to unreasonable time and space complexities [25]. To minimize redundancy, the following Eq. (1) is used. 1  I (i, j) |s|2 i, j∈S

(1)

124 Table 1 Overview of algorithms Base algorithm

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Derived counterpart

Support vector machine

Bayes point machine

Average perceptron

Neural networks

Decision tree

Decision jungle and boosted decision tree

‘|S|’ is the set of features. I(i, j) is the mutual information between ‘i’ and ‘j’ being the two features. In order to maximize relevance, Eq. (2) is used. 1  I (h, i) |S| i∈S

(2)

‘h’ refers to the target class i.e., attack or normal. Feature selection performed prior to classification aims to: • improve the classification accuracy • represent the original class distribution given only selected features and their values. Redundancy can be defined as the least correlation between features, whereas relevance is the maximum correlation a feature has with respect to class label. In mutual information, selection of informative features is done by calculating the average of all mutual information values between feature and class label. On the other hand, irrelevant features are eliminated by computing the average of all mutual information values between the two features in consideration. The Scikit learn library has a class called recursive feature elimination (RFE) which works by removing redundant attributes and those attributes which remain are termed as relevant [26]. This class presented seven features, subsequently applied on the various classification algorithms. Relevant features used for the classification process include: cookie, payload, url, contentLength, contentType, host, and method. Table 1 is an overview of base algorithm and its derived counterparts as available on MAMLS. Derived counterpart (in this problem context) can be defined as an algorithm which shares the same underlying approach as its base algorithm with slight variations. Logistic regression does not possess any derived counterparts and is an independent base algorithm on MAMLS.

7 Semantics of Classifiers SVM, a supervised learning model relies on the concept of hyperplane to distinguish between classes [27]. Let {x i } be a record in the dataset with ‘n’ features. Each

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xi corresponds to a label yi ∈ {−1, 1}. Typically, SVM optimizes the hyperplane by maximizing the margin. It is not possible to separate the samples perfectly into two classes. Therefore, slack variables are introduced [27]. The basic principle on which SVM is based is called structural risk minimization (SRM) which explores the relationship between classifier and unfamiliar or unseen observations in an efficient manner [27]. The objective function takes the following form as mentioned in Eq. (3). ‘w’ and ‘b’ refer to weigh and bias, respectively. m   1 2  ξi minw,b w + 2 i1

(3)

BPM is often referred as a sophisticated form of SVM inspired from Bayesian approximation. SVM looks at a classification problem from an optimization perspective, whereas sampling is considered by BPM to classify the given instances. BPM aims at returning the center of mass of the posterior distribution Pf|D [28].  Bayes(x)  f (x)P( f |D)d f (4) Computing center of mass is extremely challenging and this is why BPM excels [28]. P( f |D) ∝ P(D| f ).P( f ) P(D| f )  π(xi,yi)∈D l0−1 ( f (xi ), yi

(5)

l0−1 is the zero-one loss function. Each time BPM does an incorrect prediction, a penalty term is introduced which can be attributed to the zero-one loss function as given in Eq. (5). Perceptron is the simplest form of neural network [27]. MAMLS offers both twoclass perceptron and two-class neural network for classification tasks. The reason behind this is that two-class neural network, unlike two-class perceptron can be used for more perplex boundaries [24]. Perceptrons are quicker in learning than their derived counterpart namely two-class neural networks. Perceptron initializes the weights to zero. For every wrong prediction, perceptron updates the weights using an update rule as given below in Eq. (6). w j +  (yi − f (xi ))xi j

(6)

wj is the weight of feature j. yi is the class label of instance i. x i is the feature vector of instance i. f(x) is the class prediction or label. x ij is the value of feature j corresponding to instance i. In this problem scenario, a supervised two-class neural network [29] is modeled to learn the patterns pertaining to normal or attack. Weights are adjusted by the network

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to adjust the error (E) by constantly comparing the predicted and expected outputs as mentioned in Eq. (7). 2 1  Pi j − E i j 2 i1 j1 N

E

C

(7)

‘N’ refers to the total training samples. C refers to the two-class labels. ‘E ij ’ is the expected output and ‘Pij ’ is the output predicted by the neural network model. Theoretically, decision forest attempts to establish a direct relationship between number of trees and results it can produce. Based on the topology of the dataset, constructing more number of trees results in better accuracy but the problem of overfitting persists. A noticeable improvement on the underlying approach of treebased classifiers is decision jungle [30]. Decision jungle, introduced by Shotton et al., has a lower memory footprint and gives a better generalization but at the cost of longer execution time. Compared to their base counterparts, decision jungles are known to work well on nonlinear decision boundaries too. Unlike decision forests which enable one path to every node, decision jungles facilitate multiple paths from the root to leaf by using the concept of directed acyclic graph (DAG) [30]. The reason for introducing decision forest was to obtain better accuracy, but decision jungles were viewed to be memory efficient. MAMLS provides both these detectors to allow data scientists to choose an appropriate classifier for applications where memory consumption is critical [24]. The information at a particular node in a decision forest is given by Eq. (8).  −P(L i ) log P(L i ) (8) I (T )  i

‘T ’ refers to a pairing of input vectors and labels. ‘P(L i )’ is the probability of the instance belonging to a label L i. Condorcet jury theorem forms the basis for decision forest algorithm. The predilections proposed by individual trees are examined thoroughly by the algorithm, and voting is applied to eventually present an accurate prediction [31]. Boosting is another ensemble approach which converts weak learners into strong learners. Adaboost is a well-known boosting algorithm which strengthens the weights of those instances wrongly classified in previous iterations [32]. With respect to Adaboost algorithm, the prediction function H(x) is given by Eq. (9).   N  ∝m Hm (x) (9) H (x)  Sign i1

Let ‘N’ be the number of weak instances for weight has to be boosted in the next iteration. α1 …αm be the weights. ‘H’ is an estimator which decides the weight improvement factor.

A Machine Learning Approach for Web … Table 2 Hyper-parameters of SVM and BPM BA (SVM) Hyp-1 KF  RBF

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Hyp-2

Hyp-3

Lambda  0.01

Max-iter  100

Max-iter  100

DC (BPM)

The sigmoid function ‘σ ’ also called squashing function takes the following form as in Eq. (10). σ (Z ) 

1 1 + e−Z

(10)

In order to measure the efficiency of a logistic regression model, cost function (C) can be formulated as follows given in Eq. (11).  

−1  i y ∗ log(P(1) + 1 − y i ∗ log(1 − P(1)) m i1 n

C

(11)

‘m’ is the slope intercept. P(1) is the prediction of an outcome. yi is the target variable. ‘n’ refers to the number of training samples.

8 Implementation Details The implementation process is discussed in this section. A stepwise approach was followed in order to create workflows on MAMLS. 1. 2. 3. 4. 5.

Load the dataset. Random split of 70:30. Apply K-fold cross-validation (K  10). Perform feature selection using mRMR. Build classification models or workflows using SVM, BPM, perceptron, neural networks, decision forest, decision jungle, and boosted decision trees. 6. Evaluate the performance of each classifier. 7. Infer the best classifier based on their scores pertaining to performance metrics.

9 Tuning of Hyper-Parameters MAMLS requires tuning of hyper-parameters to generate desirable results. Optimal set of hyper-parameters help in solving classification problems [33]. Hyperparameters tuned with respect to SVM and BPM are shown in Table 2.

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Table 3 Hyper-parameters of AP and NN BA (AP) Hyp-1 DC (NN)

Hyp-2

LR  0.6

LR  0.01

Max-iter  100

Max-iter  100

Table 4 Hyper-parameters of DF, DJ, and BDT BA(DF) Hyp-1 Hyp-2 No of trees: 100 DC (DJ)

No of DAG: 100

Max depth  32

DC (BDT)

No of trees  100

Learning rate  0.2

Table 5 Hyper-parameters of LR LR Memory size for L-BFGS 20

Hyp-3

Max depth  32 Max-width  128

L1-weight

L2-weight

1

1

Radial basis function (RBF), normally used for nonlinear data, has been employed as the kernel function. Lambda is the regularization coefficient. Larger values tend to penalize the model thus it is set to 0.01 [24]. Polynomial kernel function was applied on the same nonlinear dataset but did not yield promising results. As seen in Table 3, the same values of hyper-parameters were set for both average perceptron and neural networks. Learning rate refers to the rate at which the perceptron/neural network learns the instances. A total of 100 iterations were used to train the models. In general, more number of trees can be constructed to obtain better results but at a certain point, the cost of constructing more number of trees certainly hampers the performance since computational cost also increases. Needless to say, hyperparameters contribute to balancing such trade-offs [24]. Owing to such practical considerations, the following hyper-parameters as mentioned in Table 4 were tuned in the process. Hundred trees were constructed in the ensemble. Increasing tree depth might improve precision but could lead to overfitting. Max depth and width values of DAG mentioned in Table 4 are default values. 0.2 refers to the initial learning rate of boosted decision tree model. Table 5 provides hyper-parameters of LR. MAMLS bestows users with L-BFGS optimization procedure while using logistic regression module for classification. L-BFGS often termed as algorithm of choice by experts calculates the inverse of the Hessian matrix thus consuming limited memory. The hyper-parameters tuned with respect to logistic regression are mentioned above in Table 5. Memory size for L-BFGS indicates the amount of memory (in MB) to be used for the optimizer. L1 and L2 are the regularization weights often used to minimize overfitting. The default value of 1 has worked well and has been retained without further contemplation [10].

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10 Results This section throws light on the significant aspects of results obtained from various algorithms. The performance of all the algorithms has been evaluated with standard performance metrics like accuracy, precision, recall, F1-score, and AUC. The following equations from (12) to (15) can be used to calculate the performance metrics. TP + TN TP + TN + FP + FN TP Precision  TP + FP TP Recall  TP + FN Precision × Recall F1-score  2 Precision + Recall

Accuracy 

(12) (13) (14) (15)

The capability of any IDS can be ascertained based on four kinds of predictions, namely True positives (TP), False positives (FP), True Negatives (TN), and False Negatives (FN). True positives are anomalous events correctly labeled as anomalous by the classifier. True negatives correspond to normal events correctly labeled as normal by the classifier. False positives refer to normal events which the classifier predicts wrongly as anomalous. False negatives are anomalous events predicted wrongly as normal by the classifier in Table 6. It can be observed from Table 6 that SVM and LR have performed exceedingly well in this problem scenario. BPM and SVM’s derived counterpart has been consistent in its performance as well as superior to average perceptron and neural networks. Generally, tree-based classifiers are known to be desirable for multiclass classification and the results indicate that neither the base algorithm, namely decision forest, nor its derived counterparts have been able to classify the instances of HTTP CSIC 2010 dataset in a convincing manner. Area under the curve (AUC) is a performance metric which summarizes the receiver operating characteristics curve (ROC). Logis-

Table 6 Results of classifiers Algorithm Accuracy SVM BPM AP NN DF DJ BDT LR

0.95 0.9 0.83 0.84 0.66 0.62 0.64 0.97

Precision

Recall

F1-score

AUC

0.94 0.89 0.8 0.83 0.68 0.63 0.65 0.92

0.92 0.87 0.79 0.82 0.69 0.6 0.68 0.95

0.93 0.88 0.78 0.79 0.64 0.62 0.65 0.96

0.97 0.92 0.82 0.86 0.67 0.68 0.66 0.97

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Fig. 2 ROC of SVM

Fig. 3 ROC of LR

tic regression has shown promising results and can be termed as the best performer. However, there are slight variations between LR and SVM with respect to different performance metrics. The highest precision is reported by SVM, whereas the highest recall is given by LR. The ROCs of the best performers can be observed from Figs. 2 and 3, respectively. Figure 4 summarizes the overall performance of all the algorithms in consideration with a depiction of bar chart for better visualization.

11 Contributions 1. MAMLS is explored reasonably well which definitely offers lot more opportunities to data scientists to experiment with various datasets.

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1.2

Accuracy

1 0.8 0.6 0.4 0.2 0 SVM

BPM

AP

NN

DF

DJ

BDT

LR

Algorithm Fig. 4 Performance of base algorithm and their derived counterparts

2. The intensity of web attacks is discussed, and the problem is suitably addressed from a machine learning standpoint. 3. This article sheds light on base algorithms as well as its derived counterparts, compares their performance and infers that SVM and LR are consistent in their predictions.

12 Conclusion This work attempted to explore algorithmic modules of MAMLS using a modern intrusion detection dataset, namely HTTP CSIC 2010. As a proof of concept to “No free lunch theorem”, eight algorithms were applied on this dataset to determine the variations in their performance. Results indicated that SVM and LR performed better than other algorithms in consideration. It is worthwhile to mention that proper tuning of hyper-parameters is a prerequisite to improve attack detection rate. When coupled with the right choice of machine learning algorithms which operationalize workflows, classification tasks have yielded encouraging results on MAMLS as illustrated by this work. As a matter of fact, instead of restricting the study to only one or two algorithms, it is quite intriguing to assess the performance of conceptually diverse algorithms belonging to different subspheres of machine learning eventually concluding the analysis with the results of optimal detectors.

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White Blood Cell Classification Using Convolutional Neural Network Mayank Sharma, Aishwarya Bhave and Rekh Ram Janghel

Contents 1 2 3

Introduction toWhite Blood Cell Classification . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Gathering and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Dataset Balancing and Image Augmentation . . . . . . . . . . . . . . . . . . . . 3.2 Downsizing Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Methodology Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Transfer Learning Using VGGNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Transfer Learning Using InceptionV3 . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Transfer Learning Using XceptionNet . . . . . . . . . . . . . . . . . . . . . . . . . 5 Experimental Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion and FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The density of white blood cells in bloodstream provides a glimpse into the state of the immune system and any potential risks such as heart disease or infection. A dramatic change in the white blood cell count relative to your baseline is generally a sign that your body is currently being affected by an antigen. A variation in a specific type of white blood cell generally correlates with a specific type of antigen. Currently, a manual approach is followed for white blood cell classification; however, some semi-automated approaches have been proposed which involves manual feature extraction and selection and an automated classification using microscopic blood smear images. In this work, we propose deep learning methodology to automate the entire process using convolutional neural networks for a binary class with an accuracy of 96% as well as multiclass classification with an accuracy of 87%. M. Sharma (B) · A. Bhave · R. R. Janghel National Institute of Technology, Raipur, India e-mail: [email protected] A. Bhave e-mail: [email protected] R. R. Janghel e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_13

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Keywords Leukocytes · White blood cell classification · Convolutional neural network · Deep learning · Transfer learning

1 Introduction to White Blood Cell Classification White blood cell classification [1–3] deals with two objectives: first, given a stained image of a white blood cell, classify it as either polynuclear or mononuclear. Note that Eosinophils and Neutrophils are polynuclear while Lymphocytes and Monocytes are mononuclear [4]. Second, given a stained image of a white blood cell, classify it as Eosinophils and Neutrophils, Lymphocytes and Monocytes. White blood cells can be distinguished from other cells due to the presence of nucleus . WBCs can be further identified into different types from their nuclear structure.

2 Literature Review Machine learning algorithms such as k-nearest neighbours, learning vector quantization [5] and support vector machines [6, 7] have been applied over extracted features of image. Extensive image processing algorithms [2, 8] have been applied to extract these features as well as for feature selection. Recently, ANN [1] has been applied over the extracted features of 70 white blood cell images. In this work, we try to automate this feature extraction [9] and selection process in neural networks by using convolutional neural network and also an experiment through transfer learning [10] using VGGNet, InceptionV3 and XceptionNet.

3 Data Gathering and Preprocessing We have used BCCD Dataset [11]. The dataset consists of 352 microscopic images of dyed white blood cells. White blood cells (also known as Leukocytes) can be easily distinguished from other types of blood cells due to the presence of nucleus. Each image is of size 640 × 480 and is in following distribution: 21 Monocyte, 33 Lymphocyte, 207 Neutrophil, 88 Eosinophil, 3 Basophil. The preprocessing was done in three stages: (1) dataset balancing and image augmentation, (2) downsizing image (Tables 1 and 2).

White Blood Cell Classification Using Convolutional Neural Network Table 1 Dataset description for multiclass classification Data label Training Testing Eosinophil Lymphocyte Monocyte Neutrophil Total

2497 2483 2478 2499 9957

623 620 620 624 2487

Table 2 Dataset description for binary classification Data label Training Testing Mononuclear polynuclear Total

4996 4961 9957

1247 1240 2487

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Total 3120 3103 3098 3123 12,444

Total 6243 6201 12,444

3.1 Dataset Balancing and Image Augmentation Since the original dataset was not balanced and images in each class were not sufficient for training, additional images were derived from the original images using operations such as rotation(the images are rotated by 90◦ , 180◦ and 270◦ ), flip (the images are flipped along the horizontal axis and the vertical axis using opencv) and shear (the images are sheared at 30◦ both vertically and horizontally). The dataset size increased from 352 images to 12,444 images after the application of these operations. Each class had 3100 images approximately, thus balancing the dataset.

3.2 Downsizing Image The images were original of the size 640 × 480. These were downsized to a size of 120 × 160 in order to reduce the computational time in training as well as testing.

4 Methodology Used Convolutional neural network [12] or CNN is a special type of deep neural networks that deal with the phenomena such as localization of the receptive field in high volume data, copying of weights forward as well as image sub-sampling using various kernels in each convolution layer. Convolution is a mathematical operation that employs feature extraction over the image.

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Pseudocode for seven layer models num_classes = 4 epochs = 20 dropout =0.7 learning_rate = 0.001 batch_size = 32 model = Sequential () model.add(Lambda(lambda x: x/127.5 − 1. , input_shape=(120, 160, 3) , output_shape=(120, 160, 3))) model.add(Conv2D(32, (3 , 3) , input_shape=(120, 160, 3))) model.add( Activation ( ’ relu ’ ) ) model.add(MaxPooling2D( pool_size=(2, 2))) model.add(Conv2D(32, (3 , 3))) model.add( Activation ( ’ relu ’ ) ) model.add(MaxPooling2D( pool_size=(2, 2))) model.add(Conv2D(64, (3 , 3))) model.add( Activation ( ’ relu ’ ) ) model.add(MaxPooling2D( pool_size=(2, 2))) model.add( Flatten ( ) ) model.add(Dense(64)) model.add( Activation ( ’ relu ’ ) ) model.add(Dropout(dropout )) model.add(Dense(num_classes)) model.add( Activation ( ’softmax ’ ) ) rms = RMSprop( l r = learning_rate , ) model. compile( loss=’categorical_crossentropy ’ , optimizer= rms, metrics=[’accuracy ’ ] )

4.1 Model Architecture LeNet [13] architecture was used primarily. This section describes in more detail the architecture of LeNet-5, the convolutional NN used in the experiments. LeNet5 comprises seven layers, not counting the input, all of which contain trainable parameters (weights) [13]. The input is a 120 × 160 pixel image. The pixel values are normalized with respect to 255, and hence, black is associated with a pixel value of 0 and white is associated with a pixel value of 1 (Figs. 1 and 2).

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Fig. 1 LeNet-5 CNN structure for a 120 × 160 input image

(a) Binary classification, epoch = 20 Lr = 0.001 Dropout = 0.7

(b) Multiclass classification, epoch = 20 Lr = 0.001 Dropout = 0.7

Fig. 2 Training and testing loss graphs for the best results so obtained in binary and multiclass classification

4.2 Transfer Learning Using VGGNet VGGNet [14] consists of 16 or 19 convolutional layers and is very appealing because of its very uniform architecture. It only performs 333 times 333 convolutions and 222 times 222 pooling all the way through. The last 10% layers of network were removed and a fully connected layer of four classes for multiclass classification and two classes for binary classification was added and fine-tuned.

4.3 Transfer Learning Using InceptionV3 InceptionV3 [15] gets rid of the linear convolutions that are the bread and butter of CNNs and instead connects convolutional layers through multi-layer perceptrons that can learn nonlinear functions. These perceptrons are mathematically equivalent to 1 × 1 convolutions and thus fit neatly within the CNN framework. InceptionV3 adopts convolution factorization and improved normalization.

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4.4 Transfer Learning Using XceptionNet Xception [16] is an extension of the Inception architecture which replaces the standard Inception modules with depthwise separable convolutions.

5 Experimental Implementation As part of dataset collection, 352 microscopic images of dyed white blood cells from BCCD Dataset [11] were collected. These files were then rotated, flipped and sheared along with image augmentation to increase the size of dataset to 12,444 images with approximately 3100 images of each white blood cell type. The dataset consists of images of size 640 × 480 which are then resized to 120 × 160 for faster computation. For multiclass classification, 9957 images (2470 of each class approximately) were used for training while 2487 images (620 images of each class approximately) were used for testing purpose. For binary classification, 9957 images (4961 images of each class approximately) were used for training while 2487 (1240 images of each class approximately) were used for testing purpose. LeNet-5 [13] architecture was implemented for both binary and multiclass classifications. String labels of each white blood cell type are encoded using one hot encoding. Binary cross entropy was used for binary class classification of polynuclear and mononuclear white blood cells. Categorical Cross entropy was used for classification into Lymphocyte, Monocyte, Eosinophil or Neutrophil. A batch size of 32 images were used. The models were implemented and executed using the Keras wrappers with Tensorflow framework as the backend to run the models on the Nvidia GTX960M GPU. Each of the models was trained using the RMSprop optimizer, and a suitable dropout was used to maintain a strong resistance to overfitting. The mean squared error function is used for designing the loss function for all the models. Additionally, Transfer learning using VGGNet [14], InceptionV3 [15] and XceptionNet [16] was performed. The last 10% layers of these models were removed, and a fully connected layer with number of neurons depending upon number of classes was added and only the last layer was trained. This was done to compare results of fine-tuning models with stand-alone Le-Net architecture described above.

6 Results This section presents the results of the experiment using a LeNet inspired architecture as well as a comparison with transfer learning-based models (Tables 3, 4, 5, 6, 7, 8 and 9).

White Blood Cell Classification Using Convolutional Neural Network Table 3 Results for binary classification using CNN architecture Epochs Learning rate Dropout 20 20 50 50 50 50 100

0.001 0.0001 0.001 0.0001 0.0001 0.0001 0.0001

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0.7 0.7 0.7 0.7 0.5 0.8 0.7

0.963 0.868 0.940 0.959 0.902 0.924 0.937

Table 4 Confusion matrix for binary classification using CNN architecture Actual\predicted Mononuclear Polynuclear Mononuclear Polynuclear

TP = 1162 FN = 15

FP = 78 TN = 1232

Table 5 Results for binary classification using transfer learning Model name Epochs Learning rate Dropout Inception Vgg16 Vgg19 Xception

20 20 20 20

0.001 0.001 0.001 0.001

0.7 0.7 0.7 0.7

Table 6 Results for multiclass classification using CNN architecture Epochs Layers Learning rate Dropout 20 20 50 50 20 20 50 50

7 7 7 7 11 11 11 11

0.001 0.0001 0.001 0.0001 0.001 0.0001 0.001 0.0001

0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7

Accuracy 0.7277 0.6558 0.5014 0.7945

Accuracy 0.8793 0.7414 0.7925 0.6489 0.8761 0.7800 0.8484 0.8383

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Table 7 Confusion matrix for multiclass classification using CNN architecture Actual\predicted Eosinophil Monocyte Lymphocyte Neutrophil Eosinophil Monocyte Lymphocyte Neutrophil

518 0 0 39

0 620 0 1

0 0 465 0

Table 8 Results for multiclass classification using transfer learning Model name Epochs Learning rate Dropout Inception Vgg16 Vgg19 Xception Inception Vgg16 Vgg19 Xception

20 20 20 20 20 20 20 20

0.001 0.001 0.001 0.001 0.0001 0.0001 0.0001 0.0001

Table 9 Comparison with other works Work Ongun et al. [5] Tai et al. [6] Manik et al. [1] Chung et al. [2] Theera-Umpon [8] Our work

0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7

105 0 155 584

Accuracy 0.5761 0.4306 0.3884 0.6529 0.5597 0.3948 0.3675 0.6083

Accuracy 0.91 0.95 0.99 0.95 0.77 0.96

7 Conclusion and Future Work The current work is aimed at automating the feature extraction and selection process along with the classification of white blood cell. A comparison with transfer learningbased models as well as previous works has been made. Experimental results show that the plain CNN architecture with seven layers shows better results than transfer learning modules as well as other previous semi-automated works. Future work can be done in accommodating medical images in transfer learning architecture to improve results over a large database of several disease images and in a short time. An unbalanced dataset [17] can also be accommodated in future using Breiman’s random forest [18].

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References 1. S. Manik, L.M. Saini, N. Vadera, Counting and classification of white blood cell using Artificial Neural Network (ANN), in IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE (2016) 2. J. Chung et al., Counting white blood cells from a blood smear using fourier ptychographic microscopy. PloS one 10(7), e0133489 (2015) 3. M. Habibzadeh, A. Krzyak, T. Fevens, White Blood Cell Differential Counts Using Convolutional Neural Networks for Low Resolution Images and Soft Computing (Springer, Berlin, Heidelberg, 2013) 4. M. LaFleur-Brooks, Exploring medical language: a Student-Directed Approach (7th ed.). St. Louis, Missouri, US: Mosby Elsevier. p. 398. ISBN 978-0-323-04950-4 (2008) 5. G. Ongun, et al., An automated differential blood count system. Engineering in Medicine and Biology Society, in 2001 Proceedings of the 23rd Annual International Conference of the IEEE, vol. 3. IEEE (2001) 6. W.-L. Tai et al., Blood cell image classification based on hierarchical SVM, in 2011 IEEE International Symposium on Multimedia (ISM), IEEE (2011) 7. H. Ramoser, Leukocyte segmentation and SVM classification in blood smear images. Mach. Graph. Vis. Int. J. 17(1), 187–200 (2008) 8. N. Theera-Umpon, S. Dhompongsa, Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Trans. Inf. Technol. Biomed. 11(3), 353–359 (2007) 9. I. Guyon, A. Elisseeff, An Introduction to Feature Extraction (Berlin, Heidelberg, Feature extraction. Springer, 2006), pp. 1–25 10. S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345– 1359 (2010) 11. GitHub - Shenggan/BCCD_Dataset: BCCD Dataset is a small-scale dataset for blood cells detection. BCCD Dataset is under MIT licence. [Online]. Available: https://github.com/ Shenggan/BCCD_Dataset 12. Krizhevsky, A., I. Sutskever, G.E. Hinton. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Proc. Syst. (2012) 13. Y. LeCun et al., Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998) 14. K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) 15. C. Szegedy, et al. Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 16. J. Carreira, H. Madeira, J.G. Silva, Xception: a technique for the experimental evaluation of dependability in modern computers. IEEE Trans. Softw. Eng. 24(2), 125–136 (1998) 17. N.V. Chawla, Data Mining for Imbalanced Datasets: An Overview. Data mining and knowledge discovery handbook (Springer, Boston, MA, 2009), pp. 875–886 18. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001) 19. I. Goodfellow et al., Deep Learning, vol. 1 (MIT press, Cambridge, 2016) 20. Agostinelli, F., et al., Learning activation functions to improve deep neural networks. arXiv preprint arXiv:1412.6830 (2014)

Analysis of Mobile Environment for Ensuring Cyber-Security in IoT-Based Digital Forensics G. Maria Jones, S. Godfrey Winster and S. V. N. Santhosh Kumar

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 System Architecture and Flow Process of Digital Forensics . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The Internet of things (IoT) is the collection of smart objects, which it collects the natural phenomena from environment, senses the data and transmits them using machine to machine (M2M) communication. In most of the existing system, providing efficient security in IoT mobile environment is a major concern. The attackers can easily exploit the vulnerability, eavesdrop and masquerade the sensitive information in the network. Because of this, there is a need to provide efficient security analysis in IoT environment. In this work, the enhanced security mechanism is proposed which uses efficient digital forensics tools to identify the threats and we demonstrated how to reconstruct the past events with forensics tools in order to provide digital evidence for legal proceedings. Keywords Internet of things · Machine to machine · Digital forensics Legal proceeding

G. Maria Jones (B) · S. Godfrey Winster · S. V. N. Santhosh Kumar Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India e-mail: [email protected] S. Godfrey Winster e-mail: [email protected] S. V. N. Santhosh Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_14

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1 Introduction The Internet of things (IoT) is defined as “a pervasive and ubiquitous network, which enables monitoring and controlling of the physical environment through the collection, processing and analysis of data that is generated by sensors or smart objects” [1]. It is the network of physical devices and things which are embedded with hardware, software, sensors and networks that form smart object to communicate with each other. IoT is rapidly emerging field with lot of technology. It provides a number of new services and business opportunities to help companies to build new innovative ideas. The interconnected nature of IoT leads to openness and collaboration across industries, which makes building business models complex. In today’s scenario, IoT makes our day-to-day life more convenient and easy, which creates new opportunities for attackers to attack the system. The traditional forensics includes appliances and devices that are considered as a source of evidence during an investigation. Security and privacy are the biggest issues for IoT devices, which give new online privacy concerns for consumers. That is because of these devices not only store the personal information (username, mail id’s and telephone), but also monitor user activities (e.g. at what time user reaches home and what they had for dinner). In IoT implementations, we have four different types of communications models, with its characteristics. These communications models are device-to-device (D2D), device-to-cloud (D2C), device-to-gateway (D3E) and back-end data-sharing. The IoT can bring business benefits, such as process optimization, complex autonomous systems and sensor-driven decision analytics. It has major combination technology areas such as cloud computing, mobile devices, computers, tablets, sensors and radio frequency identification (RFID) technologies. The source of data evidence in IoT environment can be collected into three groups: evidence can be collected from smart devices and sensors. Evidence can be collected from hardware and software that communicate between smart devices and external devices (computer, mobile, laptops, etc.), and evidence can be collected from outside the networks (cloud, social networks and mobile networks). Figure 1 gives the application of IoT. Since smart devices are mechanical and physical in nature, gathering and identifying evidence in IoT are the major challenges. Evidence can be collected from homes, buildings, moving sensors in cars, communicating devices, cloud storage mobile devices, etc. Cloud computing gives some risks due to its ways of service deployment, operations and enabling technologies than traditional IT because security controls and mechanism in traditional IT are simple. All data generated by IoT devices will be stored on cloud due to the large storage capacity. So cloud forensics plays a significant role in IoT forensics.

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Fig. 1 Applications of IoT

2 Related Works Lee et al. [2] have proposed a security process to low-energy IoT mesh network and confirmed that the third party cannot threaten the low energy due to two-factor securities. Bhattacharyaa et al. [3] have presented a framework of trust-based healthcare service for ethical issues in service ecosystem and discussed the relation between the service provider and user. Sahmim and Gharsellaoui [4] and Rajendren et al. [5] have discussed the problems of security and cloud of things to tackle the intrusions and vulnerability of the system and also they presented the risk factor and solutions. Malek et al. [6] have proposed IoT techniques to combine with big data technologies and also they conducted preliminary experiments in real-time scenario. Darwisha et al. [7] have proposed a model based on UK HMD ISI and presented a case study in technology-integrated health management (TIHM) with the analysis of threat in medical Internet of things (MIoT). Ravi Kumar et al. [8] have explored the data security issues in cloud computing and proposed a method to overcome the description of cloud computing models (deployment models and service delivery models). Luthra et al. [9] have recognized the challenges in IoT systems and analysed the grey relational analysis (GRA) and analytical hierarchy process (AHP) approaches to IoT adoption which were useful in removing the hurdles in Internet of things. Tedeschia et al. [10] have introduced a secure design for IoT which provides solution for safety in IoT with rich data, secure cloud services and supports industrial standards and interoperability of devices.

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Pereira et al. [11] have highlighted the challenges of security issues towards industry as awareness for security practices. Sfar et al. [12] have contributed an overview of security roadmap of IoT and discussed the security quires about privacy, trust, access control and identification. The standardized activities are surveyed and discussed the security components and applications of IoT. He et al. [13] have explored the IoT sensors for manufacturing and proposed statistical process monitoring (SPM) tool for cyber-manufacturing to understand the data produced by IoT sensors. Mekki et al. [14] have provided a comparative study of low-power wide area network (LPWAN) technologies, which is an efficient solution to connect devices and explained which technology fits in IoT. Zia et al. [15] have proposed a model which is applicable for digital and application-specific forensics models that enable the investigator to collect, examine, analyse and present the evidence in law of enforcement. Wang et al. [16], Santhosh Kumar [17] have proposed a security-enhanced approach for verifying the trust of remote terminal (RT) which includes the measurement module and attestation module that help to protect from malicious attacker and also they presented the policy-based services which help in the enhanced security of attestation procedure. Jones and Winster [18] performed a data acquisition of digital evidence from compromised device which will be useful for digital investigators and court proceedings.

3 System Architecture and Flow Process of Digital Forensics Figure 2 describes the architecture and flow process of digital forensics, In smart home, the commends to do things are passed from one smart object to another smart object through Internet and all the information that are used for communication purpose are stored in cloud information.

Fig. 2 Architeture of the proposed system

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If the hackers attack the sensitive information from cloud storage. Once the users know that the system is compromised, they will give all necessary details for digital forensics examiner and the investigator will examine the system by using forensics tools (OSForensics and oxygen forensics) to recover the digital evidence.

4 Experimental Analysis Real-time monitoring and notifications are one of the main features of IoT in home automation systems. Since the hub is connected to the cloud network through the Internet, we can plan various events as per our daily routine schedules. The cloud storage can send, receive and store all the inputs which send back to the hub as per the schedule. The intruders can able to access the cloud storage and get all the information which leads to misuse the data. The desired action takes places, once the hub transfers the signals to the target sensor, and it will quickly upload the new status over the cloud notifying user immediately. For example, when it detects any unwanted intrusion, the sensor will instantaneously send notification to the user through mails, messages, calls or app notifications. Once the notification is received, the user can immediately check home security smart camera and can verify the status of our home even from remote location. The main aim of forensics investigator is to extract the necessary information like call logs, SMS, MMS, e-mails, photos, videos, audio files, geolocation and various application artefacts from suspected devices. Some of the digital forensics tools UFED physical analyser, oxygen forensics, OSForensics, autopsy, magnet axiom, Internet evidence finder, SANS, CAINE, etc., are used to find out the criminals who all are directly or indirectly related to crime. These tools have the ability to acquire information from volatile memory of the devices. The following information can acquire: messages, images, audios and videos, deleted data, logs. All data that has been communicated through network has been stored in cloud. If the anonymous person accesses the data, modify the data or delete the data from cloud; we can easily reconstruct by using forensics techniques. We can be able to retrieve all necessary information through which it will be easy to find out the criminals. Here the forensics tool used is oxygen forensics and OSForensics. These tools have the capability to retrieve the past details. The messages that are deleted by anonymous person can be easily reconstructed by using digital forensics tools. From reconstructing the deleted messages, we can analyse whether it is important or not. It gives the information about source of text, message type, text and direction of text (incoming, outgoing and deleted). The timestamp gives the detail description of text message. Sometimes deleted messages are also useful to find out the criminals. The first step is to perform data acquisition from compromised device which is connecting to forensics workstation using proper cable. On connecting it, initially we must change enable the USB debugging for allowing transferring the data. The accurate mobile model should be selected from the number of mobile lists. Give the case details for further process. Now the data acquisition will be done for about

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Fig. 3 Device and case information

Fig. 4 Deleted messages with all necessary data

2 h to acquire all the information. The process will be different from each forensics workstation. The extraction of data from volatile memory brings all the necessary information which will be useful to bring out the crime plans. The initial cases and devices information will be displayed as shown in Fig. 3. Once with all devices and case history, we have to analyse all the information from suspected mobile devices. In this case, we need to find out cloud accounts. So we have to click cloud accounts from the list. Cloud accounts that are synchronized will be displayed. From specific account, we can get the call logs, images, videos, documents, e-mails, etc. Figure 4 gives the full details of deleted messages with date, time and location in which and where communication took place. This information might be useful for the digital examiners to find out the criminals. Figure 5 presents the full details of deleted call logs with time, date, country code and time logs of each call. Another digital forensics tool used is OSForensics which is used to reconstruct the deleted images. The voice call details from suspected mobile devices provide the phone number, duration and locations that help to identify the

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Fig. 5 Deleted call logs using oxygen forensics

criminal groups. We analysed the device with the help of oxygen forensics toll to find out the deleted events.

5 Conclusions and Future Work The Internet of things is an emerging technology that could potentially give “smart” services in every domain. However, the higher degree of automation, interconnectivity and transfer of sensitive private data involved in IoT services arise to ethical security and privacy concerns. The impact of the Internet is global which provides an opportunity, benefits to everyone. In this paper, we have analysed the challenges of Internet of things and used digital forensics techniques to find the crime. By using forensics techniques, we have analysed the digital evidence that has been collected from cloud infrastructure. In the future work, it will be implemented in cloud environment.

References 1. V.R. Kebande, N.M. Karie, H.S. Venter, Cloud-centric framework for isolating big data as forensic evidence from IoT infrastructures, IEEE (2017) 2. S. Lee, B. Kang, K. Choa, D. Kanga, K. Jang, L. Park, S. Park, Design and Implementation for data protection of energy IoT utilizing OTP in the wireless mesh network, in 4th International

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Payment Security Mechanism of Intelligent Mobile Terminal Seshathiri Dhanasekaran and Baskar Kasi

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Information Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Android Operation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Root Protection and Application Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Design Principle of Terminal Root Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Terminal Application Authorized Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Implementation of Application Signature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In recent years, with the vigorous development of mobile Internet and the increasing popularity of intelligent electronic products, intelligent mobile terminals are no longer just communication functions, but also related to our family life, such as smart home and smart cars. With the powerful function of intelligent mobile terminals, people pay more and more attention to mobile payment business because of its convenient and fast service, and the following security problems are worrisome. Compared with the bank card payment, the smart mobile terminal can complete a variety of transactions directly by downloading the corresponding payment procedures, including shopping payments. However, because of the open resources of the Android system, system ROOT permissions easily cracked, there is a big potential security risk in mobile payment. In addition, the Android system lacks strict application software signature verification measures, and unauthorized application software can be installed properly, and there may be malicious software containing virus. Therefore, the design of a highly secure and customized Android intelligent payment terminal has become a common concern in the field of payment and academia. S. Dhanasekaran (B) Institute of Information Sciences, Academia Sinica, Taipei, Taiwan e-mail: [email protected] B. Kasi Annamalai University, Chidambaram, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_15

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Keywords Security · Intelligent payment · Android

1 Introduction With the continuous development of mobile Internet and intelligent terminals, an emerging and brand new mode of payment—mobile payment—has developed rapidly. Mobile payment is a way of service that allows users to use their mobile terminals (usually mobile phone) to pay accounts for the goods or services consumed. Mobile payment integrates the terminal equipment, the Internet, the application provider, and financial institutions to provide the users with currency payment and pay cost and other financial services. A report from data research firm, IDC, showed that the total amount of global mobile payments exceeded $1 trillion in 2017. The powerful data show that the mobile payment business is developing rapidly and will continue to become stronger in the next few years. As the most important equipment in financial terminal, the industry is growing and expanding. The Android system developed by Google has open-source, perfect development platform and friendly interface. It occupies a large market in the field of consumer electronics. In order to meet the increasingly developing demand of financial market, the development of financial terminal platform also presents a diversified trend and gradually develops toward intellectualization and multifunction [1]. At present, the market share of multifunction Android intelligent payment terminals in financial market is increasing, which includes many functions, such as financial payment, hydropower generation payment, credit card repayment, online banking [2]. Android system draws lessons from the security mechanism of the previous operation system and makes targeted reinforcement design for all levels of the security performance of the whole system structure, so that the security aspect of the system has been greatly improved. Android system has attracted the advantages of the past operating system at the beginning of its design; it has good security, but it does not mean that there are no security risks in the Android system. On the contrary, with the high market share and its good growth trend of Android intelligent payment terminal, there will be more and more attacks on the system. There are two main problems in the primary Android system. In June 2017, a Trojan horse virus, called Xavier, was mainly affected by the southern Asian countries. Xavier uses a variety of ways to cover its traces and cover up their activities, and it is mainly lurking in high-frequency applications. Because the virus that the application may carry has brought great risk to the security of Android intelligent payment terminal, so the security of Android intelligent payment terminal has become the goal of the whole society.

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2 Related Work 2.1 Information Safety Information security is mainly divided into the information equipment security, the data security, the content security, and the behavior security [3]. Its fundamental purpose is that internal information is not threatened by internal, external, natural, and other factors. In order to ensure information security, it requires information source authentication, access control, no illegal software residency, and no unauthorized operation. The information security is constantly enriched in the development, and the information security attributes mainly include: (1) Confidentiality: It means to prevent unauthorized subjects from subject reading information. (2) Integrity: It means to prevent information from unauthorized tampering. (3) Availability: It is the ability of the authorized body to get the service in time when the information is needed. (4) Controllability: It refers to the implementation of safety monitoring and management to information and information systems. It prevents the illegal use of information and information systems. (5) Non-repudiation: It means that in the network environment, the two sides of information exchange cannot deny that they send information or receive information in the process of exchange. Cryptography has an important application in information security, and a complete cryptosystem is usually composed of five elements (M, C, K, E, D): (1) Clear Text Space M: A finite set of possible plaintext. (2) Ciphertext Space C: A finite set of possible ciphertext. (3) Key Space K: A finite set of all possible key. K  , wherein Ke represents an encryption key, Kd represents a deciphering key. The password system is different, corresponding to different encryption keys and deciphering keys. (4) Encryption Algorithm E: It is a collection of encryption keys under the control of the transformation. (5) Decryption Algorithm D: It is a set of decryption transformations under the control of the decryption key.

2.1.1

Symmetric Encryption Algorithm

For a cryptosystem K  , when Ke  Kd, the cryptosystem is symmetric cryptosystem or single key cryptosystem, otherwise called asymmetric cryptosystem or double key cryptosystem (public key cryptosystem). The basic requirements of

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Fig. 1 Cryptosystem

the cryptosystem are secure and practical, and the following conditions must be met [4]: (1) The known plaintext M and secret key encryption is Ke, calculation of C  EKe(m) effectively; C and Kd known ciphertext decryption key, calculation of M = DKd(C) effectively. Deciphers get the ciphertext, if do not know the decryption keys cannot be cracked Kd key or plaintext in the effective time. A basic functional system of traditional will be like as shown in Fig. 1, if the secret key cryptosystem is symmetric, including dotted line, symmetric key cryptographic must by the transmitting end to the receiving end transmission key, but also need to be transmitted over the secure channel, and this is the asymmetric key cryptography (public key cryptography) system model is the biggest difference. Because of the symmetric secret key cryptosystem Ke  Kd, secret key encryption and decryption key are the same, and Kd is the key to deciphering ciphertext. Therefore, the transmission of Kd must be guaranteed to be safe and reliable.

2.1.2

Asymmetric Encryption Algorithm

In public key cryptosystem, Encryption and Decryption will be independent. Two different keys will be used here, Encryption key (public key), decryption (the secret key) which will be known to decipher only.

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The basic idea of public key cryptography is [5]: (1) The secret key of K consists of two Ke and Kd, Ke used for encryption and Ke used for decryption, and Ke  Kd. (2) Can not be calculated Kd by the Ke, so Ke can be open, so that the key distribution is more simple. (3) Because of Ke  Kd, can not be calculated by Kd by the Ke, so Kd can be used as a user’s fingerprint, Because the public key has a bunch of different secret key, not only has the function of symmetric cryptography, but also provides functions such as authentication and signature. Therefore, the public key cryptosystem is divided into encryption model and authentication model according to different purposes [6]. Encryption model refers to the use of public key to encrypt information and then use private key to decrypt ciphertext. The biggest difference between the public key system secrecy model and the symmetric system model is the management of the key. In public key cryptography, public key only needs to transfer in an open letter, can guarantee its authenticity, using symmetric key system, the key must be transmitted in the security channel.

2.2 Android Operation System 2.2.1

Android Operating System

The architecture of the native Android system mainly includes the application layer, the application framework layer, the local framework layer and the virtual machine, and the Linux kernel layer. (1) Application Layer: This layer mainly has various application software written by Java code, mainly including telephone, information, browser, and contact. (2) Application Framework Layer: This layer is mainly the connection interface between the local framework layer and the application layer written by the Java code. It mainly includes activities, service, broadcast receiver, and memory provider [7]. (3) Local Framework Layer and Virtual Machine: This layer is mainly a number of C/C++ function libraries, which can be called by different components in the Android system. It provides services to developers through the Android application framework. (4) Linux Kernel Layer: This layer is the Linux operating system. Android core system service relies on Linux 2.6 kernel, mainly written in C language. It mainly includes memory management, process management, network protocol stack, and driving model.

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Analysis of Android Authority Mechanism

The security of Android kernel mainly includes the security of users and processes, and the base of the Android security model is the user and the user group. The user of the Android is represented by the user name and the user identity (UID). The user can participate in multiple user groups at the same time, and each user group is represented by the user identity (GID) [8]. The Android system defines three types of users: (1) Superuser (ROOT) has the highest system permissions, and UID is 0. (2) System pseudo-users, the Android operating system for the need of system management, but it does not want to give superusers permission. Some key system application files and all permissions must be granted to some system pseudo-users, the UID range of 1–499, and the pseudo-user cannot log in to the system. (3) Ordinary users, only with limited access rights; UID is 500–6000, and you can access the system to get shell. Android is a multiuser and multiprocess operation system, allowing multiple users to run their separate application processes at the same time, isolate, and then protect user resources through users and permissions. Process, the kernel and Device of Android, Android in the kernel to allow multiple users to simultaneously exist and run different processes, each user has multiple simultaneous processes, multiple processes belong to different users, all processes (whether or not the same user operation) in the independent memory space. The user process accesses the service of the operating system through the system call interface. The operating system kernel drives through equipment hardware devices and resources, such as data storage and network devices as shown in Fig. 2.

3 Root Protection and Application Installation Native Android operation system includes authorization mechanism, application isolation, and data sharing mechanism, but there are still loopholes in ROOT protection and application security downloading.

3.1 Design Principle of Terminal Root Protection Number equations consecutively with equation numbers in parentheses flush with the right margin, as in (1) First use the equation. The ROOT whole line in the Android terminal is obtained by illegally tampering the U-boot image [9]. The terminal support network

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Fig. 2 Connection between the user, process, kernel, and device of Android

upgrade, U disk upgrade, SD card upgrade, and other upgrades are used in this paper. The integrity and secrecy of the U-boot, kernel, and file system mirrors are the key points in the design of the upgrading of this financial terminal. ROOT protection security is mainly by encrypting the mirror image using AES algorithm and decrypting the mirroring files in U-boot, so as to achieve the security upgrade of the kernel. Its main process is shown in Fig. 3. The decryption in the process of the system kernel upgrade is completed in Uboot, and U-boot will check the mirror image after it is downloaded. The specific verification process is shown in Fig. 4. The U-boot first Reads the encrypted SPL file and will decryption it for checking it, and start the Android system when the checkout fails, erase the original SPL file in the system and write the new SPL image to FLASH when checkout success. Next

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Fig. 3 Image encryption of terminal

to the same operation for U-boot, uImage, and UBI, if the checkout fails in each process, the upgrade will be interrupted and the Android system is normally started.

3.2 Terminal Application Authorized Installation The security design for application downloads is to issue the CA certificate chain based on the trusted SSL protocol and use the certificate signature mechanism to verify the validity of the application. The signature mechanism of the application is for the sake of safety. It checks whether the program changes or prevents malicious tampering through the application signature and checkup mechanism. At the same time, multiple applications with the same signature can share resources on the basis of mutual trust. Digital signature means that a signer generates new data by some cryptographic operation to express his or her own identity. Others can confirm the signer by verifying digital signature [10]. The digital signature uses asymmetric encryption algorithm. The basic idea of the algorithm is to generate a secret key before signature, and a secret key in data encryption, and only using a secret key to decrypt. Even if you know one of the keys, you cannot deduce another key. When using it, one of the keys is only held by the signer, called the private key; another public, anyone can get, is called the public key. When the user wants to send data to others, use their private key to encrypt data, and the receiver only using the sender’s public key can decrypt the data, so the data are received from the sender. If the public key of the receiver is used to encrypt when sent, this data can be untied only if the receiver uses the private key, so that we can ensure that only the receiver can view the content of the data, and others can get the encrypted data immediately, nor can we get the real content. The most commonly used digital signature method is using digital certificates. A digital certificate is a set of data that represents identity information in communication. The digital certificates used in this article are X.509 certificates. Its structure is shown as shown in Table 1.

Payment Security Mechanism of Intelligent … Fig. 4 Kernel upgrading process in U-boot

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162 Table 1 Structure of certificate X.509 Field Version number Serial number Algorithm identifier

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Meaning Certificate of release X.509 The unique serial number of the identity certificate CA’s algorithm identifier for certificate signature and hash operation

Issuer CA

Name for issuing certificates

Term of validity

The valid period of use of certificate, including two times of entry into force and failure

Subject

The name of the certificate holder

Main public key information

Public key and algorithm information of the subject

Issuer unique identifier

The unique identifier of the certificate issuer, optional

Body unique identifier

The unique identifier of the certificate holder, optional

Extension item

Extended information held by X.509

The signature of the issuer

CA’s signature value for the certificate

3.3 Implementation of Application Signature All Android applications must be digitally signed by developer, the application is given a signed using a private key figures, in order to identify the author of the code, to detect whether the application has changed, and the establish trust between the same signature application, then application of equipment mutual trust relationship to sharing resources safe. Different applications that use the same digital signature can grant each other permission to access the signature-based API. If the application shares UID, it can run in a unified process, allowing this access to the other’s code and data. The application signature needs to generate the private key signature and public key to use the private key to sign public key certificate. Application stores and application installation packages will not install applications without verifying digital certificates. However, the signed digital certificate does not need authority to authenticate. The application signature can be completed by the third party or by the developer himself, namely the so-called self-signature. Self-signature allows developers to not rely on any third party to release applications freely [9, 10]. Android provides permissions to verify whether they have the same digital signature, and the applications have the same digital signature. They can share code and data in a safe way. Android uses a signature mechanism to protect the security of the application in order to authenticate the developer and prevent the replacement of the application package or tampering the content. It also helps to build a trust among applications

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that can share code and data by a number of applications signed by the same private key. There are two main forms of the Android system signature: ROM signatures and application APK signatures. ROM signatures are signed for the generated Android system ROM package. The application APK signature is signed for the application installation package APK developed by the developer. The former is to sign the entire Android system package, and the latter only signatures for an application APK in the Android system.

4 Conclusion In recent years, with the rapid development of mobile payment, financial terminal as the most important equipment in payment, its industry keeps growing. Because the Android system developed by Google with open source has a perfect development platform and friendly interface, it occupied a large market in the field of consumer electronics products, the financial market in financial payment, utilities payment, credit card payments, Internet banking, and other business functions of the multifunctional intelligent payment terminal based android increases the share in financial market terminal. With the high market share and its good growth situation of Android intelligent payment terminal, there will be more and more attacks on the system. The native Android system is built on the Linux kernel. In Linux, the process can get the privilege of superuser that is ROOT authority, so as to get the overall control of the system resources. Android system ROOT permissions are not open to ordinary users; the current situation is that although Google is not open to the user ROOT access, there have been a lot of vulnerabilities which can get access to the Android system ROOT permission, and malicious software can use these vulnerabilities to obtain ROOT privileges to bypass the self-protection mechanism of Android or the advanced security mechanism proposed by the third party. In addition, due to the lack of strict application software signature verification measures in the native Android system, application software can be installed arbitrarily, including some virus software, all of which bring risks to the security of Android intelligent payment terminal. The experiment shows that the updating and upgrading of the operating system kernel can be effectively secured. The terminal refuses to upgrade when the validation fails, which can effectively prevent the system from being ROOT. In addition, before the application is installed, the application software must have a certificate signature operation to avoid the unauthorized installation of the unauthorized application software.

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References 1. S. Aral, C. Dellarocas, D. Godes, Introduction to the special issue—social media and business transformation: a framework for research. Inf. Syst. Res. 24(1), 3–13 (2013) 2. M. Mann, S.E. Byun, Y. Li, Realignment strategies in the US retail industry during a recessionary time: dominant themes, trends, and propositions. Int. J. Retail Distrib. Manage. 43(8), 775–792 (2015) 3. C. Vroom, C. Von Solms, Towards information security behavioural compliance. Comput. Secur. 23(3), 191–198 (2002) 4. A. Perrig, R. Szewczyk, J.D. Tygar, SPINS: Security protocols for sensor networks. Wireless Netw. 8(5), 521–534 (2002) 5. C.H. Bennett, G. Brassard, N.D. Mermin, Quantum cryptography without Bell’s theorem. Phys. Rev. Lett. 68(5), 557–558 (1992) 6. R. Tripathi, S. Agrawal, Comparative study of symmetric and asymmetric cryptography techniques. Int. J. Adv. Found. Res. Comput. (IJAFRC) 1(6), 68–76 (2014) 7. F.C. Bernstein, T.F. Koetzle, G.J.B. Williams, The protein data bank: a computer-based archival file for macromolecular structures. Arch. Biochem. Biophys. 185(2), 584–591 (1978) 8. W. Enck, M. Ongtang, P. McDaniel, Understanding android security. IEEE Secur. Priv. 7(1), 50–57 (2007) 9. A. Wyner, J. Ziv, The rate-distortion function for source coding with side information at the decoder. IEEE Trans. Inf. Theory 22(1), 1–10 (1976) 10. A.D. Liveris, Z. Xiong, C.N. Georghiades, Compression of binary sources with side information at the decoder using LDPC codes. IEEE Commun. Lett. 6(10), 440–442 (2002)

Hybrid Neuro-fuzzy Method for Data Analysis of Brain Activity Using EEG Signals Rajalakshmi Krishnamurthi and Mukta Goyal

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In the present scenario, there exist significant challenges between the existing solutions and the needs in the medical science domain. The main objective of this paper is to propose an efficient EEG classification scheme designed for a medical environment. The proposed system is able to predict the state of mind of a disabled person. The data of the disabled person is fed as input to this proposed system. The next part of this system is based on PPCA analysis which is a feature extraction technique. Finally, the last part of this system is the hybrid technique, i.e., a combination of two classifying techniques—fuzzy logic and neural network. The hybrid algorithm (neuro-fuzzy) is used for classifying the state of mind on the given dataset. Moreover, the system also displays the result on the app installed in the user’s mobile phone. The app is built using the ionic framework. Although neural network is also an excellent classification approach, fuzzy logic provides effective knowledge for the problems need to be solved at the approximation level. However, independent solution approach using fuzzy logic is not appropriate as this technique is applied at the approximation level. Also, the membership function of fuzzy logic is not always robust. As it is a multi-class problem, a single algorithm cannot give a correct solution. It is observed that the performance of the proposed neuro-fuzzy is better than any individual classification algorithm. The accuracy of the neuro-fuzzy R. Krishnamurthi (B) · M. Goyal Department of Computer Science, Jaypee Institute of Information Technology, Noida 201307, India e-mail: [email protected] M. Goyal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_16

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system is 90%+, whereas using the only neural network as classification technique yields an accuracy of around 79%. Keywords Brain signals · Principle component analysis · Classification Artificial neural network · Fuzzy network

1 Introduction The human brain comprises of a dense network of neurons which are, in turn, are associated with each other through dendrites and axons. Whenever we think, feel, or recall something, an electric signal is generated, that is as fast as 250 mph [1–3]. The path taken by these signals is usually isolated by myelin, but sometimes these electric signals escape. Due to the potential difference between the ions present in the membrane of the neurons, this signal is generated. Researchers have found a way to detect these signals and interpret them so it can be used for wide range of applications. In recent years [4–6], the Brain–Computing Interface (BCI) technology provides interfacing tool between the brain and electronic system. The device helps your brain to direct some physical activity without using any muscle movement. For example, you can control a cursor or prosthetic limb with the help of BCI. Initially, in 1924, Hans Berger researched the field of BCI, and he developed electroencephalography (EEG), which became quite a breakthrough and helped researchers all over the world record EEG signals and use them in various fields such as medical and neuro-marketing. The signal generated by the brain is captured through the electrodes placed on the scalp of the user [7, 8]. The different processing algorithms are applied to EEG signals. The signals are converted into various control commands. However, the electrodes are separated from the neurons because of the presence of blood, skin, and fluids, and therefore, the signal tends to be noisy and little bit smoothened. The current problem with EEG investigation framework is that the efficiency of the system is very low [9, 10]. The EEG investigation framework should have the high precision of output and quality so that we can ensure that classification is right. However, the available framework lacks precision and quality.

2 Related Works In [1], authors proposed discernibility matrix for the reduction of noise and improvement of resolution in EEG signal. The major limitation of machine learning techniques is the irrelevant dimensions of input data. Therefore, the discernibility matrix focuses on resolving this limitation of irrelevant dimensioning of input matrix. In [4], authors addressed brain simulation to study the mental behavior of humans. In [7],

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the authors explained and compared the feature extraction schemes for BCI systems. Wavelet coefficients and power spectral density (PSD) were combined as a future vector for further classification step. In [11], the authors explained the methods to classify and feature reduction of the dataset of the different facial expression, movements and thought process of the human body using EEG signal and device (which is non-evasive). Through the paper, we get to know that root mean square (RMS) is the good method for the feature reduction, but it can be improved more by segmenting the output received from RMS and thus known as selecting and segmenting RMS (SRMS). In [12], the authors discuss the incorporation of electroencephalography (EEG) technique for a brain–computer interface system. The BCI system performs recognition of different activity states of brain. The activity state recognition is supported by external device, without the involvement of any muscular movements of a human body. The major drawback of such BCI systems is that the EEG signals have low signal-to-noise ratio and also low resolution. Further, these systems require high-end signal processing mechanism and machine learning techniques to produce an accurate performance.

3 Methodology The EEG dataset is very complex because various electrodes and multiple channels collect it. A single classification algorithm cannot be used to classify the data; therefore, we need to use a feature extraction algorithm in order to reduce the complexity of the data. We need a strong learning technique to train the data. One of the major challenges faced by the researchers during signal analysis is how to filter the signals on the channels. The developed framework, therefore, should be highly competent. An individual should be able to control his external environment without moving any muscle. One of the basic needs for signal acquisition is that the system should be able to differentiate the thought during the acquisition process. The classification of the signal is one of the shortcomings of brain analysis technology. The reason for that is electrode set on the scalp of the individual receives multiple signals as one can think about multiple things at the same time. To classify data, the system needs to filter the data so that the system differentiates the signal data per electrode. The objective of this work is to produce an optimized classification of EEG signals under a given medical environment. The proposed model is capable of predicting the current state of mind of any disabled person. The proposed system can predict the state of mind of a disabled person. The architecture of the proposed system is depicted in Fig. 1. There are three phases in this proposed system. The first phase is to feed the vital input data of the disabled person. The second phase is to perform feature extraction. This phase uses the PCA analysis technique to extract feature vectors from the input data. The last phase of the proposed system is to incorporate a hybrid technique for classifying the mental state based on the processed input dataset. Finally, the last phase of this system is the hybrid technique, i.e., combination of two classifying techniques, namely fuzzy logic and neural network. This is a multi-class

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Fig. 1 Architecture of the proposed system

problem. It is to be noted that a single algorithm cannot serve the purpose of finding the optimum solution. Due to the lack of sophisticated level of approximation and weak membership function definition, the sole fuzzy logic approach is not sufficient to handle this proposed multi-class problem.

4 Model Description In this work, the dataset from the University of Tuebingen, Germany, is considered in Table 1. The hyperlink for the dataset is http://epileptologie-bonn.de/cms/ frontcontent.Php?idcat=193&lang=3. As shown in Table 1, there are five different datasets, named as A, B, C, D, and E. The data is EEG time series data. These signals are sampled at the rate of 173.61 Hz with acquisition bandwidth between 0.5 and 85 Hz. Each set consists of 100 text files. Each of these text files contains 4096 different values of EEG time series data encoded in ASCII format. Each set represents a task; whenever an individual imagines such movement, an electric signal is generated in the brain. The variations in input EEG signal data are called event-related fluctuation. EEG signal is the electrical signal that flows during synaptic excitations of dendrites of pyramidal neurons in the cerebral cortex. Depending on the various physical activity or mental thinking, different electrical signal patterns are generated.

Hybrid Neuro-fuzzy Method for Data … Table 1 Input dataset No. Set

169

Sample name

Data type

Size (KB)

Data files

Sample size

1

A

Z.Zip

ASCII

536

100

4096

2

B

O.Zip

ASCII

611

100

4096

3

C

N.Zip

ASCII

560

100

4096

4

D

F.Zip

ASCII

569

100

4096

5

E

S.Zip

ASCII

747

100

4096

Principal Component Analysis (PCA) PCA is formed from optimization criteria and various starting points which ultimately help in reducing vector dimension of EEG data. PCA detects mutual orthogonal points with max variances. Next, orthogonal matrix transformation (OMT) is performed to determine the de-correlation in the given dataset. It is used for detecting patterns in high-dimensional data. It helps in finding the similarities and differences in the given dataset and also helps in determining data patterns. Researchers also refer to PCA as the best feature extraction algorithm. Classification For classification, the system has already divided the data into three categories: testing, validating, and predicting. The system has used neural network for the classification of data. Neural network is basically used for artificial intelligence and pattern recognition. Data handling used in neural system is stimulated by the way the human brain works. The data in neural network is handled by neurons. The network which consists of several layers is known as deep neural network. The layers are made up of multiply connected neurons in the human brain. The node layer can be viewed as neuron-like switches. As soon as the switch is turned off or on, the input is passed through the net. The next layer takes the input from the output of the previous layer. The proposed neural networks differ from the other neural networks on the basis of their depth, i.e., on the basis of the number of hidden layers present in the network. Each layer in the network works on the different sets of features which are decided by the previous layer output. As you go deep into the neural network, the more complex feature your nodes can determine using neural network as depicted in Fig. 2 pseudocode for the neural network. Let tr represent the set of training dataset, v represent the set of set of validating dataset, T represent the set of tasks like {eye blinking, need sleep, need food, hand movement, feeling hot, feeling cold}, αtr represent the probability of training data, βv probability of verification data, γt represent the testing data, and M represent the maximum size of sample data. Hybrid Neuro-fuzzy Network In recent trend of machine learning, one of the most popular and powerful recent data handling for classification is hybrid neuro-fuzzy algorithm. The major attraction of this method is that each of the algorithm targets to reciprocate the difficulties of the other method. That is, neural network solves the problem of fuzzy classification while fuzzy method solves the neural network problem of classification. In this work, the implementation is carried out using the adaptive

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1: 2: 3:

Load EEG signal dataset

4: 5: 6: 7:

Create zero matrix of T x M Create logical matrix for corresponding to each group Perform Principal Component Analysis Define training function using Scaled Conjugate Gradient Back Propagation (SCG) Method Create Pattern recognition network with 10 hidden layers Setup division of data for , , , Training the neural network Test the neural network View the networks Set the sample image to get the various output

8: 9: 10: 11: 12: 13:

Load .mat file for set of tasks Fetch addresses of cell location and name

Fig. 2 Pseudocode for neural network

neuro-fuzzy system toolbox (ANFIS) provided in MATLAB software. Particularly, in the classification module, three different datasets (training, testing, and validation) are incorporated. The objective of the proposed hybrid neural fuzzy system is to generate suitable membership function that satisfies the required output and also to produce graphical representation in order to validate the user satisfaction. The correctness in classification is achieved by incorporating multilayer preceptor in neural network that are completely well trained under supervised environment. The significance of hybrid neuro-fuzzy approach is that it provides an improved arrangement of pattern recognition problems. Further, the proposed method involves a vigorous and a minimal cost solution. Also, it incorporates human-like reasoning through the use of the fuzzy model and fuzzy sets which consists of a set of IF-THEN fuzzy rules. Iconic Mobile App Framework After this system predicts what the patient is feeling, the predicted data is sent through a query string to this server. The app installed in user device requests the server to send the data. The server then sends the data to the app through the query string. Finally, the predicted data is displayed in this app. The system has used ionic framework for creating this app. The iconic mobile app framework provides a universal platform for developing mobile applications using different Web technologies like HTML5, SaaS, JavaScript, and CSS. Further, the developed mobile app can be implemented on various operating system platforms like Android, IoS, and Windows.

Hybrid Neuro-fuzzy Method for Data … Table 2 System parameters Data division: random

171

Timing: scaled conjugate gradient

Performance: mean squared error

0 (min), 93(mean), 100 (max)

Performance

0.461 (min), 0.000237 (mean), 0 (max)

RMSE

Epoch (iterations)

1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

10

20

30

40

50

60

70

80

90

100

Epochs Fig. 3 Performance measurement of neuro-fuzzy algorithm

5 Results and Discussion The various system parameters set for the proposed system implementation are depicted in Table 2. It is to be noted that the epoch has mean value of 93, while maximum achieved is 100. Figure 3 depicts the performance of the proposed hybrid neuro-fuzzy system. The root mean square error is statured around 1.1, for epochs up to 70. After 70 epochs, the RMSE is reduced to around 0.15. This shows the proposed hybrid system is efficient in data analysis for the EEG signals of various brain activities. The confusion matrix C is generated for C (output class, target class) using the hybrid neuro-fuzzy system as shown in Fig. 4. The overall percentage of positive results under output classes exhibits maximum average at 100%, while minimum average at 93.8%. Further, it is observed that the target class of seven tasks has maximum average at 100% and minimum average at 89.3%.

R. Krishnamurthi and M. Goyal

Output class

172 1 30, 14.2 0, 0.0 0, 0.0 0, 0.0 0, 0.0 0, 0.0 0, 0.0 10 0%, 0.0%

2 1, 0.5 25, 11.0 1, 0.5 0, 0.0 0, 0.0 0, 0.0 0, 0.0 89. 3%, 10.7%

3 0,0 .0 1, 0.05 31, 14.6 0, 0.0 0, 0.0 0, 0.0 0, 0.0 96.9%, 3.1%

4 0, 0.0

5 0, 0.0

0, 0.0

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0.0 0,

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0, 30 , 14.2 0, 0.0 0, 0.0 10 0%, 0.0%

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30, 14.2 1, 0.5 0, 0.0 0, 0.0 96. 6%, 3.2%

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0, 0.0 28, 13.7 0, 0.0 90. 3%, 9.7%

0, 0.0 0, 0.0 29. 13.7 96. 7%, 3.3%

8 96.8%, 3.2% 96.2%, 3.8% 93.8%, 6.1% 96.9%, 3.2% 90.9%, 9.1% 96.6%, 3.4% 100%, 0.0% 95.8%, 4.2%

Target Class

Fig. 4 Confusion matrix for neuro-fuzzy method

6 Conclusions In this work, the optimum classification of EEG signal is targeted. The purpose of this classification is to identify the different mental data of disabled person. The proposed system considered the hybridization of neural network along with fuzzy approach. The hybridization is considered to overcome the cons within each of these methods. The EEG signal data fetched from disabled person is considered as the input. The experimental output exhibits that the performance of the proposed hybrid system outperforms than the performance of individual algorithm. The proposed hybrid system exhibited 97% accuracy in comparison with neural network of 76%.

References 1. R. Kottaimalai, M.P. Rajasekaran, V. Selvam, B. Kannapiran: EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications, in IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), (Tirunelveli, 2013), pp. 227–231 2. S. Chopra, A.K. Tripathy, A. Alphonso, M. Carvalho, C. Sancia, Brain computer interface and electro stimulation of muscles to assist paralyzed patients in limited movement of hands, in International Conference on Nascent Technologies in Engineering (ICNTE), (Navi Mumbai, 2017), pp. 1–6 3. H.K. Kwan, Y. Cai, A fuzzy neural network and its application to pattern recognition”. Fuzzy Syst. IEEE Trans. 2(3), 185–193 (1994) 4. K. Nakayama, K. Inagaki, A brain computer interface based on neural network with efficient pre-processing, in International Symposium on Intelligent Signal Processing and Communications. (Yonago, 2006), pp. 673–676

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5. P. Melin, O. Castillo, Hybrid intelligent systems for pattern recognition using soft computing: an evolutionary approach for neural networks and fuzzy systems, vol. 172 (Springer Science & Business Media, 2005) 6. T. KameswaraRao; M. Rajyalakshmi, T.V. Prasad, An exploration on brain computer interface and its recent trends. Int. J. Adv. Res. Artif. Intell. 1(8), (2012) 7. L. Vega-Escobar, A. E. Castro-Ospina, L. Duque-Muñoz, Feature extraction schemes for BCI systems, in 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA) (Bogota, 2015), pp. 1–6 8. R. Padmavathi, V. Ranganathan, A review on EEG based brain computer interface systems. Int. J. Emerg. Technol. Adv. Eng., 4 (2014) 9. T. Ibrahimi, Recent advances in brain-computer interfaces. EPFL, CH-1015 Lausanne (2007) 10. K. Nakayama, K. Inagaki, A brain computer interface based on neural network with efficient pre-processing, in International Symposium on Intelligent Signal Processing and Communications (Yonago, 2006), pp. 673–676 11. C. Park, D. Loonie, P. Kidmose, M. Ungstrup, D.P. Mandic, Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition. IEEE Trans. Neural Syst. Rehabil. Eng. 19(4), 366–373 (2011) 12. M.K. Goel, An overview of brain computer interface, in 2015 Recent and Emerging trends in Computer and Computational Sciences (RETCOMP) (Bangalore, 2015) pp. 10–17

Gait Recognition Using J48-Based Identification with Knee Joint Movements Jyoti Rana, Nidhi Arora and Dilendra Hiran

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gait Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Data Collection and Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Knee Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract This paper affords a method for the recognition of gait biometrics using J48 decision tree algorithm for an integrated accelerometer of mobile phone. The proposed model allows recognizing the subjects based on their gait. When a subject walks, a unique angle is created at his knee joint. Classifying the subject with the help of his knee angle enables unique recognition. The model is designed, developed, and tested in WEKA considering the parameters x-axis, y-axis, z-axis, and knee angle for the recognition of gait. Acceleration data is received from integrated sensor of cell phone placed in a front pocket of right leg’s trouser of the subject. Data is then analyzed and with the involvement of total 41 volunteers over 18–30 years old in the experiment, we achieved accuracy of 89.45%. Keywords Gait recognition · Accelerometer · Gait · WEKA · J48 · ROC · AUC J. Rana (B) Department of Computer Science, Naran Lala College of Professional and Applied Science, Navsari, Gujarat, India e-mail: [email protected] N. Arora Advicon Tech Pvt. Ltd, Ahmedabad, Gujarat, India e-mail: [email protected] D. Hiran Department of Computer Science, Pacific University, Udaipur, Rajasthan, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_17

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1 Introduction In ultramodern years, there has been an accelerated interest on correctly recognizing subjects by their gait using accelerometer sensor incorporated in portable devices. Due to compact size, incredible computing power and support of various built-in sensors, smartphones have been widely used nowadays for recognition purpose and therefore these devices have opened innovative methods in various areas for biometric research and applications. The purpose of present study is to receive gait data from accelerometer sensor of smartphone while walking naturally, to calculate the knee joint angle and to recognize the subject. We have selected android-based smartphones for recording the gait because these are open source, simple and available publically. All those smartphones have integrated triaxial accelerometer that degree acceleration in three dimensions. We can detect the orientation of the portable devices, play games, enable automatic screen rotation, record subject’s gait, and recognize activity by using these accelerometers. In order to deal with the objective of gait recognition, we gathered accelerometer data of 41 subjects while they walk at their normal speed. We have carefully selected subjects between the age group of 18–30 years people for this study because the gait of a young and elder subject is different due to the variability at the hip and knee joints [1]. Then raw time accelerometer data for each subject is gathered. Finally, an analytical model is built for the recognition of gait by using J48 classification algorithms. The study offers several purposes: • To find accuracy of recognition of same subject’s gait in different circumstances and how similar is with the walking of other subjects. • To illustrate how accelerometer data can be utilized by traditional classification algorithms. • To make the data publically available so that it is able to be used by other researchers also as we did not find such data available publically. • We consider that our work will help to convey attention to the possibilities available for biometrics research and to implement gait recognition practically in portable devices. Researchers in earlier studies [2–4] have implemented numerous machine learning algorithms which includes k-means clustering, decision tree, neural networks, and Bayesian networks for the classification and recognition of gait. In this study, we have also focused on parameter tuning using J48 algorithm. Weka is open software developed by the University of Waikato where users can provide input and for provided data perform analysis using the software’s built-in classifiers. J48 is a decision tree classifier that uses the C4.5 algorithm where data is recursively divided at attributes levels [5]. It applies to each record and provides classification result to each record of database. It constructs a binary tree for classification that makes decision of the target value based on available data. The remaining of the paper is organized as follows: Section 2 shows a few associated works. Section 3 describes gait recognition process including data collection

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and experimental setup, calculation of knee angle, and feature extraction. Section 4 covers the experiments performed and results obtained. Section 5 summarizes contribution of our work and discusses areas for future research.

2 Related Work Gait can be identified in two ways: photo-based identification and accelerometerbased identification. Image-based approach apprehends the situation from a video of their gait, typically from some sort of surveillance digicam, at the same time accelerometer-based method understands the concern by means of putting a few sort of accelerometer to be placed on subject to accumulate records. Gait identity strategies with the use of accelerometers have accomplished pretty in the previous research [6–8]. Rong et al. [8] used one sensor connected at waist and gather dataset of 21 subjects. They built unique accelerometer sensor to record data from the topics and their methods show promising effects. As this sensor could be very small, they ought to be attached to the subject, with his knowledge. Nowadays, most of the phones have integrated accelerometers and as a result they may be used to collect acceleration data from the subject carrying it [9, 10]. The benefit being that the mobile is a standard device that most subjects have already which makes data gathering process less complicated. Chan et al. [9] illustrated that data collected from phone sensors successfully recognized the subject by his gait. Kwapiz et al. [11] perform experiments where user is recognized by accelerometer readings and cell phone is positioned in the pocket of the user. They got excessive identification rate even as the usage of segments of statistics starting from 5 to 10 min. Juefei-Xu et al. [12] used commercially available android smartphone located in the pocket of the subject and they firmly believed that smartphones are not only used to identify a subject but they can also match gait patterns across different speeds. Gait recognition has lately received interest in research because accelerometers are already integrated in end-user products. Some of the earliest studies in mobile phone accelerometer-based gait recognition targeted on different classification algorithms [2–4]. Bao and Intille [2] used five biaxial accelerometers worn on the different parts of subject’s body in an effort to accumulate data from 20 users using decision tables, instance-based learning, C4.5, and Naive Bayes classifiers. They worked for activity recognition tasks and their results gave proof that the accelerometer positioned on the thigh become most effective for the recognition of gait. This finding helps our selection to have our test subjects carry the smartphone within the maximum handy area—their trousers front pocket. Kunnathu [13] developed a statistical model to recognize the subject based on how the subject picks up the phone and how he/she holds the phone to the ear. He used Weka multilayer perceptron classifier for finding threshold value for splitting data into three different phases–picking up, on-ear and placing down and achieved accuracy of 80, 80 and 82.86% for each of the phases, respectively.

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Several preceding studies carried out various classification algorithms based totally on pattern recognition and training phases. Gupta and Dallas [3] evolved an activity recognition system using a body-worn accelerometer via the use of Naive Bayes and k-nearest neighbor and got 98% accuracy for both the classifiers. Das et al. [4] extracted functions for each pattern window of a predetermined variety of 512 samples with the aid of the usage of the nearest neighbor and Naive Bayes classifiers.

3 Gait Recognition In this section, we discuss about the gait recognition process applied by us in the proposed model. In Sect. 3.1, we describe the process done by us for collecting the accelerometer data and its experimental setup; in Sect. 3.2, we describe the process of calculating knee angle from the accelerometer data; and in Sect. 3.3, we show how the subjects will be recognized.

3.1 Data Collection and Experimental Setup For data collection, subjects have been asked to walk at their regular speed carrying android-based smartphone. A total of 41 healthy and young subjects between the age group 18–30 years carried Samsung Galaxy Core mobile phone loaded with accelerometer to record data to be used in further experimentations. The smartphone has been placed in the front pocket of right leg’s trouser of the subject during data collection. The maximum walking distance was about 60 m down the hall as shown in Fig. 1. It has the integrated sensor Bosch sensortec’s 3-axis BMC 150 accelerometer which measures acceleration forces up to ±16 g. We present accelerometer readings as three component vectors based on gravity, acceleration, and motion. The sensor framework makes use of a three-axis coordinate system to store data. The device is held in its default orientation, and the phone is placed upright, and the display screen points are in walking path as shown in Fig. 2a, b. Accelerometer data from the android phone is stored in text files in terms of 150–210 samples per gait cycle for each of three axes x, y and z. The accelerometer A is denoted by   A  Ax , A y , Az

(1)

where Ax , Ay , and Az constitute the value of the forces performing on three directions, respectively. Figure 3 shows the accelerometer data acquired from normal walking style of single subject in natural conditions on a flat surface. We collected the accelerometer data every 19–21 ms.

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Fig. 1 Surface chosen for capturing gait records

Fig. 2 a Alignment of x- , y- , and z-axis with capturing device, b position and orientation of device 10

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Fig. 3 Initial 3-D gait acceleration data in terms of X- , Y- , and Z-axis for the single subject

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Fig. 4 Schematic diagram of human leg

3.2 Knee Angle Hip and knee joints are the primary joints of human leg which are used for locomotion. When a subject walks, a unique angle is created at his knee. Young and elderly people walk differently due to the distribution of joints [1], which plays an important role in calculating the knee angle. In this view, we have paid proper attention on calculating the knee angle of subject for classification purpose. When a person walks, his knee bends to form a particular angle. This angle is formed depending on his walking style and varies from person to person. Most of the researchers [14, 15] used electrogoniometer or extended Kalman filter in their study to calculate knee angle. In this work, we are performing various transformation techniques to calculate knee angle. We performed following steps to calculate knee angle. We assigned 3-D Cartesian reference frames to thigh and knee joints as shown in Fig. 4. Frames F 0 and F 1 are assigned to thigh and knee. Frame F 0 is oriented such that x-axis is in the path of movement; the y-axis is pointing upward direction while z-axis is pointing outward from thigh to the right. The coordinates of a point from one frame to another is mapped by homogeneous coordinate matrix. Length ln corresponds to the distance between the thigh and knee. This distance may vary from person to person. θ is the relative joint angle. The transformation between F 0 and F 1 is obtained by simply performing translation. Hence, cell phone is translated from frame F 0 to F 1 to sit on the new position which is just above knee. As the phone moves downward, only t y factor is affected. We received n-dimensional vectors from accelerometer for each user, i.e., X i , Yi and Z i i  1, 2, . . . , n

(2)

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where X i, Y i, and Z i denote the ith record of single user in terms of X- , Y- , and Z-axis The translated vectors are calculated as Yi  Yi + ln

(3)

where X i , Yi and Z i denotes the translated vector of X i , Y i, and Z i, respectively. Average vectors are computed as X i  abs(average(X i ))

(3)

abs(average(Yi ))

(4)

Z i  abs(average(Z i ))

(5)

Yi 

The tangent is calculated as tan(θ )  Y i /X i θ  tan−1 (59.35) θ  89◦ Knee joint angles of all the students under the same experimental setup and the same environmental condition are calculated. Results of knee angle of 41 subjects are presented in Table 1.

3.3 Feature Extraction Feature extraction is a method which is applied on a dataset containing multiple parameters. It facilitates in extracting non-redundant, non-correlated features to be used in experimentations for better results in less time. The sampling range used in this study is 150–210 data per second. We used interpolation rates of 51 FPS (Frames Per Second). We used Weka for classification of collected data. We have used 70:30 ratio for training and testing dataset. In this study, there are 41 subjects and averages of 1000 records are fetched for each subject. Hence, 50,752 total combinations of training and testing data are used in this study. A dataset of 50,752 samples was divided into two subsets resulting in a training set of 38,301 samples and testing set of 12,451 samples. The J48 decision tree was chosen to reveal the performance of distinctive sets of training and testing samples. Weka then determines the accuracy of the classifier and outputs this data as a .txt file. For this study, we have analyzed classification accuracy, mean absolute error, and root-mean-squared error. The training set for J48 is formed as a set of four-dimensional vector comprising all the records x(1), x(2), …, x(n). Table 2 represents sample data for training set of 38301 samples.

81

17

77

89

Knee angle

Subject 16 Id

Knee angle

32

65

31

Knee angle 68

Subject Id

89

2

Subject 1 Id

60

18

75

3

Table 1 Knee angle of all subjects

75

33

82

19

73

4

84

20

68

5

70

34

76

21

58

6

78

35

81

22

85

7

54

36

89

23

82

8

86

24

82

9

78

37

74

25

53

10

87

38

76

26

84

11

89

39

67

27

83

12

85

28

80

13

82

40

57

29

87

14

72

41

80

30

78

15

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−0.335 −0.976 −2.202 −1.8 −1.455 1.206 −1.024 −0.402 1.312 −0.047

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Y

Z

Knee angle

Subject Id

6.167 5.592 4.75 4.663 5.133 5.985 6.224 5.363 5.142 6.148

9.806 5.391 3.351 10.687 7.144 3.217 2.738 12.382 11.789 7.852

74 74 74 75 75 75 76 76 76 76

1 1 1 1 1 1 1 1 1 1

The experiments conducted resulted into prediction error of 0.0078 and the root mean squared error of 0.0624.

4 Results and Discussion The results of experiment conducted on the data results into data values from which a confusion matrix of 41 × 41 is generated to get more clarity on the classification done by the proposed model. A sample confusion matrix consisting of ten subjects is shown in Table 3. Every subject is denoted by letter A to J. The recognized classes are depicted vertically while the reference classes are shown horizontally. By applying J48 decision tree classifier, an overall accuracy of 89.45% is achieved. To examine the performance of binary classifier, we have used ROC curve to generate graph of true positive rate (TPR) versus false positive rate (FPR) for every classification threshold. The performance is measured by this TPR and FPR, and the results are graphically displayed using ROC curves as shown in Fig. 5. An ROC curve that hugs the higher-left corner represents a “good” classifier at the same time a curve that falls close to the line y  x represents a classifier that is not a good deal better than guessing. The area under ROC Curve (AUC) value is a way used to quantify the classifier performance and is given inside the higher-left corner of the ROC curve. AUC score of 0.5 suggests random guessing; 0.9 denotes that chosen model is good while the score 0.9999 would be too good to be true. We got AUC value between 0.90 and 1 for almost every classification threshold which shows that the J48 classifier we choose is good.

A 1443 B 0 C 0 D 0 E 0 F 0 G 0 H 0 I 0 J 0 Overall avg.

A

0 757 0 0 0 0 0 0 0 0

B

0 0 657 0 0 0 0 68 2 0

C 0 0 0 1100 0 0 0 0 0 0

D 0 0 0 0 700 0 0 0 0 0

E 0 0 0 0 0 2100 0 0 0 0

F 0 0 0 0 0 0 750 0 0 0

G 0 0 41 0 0 0 0 684 75 0

H 0 0 2 0 0 0 0 72 589 0

I

0 0 0 0 0 0 0 0 0 904

J

100 100 93.9 100 100 100 100 83 88.4 100 97.4

Acc.

Table 3 Confusion matrix of the classification using J48; (recognized classes are depicted vertically and reference classes are shown horizontally)

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Subject-1

Subject-2

Subject-3

Subject-4

Subject-5 Fig. 5 ROC curves of five subjects

5 Conclusions and Future Work The essential contribution of this study is to reaffirm the use of mobile phones with accelerometer for gait recognition. The advantage of this method over other biometric system is the unobtrusive operation which is much user friendly. Machine learning algorithm used in knee angle calculation achieves 89.44% accuracy. By using various classifiers of machine learning, the accuracy can be improved. The obtained accuracy is of 41 subjects while walking naturally. The walking pattern of the same observed

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subject changes, that is, probably due to injury, carrying load, and speed. Suggested future work is to apply the proposed method by taking deeper look into gait while carrying heavy load and in tiredness for robust gait recognition even under variable conditions. Declaration Images and the datasets used in this work are our own and not from any others work.

References 1. J. Rana, N. Arora, D. Hiran, An intensive assessment of factors affecting gait identification and recognition. Int. J. Innovations Adv. Comput. Sci. (IJIACS). 6(6), 238–244 (2017) 2. L. Bao, S. Intille, Activity recognition from user-annotated acceleration data. Pervasive Comput. 1–17 (2004) 3. P. Gupta, T. Dallas, Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans. Biomed. Eng. 61(6), 1780–1786 (2014) 4. S. Das, L. Green, B. Perez, M. Murphy, A. Perring, Detecting user activities using the accelerometer on android smartphones. Team Res. Ubiquitous Secure Technol. (2010) 5. JR. Quinlan, C4. 5: Programs for Machine Learning, vol. 1 (Morgan Kaufmann, 1993) 6. D. Gafurov, E. Snekkenes, P. Bours, Gait authentication and identification using wearable accelerometer sensor. Autom. Ident. Adv. Technol. 220–225 (2007) 7. G. Pan, Y. Zhang, Z. Wu, Accelerometer-based gait recognition via voting by signature points. Electron. Lett. 45(22), 1116–1118 (2009) 8. L. Rong, Z. Jianzhong, L. Ming, H. Xiangfeng, A Wearable acceleration sensor system for gait recognition, in Industrial Electronics and Applications (2007), pp. 2654–2659 9. H.K. Chan, H. Zheng, H. Wang, R. Gawley, M. Yang, R. Sterritt, Feasibility study on iphone accelerometer for gait detection, in Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2011), pp. 184–187 10. S. Sprager, D. Zazula, Impact of different walking surfaces on gait identification based on higher-order statistics of accelerometer data. In Signal and Image Processing Applications (2011), pp. 360–365 11. J.R. Kwapisz, G.M. Weiss, S.A. Moore, Cell phone-based biometric identification, in Biometrics: Theory Applications and Systems (2010), pp.1–7 12. F. Juefei-Xu, C. Bhagavatula, A. Jaech, U. Prasad, M. Savvides, Gait-ID on the move: pace independent human identification using cell phone accelerometer dynamics, in Biometrics: Theory, Applications and Systems (2012), pp. 8–15 13. N. Kunnathu, Biometric user authentication on smartphone accelerometer sensor data. proceedings of student-faculty research day, CS (2015) 14. T. Bennett, R. Jafari, N. Gans, An extended kalman filter to estimate human gait parameters and walking distance, in American Control Conference (ACC) (2013), pp. 752–757 15. Saito, H., Watanabe, T., Arifin, A.: Ankle and knee joint angle measurements during gait with wearable sensor system for rehabilitation, in World Congress on Medical Physics and Biomedical Engineering (Springer Berlin Heidelberg, 2009), pp. 506–509

Cyber Intelligence Alternatives to Offset Online Sedition by in-Website Image Analysis Through WebCrawler Cyberforensics N. Santhoshi, K. Chandra Sekharaiah, K. Madan Mohan, S. Ravi Kumar and B. Malathi

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 ICT and Cybercrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Cybercriminal Sedition Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Any illegal activity carried out through the usage of the Internet is cybercrime. In this paper, we present a case study of a cybercrime with respect to the Culprit organization Government of Telangana (CGoT) that has been purportedly prevalent during 2011–2015 approximately as indicated in the website home

N. Santhoshi (B) Department of CSE, Aditya College of Engineering, Madanapalle, Chittoor, Andhra Pradesh, India e-mail: [email protected] K. Chandra Sekharaiah Department of C.S.E in School of IT, JNTUH Hyderabad, Hyderabad, Telangana, India e-mail: [email protected] K. Madan Mohan Department of CSE, Malla Reddy Institute of Technology and Management, Dundigal, Hyderabad, Telangana, India e-mail: [email protected] S. Ravi Kumar Krishna University, Machilipatnam, Andhra Pradesh, India e-mail: [email protected] B. Malathi Department of CSE, Aurora’s Technological and Research Institute, Parvathapur, Uppal, Hyderabad, Telangana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_18

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page image of http://jntuhjac.com. Interestingly, the crime is registered against identity theft under Section 66-C under IT Act 2000–2008, whereas the complaint was against sedition crime. The culprit website of JNTUHJAC organization under CGoT was operational in the JNTUH University before the enactment of Andhra Pradesh Reorganization Act 2014. Through the culprit website, approximately 2000 registrations were obtained for the organizations. A snapshot of the culprit website obtained through a web crawler tool was submitted to the police to substantiate evidence. We present, in the case study, the many facets of the moot cybercrime issues related to the JNTUH University academic environment and how they are handled. The crime is on account of the abusive usage of an image in the home page of the aforementioned website. The image usage is nationally abusive during 2011–14 because of the mention of “Government of Telangana,” whereas there was no such government formed by any constitutional provision such as enactment through parliament. The analysis of the image impresses us immediately about the seditiously organized group of pupils by the JNTUHJAC, an unregistered outfit in the JNTUH academic environment. Image analytics and crime analytics show that the crime is a big data crime. We have come up with remedial forum by a website approach which is unique to spread awareness about the online sedition owing to it that the police showed abject inattention to register the crime under sedition. Keywords CGoT (Criminal government of Telangana) · Image analytics Crime analytics · Big data crime · Section 66-C ITA 2000–2008 Cyber intelligence · Cybercrime cycle · Degree of crime Seditious government of Telangana (SGoT) Cybercriminally seditious GoT (CSGoT)

1 Introduction A cybercrime is an illegal act that involves the usage of the Internet. In [1], cybercrimes such as debit/credit card frauds, hacking and unauthorized access, virus attack, denial of service attacks, fake profiles on social media, IPR violations, cyberterrorism, e-commerce/investment frauds, cyberstalking, identity theft, data diddling, source code theft, breach of privacy and confidentiality and other computer-related crimes, e-mail-related crimes (such as e-mail spoofing, e-mail spamming, e-mail bombing, sending threatening emails, e-mail frauds), phishing, lottery frauds, investment frauds, and any other online crime are mentioned. Cybercrimes are perpetrated more and more as the Internet usage rises. The crime scenario in the society has become twofold (both online and offline) with the advent of the Internet. The crime is owing to the abusive usage of an image in the home page of the aforementioned culprit Web site. The image usage is, needless to say, nationally abusive during 2011–14 owing to the mention of “Government of Telangana” during a time span when there was no such government formed by any constitutional provision such as enactment through

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Fig. 1 Snapshot 1

parliament. In other words, basically, the Web site used such mention in the image notwithstanding the unauthentic means. The image in the homepage of the culprit website is traced by retrieving the website by using an open-source cyberforensic webcrawler tool, wayback machine [2]. Snapshots of the retrieved home pages on two different dates (one, on a date when the image was there; and two, on a date when the image was not there) are presented in Figs. 1 and 2. A quick glance and analysis of the image impress us immediately about the seditiously organized group of pupils by the JNTUHJAC, an unregistered outfit in the JNTUH academic environment. The snapshot in Fig. 2 does not bear the image. Further, the present website homepage is totally redesigned. This means that the organization committed cybercrime in the background as shown in Fig. 1 and projected itself without any cyberforensic evidence of the same later in the foreground as shown in Fig. 2 to continue its activity of exploiting the academic environment with a cybercriminal and yet unpunished background. Analytical case study of multiple cybercrimes is presented anew in pursuit of our defense for registration against the cybercriminal sedition. The remaining part of the paper is organized as follows. The second section gives the details of ICT [3] and different types of cybercrimes. In Sect. 3, we present the FIR filed by the Cyberabad police at Gachibowli Police Station, Hyderabad, with respect to the said case. Section 4 presents the conclusions and future work.

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Fig. 2 Snapshot 2

2 Related Work The literature survey carried out as related to the research work presented in this paper is as follows. In [4], the first spadework work related to the cybercrimes involved in the case study was presented. In [3], elaboration of the work in [3] was taken up and the issues were presented on better grounds of issues and details. In [5], as the research work gained ground, in [5–9], the cyberforensic evidence of the cybercrimes was thoroughly captured and presented on more elaboration of reporting and recording the details. The work in [6, 10] delineates the RTI Act impact to defy the conditions that were against the pursuing for the field reporting and ensuring the registration of the cybercrime to successfully prevail upon the public authorities for right information in the information technology era we are in. The research work in [11, 12] dwells upon a cyber remedial forum against the cybercrimes case study to campaign and spread awareness about the twin cybercriminal organizations JNTUHJAC and CSGoT such that the public strength is garnered to regain the national losses owing to the impact of the cybercrimes in the case study. In [13, 14], nearly 50 interrogation-like, information-retrieving points and issues raised and laid under an RTI application are presented together with the investigation and prosecution details of the cybercrime. The impact is to delineate the maladaptive

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management in the organizational setting in the academic scenario under consideration and to foster adaptive management. In [15, 16], the charge sheet of the first cybercrime registered as regards the case study is analyzed to evaluate the search for the right information and how a cybercrime issue is tracked and handled in the information technology era in the course of investigation.

3 ICT and Cybercrime Information and Communication Technology (ICT) enables society to create, collect, consolidate, communicate, manage, and process information in multimedia and various digital formats for different purposes by using telecommunication techniques [17]. With the rapid advances in ICT, people are doing business and making social communication efficiently. ICT has now brought the countries and people closer together. But at the same time, the criminals are also allowed to do the vulnerable tasks. So, cybercrime has become now a big threat in private, professional, and public sectors. Moreover, the menace of cybercrimes is now entering directly into home to thieve and fraud the individual’s identity physically and virtually through online means. By means of ICT, these criminals are using their knowledge to gain benefits quickly and are using their expertise to gain money easily without having to do genuine daily work. Cybercrime losses are not actually measurable. In most of the financial sectors the accounts are tapped and altered to get the benefit out of it. Due to this, various industries are attacked and their money is stolen and information is altered and corrupted.

4 Cybercriminal Sedition Case Study CGOT, a cybercriminal organization has been prevalent in the JNTUH academic environment in the last several years. It maintained a Web site which is the base for its cybercrimes. This culprit organization perpetrated cybercrimes over the Internet, and the Web site was used to organize quite many a section of the university population in JNTUH. Nearly 2000 people got registered in the Web site. The cyberforensic evidence obtained from the webcrawler tool, wayback machine [2, 18], as well as that external to the Web are shown here to prove the crime or guilt. The Case Status Information Snapshot from Cyberabad Police Web site is also shown below as a proof for the complaint which is registered. The image analytics and crime analytics in the case study lead us to the following conclusions: 1. The culprit organization committed big data crime. Multiple crimes are involved and thousands of registrations are involved. We do not give a definite definition

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of big data crime here, and there is no definition as such anywhere in the literature on cybercrimes. However, we claim so on account of it that it is cognizable that there are many crimes and thousands of registrations involved. We claim that cybercrime can be computed under the term “degree of crime” in a context like the case study under consideration. For instance, “degree of crime” could mean, here, the number of crimes involved multiplied by the number of registrations involved. 2. In response to the complaint imaged and as in Fig. 3, a crime of identity theft under Section 66-C ITA 2000–2008 was registered as depicted in the FIR copy images in Figs. 4, 5, and 6. 3. It interests as to who is at loss and what is at loss in the cybercrimes case study? In other words, the impact requires analytical study. Typically, in a financial cybercrime, money is at loss and personal victim is involved. The money amount is easily figurable. For example, when an amount is fraudulently transferred online, the amount is reflected in the victim’s bank account passbook updation. Thus, the amount is easily figurable. But, in a typical sedition crime case like the present case study, money is not at loss. Then, who is at loss and what is at loss? Is it personal loss or impersonal loss? The answers are that the loss here is impersonal loss and national loss, and the loss is national amity, national solidarity, national consciousness, national unity, national integration, and national integrity. When the national sentiments are badly affected, when the people of a nation are apathetically inured to the national sentiments in regard of the aforementioned national ideas, the very concept of the national edifice is at risk and jeopardy. This may seem little to an ordinary citizen owing to impersonal loss, but the negative impact of cybercrimes in the case study percolates against the national fabric. Modern warfares are information warfares through spoiling the mindset of the people by the Internet means. A society and nation are targeted to become maladaptive toward the national motto, aims and objectives, and edifice. When the people of a society or a nation become easy subjects to accept insensitively wrong, criminal information usage through the Internet, the people lose “sense of belonging to the nation.” It is in this regard that the citizens are expected to be smart citizens. Smart citizenship does not accept nationallevel crimes such as sedition and treats the interest groups and organizations which abuse the national concept, consciousness, and spirit as “outcast” and take immediate measures for prohibition of such organizations. Smart citizens and students, in particular, do not become members of seditiously cybercriminal groups. They give smart solutions to cybercriminal sedition by seeing for orders of prohibition against cybercriminal organizations such as the JNTUHJAC and the CGoT/SGoT. On account of the sedition crime that is applicable to the CGoT, it is also termed as SGoT. 4. The concept of image processing in the case study has less relevance in the traditional sense. The image analytics presented are unique to human assessment and analysis. It is very difficult to develop and deploy a Web application that processes the images in the lakhs of websites in the Internet by a web crawler means and recognize and identify the cybercriminally seditious abusive usage presented

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Fig. 6 Copy of FIR page 3

here by us. Development of “webcrawler cyberforensic image processing software” is a challenging problem for modern AI researchers. The ability of insight and acumen is unique to human sight. When this “insight and acumen” is at loss in the citizens and, in particular, higher education-level students, who are blinded by the nationally abusive sentiments, a nation’s future becomes imperiled. Thus, there is a necessity to develop ICT for awareness and campaign for the nation building which is jeopardy and for recovering the national losses in terms of the national amity, national solidarity, national unity, and so on and so forth that are due to cybercriminal sedition and allied cybercrimes of the twin organizations JNTUHJAC and CSGoT in the case study. Our work in this paper goes a long way not merely in the identification of the cybercriminal sedition of the twin organizations but in the recording of the cybercrimes by on-the-field work by complaining against the cybercrime and pursuing a lot for the registration of the crime under some section or the other of the Information Technology Act 2000–2008. It is believed that the cybercrime is not registered under sedition because the IT Act 2000–2008 does not particularly have anything to deal with this kind of a cybercrime. Thus, it is hoped that our attempt makes initiatives to generate a think tank of IT intelligentsia and legists that paves the way for a future formulation and provision by revision of and amendment to the IT Act to deal with cybercriminal sedition. In Fig. 7, the print-screened image of the Case Status Information Snapshot from Cyberabad Police Web site testifies to it that the cybercriminal sedition complaint is registered under Section 66-C ITA 2000–2008 on January 5, 2018, with the crime number 0006/2018. In Fig. 7, on-the-site filed report of an author of this paper is recorded and captured as case diary related to the file. The various document images related to the crime registration are captured to track the lifecycle or lifespan of the

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Fig. 7 Case status information snapshot from Cyberabad police Web site

cases of the cybercrimes. It is considered that the images serve for circumstantial evidence with respect to the tracking of the case of cybercriminal sedition. The denotational semantics of the term “cybercrime cycle” is variegated. The term refers to crime unchecked, left continuing for many days or months or years. In other words, this is appearance/reappearance of the crime day by day or month by month or year by year on timescale owing to it that it is left unchecked. Another denotational semantics is as follows. It refers to cybercrime that was prevalent over a period of time, but was not convicted and whereas the stakeholders of the crime continue the activity either with continuing evidence of crime or otherwise. In other words, the activity in the cybercrime website spans beyond the overt cybercriminal usage of the Web site either in an overtly or in a covertly way either with cybercriminal activity or otherwise.

5 Conclusions Our research work is with respect to cybercrimes involved in the activities of an organized group in JNTUH, Telangana, viz. JNTUHJAC which has been purportedly under a fake Government of Telangana. An image in the Web site of JNTUHJAC during 2011–2014 captured the cybercrimes involved in the Web site by the twin organizations JNTUHJAC and CSGoT. In the cybercrimes case study of CGoT, the forensic evidence internal to the Web (obtained by Web mining) as well as that external to the Web could be put together to prove the crime. The sedition crime was neglected by the law enforcement agencies. The cybercrime is to be registered as

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sedition crime [19] because the logo used gives visual representation of mention of Government of Telangana. The results of our work indicate that the loss of national consciousness of the public particularly the teachers and the students in the JNTUH academic environment and the resultant deleterious effect on the general public. The stakeholders of the JNTUH academic environment should be sensitized about the loss, and basic awareness should be generated to see that such national losses and crime of sedition do not recur so as to ensure national solidarity and integrity. This is our guideline to the governments in India at the union level as well as at the state level to take measures to defuse the fake GoT. The work depicted the scenario of image analytics from a perspective that is different from the traditional image processing perspective. The results of cybercrime analytics in the context of the image analytics presented our successful endeavors for the development of national consciousness, national amity, national solidarity, national spirit, and national unity among the educated elites who will be the torchbearers for the promotion of the same among the general public for successful digital India.

References 1. http://www.hyderabadpolice.gov.in/Cybercrimes.html 2. http://archive.org/web/ 3. P. Usha Gayatri, S. Neeraja, Ch. Leela Poornima, K. Chandra Sekharaiah, M. Yuvaraj, Exploring Cyber intelligence alternatives for countering cyber crime, in Proceedings of the 8th INDIACom; INDIACom-2014, International Conference on Computing for Sustainable Global Development (Bharatiya Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, India, 2014) 4. P. Usha Gayatri, K.C. Sekharaiah, Encasing the baneful side of internet, in National Conference on Computer Science & Security (COCSS 2013) (Sardar Vallabhbhai Patel Institute of Technology, Vasad, Gujarat, India, 5–6 April 2013) 5. P. Usha Gayatri, K.C. Sekharaiah, D. Radhika, G. Sruthi, K. Satish, S. Mounika, K. Shravani, A. Kulshreshtha, Exploring cyber intelligence alternatives for countering cyber crime: a continuing case study for the nation, in Proceedings of the IEEE CSI-2015 International Conference @Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, (INDIA), Dec. 2015 (Presented) 6. P. Usha Gayatri, B. Tirupathi Kumar, K.C. Sekharaiah, Exploring cyber intelligence alternatives for countering cyber crime: a continuing case study for the nation, in Presented in 1st International Conference on Advancements and Innovations in Engineering, Technology, & Management(ICAIETM2017)@JBREC, Hyderabad & published in International Journal of Innovations & Advancement in Computer Science(IJIACS) 6(12), 394–397 (2017). ISSN 2347-8616 7. B. Tirupathi Kumar, K.C. Sekharaiah, P. Mounitha, A Case Study Of Web Content Mining In Handling Cybercrime, In Proceedings of 2nd International Conference on Science, Technology and Management, (Delhi University, New Delhi, 27 Sep 2015), pp. 2290–2293. 971-81-931039-6-8 & Int. J. Adv. Res. Sci. Eng. 4(01), 665–668 (2015). ISSN 2319-8354 8. P. Usha Gayatri, K.C. Sekharaiah, A Case study of multiple cybercrimes against the union of india. Int. J. Comput. Math. Sci. IJCMS ISSN 2347-8527 6(3), 71–79 (2017) presented in NCIST’2017@Manipur Institute of Technology, Manipur University, Imphal, India 9. P. Punitha, S. Vidyavathi, K.C. Sekharaiah, Spatial cognition applications towards swachch digital India. Presented in 1st International Confirence on Advancements and Innovations in

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Deep Convolutional Neural Network-Based Diabetic Retinopathy Detection in Digital Fundus Images S. Saranya Rubini, R. Saai Nithil, A. Kunthavai and Ashish Sharma

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Previous Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture of the DCNN-DRD Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Diabetic Retinopathy (DR) is a common medical disorder damaging the retinal blood vessels of diabetic patients. Regular screening of fundus images and timely detection of the initial symptoms of DR, namely microaneurysms and hemorrhages, are important to reduce the possibility of vision impairment. The proposed work explores the power of Convolutional Neural Network (CNN) in the analysis and detection of retinal disorders. An automated deep learning model named Deep Convolutional Neural Network-based Diabetic Retinopathy Detection (DCNN-DRD) has been proposed to analyze the retinal images and classify them as healthy or defective based on DR symptoms. A retinal image is fed into the DCNN-DRD model which consists of five convolution and five pooling layers followed by a dropout layer and three fully connected layers. The linear output data produced in every layer represents the weighted value based on DR symptoms and is fed into a gradient descent graph for refinement to improve the learning accuracy through several iterations. Thus, the DCNN-DRD model does not require any preprocessing and learns highlevel discriminative features of DR symptoms from the pixel intensities to categorize the retinal image as either healthy or defective. The DCNN-DRD model has been trained with a subset of images from the MESSIDOR dataset and the ROC dataset.

S. Saranya Rubini (B) · R. Saai Nithil · A. Kunthavai Department of Computer Science and Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India e-mail: [email protected] A. Sharma Lotus Eye Care Hospital, Coimbatore, Tamil Nadu, India © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_19

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Experimental results show that the DCNN-DRD model successfully predicts the retinal image as either healthy or defective with 97% accuracy. Keywords Diabetic Retinopathy · Convolutional Neural Network · Hemorrhages Microaneurysms · Deep learning

1 Introduction Diabetic Retinopathy is the prominent reason for eye blindness in recent times among the diabetes patients. People affected by diabetes for several years might encounter a drastic increase in blood sugar level at some point. The increase in sugar level damages the blood vessels leading to ruptures in the sensitive retinal blood vessels. Such damages if not identified and treated on time results in eye blindness. A recent survey made by the World Health Organization reports that currently 135 million individuals are affected by Diabetic Retinopathy and this count would reach 300 million in the next ten years [1]. Fundus images of diabetic patients can be periodically investigated to monitor the advancement and the extremity of the disease. Manual investigation on the fundus images would be a time-consuming job and error-prone at times. Hence, automation of Diabetic Retinopathy identification is much crucial in the upcoming days as it would be a challenging task for the human graders to perform manual investigation due to the increase in the number of diabetes-affected patients. The automated system can be used as a preliminary screener to scan the fundus image for DR symptoms.

2 Previous Related Work In recent times, Convolutional Neural Networks have become the trendsetter in various fields, namely computer vision [2], image recognition [3], drug discovery, crack detection, and localization of key points [4]. Convolutional Neural Networks are also experimented on various medical applications such as neuronal membrane segmentation, glaucoma identification, and many others [5–8]. Liskowski et al. [9] proposed a deep learning technique for blood vessel detection in retinal images. Raghavendraa et al. [10] proposed an 18-layer CNN framework for the diagnosis of glaucoma. Paing and Choomchuay [11] proposed artificial neural network classifier to classify the retinal image based on the features extracted. The area and perimeter of several symptoms like exudates and microaneurysm form the feature set. Diabetic Retinopathy detection based on eigenvalue analysis [12] has been proposed to locate the microaneurysm and hemorrhages. Yelampalli et al. [13] proposed classification technique based on the blood vessel segmentation through the gradient-based morphological operations. Area calculated for the blood vessel segmented images forms the characteristic toward DR classification.

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Kaur and Mann [14] applied Principal Component Analysis (PCA) to extract minimal relevant features of blood vessels followed by particle swarm optimization. The optimized features extracted are fed to a naïve Bayes classifier for final classification. Safitri and Juniati [15] applied morphological operations initially to segment the candidates. The fractal dimensions of the extracted candidates based on box-counting method aids in the classification of DR. Zhang et al. [16] proposed a sparse representation classifier that applies a dictionary-based learning to identify the blood vessel and microaneurysm structures. Sermanet et al. [17] proposed an integrated framework based on CNN for object recognition and classification. Wang et al. [18] proposed a new loss function namely positive sharing loss to fine-tune the accuracy of contour identification. The positive data is divided into several subclasses during training and parameters are tuned to fit each subclass. The new loss function outperforms the softmax classifier by extracting more discriminative features. Abbas [19] proposed CNN-based glaucoma identification which extracts the initial set of features which are then processed by deep belief network to filter out only the most discriminative features suitable for glaucoma identification. The above survey indicates that recent studies explored the power of machine learning algorithms for feature extraction in various applications. In this paper, a Deep Convolutional Neural Network has been designed for classifying the retinal images as either healthy or defective depending on the presence of DR symptoms.

3 Architecture of the DCNN-DRD Model Convolutional Neural Network is a model which accepts a raw pixel as input and flows through the set of layers defined in the network. During learning, the network autonomously extracts low-level features of DR symptoms and gradually transforms and combines them into higher-order DR features. These features are then automatically composed into a complex function that maps the retinal image as either healthy or defective. The proposed DCNN-DRD model has five convolution layers, five pooling layers, a pool flat layer, a dropout layer, and three fully connected layers as shown in Fig. 1. The architecture of the proposed DCNN-DRD model has been generated using the tool Draw.io. The architecture of DCNN-DRD applies the model used in TensorFlow [20] CNN tutorial. The architecture of DCNN-DRD model in Fig. 1 accepts an input retinal image of size 299 × 299 that can be fed into the neural network. DCNN-DRD architecture starts with a convolution layer that takes a retinal image tensor of size 224 × 224 as its input. Following that the first convolution layer uses 5 × 5 × 3 kernel filters with stride 1 × 1, and a total of 32 such filters are applied. The output from the first convolution layer is max-pooled using a pooling layer with stride 2 × 2 which reduces the input to half its size 112 × 112. The output of the initial pooling layer passes through the ReLU function which introduces some nonlinearity to the output and is fed into a next convolution layer with 64 filters of kernel size 5 × 5 × 32 and the same stride values 1 × 1. The output is max-pooled with a pooling layer of same

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Fig. 1 Architecture of the DCNN-DRD model. CONV: convolution layer; POOL: pooling layer; FC: fully connected layer

stride 2 × 2, which again halves the input size and returns an output of size 56 × 56. The output undergoes ReLU activation and is fed into a third convolution layer with 128 filters and kernel size 5 × 5 × 64 with the same 1 × 1 stride. The output is max-pooled which results in a tensor of shape 28 × 28. The output from the third pooling layer is activated using ReLU fed into a fourth convolution layer with 256 filters and kernel size 5 × 5 × 128 and the same 1 × 1 stride. This output is again max-pooled to a size of 14 × 14. The 14 × 14-sized pixels are ReLU activated and are given as input to a fifth convolution layer with 256 filters and kernel size 14 × 14 × 256 to accommodate output of all the filters from previously configured layers, and max-pooling of output from that layer with stride of size 2 × 2 produces output of size 7 × 7. Now the resulting tensor has the shape 7 × 7 × 256. This tensor is reshaped or in other words flattened to a linear unit having 12,544 neurons. The weighted values emerging out as neurons indicate the closeness to the DR symptoms. A dropout layer is being used here to randomly drop values so that the network does not overfit. From this point, the fully connected layers are used which reduce the count of neurons to the set of classes given. The first fully connected layer converts the tensor with 12,544 neurons to 2048 neurons and adds ReLU activation to the output. The second convolution layer converts the 2048 neurons to 256 neurons and activates the output neurons using ReLU. The third fully connected layer converts 256 neurons to 64 neurons and uses the same ReLU activation. The result of the fully connected layers is a tensor with 64 neurons; these 64 neurons are converted into neuron count equal to the number of classes to which the retinal image belongs, namely healthy and defective. The configuration of the DCNN-DRD model is summarized in Table 1.

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Table 1 Convolution, pooling, and fully connected layer configuration of DCNN-DRD model Layer Kernel shape Kernel# Stride Conv1 Pool1 Conv2 Pool2 Conv3 Pool3 Conv4 Pool4 Conv5 Pool5

224 × 224 × 3 112 × 112 × 3 112 × 112 × 3 56 × 56 × 3 56 × 56 × 3 28 × 28 × 3 28 × 28 × 3 14 × 14 × 3 14 × 14 × 3 7×7×3

32 32 64 64 128 128 256 256 256 256

1 2 1 2 1 2 1 2 1 2

A variation of the proposed DCNN-DRD model has been implemented with a filter of size 3 × 3 applied to the convolutional layer. The softmax activation is applied on the output to generate normalized values based on which the retinal image is classified as either healthy or defective.

4 Experimental Results The DCNN-DRD model is designed in such a way that the input retinal image of size 224 × 224 can be fed into the network which has alternate convolution and pooling layers activated using ReLU activation function. The DCNN-DRD model has been implemented in Python using Tensorflow [21, 22] with two different filter sizes, namely 3 × 3 and 5 × 5. The DCNN-DRD model has been executed using a laptop with Intel i3 core processor, 4 GB RAM, and 1 TB hard disk. In this work, 100 images from the MESSIDOR dataset and 50 images from ROC dataset are used for training. Out of 100 images from the MESSIDOR dataset used for training, 50 images are healthy and the remaining 50 images are defective; out of the 50 images from ROC dataset used for training, 40 are defective and 10 are healthy. For testing, 50 images are used, out of which 35 are from the MESSIDOR dataset, with 15 healthy images and 20 defective images, and 15 are from the ROC dataset, with 5 healthy images and 10 defective images. The images are fed into the network as tensors, and the network is trained iteratively. The hyper-parameters of the DCNN-DRD model are fine-tuned to achieve better performance. The number of iterations for every retinal image is set as 100, and learning rate is chosen as 0.0005. Parameter tuning for the DCNN-DRD model is given in Table 2.

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Table 2 Parameter tuning in the DCNN-DRD model Parameters of the DCNN-DRD model Activation function ReLU Fully connected layer

3

Convolution layer

5

Learning rate

0.0005

Epochs per image

200

Time (On CPU approximately)

3h

Image resolution

224 × 224

4.1 Result Analysis Knowledge gained by the DCNN-DRD model is observed during the training phase by calculating the mean squared error. Every retinal image is trained 100 times adding up to 15,000 iterations for 150 images. The loss value reaches a minimum as iteration count increases. DCNN-DRD model has been trained with 100 images from MESSIDOR dataset and 50 images from ROC dataset. The DCNN-DRD model achieved a training accuracy of 97.97% for a filter with size 3 × 3 and 99.87% for a filter with size 5 × 5 as shown in Table 3. The learning performance of the DCNN-DRD model is shown graphically in Fig. 2a and b for a filter with size 5 × 5. Figure 2a shows the plot of epoch in the xaxis against accuracy in the y-axis. Figure 2b shows that epochs in x-axis are plotted against loss obtained from the mean squared error in the y-axis. The graph is plotted at an equal interval of 50 iterations. It is inferred from Fig. 2a and b that as the number of iterations increases the training accuracy also increases, whereas the MSE associated with training decreases. It is also inferred that the learning phase of the DCNN-DRD model reaches a stable value for accuracy and loss at around 4000 iterations. The learning performance of the DCNN-DRD model is shown graphically in Fig. 2c and d for a filter with size 3 × 3. Figure 2c shows epoch in the x-axis plotted against accuracy in the y-axis. Figure 2d shows epoch in the x-axis plotted against loss in the y-axis. It is inferred from Fig. 2c and d that as the number of iterations increases the training accuracy also increases, whereas the loss associated with training decreases. The DCNN-DRD model with a 3 × 3 filter size undergoes learning and reaches a constant accuracy and loss at around 14,000 iterations. This shows that the DCNN-

Table 3 Training results of the DCNN-DRD model on MESSIDOR and ROC datasets Dataset No. of training images Filter size Training accuracy (%) MESSIDOR + ROC

150

3×3 5×5

97.97 99.87

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Fig. 2 a Plot of accuracy vs. epoch. b Plot of loss vs. epoch. c Plot of accuracy vs. epoch. d Plot of loss vs. epoch Table 4 Classification results by the DCNN-DRD model on MESSIDOR and ROC datasets Dataset No. of test images Filter size Testing accuracy (%) MESSIDOR + ROC

50

3×3 5×5

97 93

DRD model with 3 × 3 filter size undergoes learning for many iterations to learn discriminative features. The performance of the DCNN-DRD model has been tested on 35 images from MESSIDOR dataset and 15 images from ROC dataset. The results achieved by the DCNN-DRD model are summarized in Table 4. Table 4 represents the filter size, the number of images, and the testing accuracy achieved. The filter size 5 × 5 yields a testing accuracy of 93%. It is inferred from Fig. 2a that the network with a larger filter size 5 × 5 attains a saturated state with the minimum number of iterations; however, the testing accuracy drops. The filter size 3 × 3 yields a lower training accuracy of 97.97%, but the testing accuracy is high since the network attains a saturated level of accuracy only at the end of several iterations. The network trained with a 3 × 3 filter learns more accurate features and performs well with the testing data.

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The DCNN-DRD model shows promising results in classifying the images as either healthy or affected by scanning the presence of microaneurysms and hemorrhages. The designed model proves to be efficient in classifying the images present in the MESSIDOR and the ROC datasets.

5 Conclusion In this research, a deep learning strategy named DCNN-DRD model has been implemented which uses convolution layers built upon each other with max-pooling layers in between to reduce the dimensions. ReLU activation function is used to introduce nonlinearity between the layers. A dropout layer prevents the network from overfitting to the trained data. The output is finally classified using softmax classification. The DCNN-DRD model has been tested on a set of images from MESSIDOR and ROC datasets. The network with 3 × 3 filter achieved a testing accuracy of 97%, whereas the network with 5 × 5 filter provides an accuracy of only 93%. Hence, the network with a 3 × 3 filter performs better than the network with a 5 × 5 filter. Though the decrease in filter size affects the training accuracy, the overall testing accuracy has improved enabling the network to learn DR features accurately. So, the experimental result analysis indicates that the DCNN-DRD model results in an optimal classification rate of 97% with a filter of size 3 × 3. The results indicate that the model generated extracts the low-level discriminative features which are good enough to classify the image and thus reduce the human intervention in analyzing the images. Further, the research can be extended by considering a large dataset for training and the hyper-parameters can be fine-tuned to improve the accuracy. This work confirms that conventional neural networks can be adapted in medical applications, and it best suits for DR diagnosis and outperforms past studies as well. Acknowledgements We would like to thank Dr. Ashish Sharma, Lotus Eye Care Hospital, Coimbatore, for his continuous effort in manually grading the retinal images and verifying the results achieved.

References 1. S. Wild, G. Roglic, A Green et al., Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care, 27, l047–1053, (2004) 2. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105 3. R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 580–587 4. J.L. Long, N. Zhang, T. Darrell, Do convnets learn correspondence? in In Advances in Neural Information Processing Systems (2014), pp. 1607–1609

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5. A. Ciresan, L.M. Giusti, Gambardella, J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images, in Advances in Neural Information Processing Systems (2012), pp. 2843–2851 6. C. Cernazanu-Glavan, S. Holban, Segmentation of bone structure in X-ray images using convolutional neural network Adv. Electr. Comput. Eng. 13(1), 87–94 (2013) 7. S. Li, A.B. Chan, 3d human pose estimation from monocular images with deep convolutional neural network, in: Computer Vision? ACCV 2014 (Springer International Publishing, 2014), pp. 332–347 8. G. Levi, T. Hassner, Age and gender classification using convolutional neural networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2015), pp. 34–42 9. P. Liskowski, K. Karmic, Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging, 35(11) (2016) 10. U. Raghavendraa, H. Fujita, S. Bhandary, A. Gudigar, J.H. Tan, R. Acharya, Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Elsevier Inf. Sci. 441, 41–49 (2017) https://doi.org/10.1016/j.ins.2018.01.051 11. M.P. Paing, S. Choomchuay, Detection of lesions and classification of diabetic retinopathy using fundus images. Biomed. Eng. Int. Conf. (2016) 12. S.S. Rubini A. Kunthavai, Diabetic retinopathy detection based on eigenvalues of the hessian matrix. Procedia Comput. Sci. 47(C), 311–318 (2015) 13. P.K.R. Yelampalli, J. Nayak, V.H Gaidhane, Blood vessel segmentation and classification of diabetic retinopathy images using gradient operator and statistical analysis, in Proceedings of the World Congress on Engineering and Computer Science 2017 Vol IIWCECS 2017, October 25–27, 2017 14. S. Kaur, K.S. Mann, Optimized retinal blood vessel segmentation technique for detection of diabetic retinopathy. Int. J. Adv. Res. Comput. Sci. (2016). http://dx.doi.org/10.26483/ijarcs. v8i9.5071 15. D.W Safitri, D. Juniati, Classification of diabetic retinopathy using fractal dimension analysis of eye fundus image, in AIP Conference Proceedings, vol. 1867, p. 020011 (2017). https://doi. org/10.1063/1.4994414 16. B. Zhang, F. Karray, Q. Li, L. Zhang, Sparse Representation Classifier for microaneurysm detection andretinal blood vessel extraction. Inf. Sci. 200, 78–90 (2012). https://doi.org/10. 1016/j.media.2009.05.005 17. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, Overfeat: Integrated recognition, localization and detection using convolutional networks. Comput. Vis. Pattern Recognit. (2013). arXiv:1312.6229 18. W. Shen, X. Wang, Y. Wang, X. Bai, Z. Zhang, DeepContour: a deep convolutional feature learned by positive-sharing loss for contour detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Boston, MA, USA, 7–12 June 2015) 19. Q. Abbas, Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning (IJACSA). Int. J. Adv. Comput. Sci. Appl. 8(6), (2017) 20. M. Abadi et al. (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems, arXiv: 1603.04467 [cs.DC] (Mar. 2016) 21. CNN Tutorial, https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-GuideTo-Understanding-Convolutional-Neural-Networks/ 22. CNN tutorial, https://stackoverflow.com/questions/37340129/tensorflow-training-on-my-ownimage

A Framework for Semantic Annotation and Mapping of Sensor Data Streams Based on Multiple Linear Regression K. Vijayaprabakaran and K. Sathiyamurthy

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 SenML Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Semantic Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Mapping Between Ex-SenML and SSN Ontology . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experiments and Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In IoT, multitudes of sensors are streaming massive data which are hard to interpret meaningful information due to the presence of noise, outliers and missing value in sensor-observed data. In addition to this, heterogeneous sensors or devices in smart environment show great variations in formats, domains, and types, which stances challenges for machines to process and recognize. These challenges lead the interoperability issues in IoT. To overcome the above-mentioned issues, this work initially performs the preprocessing (i.e., removal of outlier, missing data completion) using the F-statistical tests and multiple linear regression models. Secondly, this research work proposes an Extended Sensor Markup Language for annotation of sensor-observed data and semantic mapping method to map the sensor data with standard Semantic Sensor Network (SSN) ontology for semantic interoperability. Keywords Interoperability · Semantic annotation · Regression · Feature selection SenML

K. Vijayaprabakaran (B) · K. Sathiyamurthy Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India e-mail: [email protected] K. Sathiyamurthy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_20

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1 Introduction Development in the IoT among various application increases number of sensors and network-enabled sensor devices distributed in physical environment which change the information communication network. As Cisco forecasted that in 2020, 50 billion smart devices will be linked to the Internet [1]. These devices will produce myriad of real-world data for several applications and intend to provide better services in a diverse of areas like smart homes, smart city, smart transport and logistics, smart health, smart agriculture and environment monitoring which enriches the lifestyle of the people. The related technology triggers integration of physical world data and services into the present information networking technologies are often comes under the term Internet of things (IoT) [2]. In recent years, many research works have been carried out in sensor network and implemented in various fields (e.g., health care, weather monitoring and forecasting, agriculture). However, the hindrance to achieve the full potential of the IoT is lack of interoperability and the information produced by means of various heterogeneous devices. These heterogeneous sensors observe the data which is in various format leads to ambiguity in integration and sharing the sensor data. It gradually developed into the most challenging issue in the application. Since the sensor data is defined by raw values observed via sensors, user cannot understand that what data it is generating, which sensor type it is, and what kind of unit is utilized. Hence, this raw data cannot be used effectively for analysis. To improve the interoperability, semantic technology is applied which facilitates semantic data access and integration, semantic reasoning and knowledge extraction. Several researchers propounded various semantic approaches where service-oriented approach provides the description of data as a service, the information model details only the data that fits into the specific domain. Ontology is the conceptualized model and formalized specification of domain knowledge and helps to annotate the data semantically. In order to use these sensor data completely, semantic annotation is applied for transformation of sensor data into semantic-enriched sensor data that can be easily recognized by the IoT machines. Semantic annotation is the process of adding right tag that specifies the labels and description of data. Establishing such annotation, the sensor data will be connected with each other and makes the data convenient for the application development. Most of the researchers are currently working in the conversion of sensor data to RDF and how to annotate the attributes or property. The standard RDF description enriches the meaning of the sensor data. By this way, we can make use of any sensor data to any IoT application efficiently by means of services. Though the semantic annotation provides interoperability, there exist issues of annotating sensor data due to its massiveness size, noisy value, and missing value of the observation. To overcome this issue, preprocessing operation requires to make the raw sensor data free from noisy and filling the missing values using various statistical and probabilistic models.

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The main contribution of this research work is abstracted as follows: (1) preprocessing the sensor data by removing the outliers, and selecting the appropriate feature using the multiple linear regression model and F-statistical test; (2) designing a mapping method called Extended SenML Annotation provides a schema to annotate the additional elements of the sensor; (3) the elements of Ex-SenMLAM can be mapped to the relevant logics and properties of Semantic Sensor Network ontology and converts the sensor data into RDF.

2 Related Works Semantic Sensor Observation Service (SemSOS) [3] and Linked Sensor Data were developed by the scientists of Knoesis research center in USA. SemSOS has been developed for querying and accessing the sensor-observed values over the Web. Sensor Web Enablement (SWE) group of Open Geospatial Consortium (OGS) defined a standardized Web service called Sensor Observation Service to provide interoperability among the repositories of the heterogeneous sensor observations and the applications that make use of these observations. However, these raw sensor values cannot handle most of these applications because it practically requires actionable knowledge of the environment. It can be dealt by making either the data smarter or applications smarter. SemSOS deals with making the data smarter by leveraging semantic technologies for providing meaningful representation over the raw sensor observations. The domain of sensors and its observations are modeled as ontologies for annotating the sensor data with semantics to understand sensor observations by means of the ontology models and expanding the SOS with their semantic knowledge. It allows to querying low-level raw data to high-level knowledge. Linked Data [4] is the collection of Linked Observations and Linked Sensor Data [5]. It extracts the sensor observations from devices to convert these data with observation and monitoring into RDF by adopting Sensor Web Enablement quality, and then publicize these semantic-enriched sensor data stream on the Internet. The authors took MesoWest weather dataset to semantic annotation, which is scalable datasets with more than 20,000 sensor devices, 160 million sensors observed values and 1.7 billion RDF statements. It utilized the GeoNames dataset to improve contextual information to find regions and others. Physical world sensor data has been enriched with semantic to give smarter cross-domain applications. Sense and Sens’ability framework [6] adopts semantic advancement, the Semantic Web for earth and environment technology (SWEET) ontology for observation units and Sensor Web Enablement standards. This semantic model used for representing diverse sensor data streams that adapt general standards and intelligent representational frameworks to generate a model for sensor data description as specified by the Semantic Web community. The authors created an ontology that describes how semantic connection and the operational limitations are deployed in a homogeneous format for the diversified sensor-observed data. The Linked Data Platform Sense2Web was built to distribute semantic information about sensors with an ontol-

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ogy named ‘sensor data’. The authors also stated the significance of connecting measured data to domain knowledge via Linked Data, but then again there is no method provided to utilize and connect domain ontologies relevant for IoT [7]. Digital Enterprise Research Institute (DERI) in Ireland implemented the SensorMasher and the Linked Stream Middleware (LSM) platform, which facilitates publicizing Linked Stream Data (LSD) and making it accessible to different applications [8]. The authors developed a GUI to supervise environmental semantic sensor networks. Also, proposed an infrastructure for the sensor into mashups by making it to the linked open data. The integration of sensor-observed data and exist linked open data can publicize as linked open data sources which can be adopted by others. The sensor data is available as linked data, thus enabling the dynamic discovery, integrating, and querying the heterogeneous sources at large scale. Semsor4grid4env [9] (Semantic Sensor Grids for Environmental Applications), University of Madrid in Spain, had built an extensive SSN for managing the environment, especially for the decisive domain-related cases like prevention and food control. The semantic annotations are done for providing the well-informed interactions among the services. The requirements of Semantic Sensor Web are identified with the help of food emergency response planning application CSIRO done by researchers in center of Australia works on Semantic Sensor Networks (SSN) in the domain of agriculture [10] and ocean observations [11]. Cabral et al. established a smart vineyard [12]. Another related work Kirby Smart Farm research work [13] is based on SSN ontology, Linked Data and Global Sensor Network (GSN) middleware. The Web of things is advanced on a farm [14]. Intelligent Data Analysis (IDA) is an ontology-based framework for the sensor data [15]. They developed the Temporal-Abstraction Ontology (TAO) and composed it with the existing SSN and SWRL Temporal Ontology (SWRLTO). In a dynamic environment, IDA makes use of the semantics for deriving the higher-level qualitative descriptions of the state and condition. Linked data model [16] is used to describe a sensor streams and annotated the sensor data. The authors focus on finding the unique name to determine the sensor and provided a location-based naming for addressing. This survey summarizes that the various solutions related to the key issues exist in binding the semantics with the IoT have been listed and analyzed. These systems aim to grant interoperability of the sensor data, inter-domain interoperability and interpreting the IoT data. Interoperability of sensor data deals with transforming the sensor data which can be in a form of SenML, JSON, or any other lightweight format into semantics as RDF, RDFs, or OWL.

3 Proposed Work This research work mainly focuses on semantic annotation of the sensor data streams to provide meaning to the sensor data generated by IoT devices by annotating the

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Fig. 1 Proposed framework to annotate sensor stream

SenML measurements and mapping with the SSN ontology more to that convert sensor data into RDF in Fig. 1. The proposed system consists of following modules such as data preprocessing, SenML transformation, mapping between Ex-SenML and semantic annotation and data representation.

3.1 Data Preprocessing The sensor-observed data passes through a preprocessing stage to prepare the data for further steps. The operations involved in preprocessing module reduce the volume and dimensions of the data by ignoring unwanted and repeated data. This can be done in the sensor node and base station. Due to the computational complexity, sensor node cannot perform this. Aggregation of sensor data also done in base station for further processing by computing mean, min, and max value of the data. The preprocessing steps applied in this work are described below. Feature Selection In IoT healthcare, several sensor values are generating to monitor the patient. These sensor values are complex and have irrelevant values which deviate the accuracy of prediction and decision making. To enhance the accuracy of diagnosis, appropriate features (i.e., sensor values) should be selected from the observed data. This feature selection process focuses on selecting prime subset of variable from the collected data which can effectively define the input data. It is ultimately reducing the effects of noise, irrelevant feature and providing good prediction results. There exist several feature selection methods like filter, wrapper, and other mining algorithms to remove irrelevant feature. In this work, the statistical model test is used for feature selection. The statistical model test takes the input data and calculates the significance score of the entire feature. The significance score depicts the correlation between the

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features; if the significance score of the intended feature crosses, the critical value is considered otherwise ignores the feature. T -score, F-score, and chi-square are the major statistical tests to select the appropriate feature from multidimensional data. T-score It is used for binary classification. The logic behind the T -score is to evaluate whether the feature can distinguish two classes statistically by means of computing ratio between the mean difference and the variance of two classes as given in Eq (1). Usually, more T -score obtained is considered as more important feature. |μ1 − μ2 | t - score( f i )   σ + nσ2 n1

(1)

F-score F-score is one of the statistical tests (known as F-statistical test) that perform well to checks the correlation between the features and labels. It can handle the multiclass situation by testing if a feature is able to well distinct samples from diverse classes. Seeing both within class variance and between class variance, the F-score of a feature f1 can be computed as follows in Eq (2).  f - score ( f i ) 

nj  j c−1 μ j

1 n−c

2 −μ  2   j n j − 1 σj

(2)

f i , nj, μ, μj denote the number of instances from class j, the mean feature value, the mean feature value on class j, and the standard deviation of feature value on class j, respectively. F-score with higher value is considered as the more important feature. Removal of Outliers Sensor data are more prone to inaccuracy due to inbuilt defects present in sensors, the failure of network and the limits of discharged batteries, and so on. More to that fluctuation of the environment have a great effect on the precision of observed data. In IoT, outliers are referred to as the values which quite deviate from usual regular values. It also used for finding the abnormal situation like disease diagnosing in healthcare applications. In such scenarios, anomalies and outliers are considered as noisy that needs to be removed. Machine learning algorithm of regression model with statistical techniques is used to compute the relationship model for sensor values as predefined standard range of sensor data. There are several regression models which exit with various specific tasks. Among them, multiple linear regression technique is adopted in this work for detecting outlier in the data streams.

3.2 SenML Transformation The stream of time-stamped values along with quantitative raw data is produced by several sensors deployed to observe a specific significant process. The temporal

A Framework for Semantic Annotation and Mapping … Table 1 Relationship between Ex-SenML and SSN ontology Ex-SenML element SSN class

217

Description

Type

SSN:Property

The type of the sensors

Value

SSN:ObervationValue

Unit Time Location

SSN:Unit of Measure SSN:Obervation SSN:Deployment

The values of the sensor observation The unit of the data The time of the sensor data The location of the sensors

Range

SSN:DetectionLimit

Sensor_id

SSN:Sensor

The range of sensor value observed The number of the sensor

factors are needed to be considered since sensors are event based in nature. Sensor Markup Language (SenML) is used to extract measurements of sensor devices in a non-proprietary data format which is a low-energy consuming language. SenML offers measurements such as measurement name, its units, and the value. For instance, a measurement can be the acceleration, the value is 1.4, and its unit is m/s2 . SenML is one of the lightweight protocols to obtain simple measurements and provides the simple way of describing sensor metadata. So, it is necessary to annotate the SenML data directly in specified devices to infer the sensor data.

3.3 Semantic Annotation The SenML provides the only few metadata description of sensors. In order to interpret more knowledge acquisition from these data, it is essential to present additional property and their description. In this work, inclusive of additional parameters like _location (i.e., sensor geographical location), _type (like temperature, humidity, glucose level), _range (maximum and minimum value that can be observed), sensor_id (id to specify the sensor) are manually annotated in the XML file.

3.4 Mapping Between Ex-SenML and SSN Ontology The Ex-SenML annotated the data limited to provide only the sensor and source information that information is not sufficient enough to map SSN ontology. Mapping process will define the new relationship between the Ex-SenML annotation and SSN ontology. The new relationship derived in the mapping process will help to relate the Ex-SenML elements to the SSN classes. Table 1 shows the corresponding relationship between proposed Ex-SenML annotation and SSN ontology.

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The correspondences defined in Table 1 help to fill the space among the elements of Ex-SenML and the class and their SSN ontology properties. SSN ontology [8] characterizes high-level concepts for describing sensors, sensor observation, and their nearby atmosphere. The class ‘ssn:Observation’ offers the format to define a solitary observation; thence, it is associated with a single measurement (i.e., class ssn:SensorOutput) and attributed to a single property (i.e., classes ssn:Property and ssn:Feature of Interest) and to a particular ‘ssn:Sensor’. A ssn:FeatureOfInterest shows a physical world phenomenon that can be any events or objects. ssn:Property is characterized as the quality of the phenomena to be observed (like humidity or acceleration). The sensing process outcome is modeled by the class ssn:SensorOutput. Observed real data values are depicted through ‘has Value’ relationships to an Observation Value. The class has a unit of measurement specifies the unit used for measuring the sensor value. Data Representation This module converts sensor metadata in a unified description with the help of Semantic Web technologies such as RDF. Initially, it builds the instances of the SSN ontology by utilizing the related classes and their properties of the standard SSN ontology and then, with respect to the equivalent relationships, the instances are linked via object properties and data are associated with the instances via data properties. In this way, the sensor data are changed over into RDF automatically.

4 Experiments and Result As a case study, this proposed method is implemented to the sensor data from mHealth dataset of monitoring system based on IoT [17]. The mHealth dataset comprised of readings of the shimmer2 wearable sensors placed on the chest, wrist, and ankle of the volunteers. The Shimmer2 wireless platform consists of accelerometer sensor with range of ±2 g, gyro sensor of range ±250 dps and digital magnetometer with the range of ±1.3 Ga. These values of accelerometer, gyro meter, and magnetometer sensors are taken as feature variable to classify walking, sitting, standing, jogging, jumping, and cycling activities of the 10 volunteers. Initially the dataset has total number of 15 independent variable and one class variable. In order to reduce dimension of the data for classification, F-statistic test is applied to compute the F-score of the individual variable to know their significance to the class variable. Initially, we applied F-statistic to all the variables in the dataset and calculated the F-score is shown in Table 2. Considering the F-score of the variables in dataset, the feature subset is selected for the prediction. The selected feature subset is used to design the predictive model and got the less prediction error as compared with the original dimension of data. The prediction error of the selected feature is reduced when compared to unselected feature (Table 3).

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Table 2 F-score of the individual feature in mHelath dataset

Table 3 Evaluation of feature set using prediction error Original dataset Selected features

Unselected features

No. of features No. of observations

15 155,000

7 155,000

7 15,000

Prediction error

402,405.835

312,615.151

748,332.6807

Mean pred error

3.051

2.017

4.827952779

The outliers in the mHealth suppress the efficiency of the predictive model to classify the activities of target people. The multiple linear regression model is applied to design the association model between the individual feature with the class variable and computed the Cook’s distance (Cook’s D) using Eq. (3) that helps to detect the outliers and ignore the value to make the data more accuracy [18]. Cook’s D detects the outlier by the comparing with the cutoff value obtained by three times of mean value of outlier.   ei2 hi (3) Di  (k + 1)M S E (1 − h i )2 where ei is the standard error of the residual (i.e., distance between the regression line and the ith sample data point), k is the number of independent variable, MS E is the mean squared error, and h is the hat matrix. The standard SSN ontology is utilized as the destination ontology. As stated in the proposed Ex-SenML annotation method, this research work implemented the semantic annotation and transformation of sensor data into RDF and then applied in mHealth monitoring system to confirm the correctness and achievability of ExSenML. The sensor-observed values from the heath monitoring devices are stored in the database. The observation in Fig. 2 consists of the following data about the sensors such as observation values, time of observation, the sensor type, location of the sensor, and unit of observed sensor value. According to the Ex-SenML, the

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Fig. 2 Outlier detection in mHealth using multiple linear regression

Fig. 3 Structure of the semantic annotated mHealth data

sensors which are used in the mHealth monitoring are annotated and described the needed information about data. Figure 3 shows part of the Ex-SenML file. In this experimental study, the data source taken from the mHealth monitoring system using wearable sensors. The includes the base name (“bn”), base time (“bt”), version (“ver”), and base unit (“bu”), respectively. The (entity) tag annotates the information of the sensors which are used in mHealth monitoring system. The annotations in the tag denotes the name (“n”), time (“t”), unit (“u”), value (“v”), and location (“l”) of the sensors data. Then, mapping the generated Ex-SenML to SSN ontology according to the corresponding relationships is defined. To illustrate the result, the measure of acceleration using wearable sensors in mHealth monitoring system has taken as a case to illustrate the result RDF segment. The equivalent instances are formed with respect to the database model and the properties of the instances as the SSN ontology. This work implements the proposed system using Netbeans8.0.2 and provides a simple interface which eases the process of semantic annotation and mapping. The prototype of the experimental framework interface is depicted in Fig. 4. In the first tab of the prototype, the data source is selected. Then, the sensor data is annotated according to the Ex-SenML schema. It selects the information (e.g., name, time, unit, observation value, and location) of the sensors from the table. Users can also tag the intended description of the sensors devices manually. This annotated

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Fig. 4 Prototype of proposed system

information of the sensors will be displayed on the right side of GUI. When all annotation is finished, the XML representation of Ex-SenML file is constructed. This XML file is used to generate the RDF by mapping with SSN ontology in the second tab of the GUI provided.

5 Conclusion and Future Work This research work implements the annotation of sensor data stream and converted into RDF using the proposed Ex-SenML method. To achieve semantic annotation of sensor data, this work applied multiple linear regression to remove the outlier and F-statistical test for feature selection to preprocess the collected data. The proposed Extended SenML comprises the annotations of the sensor data stream and can be utilized to add the essential details of sensor data from various data sources which are stored in the database. Further, a framework of the proposed system is developed to ease the mapping process. The sensor data from the mHealth monitoring system are taken as the investigational data source to verify the achievability and efficacy of proposed method. The future work can be extended with building semantic reasoning to extract the concealed data for developing health recommendation system.

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References 1. L.L. Li, S.F. Yang, L.Y. Wang, X.M. Gao, The greenhouse environment monitoring system based on wireless sensor network technology, in Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), in 2011 IEEE International Conference on (IEEE, Chicago, 2011), pp. 265–268 2. S. Sivamani, N. Bae, Y. Cho, A smart service model based on ubiquitous sensor networks using vertical farm ontology. Int. J. Distrib. Sens. Netw (2013) 3. C.A. Henson, J.K. Pschorr, A.P. Sheth, K. Thirunarayan, SemSOS: semantic sensor observation service, in International Symposium on Collaborative Technologies and Systems, 2009 CTS’09, IEEE (2009), pp. 44–53 4. Linked data, http://linkeddata.org/ 5. C. Bizer, T. Heath, T. Berners-Lee, Linked data-the story so far. Semantic services, interoperability and web applications: emerging concepts (2009), pp. 205–227 6. P. Barnaghi, S. Meissner, M. Presser, K. Moessner, Sense and sens’ ability: semantic data modelling for sensor networks, in Conference Proceedings of ICT Mobile Summit 2009 (2009) 7. S. De, T. Elsaleh, P. Barnaghi, S. Meissner, An internet of things platform for real-world and digital objects. Scalable Comput.: Pract. Experience 13(1), 45–58 (2012) 8. D. Le-Phuoc, M. Hauswirth, Linked open data in sensor data mashups, in Proceedings of the 2nd International Conference on Semantic Sensor Networks-Volume 522, pp. 1–16. CEUR-WS. org (2009) 9. A.J. Gray, R. García-Castro, K. Kyzirakos, M. Karpathiotakis, J.P. Calbimonte, K. Page, et al., A semantically enabled service architecture for mashups over streaming and stored data, in Extended Semantic Web Conference (Springer, Berlin, Heidelberg 2011), pp. 300–314 10. K. Taylor, C. Griffith, L. Lefort, R. Gaire, M. Compton, T. Wark, et al., Farming the web of things. IEEE Intell. Syst. 28(6), 12–19 (2013) 11. M.A. Cameron, J.X. Wu, K. Taylor, D. Ratcliffe, G. Squire, J. Colton, Semantic solutions for integration of federated ocean observations, in Proceedings of the 2nd International Conference on Semantic Sensor Networks-Volume 522, pp. 64–79. CEUR-WS. org (2009) 12. L. Cabral, M. Compton, H. Müller, A use case in semantic modelling and ranking for the sensor web, in International Semantic Web Conference. (Springer, Cham), pp. 276–291 (2014) 13. K. Aberer, M. Hauswirth, A. Salehi, A middleware for fast and flexible sensor network deployment, in Proceedings of the 32nd international conference on Very large data bases, pp. 1199–1202. VLDB Endowment (2006) 14. R. Gaire, L. Lefort, M. Compton, G. Falzon, D. Lamb, K. Taylor, Semantic web enabled smart farm with GSN, in Proceedings of the 2013th International Conference on Posters & Demonstrations Track-Volume 1035, pp. 41–44. CEUR-WS. org (2013) 15. F. Roda, E. Musulin, An ontology-based framework to support intelligent data analysis of sensor measurements. Expert Syst. Appl. 41(17), 7914–7926 (2014) 16. P. Barnaghi, W. Wang, L. Dong, C. Wang, A linked-data model for semantic sensor streams, in Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing (pp. 468–475). IEEE (2013) 17. O. Banos, R. Garcia, J.A. Holgado, M. Damas, H. Pomares, I. Rojas, A. Saez, C. Villalonga, mHealthDroid: a novel framework for agile development of mobile health applications, in Proceedings of the 6th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2014), Belfast, Northern Ireland, December 2–5 (2014) 18. S.M.A. Khaleelur Rahman, M. Mohamed Sathik, K. Senthamarai Kannan, Multiple linear regression models in outlier detection. Int. J. Res. Comput. Sci. 2(2), 23–28 (2012). https://doi. org/10.7815/ijorcs.22.2012.018

Cyclostationarity Analysis of GPS Signals for Spoofing Detection R. Lakshmi, S. M. Vaitheeswaran and K. Pargunarajan

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GPS System and Effect of Spoofing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 GPS System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Effect of Spoofing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cyclostationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Analysis of cyclostationarity property in GPS signals for spoofing detection is presented in a software-defined GPS receiver framework. GPS spoofing is an attempt to mislead GPS receivers by sending false GPS signals. For the analysis of spoofed GPS signals, cyclic autocorrelation function (CAF) and spectral correlation density (SCD) graphs are considered. For the validation of the proposed method, a set of recorded GPS spoofed data known as Texas Spoofing Test Battery (TEXBAT), which is an evolving standard test for evaluating GPS spoofing countermeasures, is used. When CAF and SCD of a GPS signal with and without spoofing are calculated, results showed that the fundamental cyclic frequency of GPS signal changes due to spoofing. Keywords GPS · Cyclostationarity · Spoofing · Software-defined radio

R. Lakshmi (B) · K. Pargunarajan Department of Electronics and Communication Engineering, Amrita Viswa Vidyapeetham, Coimbatore, Tamil Nadu, India e-mail: [email protected] S. M. Vaitheeswaran Aerospace Electronics and Systems Division, National Aerospace Laboratories, Bangalore, Karnataka, India © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_21

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1 Introduction Global positioning system (GPS) is commonly used for navigation in both civil and military sectors by acquiring signals from different GPS satellites and processing them. GPS signals have two main drawbacks: Civilian signal format is available in the public domain, and the power with which a GPS signal reaches the earth surface is very low (−160 dBw) [1]. Since a civilian GPS signal is not encrypted, anyone can easily reproduce it. Thus, GPS is easily prone to spoofing and jamming. GPS spoofing is an attack to mislead the GPS receivers by giving false information. This is done by transmitting signals similar to original GPS signals or recorded GPS signals [2]. Jamming is another GPS attack where unwanted signals of same frequency are transmitted deliberately to prevent acquisition and tracking of the signal. Spoofing is more severe than intentional jamming because the targeted receiver fails to realize that it has undergone spoofing. One such example of spoofing could be the changing of the landing position of an aircraft by sending fake GPS signals. Spoofing, in general, can be done in three ways [3]. The simplest method is a GPS signal simulator-based spoofing. The signals generated in a GPS simulator are different from the real GPS signals received from satellites in terms of navigation data, timing, code delays, and Doppler shifts. Receiver-based spoofing uses a GPS receiver to extract the position, time, and satellite ephemeris. This information is used to generate signals that mimic GPS signals received from satellites in all respect. To generate a spoofing signal, an offset is also added to the extracted parameters so that the resulting position is shifted. A sophisticated receiver-based spoofing uses more sophisticated techniques and equipment such as multiple transmit antennas. This type of spoofing can control the code and carrier phases of signals transmitted by each antenna and thereby synchronize the spoofing signal with the real GPS signal. Sophisticated spoofing is difficult to realize in practice [3]. There are different approaches to spoofing detection which is the discrimination of spoofed signal from the real GPS signal. Some very commonly used spoofing detection methods are signal power monitoring, spatial processing, and signal processing methods [4]. Spatial processing technique uses moving antenna or multi-antenna to detect GPS spoofing. Paper [5] proposes a spoofing countermeasure with a single rotating antenna that can be fixed on a static receiver. This method works on the assumption that the spoofing GPS signals are transmitted from a single source but that authentic GPS signals come from different satellites. When the antenna rotates, the power of GPS signals coming from the same direction (spoofing signals) changes similarly, whereas the power of signals coming from different directions (authentic signals) changes differently. Signal power monitoring technique is based on the idea that whenever a spoofing signal arrives, the power level of received GPS signal shows fluctuations. Paper [6] proposes a low-cost power and distortion monitoring technique that enables GPS receivers to detect spoofing and jamming. By observing the received power and

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correlation function distortion, it classifies received signals as spoofed, jammed, or interference-free signal. Detection and mitigation of GPS spoofing based on maximum likelihood estimation (MLE) is a good example for a signal processing-based spoofing detection [7]. In this, the relation between GPS position and signal parameters is used to derive the MLE cost function. Any shift in position due to spoofing attack is reflected on the cost function, which can be detected. Some recent methods using signal processing techniques have been applied for interference detection with GPS signals. However, the interference signals considered have been mostly chirped signals unlike a spoof counterfeit GPS signal used for spoofing attack which has a very similar form to the authentic signal and can be masqueraded as a GPS signal. This paper considers the periodic properties of the GPS signal in a time–frequency framework as it is simple and more convenient. Toward this, we use the cyclostationary detection techniques, a method which accurately shows the spectral occupancy in very low SNR bands, wherein use is made of the property that the GPS time-varying periodic signal has some correlation between certain frequencies, at certain frequency shifts [8]. Cyclostationary features have been widely used in condition monitoring of mechanical systems [9]. Software-defined radio (SDR) framework allows us to develop a receiver in PC instead of using an extra hardware. The advantages of a SDR are as follows: lesser implementation cost, reduced hardware complexity, and small size requirement. Spoofing detection by signal processing techniques is more relevant in a SDR framework because it allows direct manipulation and coding of the carrier and code waveforms unlike hardware-based GPS receivers. The paper is organized as follows. Section 2 describes the GPS system and effects of spoofing on the GPS signal. Section 3 discusses cyclostationarity of signals in general. This section also discusses cyclostationary properties of GPS signal. Section 4 deals with the simulation results and further discussions. It also presents a real-time implementation of the proposed method in SDR framework.

2 GPS System and Effect of Spoofing 2.1 GPS System GPS system contains a space component which broadcasts the signals, a ground component which receives the signals, and a control part which maintains the space components [10]. GPS satellite acts as the space component that transmits the GPS signals in two frequencies: L1 of 1.575 GHz and L2 of 1.227 GHz. A GPS signal is made of three components: navigation data, spreading code, and carrier signal. L1 and L2 are the carrier signals. The two spreading codes used in GPS signal are coarse/acquisition (C/A) code that is not encrypted and used for civilian purpose and P(Y) code that

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is encrypted and used for military purpose. C/A code is only added with L1 signal, whereas P(Y) code is added with both L1 and L2 carriers. C/A code belongs to a family of gold codes, and it is unique for each satellite. The most important component of GPS signal is its navigation data that carries all required information such as position, velocity, clock correction parameters, regarding a GPS satellite. The ground component is the user [10]. A GPS receiver finds the location, time, and velocity of the user by processing the GPS signal. The three functions of the GPS receivers are acquisition, tracking, and navigation solution calculation. Acquisition identifies the visible satellites and calculates a rough estimate of code phase and carrier frequency of received signal. Code phase is the time alignment of the spreading code sequence in the received data. Doppler frequency shift is the change in frequency of a GPS signal caused by orbital motion of satellites. Its maximum range is 10 kHz. In order to remove the code and carrier part and thereby extract the navigation message, the receiver necessarily needs to know the code phase and Doppler frequency [11]. Purpose of tracking is to calculate the exact value for code phase and carrier frequency and extract the navigation data from the received signal. Carrier tracking often uses a phase lock loop (PLL), and code tacking uses a delay lock loop (DLL). Code tracking is to track the code phase of received signal until the local code perfectly aligned with the input code [11]. Function of GPS control segment is to maintain the space segment. This includes monitoring working condition and health status of satellites, updated navigation data parameters such as ephemeris, almanac.

2.2 Effect of Spoofing As discussed earlier, spoofing signal utilizes two drawbacks of GPS system. GPS signal power is very low at the earth surface. So spoofing signals are generally transmitted with more power than the received signal. As a result, in acquisition, spoofing signal with more power shows higher correlation peak compared to authentic signal. Acquiring wrong signals leads to wrong navigation solution. Figure 1 shows the satellites detected by the acquisition block before spoofing attack. Once the receiver is attacked by spoofing signal, acquired satellites will be detected incorrectly. In Fig. 2, satellites detected by acquisition block after spoofing attack are shown.

3 Cyclostationarity Cyclostationary signals are random signals whose statistical properties change periodically with time. If we assume a signal a(t) and define C(t) as some nth-order nonlinear transformation of ‘a’, then s(t) can be considered as nth-order cyclosta-

Cyclostationarity Analysis of GPS Signals … Fig. 1 Acquired satellites before spoofing attack. Signals with acquisition metric above a threshold are acquired signals shown as green bar. Blue bar represents remaining signals

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tionary signal only if it repeats with a periodicity. The time with which C(t) repeats is called as its cycle period. The second-order cyclostationarity of a signal is described in terms of its autocorrelation function. Autocorrelation is a very common statistical property of a signal which measures the degree of similarity of a signal and its delayed versions. Autocor-

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Fig. 3 Different domains in which cyclostationarity property is analyzed

relation of a signal x over a time interval T becomes the time-averaged autocorrelation function which is defined as C x x (t, τ )  lim T →∞

1 T

T /2 x(t + τ/2)x(t − τ/2)dx

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−T /2

where τ is the delay. The autocorrelation function shows a periodic nature if the function Rx x (α, τ )  limT →∞

1 T

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x(t + τ/2)x(t − τ/2)e− j2πnaα dt

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is nonzero for all values of α. Here, Rxx (α, τ ) is known as the cyclic autocorrelation function (CAF). It is the Fourier series representation of autocorrelation, and α is the cycle frequency. The CAF function can be interpreted as the measure of correlation of different frequency-shifted components of the signal. To analyze its periodic nature in frequency domain, we can calculate    1 α ∗ α  −j2π f n XT f + XT f − e (3) Sx x (α, f )  lim T →∞ T 2 2 where S xx (α, f ) is the Fourier transform of the CAF, called as spectral correlation density (SCD). X T represents the Fourier transform of x. The second-order cyclostationarity analysis of signals can be summarized as shown in the diagram (Fig. 3). There are three domains based on four variables time, lag, cyclic frequency, and spectral frequency [10].

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4 Results and Discussions To evaluate our methodology, open and available spoofing test data TEXBAT is used. TEXBAT is a spoofing test battery that has been used for evaluating anti-spoofing methods. It records GPS signal with static and dynamic receivers and applies various spoofing methods. A MATLAB code was generated to compute CAF and SCD. To calculate CAF, first autocorrelation of the signal is calculated. The Fourier series expansion of autocorrelation function gives cyclic autocorrelation. The cyclic frequency ‘α’ will be different for different types of signals. So a trial-and-error method is followed to find the range of α. The spectral correlation density (SCD) is the frequency-domain representation of CAF. So Fourier transform of CAF will give SCD. The number of data samples considered is 10,000. Range of cyclic frequency α is −1000–1000. Range of α considered here is −10–10. Figures 4 and 5 show the CAF and SCD of a normal GPS signal, respectively. From the above figures, we can observe that the CAF and SCD functions of a GPS signal are periodic. The fundamental cyclic frequency is observed to be 355 Hz. To show the cyclic property of normal GPS signal, the clean static data from TEXBAT data set is used. In Fig. 6, the cyclic autocorrelation of spoofed GPS signal TEXBAT sample ds1 is plotted. It can be observed that the fundamental cyclic frequency of GPS signal is changed from 335 to 710. Figure 7 shows GPS signal after spoofing. The real-time implementation of the proposed methodology is shown in the block diagram in Fig. 8. The proposed GPS spoofing detection part is placed in between the RF front end the software receiver. If a GPS signal is spoofed, with all other authentic signals, spoofed one will also be received by the antenna and processed

Fig. 4 Cyclic autocorrelation function of a GPS signal

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Fig. 5 Spectral correlation density function of a GPS signal

Fig. 6 Cyclic autocorrelation function of a GPS signal after spoofing attack

by the front-end part. Once a spoof signal reaches the acquisition, it will corrupt the position solutions. Therefore, the corrupted signal should be detected/eliminated before it enters the acquisition block.

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Fig. 7 Spectral correlation density function of a GPS signal after spoofing

Fig. 8 Block diagram for real-time implementation of the proposed methodology

5 Conclusion The cyclic autocorrelation function and spectral correlation density function of a GPS signal before and after spoofing attack are calculated and observed. For a normal GPS signal, these functions show a periodicity that ensures GPS is a cyclostationary signal. Analyzing the figures of a spoofed signal, it can be observed that the cyclostationary nature of an authentic GPS signal has changed due to the presence of spoofing signal. This shows that spoofing affects the cyclostationarity of a GPS signal. Thus, we can conclude that cyclostationarity is a good tool to detect spoofing attacks in GPS receivers.

References 1. J.S. Warner, R.G. Johnston, GPS spoofing countermeasures. Homel. Secur. J. 25(2), 19–27 (2003) 2. Z. Haider, S. Khalid, Survey on effective GPS spoofing countermeasures, in Sixth International Conference Innovative Computing Technology (INTECH), IEEE (2016), pp. 573–577

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3. P.Y. Montgomery, Receiver-autonomous spoofing detection: experimental results of a multiantenna receiver defence against a portable civil GPS spoofer, in Radionavigation Laboratory Conference Proceedings (2011) 4. A. Jafarnia-Jahromi, A. Broumandan, J. Nielsen, G. Lachapelle, GPS vulnerability to spoofing threats and a review of antispoofing techniques. Inter. J. Navigation Obs. (2012) 5. F. Wang, H. Li, M. Lu, GNSS spoofing countermeasure with a single rotating antenna. IEEE Access. 5, 8039–8047 (2017) 6. K.D. Wesson, J.N. Gross, T.E. Humphreys, L. Evans, GNSS signal authentication via power and distortion monitoring. IEEE Trans. Aerosp. Electron. Syst. 54(2), 739–754 (2018) 7. F. Wang, H. Li, M. Lu, GNSS spoofing detection and mitigation based on maximum likelihood estimation. Sensors 17(7), 1532 (2017) 8. K. Divakaran, N. Manikandan, S. Hari, Wavelet based spectrum sensing techniques for cognitive radio-a survey. Int. J. Comput. Sci. Inf. Technology (IJCSIT) (2011) 9. S.S. Kumar, P.P. Mohan, K.P. Soman, Condition monitoring in roller bearings using cyclostationary features,. in Proceedings of the Third International Symposium on Women in Computing and Informatics (2015), pp. 690–697 10. E. Kaplan, C. Hegarty, Understanding GPS: principles and applications. Artech house (2005) 11. K. Borre, D.M. Akos, N. Bertelsen, P. Rinderm, S.H. Jensen, A software-defined GPS and Galileo receiver: a single-frequency approach. (Springer Science Business Media, 2007)

Implementation of Fingerprint-Based Authentication System Using Blockchain Dipti Pawade, Avani Sakhapara, Melvita Andrade, Aishwarya Badgujar and Divya Adepu

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Biometric Authentication System Using Fingerprints . . . . . . . . . . . . . . . . . . . . . . 2.2 Overview of Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Implementation Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Enrollment Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Authentication Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Authentication is the most crucial part of security concerns. From last few years, biometric authentication is considered as the most reliable authentication method. With the advent of technology, security threats related to biometric data theft are also increased. But recently, blockchain technology has emerged as a robust system which is immutable and almost completely immune to the security threats. So in this paper, we have designed the system exploring the advantages of blockchain technology for secure and immutable storage of biometric data. The system is implemented and the results are discussed.

D. Pawade (B) · A. Sakhapara · M. Andrade · A. Badgujar · D. Adepu Department of Information Technology, K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, India e-mail: [email protected] A. Sakhapara e-mail: [email protected] M. Andrade e-mail: [email protected] A. Badgujar e-mail: [email protected] D. Adepu e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_22

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1 Introduction Nowadays, secure authentication is a major concern. The most widely used authentication is using a password. The worth of using a password authentication is based on the strength of the password and how well the confidentiality is maintained. Weak password or the password that can be easily guessed is a major security threat [1]. Even if the password is strong enough, it can be compromised through phishing or many times unknowingly one shares the password and gets victimized. There are certain systems where along with a password, RFID cards are also used [2]. But if the card is lost or the clone card is made, then also the security is at stake. With technology infiltrating every aspect of our daily routine, the lifestyle is also digitized to great extent. This fact demands a secure authentication system in order to maintain confidentiality. If passwords and RFID cards are feeble to provide security, why not go for using biometric modalities for authentication? There are certain advantages associated with using biometric such as it cannot be stolen, no need to carry it like smart RFID-based cards or no need to remember it like a password. Hence, it is considered as a very secure way of authentication. In a typical fingerprintbased authentication system, fingerprint scanner is used to scan the fingerprint and is compared to the stored fingerprint database which is created during the initial registration phase. If one’s fingerprints are found in the database, he/she is considered as authenticated user otherwise not. Now, the next security concern is how to secure the database carrying the fingerprint information? This issue inspires us to develop a fingerprint authentication system using blockchain. A blockchain is a distributed technology where data is stored in an encrypted manner [3]. Thus, it guarantees the data fortification against tampering or destruction.

2 Background Work 2.1 Biometric Authentication System Using Fingerprints Many researchers are working in this area of image processing which focuses on to improve the accuracy and efficiency of biometric authentication using a fingerprint. Jin et al. [4] have considered only the core part of fingerprint for preprocessing and matching process. They used backpropagation neural network for training the dataset and matching is performed by considering the Euclidian distance between the two neurons. For fingerprint matching, they have considered only the core part of the fingerprint. Yang et al. [5] have used FVC2002 database. They have chosen local and global invariant moment feature and PCA feature selection and extreme learning machine (ELM) and regularized extreme learning machine (R-ELM) for fingerprint matching. In 2015, Chavan et al. [6] proposed Gabor filter matching algorithm for fingerprint authentication. Firstly, the fingerprint image is preprocessed using normalization technique to achieve the preferred contrast of fingerprint. The

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Fig. 1 Working of blockchain

normalized image is then segmented and cropped to pick up the region of interest (ROI). After that, ROI reference point is selected by counting the different number of orientation for every pixel and selecting one pixel which as unique orientation. Then, the fingerprint image is further filtered using Gabor filter technique. Lastly, feature vector map (template) is created using circular tessellation. For FVC 2000 database, the average efficiency is 82.95% and average error rejection rate (ERR) of 18.097. While for DBIT database, average efficiency is 89.68% and ERR is 10.883. In 2016, Ochieng and Harsa [7] proposed a novel downsampling pixel preprocessing technique to compress original fingerprint pixel matrix to a unit input vector for artificial neural network for fingerprint authentication system. The discussed methodologies include binarization of grayscale image, formulation of downsampling model which compress the original pixel matrix by computing the arithmetic mean of the sum of the pixel values on each input row matrix to generate a unit input vector for ANN, backpropagation using neural network which trains the system to match fingerprint samples and relates them to the number provided for each authorized user. The comparative evaluation shows the proposed method outperforms backpropagation normal pixel, perceptron with downsampled pixel and perceptron with normal pixel achieving 98.03% precision with minimum convergence time of 30 s and mean square error of 0.05%. Umesh et al. [8] discussed the novel approach for fingerprint recognition. Here, the binary image is enhanced using morphological operations and then a matrix of the minutiae distance is made. The hybrid optimization is then calculated using two distance functions and also the stance of the image. It reduces the size and complexity of the program with the increase in speed. It has an accuracy of 99%.

2.2 Overview of Blockchain The blockchain is a distributed, decentralized system that is shared, replicated, and consistent among the members of its network which record the transaction of assets or data in its network. Similar to a digital ledger, blockchain also records transactions in a public or private peer-to-peer network. All the records in blockchain are stored in a sequence of cryptographic hash-linked blocks [9]. The working of blockchain is depicted in Fig. 1.

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Associated with blockchain, there is a concept of sidechain. Sidechains are implemented to cover up the fact that blockchain networks cannot scale in terms of performance. It is a mechanism that allows tokens from one blockchain to be securely used within a completely separate blockchain but still moved back to the original chain if necessary. The main chain is called an original blockchain while sidechain is referred to as additional blocks which allow users to transact within them in the tokens of the main chain. It is implemented such that there is no effect on the main chain and the main chain is as secure as possible while providing the freedom to explore options which would never be considered for use on the main chain [10, 11].

3 Implementation Overview The proposed system implementation has two main phases: enrollment phase and authentication (log in) phase.

3.1 Enrollment Phase The new user has to first enroll him/her. Following are the enrollment steps for the new user. • Aadhar card number verification and pushing it on the blockchain; • Uploading fingerprint feature vector and other details on sidechain against the block created for Aadhar number in the previous step. Initially, the user needs to provide his Aadhar card number. This number is verified against the blockchain. If there is any block present with same Aadhar number, the user is notified with the appropriate popup and is redirected to the login page for authentication. Else, for that Aadhar number, a block is created and the user continues with filling up the enrollment details such as phone number, address and the user’s fingerprints are scanned using the fingerprint scanner. Thereafter, the features are extracted from the scanned fingerprint image and stored on the blockchain. The fingerprint feature extraction process is given as follows: 1. Color to grayscale conversion: A colored fingerprint image is converted to better intensity by extracting the red, blue, and green colors from its pixels and bringing them in the 0–255 grayscale range. Figure 2a shows the sample fingerprint image, and Fig. 2b represents its grayscale conversion. 2. Binarization: Next step is binarization. It is done to obtain pixel values in form of 1’s (white) and 0’s (black) using a threshold value. If the average of RGB colors for a particular pixel is greater than the threshold value of 50, set it to white otherwise set it to black. Figure 2c shows the fingerprint image after binning.

Implementation of Fingerprint-Based Authentication …

(a)

(b)

(c)

(d)

(e)

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(f)

(g)

Fig. 2 a Sample fingerprint image; b grayscale image; c image after binning; d fingerprint image after thinning; e fingerprint image after edge detection; f fingerprint image after CN; g pixel matrices

3. Thinning: This is used to remove redundant pixels from a binary image. It is a morphological operation that makes the fingerprint lines only 1 pixel thick by eliminating the selected foreground pixels. The result is also a binary image. Thinning is carried out using T. Y. Zhang and C.Y. Suen algorithm. A pixel is considered for thinning when its value is 0 (black), and the sum of its local neighborhood pixels lies between 3 and 6 [12]. The neighborhood pixels of a point are labeled as B2–B9 (Fig. 2g). For even iterations: A point is set to 1(white) if At least one of the pixels B2, B4, B6 is white or At least one of the pixels B4, B6, B8 is white. For odd iterations: A point is set to 1(white) if At least one of the pixels B2, B4, B8 is white or At least one of the pixels B2, B6, B8 is white. Figure 2d represents the processed image after thinning operation. 4. Edge Detection: For edge detection, the Sobel operator is used which uses a 3x3 mask to obtain the horizontal and vertical gradient Gx and Gy. Figure 2e depicts the results of edge detection [13]. 5. Minutiae extraction using Cross-numbering (CN): This method extracts the minutiae points by identifying the bifurcation points using a 3 × 3 mask on the local neighborhood pixels of the ridge pixel. The cross-numbering method identifies five properties that are—isolated point, ending point, connecting point, bifurcation point, and crossing point having CN number 0–4, respectively. Figure 2f shows the image after applying the CN operation from which feature set is extracted [14]. The extracted feature set along with the personal details of the person is pushed on to the blockchain using Metamask. Metamask is used to connect the browser to the Ethereum network. Here, the Aadhar number is the address of block on blockchain, and fingerprint feature vector and personal details are stored on the side chain. We have used Metamask and Ethereum platform for blockchain implementation. Ethereum is a distributed public blockchain network providing open-source platform for blockchain technology that allows the developers to build and develop dApps that run on the blockchain network. In Ethereum blockchain, miners work for a crypto token called Ether. The prime advantage of using Ethereum as the decentralized plat-

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form is that it provides all the benefits of the blockchain. Ethereum has Ethereum Virtual Machine which provides great support for the easy and efficient implementation of blockchain applications [15]. MetaMask is a browser plugin that allows users to make Ethereum transactions through regular Web sites. It acts as a bridge between the Ethereum interfaces and the regular Web without actually running a full Ethereum node on the browser. Accessing decentralized applications without Metamask can become a tedious task as it requires running the full Ethereum node on the computer. Metamask plugin allows anyone using the Web browser to directly access Ethereum dApps. It simplifies the entire process of running and accessing a dApp making it convenient for novice users along with providing the technical requirements necessary for the Ethereum ecosystem [16].

3.2 Authentication Phase Just like login phase, here also first Aadhar number validation is done to check if the Aadhar is present on the blockchain or not. If it is not present, the user is redirected to enrollment page. Else, system prompts for providing user’s fingerprint. The fingerprint image is scanned and feature extraction is done using the steps like grayscale conversion, binarization, thinning, edge detection, and minutiae extraction using cross-numbering as discussed in enrollment phase. The features stored on the side chain against that blockchain containing Aadhar number are extracted. The two features are compared using orientation matching and L2 norm methods for matching purpose. If the two images match or have a high similarity score, the user’s details are fetched from the block and displayed on the Web site. Else, the user is informed about the mismatch in the fingerprints. Figure 3 gives a pictorial representation of the various activities performed in enrollment and authentication phase.

4 Result Analysis As discussed in the previous section, during the registration phase, Aadhar number of each unique user is stored on blockchain and their fingerprint features and other details are stored on sidechain. For authentication, the Aadhar number provided by the user is matched against the Aadhar number stored on the blockchain. If it does not exist, fingerprint authentication won’t be carried out thus save the processing time. If the Aadhar number exists, then the fingerprint features of the user are matched against the features stored on the sidechain associated with that particular block using two different methods, viz. Euclidian distance (method 1) and percentage method (method 2).

Implementation of Fingerprint-Based Authentication …

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Get Aadhar Card Number

START

Yes

Does the block with same Aadhar Card Number exist?

No

Create block on Blockchain against Aadhar Card Number Get users personal information and scan fingerprints image

Convert RGB fingerprint image to grayscale Apply Binning and Thinig Apply Edge Detection

Display user already exists in the system Redirect to the log-in page Convert RGB fingerprint image to grayscale

Scan fingerprints Apply Edge Detection

Apply Binning and Thining

Calculate feature set by Minutiae extraction using Cross Numbering

Calculate feature set by Minutiae extraction using Cross Numbering (CN) Store fingerprint feature and personal information in sidechain against the main block created for Aadhar number

Match the feature vector against the feature vector store in sidechain of the matched Adhar number No Authentication Unsuccessful

Registration Phase

Authentication Successful. Display Personal Details END

Fig. 3 System flowchart

Authentication Phase

Yes

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For experimental purpose, we have considered the dataset consisting of around 854 images of fingerprints. The efficiency of method 1 and 2 is measured in terms of accuracy, sensitivity, specificity, and error rate for which the following interpretation is used: • True Positive (TP): When the fingerprint is supposed to match and it is matching. • True Negative (TN): When the fingerprint is not supposed to match and it is not matching. • False Positive (FP): When the fingerprint is not supposed to match and it is matching. • False Negative (FN): When the fingerprint is supposed to match and it is not matching. The accuracy, specificity, sensitivity, and error rate is calculated as follows: Accuracy  (TP + TN)/(TP + FP + TN + FN) Specificity  TN/(TN + FP) Sensitivity  TP/(TP + FN) Error rate  (FP + FN)∗ 100/(TP + TN + FP + FN) Table 1 presents the confusion matrix for method 1 and method 2. It is observed from Table 2 that the accuracy of method 2 is higher than method 1. So in our system, we have preferred method 2, i.e., percentage method for fingerprint matching using feature vectors which are stored on the blockchain. According to our experimental observation, the discussed system serves as a secure biometric authentication system where the biometric data is also impregnable as it is stored on the blockchain.

5 Conclusion and Future Scope In this paper, we have proposed and implemented a novel technique of securing biometric data by using blockchain technology, thus enabling one to implement a secure biometric-based authentication system. In our system, the biometric data is stored temporarily to extract the features, and thereafter, it is deleted from the system. Also, the biometric information is stored in the form of feature vectors on the blockchain which is hashed. This eliminates the possibility of biometric data tampering making system more secure. According to the experimental results, the accuracy of our system is 82.55% and the error rate is 17.48%. In future, we can

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Table 1 Confusion matrix for method 1 and method 2 Predicted matching NO Predicted matching YES Method 1 Method 2 Method 1 Method 2 Actual 378 TN) 469 (TN) 83 (FP) 65 (FP) matching no Actual 172 (FN) 84 221 236 (TP) matching (FN) (TP) yes Total

550 (TN + FN)

Table 2 Comparison of method 1 and method 2

553 (TN + FN)

304 (FP + TP)

301 (FP + TP)

Total Method 1 461 (TN + FP)

Method 2 534 (TN + FP)

393 (FN + TP)

320 (FN + TP)

854 (TN + TP + FN + FP)

854 (TN + TP + FN + FP)

Method 1 (%)

Method 2 (%)

Accuracy

70.14

82.55

Specificity

81.10

87.82

Sensitivity

56.23

73.75

Error rate

29.86

17.48

try to improve the accuracy of the system. Also to make the system more secure, multiple biometric modalities like Eris, palm print can be considered.

References 1. A. Sakhapara, D. Pawade, P. Sharma, R. Dalia, S. Khandelwal, Animation based hybrid shoulder surfing attack proof approach for user authentication, in Third International Conference on Computing, Communication, Control and Automation, Pune (2017) 2. P. Joo-Sang, K. Young-I, L. Yong-Joon, Security considerations for RFID technology adoption, in 7th International Conference on Advanced Communication Technology, ICACT 2005, (Phoenix Park, South Korea, 2005), pp. 797–803 3. S. David, L. Harvey, G. Vimi, S. Alexander, M. Stephen, W. Tyler, What is blockchain? blockchain enigma. Paradox. Opportunity—Deloitte (2016) 4. A.L.H. Jin, A. Chekima, J.A. Dargham, L.C. Fan, Fingerprint identification and recognition using backpropagation neural network, in Student Conference on Research and Development proceedings, Malaysia (2012), pp. 98–101 5. J. Yang, S. Xie, S. Yoon, D. Park, Z. Fang, S. Yang, Fingerprint matching based on extreme learning machine. Neural computing and application (Springer-Verlag, London, 2013) 6. S. Chavan, P. Mundada, D. Pal, Fingerprint authentication using gabor filter based matching algorithm, in International Conference on Technologies for Sustainable Development (ICTSD2015), Mumbai, India (2015) 7. P.J. Ochieng, H. Harsa, Fingerprint authentication system using back-propagation with downsampling technique, in: Second International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia (2016), pp. 182–187

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8. S.T. Umesh, V. Abhinav, S. Pankaj, Fingerprint recognition by hybrid optimization based on minutaies distance and pattern matching, in International Conference on Signal Processing, Communication, Power and Embedded System. India (2016), pp. 2047–2051 9. B. Sloane, P. Bhargav, Blockchain basics: introduction to distributed ledgers (2016). Online available at: https://www.ibm.com/developerworks/cloud/library/cl-blockchain-basics-introbluemix-trs/index.html 10. https://www.e-spincorp.com/2017/11/24/pros-and-cons-of-blockchain-technology/ 11. N. Alex, Designing a smart-contract application layer for transacting decentralized autonomous organizations, in International Conference on Advances in Computing and Data Sciences (2016) 12. P. Shiny, Gladis: analogizing the thinning algorithm on minutiae extraction, in International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, India (2015), pp. 867–870 13. P. Dipti, C. Pranchal, S. Harshada, Comparative study of different paper currency and coin currency recognition method. Int. J. Comput. Appl. 66(23) (2013) 14. https://github.com/yoga1290/Fingerprint-Recognition 15. https://www.ethereum.org/ 16. https://metamask.io/

NSGLTLBOLE: A Modified Non-dominated Sorting TLBO Technique Using Group Learning and Learning Experience of Others for Multi-objective Test Problems Jatinder Kaur, Surjeet Singh Chauhan and Pavitdeep Singh Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Non-dominated Sorting TLBO Algorithm for Multi-objective Optimization . . . . . . . . . . Non-dominated Sorting TLBO using Group Learning and Learning Experience from Others (NSGLTLBOLE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Inspiration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Grouping Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Simulation Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Benchmark Problems and Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Quality Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Comparison of Solution Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In this paper, we have proposed a modified algorithm of non-dominating sorting TLBO (NSTLBO) using group learning and learning from other experienced learners to solve multi-objective optimization problems (MOOPs). The proposed algorithm (called NSGLTLBOLE) is based on the concept of micro-teaching in which a class (called population) is divided into different smaller groups (called sub-populations) and algorithm is individually run for all the sub-populations before being merged together after certain generations to improve the diversity of the population. Within each sub-population, the algorithm uses the non-dominating sorting and crowding distance techniques to find the optimal set of solutions which are passed to the next iteration. Additionally, learner phase makes use of learning from other experienced learners within the sub-population. The algorithm hugely benefits from J. Kaur (B) · S. S. Chauhan Chandigarh University, Mohali, India e-mail: [email protected] S. S. Chauhan e-mail: [email protected] P. Singh Royal Bank of Scotland, Gurgaon, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_23

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exploitation perspective by strategizing group concept incorporated in the algorithm, whereas the exploration search immensely benefits from randomly regrouping of sub-populations and learning experience mechanisms inculcated into the algorithm. The proposed algorithm is tested on several bi-objective benchmark functions which proved that NSGLTLBOLE has some good performances when compared with other established algorithms. Keywords Multi-objective optimization problem (MOOP) · Meta-heuristic technique · Teaching–learning-based optimization (TLBO) · Non-dominating sorting TLBO using group learning and learning from other experienced learners (NSGLTLBOLE)

1 Introduction In recent years, a new meta-heuristic and nonparametric algorithm called teaching– learning-based optimization (TLBO) is proposed by Rao in 2012. The algorithm mimics the teaching–learning process, and each student signifies a probable solution of the optimization problems, whereas each subject pertains to the dimensionality of the problem. Some results indicate that meta-heuristic TLBO algorithm yielded better objective function values in comparison with other meta-heuristics for constrained benchmark functions and nonlinear numerical optimization problems. TLBO technique has been successfully applied to various engineering optimization problems like automatic voltage regulator, power flow problem, heat exchanger, DC link placement problem , NRP, plate fin heat sink, job shop scheduling problem and thermoelectric cooler problem. In the past few years, several variants of TLBO algorithm have been proposed in the literature. Rao and Patel in 2012 proposed elitist TLBO algorithm [7] in which worst solutions are replaced by the elitist solutions. Yu et al. [9] proposed a selfadaptive multi-objective teaching–learning-based optimization (SA-MTLBO) technique in which the learners can self-adaptively select the modes of learning according to their levels of knowledge in classroom. Non-dominated sorting TLBO (NSTLBO) algorithm proposed by Rao [6] uses non-dominating sorting and crowding distance technique to find the optimal set of solutions for multi-objective optimization problems. Few hybrid algorithms have been developed to solve various optimization problems, estimation of unknown proton exchange membrane fuel cell (PEMFC) model [8] and constrained optimization problems [5]. Chen et al. [1] proposed a variant of TLBO algorithm with multi-classes cooperation and simulated annealing operator (SAMCCTLBO) to solve benchmark functions. Analogous to other meta-heuristics methods, TLBO also suffers from local optima while solving problems of higher complexity yielding multiple local optimal solutions. To improve the performance of NSTLBO algorithm for solving multi-objective optimization problems, a modified NSTLBO technique using group learning and learning experience of others for multi-objective optimization is proposed in this paper.

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2 Non-dominated Sorting TLBO Algorithm for Multi-objective Optimization Rao proposed a multi-objective optimization technique based on TLBO called NSTLBO which is posteriori approach for solving MOOPs and generates sets of solution of an approximation of the entire true Pareto front. NSTLBO makes use of the non-dominating sorting approach and crowding distance (proposed by Deb [2]) in the teacher and learner phases of the algorithm. As the algorithm is multi-objective in nature, there is not a single solution for the conflicting objectives to the problem. The diverse solution sets are assigned rank to find the best solution among them. The solution set having highest rank (rank = 1) are considered the best solution. The highest rank solution set is considered as a teacher. In case of a tie between different solution sets, crowding distance is used to find the teacher in the class (having the highest crowding distance among the competing solution sets). Once the teacher is selected, the teacher and learner phases are updated using the selected teacher for the iteration.

3 Non-dominated Sorting TLBO using Group Learning and Learning Experience from Others (NSGLTLBOLE) 3.1 Inspiration The inspiration behind our method is to leverage the concept of micro-learning to improve the scores of learners within small groups and a learning from other experienced learners, thus providing an effective and efficient population-based algorithm. TLBO exhibits a greedy search process. It can be seen that only better individuals are promoted to the next phase/generation of the algorithm which can impact the diversity of the learners participating in the algorithm. In a real classroom, students are more attentive and tend to communicate more effectively within small groups rather than one big class. They try to improve their learning by exchanging information among themselves. In this paper, groups within a class are introduced into TLBO combined with learning from other experienced learners within the group to maintain a proper trade-off between exploration and exploitation capabilities, as a consequence of enhancing the global optimization performance.

3.2 Proposed Methodology In this paper, we extend the non-dominated sorting TLBO algorithm for multiobjective optimization (NSTLBO) to incorporate the concepts like micro-learning and learning from other experienced learners in the original NSTLBO proposed by Rao. As NSTLBO, NSGLTLBOLE algorithm also uses a posteriori approach

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for solving MOOPs and maintains a varied set of solutions. The NSGLTLBOLE algorithm works quite similar to NSTLBO algorithm using non-dominated sorting approach and crowding distance computation mechanism proposed by Deb [2]. However, there are subtle differences in the teacher and learner phases. First and the foremost is the introduction of micro-learning concepts. Initial population is randomly divided into different groups depending upon the group size specified in the algorithm. Best learner or teacher is selected among all the groups. In the teacher phase, the learners within a group are updated similar to the teacher phase of NSTLBO. In the learner phase of the NSGLTLBOLE, however, the learners are updated based on the learning phase of NSTLBO or using the experience of other learners belonging to the same group, while the teacher and learner phases of the NSGLTLBOLE assist in ensuring good exploitation and exploration of the search space. At the same time, the non-dominated sorting ensures good selection of solutions which are towards the true Pareto front. The crowding distance technique helps in selecting the teacher from the sparse region of the search space, thus drastically reducing the risk of premature convergence of the algorithm. The flow chart of the proposed NSGLTLBOLE algorithm is shown in Fig. 1.

3.3 Grouping Strategy The algorithm uses a random selection grouping strategy to create various groups and apply the teacher and learner phases on these groups. Diversity of the groups is improved by regrouping the existing groups after a certain number of generations (say a period T ) which is an input to the algorithm. The process is quite simple as individuals are allocated to groups on random basis as shown in Fig. 2, and later they are merged together.

4 Simulation Results and Discussion The experiments are conducted on jMetal Framework 4.3 [3, 4], which is a Javabased framework that is aimed at facilitating the development of meta-heuristics for solving multi-objective problems. For assessing the performance of the algorithms, we have used the various key quality metrics defined in the next section.

4.1 Benchmark Problems and Experimental Settings We have used ten test benchmark functions (bi-objective in nature) to authenticate our proposed algorithm. Table 1 depicts various function names along with the reference points ( rmin and rmax ) which contain the minimum and maximum objective values of a

NSGLTLBOLE: A Modified Non-dominated Sorting TLBO Technique …

Fig. 1 Flow chart of the NSGLTLBOLE algorithm

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Fig. 2 Grouping strategy of NSGLTLBOLE Table 1 Benchmark problem functions Function Function Reference points name rmin (Obj1min ,Obj2min ) F1

Fonseca

F2

Kursawe

F3

Schaffer

F4 F5 F6 F7 F8 F9 F10

DTLZ1 DTLZ2 DTLZ3 ZDT1 ZDT2 ZDT3 ZDT4

(0.000005469665695, 0.000005469665695) (−20.0, −11.62641325) (5.844598067E-19, 5.844575628E-19) (0.0, 0.0024999999999996136) (0.00785390088871024, 0.0) (0.00785390088871024, 0.0) (0.0, 0.0) (0.0, 0.0) (0.0, −0.773354) (0.0, 0.0)

rmax (Obj1max ,Obj2max ) (0.9815123166, 0.9815123166) (−14.44665867, 0.00000000008180035271) (3.999999997, 4.000000003) (0.49750000000000039, 0.5) (1.0, 0.99996915764478977) (1.0, 0.99996915764478977) (1.0, 1.0) (1.0, 1.0) (0.852, 1.0) (1.0, 1.0)

true Pareto front for these functions. These values are taken from the true Pareto front values defined in the jMetal Framework 4.3 [3, 4].Well-known existing optimization techniques like NSGA-II and SPEA2 are used for comparing the performance of NSGLTLBOLE algorithm. The major experimental settings for carrying out the simulation results are: population size is 100; maximum function evaluations are 50,000; independent runs are 10; number of variables defined are 5; group size is 20; period T is set to 40.

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4.2 Quality Indicators The performance of multi-objective algorithms is measured in terms of convergence and diversity factors. These factors are determined by various quality indicators as mentioned: – Convergence: hypervolume (HV), generational distance (GD), inverse generational distance (IGD), epsilon – Diversity: hypervolume (HV), inverse generational distance (IGD), spread.

4.3 Comparison of Solution Accuracy Comparison of results of our proposed approach (NSGLTLBOLE) with NSGA-II and SPEA2 algorithms for various quality indicators is shown in Table 2. The algorithms are simulated for results using the jMetal Framework for multi-objective optimization. For a few problems, the HV values are higher for NSGLTLBOLE compared with NSGA-II and SPEA2 algorithms which is the desired result. NSGLTLBOLE algorithm produces lower mean GD values in comparison with other two algorithms. As GD is a key factor for measuring the convergence, it proved that our proposed algorithm is better in terms of faster convergence to global optimum value. NSGLTLBOLE performed better for all the benchmark problems compared with NSGA-II for spread values. However, it yields better results as compared with SPEA2 for a few benchmark problems. For IGD and epsilon values, NSGLTLBOLE performed better for a few of the benchmark problems.

5 Conclusions and Future Scope This paper presents a modified NSTLBO algorithm called NSGLTLBOLE leveraging group-based learning to enable the micro-learning concept to be incorporated in the original NSTLBO algorithm along with learning from other experienced learners. The learners (or individuals) seek knowledge from others within the same group during the teacher and learner phases of NSGLTLBOLE. The diversity of the population within different groups is increased by regrouping strategy which can be easily controlled by changing the period value parameter defined at the beginning of the algorithm. Several experiments were carried out on benchmark functions (biobjective in nature) in this paper. Comparing the experimental results, few conclusions can be drawn from the modified algorithm. First of all, it may not be best for all the test functions, but it has excellent performance of solution accuracy and high convergence speed based on the various quality indicator values produced. As NSGLTLBOLE is in its initial stages in terms of its applicability to many practical problems, still there is a lot of scope for its application to various engineering and other fields. Future work will include tweaking the algorithm in terms

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Table 2 Comparison among the three meta-heuristics for hypervolume (HV), generational distance (GD), inverted generational distance (IGD), spread and epsilon metrices Quality indicators

Function name

Mean

Std. dev.

Mean

Std. dev.

Mean

HV

Fonseca

0.307526

5.55E−17

0.310733

0.0001392

0.311037

1.32E−17

Kursawe

1

0

1

0

1

0

Schaffer

0.6908635

1.11E−16

0.7071113

0.1018428

0.829795

2.12E−13

DTLZ1

0.4932417

5.55E−17

0.4944482

0.0003223

0.494768

0

DTLZ2

0.2111698

5.55E−17

0.2118685

0.0001005

0.21238

1.08E−06

GD

IGD

Spread

NSGA-II

SPEA2

NSGLTLBOLE Std. dev.

DTLZ3

0.2111722

2.78E−17

0.2118612

0.0004893

0.212387

2.08E−09

ZDT1

0.6610385

1.11E−16

0.661858

4.01E−05

0.661949

1.41E−11

ZDT2

0.3277006

0

0.3285607

6.43E−05

0.328672

1.51E−09

ZDT3

0.5154983

1.11E−16

0.5157937

4.69E−05

0.515971

3.43E−13

ZDT4

0.6610055

0

0.6618015

0.0001841

0.661967

0 0

Fonseca

0.0003804

0

0.000218

1.71E−05

0.000184

Kursawe

0.2741445

5.55E−17

0.2759538

0.0008602

0.275166

1.32E−16

Schaffer

0.020402

0

0.0116889

0.0192581

0.000237

3.11E−14

DTLZ1

0.0002365

5.42E−20

0.0316167

0.0941976

0.000226

0

DTLZ2

0.0002593

0

0.0002478

7.42E−06

0.000229

4.35E−18

DTLZ3

0.0002395

0

0.0002437

1.12E−05

0.000232

0

ZDT1

9.06E−05

1.36E−20

0.0001652

7.17E−06

0.000123

0

ZDT2

4.90E−05

6.78E−21

5.48E−05

1.39E−05

4.94E−05

3.17E−12

ZDT3

0.0001979

5.42E−20

0.0002163

1.08E−05

0.0002

0

ZDT4

0.0001929

2.71E−20

0.0001578

1.59E−05

0.00014

7.15E−19

Fonseca

0.0003217

5.42E−20

0.0002305

4.24E−06

0.000225

1.75E−17

Kursawe

0.0861451

1.39E−17

0.0863145

0.0001595

0.086224

0

Schaffer

0.0112793

0

0.0096868

0.0044857

0.00036

0

DTLZ1

0.0003728

0

0.0003066

4.67E−06

0.000305

2.45E−14

DTLZ2

0.0004461

1.08E−19

0.0003468

4.70E−06

0.000354

4.25E−18

DTLZ3

0.000428

1.08E−19

0.0003377

5.68E−06

0.000351

2.18E−19

ZDT1

0.0001763

2.71E−20

0.0001423

9.82E−07

0.00014

1.31E−12

ZDT2

0.0002158

2.71E−20

0.0001435

1.23E−06

0.000144

0

ZDT3

0.00025

5.42E−20

0.0002228

4.73E−06

0.000203

2.13E−16

ZDT4

0.0001734

0

0.0001423

2.08E−06

0.00014

0

Fonseca

0.3778795

5.55E−17

0.1323873

0.0147332

0.161414

0

Kursawe

0.8413194

2.22E−16

0.823473

0.0038358

0.816081

1.79E−17

Schaffer

0.6084643

1.11E-16

0.6696008

0.1378416

0.184865

0

DTLZ1

0.3482016

0

0.264201

0.3976021

0.14958

2.41E−16

DTLZ2

0.3740817

5.55E−17

0.1544089

0.0127528

0.189656

0

DTLZ3

0.3687684

0

0.1259812

0.0095585

0.188812

0

ZDT1

0.3550358

0

0.1388798

0.012362

0.16225

2.56E−17

ZDT2

0.4259352

1.11E−16

0.1439276

0.0123237

0.143361

2.38E−19

ZDT3

0.7448631

0

0.7027418

0.0017841

0.700436

0

ZDT4

0.3401807

5.55E−17

0.1310697

0.0104533

0.13595

1.91E−16

(continued)

NSGLTLBOLE: A Modified Non-dominated Sorting TLBO Technique …

251

Table 2 (continued) Quality indicators

Function name

NSGA-II Mean

Epsilon

SPEA2 Std. dev.

Mean

NSGLTLBOLE Std. dev.

Mean

Std. dev.

Fonseca

0.307526

5.55E−17

0.310733

0.0001392

0.311037

1.32E−17

Kursawe

1

0

1

0

1

0

Schaffer

0.6908635

1.11E−16

0.7071113

0.1018428

0.829795

2.12E−13

DTLZ1

0.4932417

5.55E−17

0.4944482

0.0003223

0.494768

0

DTLZ2

0.2111698

5.55E−17

0.2118685

0.0001005

0.21238

1.08E−06

DTLZ3

0.2111722

2.78E−17

0.2118612

0.0004893

0.212387

2.08E−09

ZDT1

0.6610385

1.11E−16

0.661858

4.01E−05

0.661949

1.41E−11

ZDT2

0.3277006

0

0.3285607

6.43E−05

0.328672

1.51E−09

ZDT3

0.5154983

1.11E−16

0.5157937

4.69E−05

0.515971

3.43E−13

ZDT4

0.6610055

0

0.6618015

0.0001841

0.661967

0

of dynamically changing the period value based on the learners’ position in order to improve the diversity of the population further. Group size also plays an important role towards the convergence speed, and finding the right group size for a problem will definitely invite the researchers’ intention in future.

References 1. D. Chen, F. Zou, J. Wang, W. Yuan, Samcctlbo: a multi-class cooperative teachinglearning-based optimization algorithm with simulated annealing. Soft Comput. 20 (2015). https://doi.org/10. 1007/s00500-015-1613-9 2. K. Deb, Multi-objective Optimization Using Evolutionary Algorithms (Wiley, Hoboken, 2001) 3. J. Durillo, A. Nebro, The jmetal: a java framework for multi-objective optimization. advances in engineering soft-ware. IEEE Trans. Evol. Comput. 10, 760–771 (2011) 4. J. Durillo, A. Nebro, E. Alba, The jmetal framework for multiobjective optimization: design and architecture, in Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010) 5. J. Huang, L. Gao, X. Li, A teachinglearning-based cuckoo search for constrained engineering design problems. Adv. Global Optim. 95, 375–386 (2015) 6. R.V. Rao, Teaching-Learning-Based Optimization (tlbo) Algorithm and Its Engineering Applications (Springer, Berlin, 2015) 7. R.V. Rao, V. Patel, An elitist teaching learning based optimization algorithm for solving complex constrained optimization problems. Int. J. Ind. Eng. Comput. 3, 535–560 (2012) 8. O.E. Turgut, M.T. Coban, Optimal proton exchange membrane fuel cell modelling based on hybrid teaching learning based optimization differential evolution algorithm. Ain Shams Eng. J. (2015). https://doi.org/10.1016/j.asej.2015.05.003 9. K. Yu, X. Wang, Z. Wang, Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemom. Intell. Lab. Syst. 146, 198–210 (2015)

Homomorphic Encryption Scheme for Data Security in Cloud Using Compression Technique D. K. Chandrashekar, K. C. Srikantaiah and K. R. Venugopal

Contents 1 2 3 4 5

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CHET: System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Model and System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

254 255 255 255 256 256 258 258 258 259 260

Abstract Data generation and consumption are becoming a main part of daily life. Huge volume of data is available over the Internet; preprocessing of these data is a challenging task. The data preprocessing is used to process the raw data into precise data and store in the cloud. Providing security to this data is another challenging issue. Earlier, data owners were not ready to send their data to centralized sites because of the privacy issue. Nowadays, two types of encryption techniques are applied to data stored in the cloud and data is more secured. In this paper, we proposed compressed homomorphic encryption technique (CHET) which combines the compression technique to encrypt the data so it reduces the file transfer time and encrypt time, and it provides security. Data owner can decrypt the data when required. The homomorphic function is allowed to compute encryption time. Our experiment results show that CHET outperforms 10% in compression and encryption time and provides high security than RSA and BGV.

D. K. Chandrashekar (B) · K. C. Srikantaiah Department of Computer Science and Engineering, S J B Institute of Technology, Bangalore, Karnataka, India e-mail: [email protected] K. R. Venugopal Bangalore University, Bangalore, Karnataka, India © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_24

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Keywords Big data · Cloud · Compression · Decryption · Encryption · Privacy Security

1 Introduction Data is not just information. It requires interpretation to become information. There must be several factors to be considered to translate data into information. Those factors are decided by the creator of data. Data can be measured, collected, reported, and visualized using graphs, images, etc. Data becomes information only when it is analyzed. Data mining is used to find the actionable information from large sets of data; it uses mathematics to derive patterns and trends in data. As there is huge quantity of information available in the industry, this data is not useful until it is converted into useful information So, it is necessary to know about a large amount of data and extract useful information. Data mining not only does extraction but also performs data cleaning, data integration, and data transformation. As the amount of data increased, many organizations are concerned in mining. The identification of relationship in transaction record helps in the business process. The transaction database is used in business for catalog design, cross-marketing, behavior analysis and market analysis where the items are analysed. The output of the analysis is stored in the cloud and used by many people every day without having the knowledge of analysis. For instance, in all renditions of email (Gmail or Webmail) and access to the applications that are not physically near as Excel and Microsoft Word this utilization is done on account of Web; yet, clients may not know the area of the servers that put away their messages and facilitate the source code of the applications that they utilize. Motivation: Security is the prime prerequisite since information is expanding these days in the cloud. Cloud is a third-party server where security is primeconcerned; every time when we upload a data, it consumes time for encryption and decryption of data. The main goal is to reduce the encryption time and storage space in the cloud. Contribution: There are many encryption and decryption techniques which lead to time-consuming to overcome with the problem. We have a new scheme, compression homomorphic encryption technique (CHET). This scheme is comprised of compression and encryption techniques, when an entire plaintext is converted into ciphertext is time consuming. To reduce this encryption time, the LSW method is used to compress the plaintext; later, the compressed file is encrypted and stored in the cloud. The same reverse process is applied to retrieve the original plaintext; by doing this, we can reduce the encryption and decryption time and reduce the storage capacity in the cloud. The rest of the paper is organized as follows: Related work is described in Sect. 2, background work is described in Sect. 3, the problem is defined in Sect. 4, the entire structure of the model is described in Sect. 5, experimental results are described in Sect. 6, and Sect. 7 concludes and highlights the future work.

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2 Related Works Paillier [1] presented new number-theoretic problem and a related trapdoor mechanism. Additive homomorphic function is used to provide security for e-voting system and threshold scheme. Elgamal [2] present four additive primary homomorphics; in this, two of them are secured under a cipher attack, and other two methods are broken by plaintext attack. To overcome with this problem, they have introduced R-additive privacy homomorphic where R messages are added to function and provide security to banking and credit card transaction. Brickel and Yacobi [3] proposed a new signature scheme with Diffie–Hellman key distribution scheme and provided security to discrete algorithm; it estimates the running time of the data in hybrid systems. Fau et al. [4] present an encryption scheme which provides a prototype of a compilation and execution time by using FHE scheme, which reduces the execution time and processor capacity by using integer polynomials. Rao and Uma [5] proposed a new scheme for secured transmission of message in MANET to overcomes the problem of lageane interpolation and reduces the encryption time all the split messages.

3 Background The time taken to encrypt and decrypt in the existing method is high; if encryption and decryption keys are lost, the security to the data is lost [6]. To avoid this, we have to provide a security to the data in the cloud, but providing security to the cloud is time consuming. To overcome this we propose CHET, which compresses the data using Levenshtein similarity weight: compressed data is Encrypted and stored in the cloud and decryption, decompression techniques are applied during read operation, this reduces storage space and time.

4 Problem Definition Given transaction data D with n items is n 1 , n 2 , n 3 …n, where n i is the item set of data. Our objective is to compress the data by using LSW, to encrypt using fully homomorphic encryption to store encrypted compressed data in the cloud and to retrieve the original data from the cloud using reverse process of encryption. The objective of this work is to reduce the encryption time and provide high security to data which is uploaded in the cloud.

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5 CHET: System Model 5.1 Model and System Architecture The system architecture consists of the following components: (i) big data, (ii) preprocessing, (iii) data compression, (iv) data encryption, (v) cloud, (vi) decompress the data, (vii) data decryption, and (viii) original sata as shown in Fig. 1. Data owners have multiple data, and they are not ready to send their data to centralized database because of privacy issue where the data is stored in central sites. Multiple data owners have multiple joint data. Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. It has four types of categories: data cleaning, data integration, data transformation, and data reduction. After the data is preprocessed, compress the data by using Levenshtein similarity weight method. Once the data is in understandable form, apply compression technique, i.e., LSW. LSW identifies the similar words; then, it performs mapping to the words and the plaintext is converted to string. Later, this string is encrypted by using HME method and uploaded to the cloud. The same reverse process is applied to obtain original file, and the data owner is the only person who decrypts the data because the data owner is having the key values (addition or multiplication). The architecture of the system is shown in Fig. 1. Fully homomorphic encryption enables complex numerical activities to be performed on encoded information without utilizing the first information. For plaintexts P1 and P2 and comparing ciphertexts C1 and C2, a homomorphic encryption conspire licenses the calculation of P1 θ P2 from C1 and C2 by utilizing K1 θ K2. In the event of that: FromEnc(a) and Enc(b), it is conceivable to process Enc(f (a, b)), where f can be: +, X, and by utilizing the private key. Multiplicative homomorphic encryption is computed using when Ek is an encryption algorithm with key k. Dk(Ek(n)Ek(m)) = nm OR Enc(x ⊗ y ) = Enc(x) ⊗ Enc(y)

Fig. 1 System architecture

(1)

Homomorphic Encryption Scheme for Data Security in Cloud Using …

257

Additive homomorphic encryption is achieved using when Dk is a decryption algorithm. Dk(EL(n)EL(m)) = n + m OR Enc(x ⊕ y ) = Enc(x) ⊗ Enc(y)

(2)

The encrypted data is uploaded to the cloud for the mining purpose where the mining process with another encryption is done here. By using this encryption, security for the cloud is increased and outsourced scheme is built securely. Previously used homomorphic encryption is of asymmetric, but here the more efficient symmetric method is used. This technique is used to help the addition and multiplication with comparison of algorithm of homomorphic encryption and decryption which is shown in Tables 1 and 2.

Table 1 Algorithm for encryption and decryption Encryption Decryption Input : public key pk = (q, α, β) and message m Output : Ciphertext c 1: Function encrypt (m) 2: Select k  2, q - 2 3: a = α k mod q 4: b = α m β · k mod q 5: Return c = (a, b) 6: End function

Table 2 Algorithm for key generation

Input: private key k pr = a and ciphertext c = (x, y) Output: message m 1: Function decrypt (c) 2: m ∗ = x a · y mod p 3: Recover m from m ∗ = α m 4: Return m 5: End function

Key generation Input: plaintext Output: public key pk and private key pik 1: Function KeyGen 2: Select a large prime q 3: Select a primitive element α  Z ∗ q 4: Select an integer a  0, q 2 5: = α a 6: Return pk = (p, α, β), pik = a 7: End function

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6 Experimental Results 6.1 Experimental Setup The algorithm CHET has been implemented using Java language JDK 10.1 version, the health data set is considered in the form of GB, and the preprocessing of data is done using Hadoop tool. The data is encrypted by using CHET method for privacy; to set up this, the code designed should be in machine-understandable way. This step is performed by key generator. There is no complication for the key generation when the design is correctly executed. For coding, high-level programming languages are used like Java. The encrypted data will be stored in DriveHQ cloud. First, DriveHQ should create the bucket for the storage, and then, it will load the data to the cloud. The configuration of DriveHQ cloud is 8 GB storage, bandwidth 100 mbps, and Intel Core i7 processor has been used. By this setup, it provides high security to the original data.

6.2 Performance Evaluation We have chosen three different text files which are given as input to RSA, BGV, and CHET. Table 3 shows the encryption time taken by the algorithms. The server is running time on a single machine on DriveHQ cloud for growing database array size over encrypted database. In table, column represents the different algorithms and row represents the plaintext in bits; this table shows the comparison results between existing model and CHET model. Figure 2 shows the encryption time taken by the algorithms. The server is running time on a single machine on DriveHQ cloud for growing database array size over encrypted database. In this graph, y-axis represents the ciphertext in bits and x-axis represents the plaintext in bits; this graph shows the comparison results between existing model and CHET model. Table 4 shows the decryption time taken by the algorithms. The server is running time on a single machine on DriveHQ cloud for growing database array size over decrypted database. In table, column represents the different algorithms and row

Table 3 Encryption time Size of plaintext in bits 10 20 30

RSA

BGV

CHET

10.34 4.87 2.8

6.24 3.76 3.2

2.43 1.53 1.2

Homomorphic Encryption Scheme for Data Security in Cloud Using … Fig. 2 Encryption time

259

Ciphertextinbits

10

RSA BGV CHET

5

0 0

10

20

30

Plaintextinbits Table 4 Decryption time

Size of plaintext in bits

RSA

BGV

CHET

10 20 30

4.92 3.1 2.2

3.82 2 1.1

2.16 0.96 0.85

Ciphertextinbits

Fig. 3 Decryption time RSA BGV CHET

4

2

0 0

10

20

Plaintextinbits

30

represents the plain text in bits; this table shows the comparison results between existing model and CHET model. Figure 3 shows the decryption time taken by the algorithms. The server is running time on a single machine on DriveHQ cloud for growing database array size over decryption database. Y -axis represents the ciphertext in bits, and x-axis represents the plaintext in bits; this graph shows the comparison results between existing model and CHET model.

7 Conclusion and Future Work The existing methods were not combined with compression technique to encrypt the data. The proposed technique CHET reduces the encryption time and fast file transmission by using LSW compression and fully homomorphic techniques. This has

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been evaluated using number of bits in plaintext and ciphertext. The computational complexity of our method is o(logp). This work can be extended to provide key to the authorized cloud users for further computations.

References 1. P. Paillier, Public-key cryptosystems based on composite degree residuosity classes, in International Conference on the Theory and Applications of Cryp- tographic Techniques (Springer, Berlin, 1999), pp. 223–238 2. T. Elgamal, A public key cryptosystem and a signature scheme based on dis-crete logarithms. IEEE Trans. Inf. Theor. 31(4), 469–472 (1985). IEEE 3. E.F. Brickell, T. Yacobi, On privacy homomorphisms, in Workshop on the Theory and Application of of Cryptographic Techniques (Springer, Berlin, 1987), pp. 117–125 4. S. Fau, R. Sirdey, C. Fontaine, C. Aguilar-Melchor, G. Gogniat, Towards practical program execution over fully homomorphic encryption schemes, in Eighth International Conference P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC) (IEEE Press, 2003), pp. 284–290 5. G.V.S. Rao, G. Uma, An efficient secure message transmission in mobile ad hoc networks using enhanced homomorphic encryption scheme. Global J. Comput. Sci. Technol., 543–552 (2013) 6. M.J. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

Efficient Query Clustering Technique and Context Well-Informed Document Clustering Manukonda Sumathi Rani and Geddati China Babu

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hierarchical Document and Query Clustering Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Feedback-Related Query Clustering (FQD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Feedback Content-Related Query Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 FQC Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 FQDC Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Merger of Clustered Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Merger of Clustered Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Enhancement Clustering Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Time Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

262 264 265 265 265 266 266 267 267 267 268 270 271

Abstract Data clustering plays a crucial role in extracting useful information based on the user interest. Traditional query clustering algorithms work on the collection of previously available data from the query stream. As we observe day by day the topic of interests, popularity, query meaning is changing. However, it is quite challenging as the queries are incomplete, ambiguous and short. Existing clustering methods like k-means or DBSCAN cannot assure to perform well in such fully measurable environment. There is high demand for enhancement of algorithms that can indulge in the prediction of characteristics, as the new data is being added to the data mob without implementing a complete re-clustering. So, proposing a new enhancement paradigm for query and context well-informed query document clustering. Even through analysis of user’s click-through log and hierarchical agglomerative clustering, we can M. S. Rani (B) Department of CSE, Keshav Memorial Institute of Technology (KMIT), Hyderabad, India e-mail: [email protected] G. C. Babu Department of MBA, Bandari Srinivas Institute of Technology (BSIT), Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_25

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achieve good results, but, however, it is computationally quite expensive. In order to overcome the problem, the proposed enhancement model attains both the query and document cluster quality. This model in regular intervals updates the new information which is being produced and can be applied in a distributed environment. And also, the suggested paradigm can be related to the outcome of hierarchical query clustering algorithms which produces query clusters and as well as document clusters. This proposed system not only concentrates on achieving accuracy, but also can show a remarkable speedup. Keywords Query context · Click-through log · Document clustering Hierarchical clustering · Query clustering · Web search · Cluster validation Data analysis

1 Introduction As we observe day to day, there is a drastic increase in the World Wide Web. The complete information and every classification for information retrieval made difficult to the user. Navigation is handled by search engine like Google, but inaccurate list of user query construction makes this facilitated point of view as a big challenge. One approach is to make the Web access friendly to the users and to cluster the results in a clearly related topic. This way helps the user to filter the query which is unclear and ambiguous query and accordingly finds out the irrelevant results relating to the topic. Effective query clustering is based on finding similar queries and contextbased document query clustering. Most of the models for grouping alike queries are classified into content-based and feedback-based models. In content-based, the similarity measure is based on the content [1], and in feedback-based, the similarity measure is based on the user’s click-through logs [2]. Research is carried out relating to one of these techniques to find alike queries. In isolation when the above-mentioned two techniques are used can have a certain limitation. As in the content-based, one can use the content of the relating query session by the other users that are saved in the query ledger. Anyways user’s requirement is not captured fully by the query and document data text in content-based context. Ambiguity and session division are the major concern with the above two techniques. Required information is the criteria that can be captured in user’s click-through logs, but click-through log can contain unstructured data which can disturb the quality of Web search results. In order to overcome these problems, both the content-based and feedback-based techniques can be combined to extract the user’s requirement by constructing snippets of information click model which uses the clicked document snippet [3]. Document clustering is used to sort large amounts of documents into small number of groups of related documents that consists of meaningful data in the manner of topics and subtopics that give feasibility to retrieve information easily according to the user’s requirement. When we observe document, clustering suffers from few problems like clustering without considering the list of topics needed, clustering without domain information,

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clustering without considering the number of document classification, etc.; when the written text or query is too large, document clustering retrieves inaccurate results. Generally, queries work on the theme of text which considers the particular subjects and retrieves the keywords, and the concepts from the result search and cluster the query-related documents called as query context well-informed document clustering. The key idea of the query context well-informed document clustering is to cluster small group of documents that are query relevant by classifying the Web content, generalising the query search and by ranking the relevant concepts, etc. Hierarchical clustering provides the data in different levels of abstracting the information and provides the Web search users to interact and find the large collection of documents along with analysing the information. This type of clustering uses both the contentbased and feedback-based techniques for the query-related information retrieval. The clustering algorithms are mostly on feedback or content-based that works on former collected information for query transmission. These techniques are very less effective nowadays as the user’s requirement became outmost based on their interest, importance of topics and query meaning. So, there is a need for enhancement of algorithms for the data being added day by day to the previously collected data. The enhancement model can be capable enough to cluster large amounts of documents based on the context-aware query approach within less time and space. By reducing the noise in click-through logs and produce accurate results is also one of the main concerns to produce the results without manual intervention like session time, number of clusters and parameter tuning. The enhancement model finds the similar queries based on the query term similarity by considering the query sessions [4]. This approach also lacks to capture the user’s requirement as it uses only query content. Another model provides the cluster queries by grouping the relevant queries [5]. Query relevance measure is mainly based on the same clicked URLs and the relevant query terms. Some times we may see queries are short and ambiguous which are not precise, that users may try to retrieve the data from corpus. By considering the same query, different users may click different URLs except for the popular hot topics, there may be a chance of clicking same URLs. Query similarity measure is done by cosine similarity by considering the frequently clicked URLs based on the query vectors [6]. This approach also has drawbacks as mentioned earlier. Enhancement hierarchical clustering algorithms can streamline the new information successfully to the Web content search and find the exact cluster to update the new object. In this paper, the proposed method and its variant efficiently use the hierarchical clustering algorithms by considering both the feedback-based and content feedback-based hierarchical clustering algorithms [3]. The proposed models are applied to query concept (FQC) which is based on feedback content, query document (FQD) based on feedback and query document concept (FQDC) based on feedback content-based. An efficient hierarchical model and its variant for clustering query and documents are proposed. It updates the new information and provides query and document cluster at different time intervals that are close to the relevant clusters applied by re-clustering on the whole or the complete set of data. The time complexity of the

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proposed model including its variant is considered and also applied to hierarchical clustering algorithms.

2 Existing Techniques Query clustering is a constructive method to retrieve relevant queries [1]. String coinciding features are used to find the relevant queries. Content-based approach for finding relevant queries has been considered [7]. Content browsed by user’s historical liking is recorded to derive the current queries semantic analysis [8]. Clickthrough data is clustered frequently done by combining two most similar queries followed by combining two similar URLs that use agglomerative clustering and bipartite graph, but this method completely ignores the content [9]. Another method which is addressed to find the list of relevant queries by mining interconnection rules from the logs of previously submitted queries to the search engine has been considered [10]. Query similarity finding using click-through data was addressed [9]. However, the preliminary concentration is on document ranking. Relevant queries are also retrieved by 1. Analysing users’ time interval. 2. Deriving query-flow graphs [11]. 3. Classifying types of query reformulation. Click-through-based clustering is implemented to find particular time interval of the user-related query information and accordingly with the given context awareness helps to find more similar queries, but they did not analyse it [4]. Document clustering methods have been examined by many scientists. Document clustering techniques that are familiar have been implemented with the results of experimental study with the usage of hierarchical clustering algorithm and k-means clustering [12]. Finally, we can say that there exist some more issues which are to be concentrated in document clustering. Query particular clustering is more effective when compared to the traditional clustering. Once the query set keywords are retrieved from the documents, those keywords can be utilised to group the relevant documents. An enhancement clustering process is presented by introducing a tree structure called DC tree [13]. Various advanced methods have been proposed focusing on noise tolerance to achieve efficiency and produce accurate results. But this is not efficient as the centroid vector computation is more when compared to the original algorithm computation time. An incremental query clustering approach is provided that specifically gives phrases to a user’s query [4]. These phrases are extracted from related query time intervals rather than considering from the retrieved documents. The design introduced for enhancement query clustering provides a method that can run on online and as well as offline will predict the identical data based on the user queries. Relating

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to that, rules and cover graph has been presented in the enhancement query clustering approach. However, there are specific limitations in query proposed algorithms.

3 Hierarchical Document and Query Clustering Algorithms The hierarchical clustering algorithms based on the query are classified into feedbackrelated and feedback content-related clustering algorithms. We shall see in detail about feedback-related query document (FQD), feedback content-related query concept (FQC) and feedback content-related query document concept (FQDC).

3.1 Feedback-Related Query Clustering (FQD) In this feedback-related query, user’s click-through logs are considered to retrieve the similar queries. In this algorithm, a bipartite graph is constructed on a query document that has queries on the one way and the clicked documents on the other way in uniquely precise manner. In this, each object or vertex is initially considered as one single unique cluster. In order to obtain relevant queries and relevant documents, a hierarchical agglomerative clustering algorithm is utilised.   n(xi ) ∩ n(x j ) (3.1) Sim  (xi , x j )  |n(xi ) ∪ n(xi )| The first two relevant queries and relevant documents are combined based on their same relevance function. This combining process is repeated until all the clusters have relevant greater threshold values with the respective parameters. This algorithm utilises only query document links present in the constructed bipartite graph; i.e., keywords are not taken into consideration in both the queries and documents as the similarity function matters a lot. The similarity measure between the two query clusters qi and qj and document clusters d i and d j are as follows: where Xi refers to qi or d i and n(X) represents the count of links related to X.

3.2 Feedback Content-Related Query Clustering Feedback content-related query clustering algorithm uses both the user click-through action and content to interpret the user’s query purpose. This algorithm first selects the features or concepts, and next, it clusters the conceptually related queries. FQC and FQDC algorithms as discussed earlier are based on the feedback content-based query

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clustering which considers the constructed snippet instead of the whole documents to retrieve efficiency. In order to get the required quality of the documents, top priority snippets are considered from the query and filtered. First and foremost, in primary filtration phase works on the stemming, stop word removal and then nouns, noun phrases and verbs are taken into consideration for predicting the quality of the content of documents. Porter stemming algorithm can be used for information retrieval as a open source. Second, filtration is done by query and feature relevance that is dependent on the terms extracted in the primary filtration and again finally filters the item sets that are relevant because in the primary filtration there may be a chance of getting irrelevant terms to the given user query.

3.3 FQC Clustering Algorithm Both FQC and FQD algorithms are similar, but in FQC there are concepts instead of documents or URLs in bipartite graph at one side. Hierarchical agglomerative algorithm is then implemented to retrieve the relevant queries and relevant concepts. Similarity function is same in both the algorithms to retrieve the relevant queries and relevant concepts. As a resultant, query clusters and concept document clusters are formed which in turn give the query and concept-based documents. As discussed above, the union of the two relevant query clusters and two relevant document clusters is considerably dependent on the relative function which is repeated until the related threshold values are retrieved.

3.4 FQDC Clustering Algorithm In this algorithm, the first bipartite graph is constructed wherein it consists of unique queries at one position of the graph, on the other position, it consists of concepts, and in the middle, user’s clicked documents are available. This graph can also decouple the relation between concepts and queries. It is capable enough to store complete information about the concepts retrieved from the documents, particular documents clicked by the user for a particular query, document numbers clicked for a query and documents relating to the sharing of the concepts between the different queries, and some more features are maintained by this algorithm. Hierarchical agglomerative clustering algorithm is implemented to retrieve the relevant query, concepts and document relevant clusters. Two different types of merging are done between the relevant queries and relative concepts until the relative threshold is achieved through repetition. In this algorithm, query and concept relevance are considered by finding frequent data set items by calculating the support value S as follows: m  i ti (3.2) s(q, ci )  N

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where t i is the count of terms in the content or concept ci .

3.5 Merger of Clustered Concepts The relevance between the two concept clusters is considered by taking the ratio between the number of clicked documents containing the concepts in both the clusters and the documents clicked, which contains at least one concept.

3.6 Merger of Clustered Queries The similarity measure is compared between the two queries or between two cluster queries that can be obtained by measuring the concepts retrieved from the documents clicked that are related to the user’s query search or query clusters. The relevance between query and concept is evaluated by the following [14]: rel(q, ci )

m i N

(3.3)

where m i is the documents clicked that contain the relevant concept ci and N  is the documents clicked count for the relevant query. If both the concept sets for the relevant query are identical, then the two query clusters are considered as equivalent. In order to retrieve the accurate search results, both the FQC and FQDC algorithms retrieve document clusters further with query and concept clusters, as the document query clusters contain both the content and user’s feedback.

4 Enhancement Clustering Techniques The proposed enhancement clustering technique along with its variant is considered. The existing hierarchical clustering algorithms are considered in this proposed model, which does not give any constraints. • The clustering algorithm is implemented on the collected data set at a time t i and retrieves the relevant clusters. • The new set of data is added at time t i + 1 after t i , and this is separately grouped by the clustering algorithm. • The two sets of data are added and then clustered again by using the same clustering algorithm. As a result, set clusters are retrieved at the time t i + 1 . • In similar way, next data set of data also clustered and the above steps are repeated until the threshold cluster value is retrieved for the relevant cluster formations for the newly added data objects.

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Fig. 1 System model

Variation is considered in the above step where the new data set is added to the existing data set, and hence, the proposed technique comes up with two submodels; i.e., one is enhancement model with the cluster data (EMCD) which first clusters the data, and another one is enhancement model with raw data (EMRD) which considers individual query or concepts as individual unit clusters. The presentation of the recommended models is measured with the existing models that can cluster entire data set at the different time intervals. The proposed models are also considered in the distributed environment that individually clusters the data that are relevant. These clusters are merged with the new set of data that is clustered, and repetition of the merging is done until the global data set according to the relevant threshold is achieved. EMCD is more efficient according to the time stamps considered when compared with the existing static model, and this proposed model takes the average processing time. EMRD also takes the average processing time, but it is slightly more than EMCD processing time because EMRD considers the individual unit clusters based on the query, concept, or document in Figs. 1 and 2. Hence, the enhanced model results of EMCD and EMRD are very much similar to the existing constant model. It lessens the clustering procedure time for the individual data sets, and merging the two clustered data sets is very efficient instead of clustering the whole data set again in Fig. 3.

5 Time Complexity Time complexity of the two enhancement models with FQDC clustering algorithm is same as we compare to the other two algorithms FQD and FQC. The time complexity of agglomerative algorithm for clustering the data is O(n)2 , n being the number of points [15]. Let qi and ci be the number of queries and number of contents in ith bundle data set. Cost of processing ith bundle of data set alone is

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Fig. 2 Clustering process

Fig. 3 Query log with search results data

  O qi2 + ci2

(5.1)

For enhanced proposal models, let n qi and n ci be the number of queries and contents originated by enhanced models. Firstly, for bundle set of clusters, cost is   O q12 + c12 Appending second bundle set of clusters to the first is

(5.2)

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O(1) So total cost of clustering both the first and second after appended is  2  2  O n q1 +q2 + n c2 +c2

(5.3)

(5.4)

After ith bundle data set total cost is O((q12 + q22 + · · · + qi2 ) + (c12 + c22 + · · · + ci2 ))

(5.5)

For Original model Cost of processing the first bundle set of data is   O qi2 + ci2

(5.6)

Processing cost for the first and second bundle set of data is   O (q1 + q2 )2 + (c1 + c2 )2

(5.7)

After ith bundle data set, total cost is   O (q1 + q2 + · · · + qi )2 + (c1 + c2 + · · · + ci )2

(5.8)

When we compare the original model of Eq. (5.8) to Eq. (5.5), original model is much greater than the enhanced model of the ith iteration.

6 Conclusion In this paper the design and its transformation for query and concept clustering is considered, when the proposed model is applied to the existing hierarchical clustering algorithm then it achieves efficient and accurate results for both query and document clustering. It also takes less time when compared with the static model. Cluster updating means adding and then clustering, i.e., added data considers negligible time when observed to the constant cluster algorithm technique. The elevated accomplishment is achieved when the data has enough click information, and the clustering algorithm is powerful when it produces the relevant alike clusters. Query context-related document clustering clusters small group of relevant documents according to given query and by differentiating the unrelated documents from the search result set. This achieves high accuracy.

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References 1. J. Wen, J. Nie, H. Zhang, Clustering user queries of a search engine, in WWW: Proceedings of the 10th International World Wide Web Conference (ACM, Hong Kong, 1–5 May 2001), pp. 162–168 2. D. Beeferman, A. Berger, Agglomerative clustering of a search engine query log, in ACM SIGKDD: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA USA, 20–23 Aug 2000, pp. 407–416 3. H. Chien-Kang, C. Lee-Feng, O. Yen-Jen, Clustering similar query sessions toward interactive web search, in Proceedings of Research on Computational Linguistics Conference XIII (2000) 4. H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, H. Li, Context-aware query suggestion by mining click-through and session data, in KDD: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, Las Vegas, NV, USA, 24–27 Aug 2010), pp. 875–883 5. K.W-T. Leung, W. Ng, D.L. Lee, Personalized concept-based clustering of search engine queries. IEEE Trans. Knowl. Data Eng. 20(11), 1505–1518 6. G. Ranjna, D. Neelam, A.K. Sharma, A. Neha, Query based duplicate data detection on WWW. Int. J. Comput. Sci. Eng. (IJCSE) 2(4), 1395–1400 7. R. Zaiane, A. Strilets, Finding similar queries to satisfy searches based on query traces, in EWIS: Proceedings of the International Workshop on Efficient Web-based Information Systems, Montpellier, France, 2 Sept 2002, pp. 207–216 8. R. Baeza-Yates, C. Hurtado, M. Mendoza, Query recommendation using query logs in search engines, in EDBT Workshop on Current Trends in Database Technology (Springer, Berlin, Heidelberg, 2004), pp. 588–596 9. G. Dupret, M. Mendoza, Recommending better queries from click-through data, in SPIRE: Proceedings of the 12th International Symposium on String Processing and Information Retrieval (Springer, Buenos Aires, Argentina, 2–4 Nov 2005), pp. 41–44 10. S.S. Kumar, S. Ugrasen, An efficient semantic clustering of URLs for web page recommendation. Int. J. Data Anal. Tech. Strat. (IJDATS) 5(4), 339–358 11. P. Boldi, F. Bonchi, C. Castillo, D. Donato, S. Vigna, Query suggestions using query-flow graphs, in WSCD: Proceedings of the Workshop on Web Search Click Data (ACM, New York, USA, 9 Feb 2009), pp. 56–63 12. M. Steinbach, G. Karypis, V. Kumar, A comparison of document clustering techniques, in KDD Workshop on Text Mining, Boston, MA, pp. 109–111 (2000) 13. W-C. Wong, A.W. Fu, Incremental document clustering for web page classification, in International Conference on Information Society in the 21st Century: Emerging Technologies and New Challenges, Fukushima, Japan [online] (2000). http://citeseer.nj.nec.com/article/ wong01incremental.html. Accessed July 2011 14. B. Daniele, F. Ophir, M. Franco, P. Raffaele, S. Fabrizio, Incremental algorithms for effective and efficient query recommendation, in SPIRE: Proceedings of the 17th International Symposium on String Processing and Information Retrieval, (Springer, Los Cabos, Mexico, 11–13 Oct 2010), pp. 13–24 15. P. Goyal, N. Mehala, Concept based query recommendation, in AusDM: Proceedings of the Ninth Australasian Data Mining Conference, vol. 121 (ACM, Ballarat, Australia, 1–2 Dec 2011), pp. 69–78

Motif Shape Primitives on Fibonacci Weighted Neighborhood Pattern for Age Classification P. Chandra Sekhar Reddy, P. Vara Prasad Rao, P. Kiran Kumar Reddy and M. Sridhar

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Age classification from facial images is increasingly receiving attention in age-based computer vision applications. To address this classification problem, the present paper proposes a new method of age grouping with motif shape primitives on the fibonacci weighted neighborhood pattern. FWNP on the image is computed, and motif shape primitives are evaluated on this FWNP image. These shape primitives are used for age variation of different persons. This method is investigated on facial image datasets of FG-NET database. The experimental study has shown the good performance of our proposed method against the other existing methods. Keywords Age classification · FWNP · Motif shape primitives

P. Chandra Sekhar Reddy (B) · P. Vara Prasad Rao · M. Sridhar CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India e-mail: [email protected] P. Vara Prasad Rao e-mail: [email protected] M. Sridhar e-mail: [email protected] P. Kiran Kumar Reddy CSE Department, Malla Reddy Institute of Engineering and Technology, Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_26

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1 Introduction Age classification is an active research task in image processing domain. In the past two decades, age grouping of persons with facial features is an area in which many researchers are focusing due to its potential applications like electronic-CRM, human and computer interaction, and video surveillance monitoring. The age estimation with textural properties [1, 2], fusion of features on face primary parts and wrinkle [3, 4]. An extraction of skin feature for automatic skin aging estimation [5]. Age classification methods are classified into three categories [6]. They are an anthropometric model [3, 7], KAGES [8], and age regression [9–12] categories. In anthropometric model, distance measures of facial parts and wrinkle analysis are used. The active appearance model (AAM) is used in age regression methods to extract facial features related to shape and appearance. Recently, facial emotion algorithms based on spectral features in ECG signals [13], LBP models [14] are developed. Recently, various methods for age classification and age grouping are developed by Vijaya Kumar and Chandra Sekhar Reddy [15–17]. To address this research in age grouping, the present paper considers two groups, child and adult, for age classification. The present paper evaluates motif shape primitives as features on FWNP of facial images. This paper is organized as: In Sect. 2, method is described, Sect. 3 covers results and discussion, and conclusions are drawn in Sect. 4.

2 Methodology In this proposed method, the color image is converted to gray image, on this image fibonacci weighted neighborhood pattern (FWNP) is computed, and then motif shape parameters are evaluated for age classification as illustrated in Fig. 1. Step 1: Conversion to Gray-level Image In conversion process, RGB colors are mapped to 256 gray levels to gray image. Step 2: Fibonacci Weighted Neighborhood Pattern The FWNP is computed on the image for obtaining local neighborhood information of pixels. The process of computation of this pattern with an example is illustrated in Fig. 2.

Color Image

Conversion to Gray level image (Step-1)

Find FWNP on gray image

Calculate Motif Shape Primitives on FWNP (Step-3)

(Step-2)

Fig. 1 Age classification using motif shape patterns on FWNP images

Age classifier

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Consider 3 × 3 block of pixels with nine elements, PI  {PIc , PI0 , PI1 … PI7 }, where PIc is the intensity value of the central pixel and PIk (0 ≤ k ≤ 7) represent the intensity values of surrounding eight pixels. Each 3 × 3 image block can be represented by {0, 1} values using d k , which is computed from Eq. (1).  1 PIk − PIc ≥ 0 where 0 ≤ k ≤ 7 (1) di  0 PIk − PIc < 0 For each 3 × 3 neighborhood, FWNP is deduced from Eq. (2). FWNP 

8 

di × fibi

(2)

i1

where fib1, 2, 3, … 8  {1, 1, 2, 3, 5, 8, 13, 21}. Each pixel in the image generates FWNP, and this can represent local pixel information around a pixel by an integer code in between 0 and 54. The range of values for encoding local information is reduced to 20 percent to local binary pattern (LBP) values [18] ranging 0–255. Step 3: Motif Shape Primitives The motif shape primitives are defined over a 2 × 2 grid, each depicting a distinct sequence of pixels starting from the top left corner as shown in Fig. 3 denoted as Z, N, U, C, gamma, and alpha, respectively [19]. The present paper considers motif shape primitives on FWNP of the facial image. The FWNP image is divided into 2 × 2 sub-image blocks. Each block is then replaced by a scan which traverses block starting from top left corner in a direction with minimal variation in the incremental difference of FWNP values. The frequency occurrences of all these motif shape primitives are evaluated on FWNP of the image with a 2 × 2 block from left to right and top to bottom in non-overlapped fashion. The process of finding motif shape primitives on FWNP is shown in Fig. 4.

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Fig. 3 Motif shape patterns Z, N, U, C, gamma, and alpha

3 Results and Discussion The proposed method is investigated on FG-NET database images. Sample images are given in Fig. 5. In this method, images are classified into two groups as a child (up to 18 years) and an adult (above 18 years) based on frequency occurrences of these shape primitives. These features are evaluated on the considered database of facial images, and these computed features are listed in Table 1. The frequency occurrences of Z, N, C, U, gamma, and alpha are represented with FZ, FN, FC, FU, FGAMMA, and FALPHA, respectively. From the results, it is observed that frequency occurrences of motif shape primitives are having a significant role in classifying facial images of different persons. From this observation, Algorithm 1 is proposed. This algorithm considers all shape primitives except FU in classifying images. This algorithm has given 97% correct classification rate for age groups into adult and child. Algorithm 1: Age Classification using Frequency Occurrences of Motif Shape Primitives on FWNP Image Let FZ, FN, FC, FU, FGAMMA, and FALPHA be frequency occurrences of Z, N, C, U, gamma, and alpha motif shape primitives. Begin if ((FZ < 1500) & (FN > 1300) & (FC < 1900) & (FGAMMA > 940) & (FALPHA > 8000))

Table 1 Frequency occurrences of motif shape primitives on FWNP images Image FZ FN FC FU FGAMMA 002A36 037A19 008A35 011A40 013A25 011A02 002A04 002A05 002A07 002A15

2315 2860 2344 2493 2420 1112 1003 1495 1115 1048

1137 975 1083 1281 1262 1505 1727 1373 1608 1558

2763 3200 2786 2910 2688 1329 1237 1814 1395 1300

1635 1381 1525 1806 1703 1667 1944 1766 1845 1813

839 468 794 712 713 1148 971 948 1079 1057

FALPHA 741 709 746 744 722 953 808 970 887 871

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(b) Motif Shape Primitives on 2x2 grid of FWNP Fig. 4 Computation of motif shape primitives on FWNP image

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Fig. 5 Sample images from FG-NET database

write (“Child Image”) else write (“Adult Image”) End The algorithm classified the facial images of the considered database into two groups with 97% correct classification rate. This method in classification of FGNET database is compared with the existing methods for age classification. Age classification with shape features on lbp based text on by P. Chandra Sekhar Reddy et al. [20]. Child and adulthood classification with geometrical features by Chandra Mohan et al. [21] and other age classification methods. The classification rates of our

Table 2 Comparison of age classifier methods S. No. Authors Name of the method

% of classification rate Motif shape primitives on FWNP 97

Category of age classification

1

Proposed method

Child and adulthood Child and adulthood

2

Chandra Sekhar Reddy et al. [20]

Shape features on IT-LBP

95

4

Chandra Mohan et al. [21]

Child and adulthood classifications based on geometrical features

94.5

Child and adulthood

3

Kwon and Lobo [3]

Age classification from facial images

78

4

Kanno [22]

Age classification using mosaic features and KL features

80

Babies, adults, and senior adults Young male age groups

5

Horng et al. [4]

Geometric features and wrinkle features with neural networks

90.52

Four age groups

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method with other methods are given in Table 2, and it has shown that the proposed scheme outperforms with other methods.

4 Conclusion In this paper, we developed a new pattern FWNP for representing local neighborhoods. Motif shape primitives are evaluated on FWNP image. The motif shape primitives are used as features for age grouping. Algorithm 1 classified images with a good classification rate compared to the other existing methods. This method is very simple, efficient, and more accurate for age grouping. The FWNP with other shape parameters and statistical properties can be extended in future work.

References 1. R. Iga, K. Izumi, H. Hayashi, G. Fukano, T. Ohtani, A gender and age estimation system from face images, in SICE Annual Conference in Fukui, pp. 202–209 (2003) 2. A. Lanitis, On the significance of different facial parts for automatic age estimation, in 14th International Conference on Digital Signal Processing, vol. 2, pp. 1027–1030 (2002) 3. Y. Kwon, N. Lobo, Age classification from facial images. Comput. Vis. Image Underst. 74(1), 1–2 (1999) 4. W. Horng, C. Lee, C. Chen, Classification of age groups based on facial features. Tamkang J. Sci. Eng. 4(3), 183–192 (2001) 5. Y.-H. Choi, K. Kim, E. Hwang, Classification based skin aging analysis, in International AsiaPacific Web Conference, pp. 347–349 (2010) 6. F. Yun, X. Ye, T.S. Huang, Estimating human age by manifold analysis of face pictures and regression on aging features, in Proceeding of 2007 IEEE International Conference on Multimedia and Expo, pp. 1383–1386 (2007) 7. R. Nathan, R. Chellappa, Modeling age progression in young faces, in Proceeding of IEEE Conference on CVPR, vol. 1, pp. 387–394 (2006) 8. X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, H. Dai, Learning from facial aging patterns for automatic age estimation, in Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 307–316 (2006) 9. A. Lanitis, Drag nova, and Christodoulou: comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B 34(l), 621–628 (2004) 10. Y. Fu, T.S. Huang, Human age estimation with regression on the discriminative aging manifold. IEEE Trans. Multimedia 10(4), 578–584 (2008) 11. A. Lanitis, C.J. Taylor, T.F. Cootes, Towards automatic simulation of aging effects on facial images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002) 12. S. Yan, T.S. Wang, T.S, Huang, X. Tang, Ranking with uncertain labels, in IEEE International Conference on Multimedia and Expo, pp. 96–99 (2007) 13. M. Hashemian, H. Pourghassem, Facial emotion processing in autism spectrum disorder based on spectral features of EEG signals. Int. J. Imaging Rob. 11(3), 68–80 (2013) 14. J. Ylioinas, A. Hadid, M. Pietikäinen, Age classification in unconstrained conditions using LBP variants, in Proceeding of ICPR, pp. 1257–1260 (2012) 15. P. Chandra Sekhar Reddy, Age classification with motif shape patterns on local binary pattern. Int. J. Comput. Trends Technol 39(3), 134–138 (2016)

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16. P. Chandra Sekhar Reddy, Age classification with shape patterns derived from central pixel flooding matrix (CPFM) on facial images. Int. J. Latest Trends Eng. Technol. 7(4), 205–211 (2016) 17. P. Chandra Sekhar Reddy, B. Eswara Reddy V. Vijaya Kumar, New method for classification of age groups based on texture shape features. Int. J. Imaging Rob. (IJIR) 15(1), 19–30 (2015) 18. T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996) 19. N. Jhanwar, S. Chaudhuri, G. Seetharaman, B. Zavidovique, Content-based image retrieval using motif co-occurrence matrix. Image Vision Comput. 22, 1211–1220 (2004) 20. P. ChandraSekhar Reddy, B. Eswara Reddy, V. VijayaKumar, Texton based shape features on local binary pattern for age classification. Int. J. Image Graph. Signal Process. (IJIGSP) 4(7), 54–60 (2012) 21. M. Chandra Mohan, V. Vijaya Kumar, B. Sujatha, Classification of child and adult based on geometric features of face using linear wavelets. Int. J. Signal Image Process. 1(3), 211–220 (2010) 22. T. Kanno, Classification of age group based on facial images of young males by using neural networks. IEICE Trans. Inf. Syst. 84(8) (2001)

A Novel Virtual Tunneling Protocol for Underwater Wireless Sensor Networks A. M. Viswa Bharathy and V. Chandrasekar

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Virtual Tunneling Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Selection of Relay Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Tunneling of Relay Nodes to Base Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Data Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 VTP Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The wireless sensor networks are prime components in automation and help in accelerating the technology to the next level. The sensor nodes are deployed in adverse conditions to monitor and collect critical data around the environment and relay the same to the server sensor node. The underwater wireless sensor networks (UWSNs) are prone to high danger and are designed to withstand extreme climate conditions. The UWSNs performance is evaluated by the metrics low energy consumption, high packet delivery rate (PDR), low jitter, and shortest path in transmitting the sensed data to the server sensor node (SSN). In this paper, we have proposed a virtual tunneling protocol (VTP) to increase the aforementioned factors associated with the underwater wireless sensor networks. The simulation yielded good results, and the same has been recorded here. Keywords Virtual tunneling protocol · Wireless sensor networks · Underwater Data transmission

A. M. Viswa Bharathy Jyothishmathi Institute of Technology and Science, Karimnagar, Telangana, India e-mail: [email protected] V. Chandrasekar (B) Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_27

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1 Introduction The integrity among the sensor nodes is important in aligning the network. The power consumption of the sensor nodes can be reduced with the help of node synchronization, Gowrishankar et al. [1]. The clustering technique keeps the sensor nodes linked to one another, reduces the routing overhead, and also helps in increasing the average life of the network, Sen [2]. When all the sensor nodes are synchronized, aligned, and clustered in the network, then such network is safe and secure with minimal infusion of attack packets. This external injection of attack packets is the main cause of traffic congestion, Khare and Ali [3]. The key points to be noted in underwater wireless sensor network setup are power consumption, self-configuration, reliability, and channel utilization, Rasal et al. [4]. The key issues associated with maintaining the topology of the sensor networks are power control and power management, Sengupta and Roy [5]. There are different types of contacts established between sensor nodes and server sensor node for transmitting the sensed data. They are scheduled and unscheduled contact, and unscheduled is divided into predicted and opportunistic contacts. The metrics’ average end-to-end delay, packet delivery ratio, and energy consumption are critical parameters in evaluating the performance of the underwater wireless sensor networks. The scheduled schemes show good performance for UWSNs with a higher cost for base station planning. Opportunistic and unscheduled contacts are used in partially known and unknown environments, respectively, Cho et al. [6]. The tree topology is commonly used to set the shortest path to each sink node and for dynamic balancing of the load among sink nodes, Le et al. [7]. The two major energy-consuming operations are sending and receiving of messages, Wu et al. [8]. Energy consumption is the primary cause of the performance of the sensor networks. The topology control techniques must lower the energy exhaustion rate of the sensor nodes, Luo et al. [9]. The load between mesh routers could be shared to distribute the load evenly in a mesh topology, Riggio et al. [10]. The clustering of the nodes within the grid and dynamic selection of cluster head reduces the energy dissipation and extends the lifetime of the sensor nodes, Wei et al. [11]. The preservation of topology and energy consumption of the UWSNs has been highlighted in the survey by Sharma et al. [12]. The performance of various energy-efficient and cluster-based routing schemes in the wireless sensor networks is studied, Dehghani et al. [13].

2 Related Work Taherian et al. [14] proposed an optimal and secured routing protocol for the wireless sensor networks using the particle swarm intelligence (PSI) algorithm. The main focus on the work was to find a safe, efficient, and secure routing scheme for the wireless sensor networks using the clustering algorithm and PSO. Barekatain et al. [15] proposed a new combination of improved genetic algorithm (IGA) and K-means

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algorithm. The proposed work claimed to improve the energy consumption of the sensor nodes and thereby increased the lifetime of the sensor network. Nighot and Ghatol [16] proposed a GPS-based distributed communication protocol (GDCP) for static wireless sensor networks (SWSNs). In this method, a neighboring table (NT) is maintained by the sensor nodes. This table is used to store data such as location, distance to the neighbor node, and distance to the server sensor node. The neighbor node to become the next hop node should satisfy two parameters, namely high remaining energy and lowest distance to the server sensor node. Rakhee and Srinivas [17] demonstrated a new technique by combining ant colony optimization (ACO) and breadth-first search (BFS). In this method, the choice of cluster head is made level by level and on rotation basis to make sure that the connectivity is not lost between nodes. Amutha et al. [18] proposed an ECOSENSE protocol for the wireless sensor networks. It was claimed to be the energy-efficient routing protocol. The work compared S-MAC with ECOSENSE and proved to be fruitful in terms of latency and energy saving. S-MAC operates on duty cycle, and traffic-adaptive medium access (TRAMA) operates on load balancing. ECOSENSE used both duty cycle and load balancing. Jaibheem et al. [19] came up with the new routing protocol for underwater wireless sensor networks (UWSNs) using the multilayered routing protocol (MRP) strategy. Many existing routing protocols make use of the sensor node localization. This multilayered routing protocol (MRP) utilized super-sensor nodes to eliminate the necessity of localization. Ahmed et al. [20] proposed a routing protocol for improving the performance of the network stability and packet delivery ratio in UWSNs. Khan et al. [21] experimented the idea of sinks moves toward the densest region of the network in terms of the number of sensor nodes. Ilyas et al. [22] proposed an AUV-aided efficient data-gathering (AEDG) routing protocol. In this method, an autonomous underwater vehicle (AUV) collected the sensed data from the sensor nodes. The shortest path tree (SPT) algorithm was used to save the energy of the sensor nodes. Ilyas et al. [23] proposed another data-gathering and routing protocol. Over the years, autonomous vehicles are used under water to collect the data from the sensor nodes. Joshi et al. [24] implemented a protocol stack for three-dimensional wireless sensor networks (WSNs). This paper presented terrestrial three-dimensional network architecture and a protocol stack for static sensor nodes placed at different heights. Optimization techniques help a lot in fine tuning the optimal path by segregating the nodes based on a classification algorithm [25–27].

3 The Virtual Tunneling Protocol The virtual tunneling protocol works in three phases, namely 1. Selection of relay nodes 2. Tunneling of relay nodes to base station 3. Data transmission. The detailed description of all the phases is given below in the following sections.

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3.1 Selection of Relay Nodes This is the most crucial phase of the VT protocol. If this phase goes well, then everything is done well with nothing to be wrong. The relay nodes are nodes which help in transferring the data packets to the server sensor node from the client sensor node. These intermediate nodes are called the relay nodes. The relay nodes are selected based on the following criteria (a) All groups in the path between the source and base station are selected. (b) A group is avoided, unless if it is really unnecessary making the path too long. (c) Border nodes are given high priority for being the relay nodes, because the connectivity between these nodes to the border nodes in adjacent group is high. (d) At the maximum, only two nodes are selected from each border in a group: one being the entry and another being the exit. Rarely, more than two nodes are selected from a group. (e) Nodes which have most recently transmitted the sensed data are given more preference. If at any case the connection during data transmission is lost, the node which holds the data at the time of connection is lost is responsible for establishing a new tunnel from itself to the base station. This is the reason why the nodes with the recent transmission are selected. So considering all these conditions, relay nodes are selected.

3.2 Tunneling of Relay Nodes to Base Station A strong connection is established between these relay nodes forming a tunnellike structure from the client to the server sensor nodes. This tunnel is used to continuously transfer the collected data from the source sensor node to the server sensor node. It acts like a pipe carrying water. The connection is established just like the TCP three-way handshake.

3.3 Data Transmission In this phase, the data to be transferred from the node to the server sensor node is sent continuously without any interruption. Figure 1a depicts the three phases of VTP in detail. The nodes in red inside the clustered group are the potential candidates for relay nodes, as they are the border nodes which make connectivity among the group easier. Figure 1b clearly shows the three-way handshake connection formation between nodes, and Fig. 1c depicts the creation of virtual tunnel between the nodes.

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Fig. 1 a Selected relay nodes in group. b Establishing connection between relay nodes using a three-way handshake 1. c Virtual tunnel between relay nodes

3.4 VTP Packet Format The virtual tunneling protocol has a defined packet format for communicating between the nodes in the underwater wireless sensor networks (UWSNs). This packet format is used for all the three phases of VTP for transmission of data from the source sensor node to the server sensor node. Figure 2 represents the packet format, and the explanation of the same is given following the format. The VTP version denotes the current version of the VTP that is being utilized. The URG bit is set to 1 if the data being sent is urgent and set to 0 if not urgent. The source address denotes the address of the sensor node which originates the packet. It is usually the node which starts the transmission of the data to the server sensor node. Destination address is generally the base station. 0

4 VTP Version

8 URG

16 Source Address

No. Relay_nodes

Fig. 2 Packet format of VTP

24 Intermediate Destination Address

Seq_tunnel Reserved TTL Relay_nodes …….. …….. Data Padding Tail

32 Destination Address No. of bytes

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Intermediate destination address is the next hop address of the sensor node to which the packet should be forwarded. Number of Relay_nodes denotes the number of relay nodes in the path from the source to the destination. The Seq_tunnel denotes the sequence number of the tunnel being established. Number of bytes denotes the total number of bytes in the data section. Reserved is for future use. TTL is a timer usually set to some fixed value after which expiry the packet is dropped by the nodes. The list of relay nodes with its complete details such as address, ID, and cluster ID is given in this section. The actual data that is being sent from the client sensor node to the server sensor node is denoted by the data. Padding denotes the character used to pad between the relay node list section and the actual node. This padding is usually done to separate the section more visibly. Usually, the character * is used for padding. Tail denotes the end of the VTP packet.

4 Metrics Tot. no. of data packets  Time taken No. of packets received Packet delivery ratio   No. of packets send Throughput 

(1) (2)

Jitter is the difference in time between the packets received at receiver with respect to the sender. If Si is the time in which packet i was sent by the sender and Ri is the time received by the receiver, jitter sample J i is given by Ji  |(Ri+1 − Ri ) − (Si+1 − Si )|

(3)

Energy per bit is the total energy dissipated to send a bit from client to server sensor node.

5 Simulation Results In this section, we present the simulation results of the virtual tunneling protocol and compare it with the existing routing protocols in Table 1 and Fig. 3. The experimental setup in MATLAB is done with 100 sensor nodes. Figure 4 represents graphical representation.

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Table 1 Comparison of VTP with other routing protocols S. no. Model PDR Jitter 1 2 3 4

VTP MRP CARP MURAO

96 89 91 87

RL

1.43 1.59 1.76 2.31

39 51 45 63

PDR—packet delivery rate (packets per second); jitter is in milliseconds; RL—route length (number of hops) Table 2 Number of nodes from source to BS S. no. Method 1 2 3 4

VTP CARP MRP MURAO

Average no. of nodes from source to BS 15 19 23 31

Fig. 3 Graphical representation of Table 1

Fig. 4 Graphical representation of Table 2

6 Conclusion The virtual tunneling protocol has showed good results and is visible through the simulation results. The protocol is tested for PDR, jitter, route length and yielded

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good results. The future works include the measuring of the performance of the protocol with more parameters.

References 1. S. Gowrishankar, T.G. Basavaraju, D.H. Manjaiah, S.K. Sarkar, Issues in wireless sensor networks, in Proceedings of the World Congress on Engineering, vol. I, London, U.K (2008) 2. J. Sen, A survey on wireless sensor network security. Int. J. Commun. Netw. Inf. Secur. (IJCNIS) 1(2), 55–78 (2009) 3. P. Khare, S. Ali, Survey of wireless sensor network vulnerabilities and its solution. Int. J. Recent Dev. Eng. Technol. 2(6), 84–88 (2014) 4. R.J. Rasal, S.V. Gumaste, G.S. Deokate, Survey on different routing issues and design challenges in WSN. Int. J. Sci. Eng. Appl. Sci. (IJSEAS) 1(4), 189–192 (2015) 5. D. Sengupta, A. Roy, A Literature Survey of Topology Control and Its Related Issues in Wireless Sensor Networks, in International Journal of Information Technology and Computer Science (IJITCS), vol. 10, pp. 19–27 (2014) 6. H.-H. Cho, C.-Y. Chen, T.K. Shih, H.-C. Chao, Survey on underwater delay/disruption tolerant wireless sensor network routing. IET Wireless Sens. Syst. 4(3), 112–121 (2014) 7. H.K. Le, D. Henriksson, T. Abdelzaher, A control theory approach to throughput optimization in multi-channel collection sensor networks, in IPSN (2007) 8. Y. Wu, S. Fahmy, N.B. Shroff, On the construction of a maximum-lifetime data gathering tree in sensor networks: NP-completeness and approximation algorithm, in INFOCOM (2008) 9. D. Luo, X. Zhu, X. Wu, G. Chen, Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks, in IEEE Proceedings INFOCOM, pp. 1566–1574 (2011) 10. R. Riggio, T. Rasheed, Tinku, S. Sicari, Performance evaluation of an hybrid mesh and sensor network, in IEEE International on Global Telecommunications, pp. 1–6 (2011) 11. W.-d. Liu, Z.-d. Wang, S. Zhang, Q.-q. Wang, A low power grid-based cluster routing algorithm of wireless sensor networks, in IEEE International Forum on Information Technology and Applications (IFITA), vol. 1, pp. 227–229 (2010) 12. S. Sharma, D. Kumar, K. Kishore, Wireless sensor networks—a review on topologies and node architecture. Int. J. Comput. Sci. Eng. 1(2), 19–25 (2013) 13. S. Dehghani, M. Pourzaferani, B. Barekatain, Comparison on energy-efficient cluster based routing algorithms in wireless sensor network. Procedia Comput. Sci. 72, 535–542 (2015) 14. M. Taherian, H. Karimi, A.M. Kashkooli, A. Esfahanimehr, T. Jafta, M. Jafarabad, The design of an optimal and secure routing model in wireless sensor networks by using PSO algorithm. Procedia Comput. Sci. 73, 468–473 (2015) 15. B. Barekatain, S. Dehghani, M. Pourzaferani, An energy-aware routing protocol for wireless sensor networks based on new combination of genetic algorithm & k-means. Procedia Comput. Sci. 72, 552–560 (2015) 16. M. Nighot, A. Ghatol, GPS based distributed communication protocol for static sensor network (GDCP). Procedia Comput. Sci. 78, 530–536 (2016) 17. M.B. Srinivas, Cluster based energy efficient routing protocol using ANT colony, optimization and breadth first search. Procedia Comput. Sci. 89, 124–133 (2016) 18. B. Amutha, B. Ghanta, K. Nanamaran, M. Balasubramanian, ECOSENSE: an energy consumption protocol for wireless sensor networks. Procedia Comput. Sci. 57, 1160–1170 (2015) 19. Jaibheem, M. Gokavi, K. Patil, Enhanced routing protocols for Uwsn using Mrp technology. Int. J. Res. Eng. Technol. 04(05), 117–121 (2015) 20. S. Ahmed, M. Akbar, R. Ullah, S. Ahmed, Atta-ur-Rehman, M. Raza, Z.A. Khan, U. Qasim, N. Javaid, ARCUN: analytical approach towards reliability with cooperation for underwater WSNs. Procedia Comput. Sci. 52, 576–583 (2015)

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21. A.H. Khan, M.R. Jafri, N. Javaid, Z.A. Khan, U. Qasim, M. Imran, DSM: dynamic sink mobility equipped DBR for underwater WSNs. Procedia Comput. Sci. 52, 560–567 (2015) 22. N. Ilyas, T.A. Alghamdi, M.N. Farooq, B. Mehboob, A.H. Sadiq, U.N. Javaid, AEDG: AUVaided efficient data gathering routing protocol for underwater wireless sensor networks. Procedia Comput. Sci. 52, 568–575 (2015) 23. N. Ilyas, M. Akbar, R. Ullah, M. Khalid, A. Arif, A. Hafeez, U. Qasim, Z.A. Khan, N. Javaid, SEDG: scalable and efficient data gathering routing protocol for underwater WSNs. Procedia Comput. Sci. 52, 584–591 (2015) 24. A. Joshi, S. Dhongdi, K.R. Anupama, P. Nahar, R. Sethunathan, Implementation of protocol stack for three-dimensional wireless sensor network. Procedia Comput. Sci. 89, 193–202 (2016) 25. A.M.V. Bharathy, A.M. Basha, A multi-class classification MCLP model with particle swarm optimization for network intrusion detection. Sadhana: Acad. Proceed. Eng. Sci. 42(5), 631–640 (2017) 26. A.M.V. Bharathy, A.M. Basha, A hybrid intrusion detection system cascading support vector machine and fuzzy logic. World Appl. Sci. J. 35(1), 104–109 (2016) 27. A.M.V. Bharathy, A.M. Basha, A hybrid network intrusion detection technique using variable multiplicative K-Means with self-organising PSO. Middle East J. Sci. Res. 24(12), 3812–3819 (2016)

Garbage Monitoring System Using Internet of Things Arpan Patel and Nehal Patel

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

292 292 292 295 295 297 298

Abstract Garbage management is becoming the chief issue owing to escalate in population. In most of the metropolises, the overflowed garbage bins are producing an unsanitary atmosphere. Moreover, it leads to emerging of diverse varieties of anonymous illnesses. However, it damages the living standard. So, we need to take some responsible actions for garbage management. We have to improve garbage management level by decreasing the time for tacking garbage and finding the more efficient way. In this paper, we had done the literature survey and also proposed IoT-based garbage monitoring system which checks the level of garbage in bins and sends that information to authorized worker through SMS. Information contains level of garbage and Google map link of bin. Using the garbage bin link, worker reaches the garbage bin when it is full and saves the time which is unnecessarily used to go through for garbage bin even it is not full. Keywords NodeMCU (Esp8266 12-E) · Ultrasonic sensor · Geolocation API IFTTT service · IoT

A. Patel · N. Patel (B) Department of Information Technology, CSPIT, CHARUSAT, Changa, Gujarat, India e-mail: [email protected] A. Patel e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_28

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1 Introduction In current decade, people are migrating from rural to urban areas; therefore, garbage is increasing in urban areas rapidly, but technology is same in rural as well as urban areas to control the garbage. Hence, the technology used for controlling garbage is real-time monitoring using Internet of things (IoT) because communication through the Internet has evolved from user to user, and new technologies took a birth like IoT, cloud computing. IoT can link services with modern techniques and try to metamorphose urban centers into the smart cities by optimizing system. In garbage monitoring system, sensor senses the information and sends to authorized worker in the form of message with the location of bin. After garbage is taken, they will send that garbage for disposal to organic, plastic, and metal [1, 2]. So it is getting easy to regenerate another thing using this garbage which is helping to make easy day-to-day life. We implement this system using embedded system with IoT. For that we need to take the information from ultrasonic sensor and check that information is above garbage limit or not; if it is above the limit, then send that information to authorized worker in the form of message with Google map location. Thus, worker can track that bin and easily collect the bin [3]. Ultimately it helps to retain cleanness in the society.

2 Literature Survey See Table 1.

3 Proposed Work As depicted in Fig. 1, first we start the NodeMCU. After starting NodeMCU, it will connect to router then it will request to ultrasonic sensor for information. Ultrasonic sensor collects the information and lends that information to NodeMCU [4]. Afterward, NodeMCU checks that information and figures out whether the information reaches the garbage limit or not. Nevertheless, if garbage level is less than 70% then NodeMCU will stop for some time and check again for newly arrived data from ultrasonic sensor. If garbage level is greater than 70% but less than 90%, then NodeMCU will get the longitude and latitude of that bin using geolocation API and send the message, “bin is 70% full,” with “Google map link” of that particular bin to authorized worker. If garbage level is greater than 90%, then it sends the message, “bin is full,” with “Google map link” of that particular bin to authorized worker.

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Table 1 Comparison of diverse existing garbage monitoring system S. No. Title of papers Year Sensors used

Information transfer methods and technology used Ultrasonic sensor Microcontroller, Wi-Fi modem, IoT, GSM

1

Smart Garbage Monitoring System Using Internet of Things [6]

2017

2

Smart garbage monitoring and clearance system using Internet of things [3]

2017

Ultrasonic sensor, force sensor

Embedded, IoT, GSM, Microcontroller, Web server

3

Smart waste management using Internet of Thing [7]

2017

Ultrasonic ranging module HC-SR04

Wi-Fi, Embedded, IoT, MySql, AI

4

Smart Garbage 2017 Monitoring System for Waste Management [8]

HC-SR04 ultrasonic sensor

SIM900A GSM Module, Arduino Uno board

5

Smart city 2016 technology based architecture for refuse disposal management [9]

Proximity, light, odor, force sensitive sensor

6

A Cloud-based 2016 Dynamic Waste Management System for Smart Cities [10]

Load sensor SEN-10245, ultrasonic sensor

Embedded, Arduino UNO microcontroller board, breadboard, GSM/GPRS, Wi-Fi Cloud server, Microcontroller, and GPRS

7

Automatic Waste 2016 Segregator and Monitoring System [11]

Ultrasonic Arduino Uno sensor, proximity board, sensor Microcontroller, GSM

8

Cloud-based Smart Waste Management for Smart Cities [2]

RFID, load cell sensor

9

Smart 2016 Dustbin-An Efficient Garbage Monitoring System [12]

2016

Cloud, Big Data Analytics

Ultrasonic sensor GSM, Arduino HC-SR04 Uno

(continued)

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Table 1 (continued) S. No. Title of papers

10

Real-time solid waste bin monitoring system framework using wireless sensor network [13]

Fig. 1 System architecture

Year

Sensors used

2014

Accelerometer, hall effect, ultrasound, temperature, humidity, load cell sensor

Information transfer methods and technology used Zigbee PRO, GPRS, central server database

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Fig. 2 NodeMCU (Esp8266 12-E)

4 Implementation A. Ultrasonic Sensor This module is connected to NodeMCU and waits for NodeMCU reply. When it gets a reply from NodeMCU, it sends the signal and waits for receiving that signal and calculates that amount of time and gives it to NodeMCU. B. NodeMCU This module gives the instruction to ultrasonic sensor to sense the time. After retrieving that time, it performs some operations and calculates distance. If distance is more than 70%, then this module is connected to geolocation API and gets the longitude and latitude of that location and sends the Google map link through IFTTT service. C. Geolocation API NodeMCU connects to geolocation API server and gives the information about nearby Wi-Fi network or cell tower. Geolocation API performs the calculation and gives the longitude and latitude with accuracy. D. IFTTT Service After getting longitude and latitude, NodeMCU connects to IFTTT service and triggers the message using Webhook service to assign number with value. Value contains percentage of garbage level and location of garbage bin.

5 Results and Discussion The hardware mechanisms must be associated correctly. Moreover, ensure that the android phone and the server must be connected to the Internet. In Fig. 2, this module is called NodeMCU which is used to load program and connect to the server. In Fig. 3, ultrasonic sensor is used to measure distance of garbage. It detects the range from 3 cm up to 3 m. Figure 4 shows the diagram of implemented system and Fig. 5 shows bin with garbage.

296 Fig. 3 Ultrasonic sensor

Fig. 4 Diagram of implemented system

Fig. 5 Bin with garbage

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Fig. 6 Received message

Fig. 7 Garbage bin location

In Fig. 6, when garbage reaches the threshold value, message will be received to phone via SMS with garbage bin location link [5]. In Fig. 7, we can see the garbage bin location using Google map link.

6 Conclusion In this paper, we presented a smart garbage monitoring system for single bin using IoT. It is accountable for computing the waste level in the bins and later sends the information to authorized worker through SMS. This information helps to calculate

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to optimized routes for the workers. Furthermore, we get the location without using GPS module and send the message deprived of using GSM component. In the future, we would like to elongate this system for different kinds of wastes such as metallic, organic, and dry waste segregator and monitoring system.

References 1. G. Soni, S. Kandasamy, Smart garbage bin systems–a comprehensive survey, in International Conference on Intelligent Information Technologies (Springer, Singapore, 2017), pp. 194–206 2. M. Aazam, M. St-Hilaire, C.-H. Lung, I. Lambadaris, Cloud-based smart waste management for smart cities, in 2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD) (IEEE, USA, 2016), pp. 188–193 3. S.V. Kumar, T. Senthil Kumaran, A. Krishna Kumar, M. Mathapati, Smart garbage monitoring and clearance system using internet of things, in 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM) (IEEE, USA, 2017), pp. 184–189 4. R.M. Saji, D. Gopakumar, H. Kumar, A survey on smart garbage management in cities using IoT. Int. J. Eng. Comput. Sci. 5(11) (2016) 5. M.S. Kumbhar, P.S. Yalagi, Survey on technology tools for water and garbage management for smart city planning, in International Journal of Computer Applications (0975–8887) National Seminar on Recent Trends in Data Mining (RTDM 2016) (2016) 6. S.M. Chaware, S. Dighe, A. Joshi, N. Bajare, R. Korke, Smart garbage monitoring system using internet of things (IoT). Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 5(1) (2017) 7. G.K. Shyam, S.S. Manvi, P. Bharti, Smart waste management using Internet-of-Things (IoT), in 2017 2nd International Conference on Computing and Communications Technologies (ICCCT) (IEEE, USA, 2017), pp. 199–203 8. N.M. Yusof, A.Z. Jidin, M.I. Rahim, Smart garbage monitoring system for waste management, in MATEC Web of Conferences, vol. 97 (EDP Sciences, France, 2017) 9. J.O. Adeyemo, O.O. Oludayo E. Adetiba, Smart city technology based architecture for refuse disposal management, in IST-Africa Week Conference (IEEE, USA, 2016) 10. S. Sharmin, S.T. Al-Amin, A cloud-based dynamic waste management system for smart cities, in Proceedings of the 7th Annual Symposium on Computing for Development (ACM, USA, 2016) 11. A. VJ, K. Balakrishnan, T.B. Rosmi, K.J. Swathy Krishna, S. Sreejith, T.D. Subha, Automatic Waste Segregator and Monitoring System 12. K.A. Monika, N. Rao, S.B. Prapulla, G. Shobha, Smart dustbin-an efficient garbage monitoring system. Int. J. Eng. Sci. Comput. 6(6), 7113–7116 (2016) 13. M.A. Al Mamun, M.A. Hannan, A. Hussain, Real time solid waste bin monitoring system framework using wireless sensor network, in 2014 International Conference on Electronics, Information and Communications (ICEIC) (IEEE, USA, 2014)

Mobile Learning Recommender System Based on Learning Styles Shivam Saryar, Sucheta V. Kolekar, Radhika M. Pai and M. M. Manohara Pai

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Felder-Silverman Learning Style Model (FSLSM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 App Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Methodology for Identification of Learning Style . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 The Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Sample Learning Style Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 The Optimized Learning Style Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Learning Style Computation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Recommendation System for the learner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Activities of Mobile Learning Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In the Internet era, more and more learners now have the option of using multimedia to engage in a learning environment, for example, videos, text, pictures. They also prefer more control over their learning sessions, i.e., being able to choose which topics, which mode of multimedia, as that is one thing which classroom learning cannot provide. Classroom learning does not give the freedom of choosing a pace, a learning style, or a suitable medium for learning. Moreover, the existing S. Saryar · S. V. Kolekar (B) · R. M. Pai · M. M. Manohara Pai Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India e-mail: [email protected] S. Saryar e-mail: [email protected] R. M. Pai e-mail: [email protected] M. M. Manohara Pai e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_29

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teaching methods do not encourage from exploring other possible means of learning which could turn out to be more helpful. Also, classroom learning or learning over the Internet, most learners are still not well aware of their learning styles. In this paper, an approach is proposed to develop a mobile learning (M-learning) Android application which implements a learning style (LS) model as per Felder-Silverman learning style model (FSLSM) and recommendation component (RC) model. LS model is used to identify the learning behavior and characteristics of each learner. According to the identified learning style as well as the user’s other in-app activities, it uses a recommendation system to recommend relevant course material to the user which he/she might find useful. This gives the learner a greater insight into his/her own learning pattern and becomes self-aware about what mode of learning suits them more or what might be more useful to them. This mobile learning application provides seamless availability of course material to the learners on the go. As opposed to the e-learning platforms, this approach has been implemented as a mobile application, which allows learners to access course material whenever and wherever they want. Keywords M-Learning · FSLSM · Recommendation system · LS

1 Introduction Mobile learning (M-learning) is the advanced method of learning, and it is accessible from anywhere and at any time. This method is useful for sharing of an course content instantly and appropriately among all learners, which makes learners to receive the contents and provides mechanism for instant feedback and tips. This method has proven to increase academic performance and decrease the dropout rate. M-learning is also the most portable learning method anywhere and anytime which replaces books and notes with small handy devices such as tablets and kindles. M-learning is a trending technology in the field of education which supports lifelong learning. These frameworks still face technical and accessibility problem in many parts of the world. Some problems are such as accessing the course material online, no adaptation as per course-ware to individual learners, and the interaction between the learner and the system is difficult to access due to the Internet bandwidth limitations. Also, systems are not designed with the consideration of pedagogical infrastructure for mobile learning. The proposed approach aims to develop a mobile learning application using Android platform, which provides learners a platform to access course material in various formats like audio, video, text, demos, samples. This has been implemented using a learning style model, where each of the learners, through the ILS questionnaire approach [1, 2] proposed by Felder and Soloman, has been analyzed initially before starting the course to capture and understand their learning styles. After the identification of the learning styles of the user, during use of the learning portal by the user, a recommendation system will be provided with the suggestions to the learner

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based on his/her learning style, the appropriate relevant content for the topic which might be helpful to them. The organization of the remaining paper is as follows: The related work in the area of mobile learning application is discussed in Sect. 2. The basics of FSLSM are explained in detail in Sect. 3. The architecture and methodology of the proposed work are explained in Sect. 4. Section 5 mentions about the implementation of methodology, followed by results of approach in Sect. 6. Section 7 discusses the concluding remarks and future scope.

2 Related Works Kinshuk and Lin [3] in their paper on mobile learning using learning styles have explored the prospect of improving learning by providing the course contents to the learner adaptively in multi-platform end devices such as PCs, mobiles, tablets, and PDAs (personal digital assistant). An approach has been defined to comprehensively identify the learners’ learning styles and present the appropriate contents and formats of content as per suitability of an individual learner. The work is based on the various categories of FSLSM. The system does not provide online recommendations of contents and other materials. Kinshuk et al. [4] have discussed the two approaches to achieve personalization. With the approach, learners’ characteristics such as learning styles, requirements, status, performances, preferences, profiles, and context surrounding the learners have to be adapted. The first approach is easy to implement, but the second approach requires context-awareness ability to capture and understand the LS and deliver the materials. Tortorella and Graf [5] have discussed in their research about how to provide adaptivity based on learners’ behavior on Moodle-based e-learning systems. They proposed a framework which captures and understands the LS of the learner and then provides the appropriate contents and information through mobile technology based on the context of the learning information. As mentioned in recommender systems—Melville and Sindhwan [6]—it refers to a filtering technique that tries to provide content items according to learners’ interests and requirements and reduces the problem of information overloading. M-learning system should suggest suitable learning contents, exercises, various activities, in order to improve the efficiency of the system along with the satisfaction of learners. While improving system, recommendation approaches should be adapted according to the new requirements of learning.

3 Felder-Silverman Learning Style Model (FSLSM) Felder and Silverman (1988) have developed the model to understand the LS for engineering students. The M-learning system is built based on FSLSM which is adopted for providing recommendations. The system classifies learners’ responses

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as per the scale defined with the positions that evaluate how the learning contents are studied and applied for the specific course. The FSLSM model is essential for engineering teachers to design learning materials that would address the learning needs of all learners. The following are the details of FSLSM: • Number of dimensions: four • Number of categories: eight • Names of dimensions and categories: preprocessing (active/reflective), perception (sensing/intuitive), input (visual/verbal), and understanding (sequential/global). These dimensions can be viewed as a continuum with one learning preference on the far left and the other on the far right. A combination of these styles makes up the individual’s learning preferences [7]. To identify the learning styles, FSLSM ILS questionnaire [1] is used in the system. After completing the questionnaire, the application computes, using appropriate algorithms, as to where among the learning style dimensions the learner falls and categorizes the learners accordingly. Then, based on the learning styles of each learner, a recommendation system suggests an appropriate available content to the learners during the course according to their learning styles to provide them with a familiar and comfortable means of learning the particular course topics. In M-learning systems, the suggestion of available learning components and contents is an important approach which focuses on learners’ requirements and behavior.

4 Architecture Overview The architecture depicts the work flow of the above-mentioned objectives of mobile application which is illustrated in Fig. 1. As seen in the architecture overview, the application first launches with the Splash Screen Activity, which checks with Firebase Authentication to determine the sign-in status of the learner in the app. If the learner has previously signed in with the app, he is taken directly to the Dashboard Home activity; else, if he is a new user/learner, he is redirected to the Google Sign-In activity, where s/he can choose any existing Google accounts currently used on their personal device to sign in to the app. Once the learner signs in, he/she is directed to the questionnaire activity. In the questionnaire activity, the learner is required to answer all the questions of the questionnaire. Once he/she has done that, his learning styles are computed, and this data, along with the learner account details, is uploaded to the firebase real-time database. Once done with this, he/she is redirected to the Dashboard Activity. In the Dashboard Activity, the learner can choose from the Navigation Drawer to view his/her profile information in the Profile section, which includes the learning styles identified for him/her. The learner is also provided with a Help option, which lets the learner read about all the various learning style patterns that the application implements. The learner can go to the Discover section from the Navigation Drawer to view all the courses that the

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Fig. 1 Architecture overview

application is offering. He/she can view the course details from there and choose to enroll for the course if they wish to. Once enrolled, the course is made visible for the learner in the Home activity. Upon clicking that, he/she can see all the topics in the Topic List activity for that course and choose a topic in order to view all the course materials available for that course topic. From here, he/she can choose from options including video, pdf, quiz, or intuitive test. Inside the video and pdf activities, the learner is given an option to view the respective topic material and is recommended other relevant material according to their learning styles (visual/verbal or sequential/global). In the Recommendations section in the Dashboard Navigation Drawer, the learner is given recommendations for quiz and intuitive test based on their learning styles (active/reflective or sensing/intuitive). The paper focuses mainly on the following two objectives: 1. LS identification system: The Felder-Silverman ILS questionnaire is used for identification of the learning styles of the learner. It has to be filled out by the learner at the beginning of his usage of the mobile application. Using the answers given by the learner, a computation has been carried out using an algorithm in order to find out the learning style of the learner. 2. Recommendation system: Once the learning styles of the learner are identified, the recommendation system suggests appropriate learning objects and material (videos, text, quiz, etc.) to the learner in order to provide a comfortable learning environment to the learner.

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4.1 App Modules The various modules developed in the Android application are explained as follows. • Google Sign-In: Firebase Auth provides the Google Sign-In API for learners to conveniently sign in to the app using their Google accounts with a touch of a button. Once the learner signs in, he is taken to the questionnaire activity if he is a new learner, else he is taken to the Dashboard. • Questionnaire: The questionnaire activity is where the learner is first introduced to the Felder-Silverman learning style computation questions which the learner must answer before starting to use the app. It is a set of 44 questions. Upon completion of which the algorithm for computing the learning style of the learner is run, then this data is uploaded along with the learner’s profile data to the firebase real-time database. • Dashboard: The Dashboard is the main learner interface element of the app. It provides a Navigation Drawer with options of Home, Profile, Discover, and Recommendations. • Home: This activity contains all the courses that the learner has enrolled in. Clicking on any of these courses, the learner takes to the list of topics for the course. Upon selecting the topics, the learner can choose the type of course material he/she wants to view for that topic or choose to take a quiz or intuitive test on the topic for self-evaluation. The types of materials available to the learner include pdf’s and videos. The course material files are stored online using the Firebase Storage API for online storage of files for easy fetching into the app. The quiz contains questions with multiple choice with one correct answer. • Profile: This activity shows the profile information of the signed-in learner, along with his/her learning styles computed earlier. A help button is provided for the learner to further read upon all the learning styles and their general behavior. • Discover: This activity displays all the courses made available to the learner by the app for enrolling and viewing. Selecting any of these takes the learner to the details of the course. The learner can enroll for the course from here. • Recommendations: This activity shows the learner any relevant course material that he/she may find useful based on their learning styles and their in-app activity.

5 Methodology for Identification of Learning Style The methodology implemented for identifying the learning styles of the learner is explained in this section.

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5.1 The Method Using an Android application, first provide a log-in to a new learner, through which his/her data is collected in order to create a learner profile. After this, the ILS questionnaire [1] is provided through the application and the selected responses from the learner are recorded. Using these responses, a calculation has been done in order to identify the learning style of the learner. The classification of different learning styles follows the following scales. • If the score on a scale is 1–3, he/she is fairly well balanced on the two dimensions of that scale. In this case, a balance of the two learning style objects has been recommended to the learner. • If the score on a scale is 5–11, he/she has a higher preference for one dimension of the scale than the other and will learn more easily in a teaching environment which favors that dimension. In this case, the learning objects recommendations have been inclined toward the higher preference dimension. The identification of the learning style of the learner has been carried out using the following computation: • Assign the number 1 to the option selected for each of the 44 questions in the questionnaire and 0 to the other, where the questions are grouped according to the class for which it is a deterministic question (active/reflective or sequential/global). • For each of these groups, we get the sum of number of 1s for both the options. • The sum of the option which scored a lower sum than the other is subtracted from the option with the higher sum. • This number obtained is the value on the scale from 1 to 11 determining the degree to which the learning style is inclined to the option with the higher total sum.

5.2 Sample Learning Style Response The computational result shown in Fig. 2 is an example of how the learning pattern of a user has been identified: Active/reflective: 8(A) − 3(B) = 5A =⇒ Higher preference toward ‘Active’ Sensing/intuitive: 9(A) − 2(B) = 7A =⇒ Higher preference toward ‘Sensing’ Visual/verbal: 7(A) − 4(B) = 3AB =⇒ Fairly well balanced Sequential/global: 7(A) − 4(B) = 3AB =⇒ Fairly well balanced.

5.3 The Optimized Learning Style Response The method shown in Fig. 2 for computing the learning styles was further optimized for the Android application in order to obtain a cleaner and more efficient code. If the positive value belongs to option A, then preference is toward A, otherwise toward B. Figure 3 shows the computation done with the optimized code.

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Fig. 2 Screenshot of sample learning style response Fig. 3 Screenshot of optimized learning style responses

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5.4 Learning Style Computation Algorithm For every group which represents one of the 44 questions, each has two radio buttons, only one of which can be selected. Then, for each question, if A option is selected, the position in the result[i] array corresponding to that question number is set as 1; otherwise, it is set as −1. Since every same learning style-related question is at a gap of four places from each other, for each of the four learning styles every fourth position’s result[] value is added up. So, for a particular learning style, if the sum is greater than 3, then it means that the learning style is inclined toward the left side of the scale; else, if it is less than −3, it means it is inclined toward the right side of the scale. Also, if it lies between −3 and 3, it means that learning style of the learner is in a balanced range. This computation can be better understood using the algorithms shown in Algorithm 1. Algorithm 1 Compute Learning Style initialize int r esult[44] = 0 initialize four integers lear ningSt yle1, lear ningSt yle2, lear ningSt yle3, lear ningSt yle4 for (i in radioGroup[i]) do if option ‘A’ selected then r esult[i] ← 1 else if option ‘B’ selected then r esult[i] ← −1 end if end for lear ningSt yle1 ← sum of every 4th element of r esult[i] starting with i = 0 lear ningSt yle2 ← sum of every 4th element of r esult[i] starting with i = 1 lear ningSt yle3 ← sum of every 4th element of r esult[i] starting with i = 2 lear ningSt yle4 ← sum of every 4th element of r esult[i] starting with i = 3 //Determine first learning style if (lear ningSt yle1 > 3) then Active learner else if (lear ningSt yle1 < −3) then Reflective Learner else if (−3 ≤ lear ningSt yle1 ≤3) then Balanced between Active and Reflective

end if //Determine second learning style if (lear ningSt yle2 > 3) then Sensing learner else if (lear ningSt yle2 < −3) then Intuitive Learner else if (−3 ≤ lear ningSt yle2 ≤3) then Balanced between Sensing and Intuitive end if //Determine third learning style if (lear ningSt yle3 > 3) then Visual learner else if (lear ningSt yle3 < −3) then Verbal Learner else if (−3 ≤ lear ningSt yle3 ≤3) then Balanced between Visual and Verbal end if //Determine fourth learning style if (lear ningSt yle4 > 3) then Sequential learner else if (lear ningSt yle4 < −3) then Global Learner else if (−3 ≤ lear ningSt yle4 ≤3) then Balanced between Sequential and Global end if

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Table 1 Mapping of FSLSM parameters to contents Contents Act Ref Sen Int Text Videos Demo/PPT Exercise/Quiz Forum Index of topics

X X X X X X

X X

X X

Vis

Ver

Seq

X X X

X

X

Glo

X

X X X

X

5.5 Recommendation System for the learner After the learning pattern of the learner has been identified, it is stored for that learner’s profile. Then, throughout the course of the usage of the application, the learner’s clicks on the course material are monitored by the application which, along with the identified learning styles of the learner, is used to appropriately suggest relevant learning material to the learner. The various learning objects/parameters that can be incorporated into this recommendation are as mentioned in Table 1. The X marks indicate which learning objects/parameters are recommended for which of the learning styles. For example, for an active learner, an exercise will be more helpful than videos. Similarly, an index of topics will not be of any use to a sequential learner who is anyway going to follow a sequential learning pattern of the course as opposed to a global learner who will need the index of topics to choose which topic he wants to jump to and which ones to skip or view later.

5.5.1

Algorithm for Recommendation System

The recommendation system of this app monitors the user’s activity for their enrolled courses and their corresponding course material. Different forms of recommendations are given for different types of learning styles of the learners.

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Algorithm 2 Recommendation System Algorithm for Active/Reflective Recommendation do if (learner is active learner) then show option for quiz else if (learner is reflective learner) then show recommendation for quiz end if end for for Visual/Verbal Recommendation do if (inside video activity) then if (user is visual learner) then show recommendation for pdf end if else if (inside pdf activity) then if (user is verbal user) then show recommendation for video end if end if end for for Sensing/Intuitive Recommendation do if (learner is sensing learner) then show option for quiz

give recommendation for intuitive test else if (learner is intuitive learner) then show option for intuitive test show recommendation for quiz end if end for for Sequential/Global Recommendation do if (inside video activity) then if (user is sequential learner) then show next video option else if (user is global learner) then show all other video suggestions end if else if (inside pdf activity) then if (user is sequential learner) then show next pdf option else if (user is global learner) then show all other pdf suggestions end if end if end for

6 Results and Discussions The following subsections explain some functionalities of mobile app and the analysis of app on the learners.

6.1 Activities of Mobile Learning Application The following are the screenshots of intuitive quiz activity and recommendation Activity. • Intuitive Test Activity: As shown in Fig. 4a, this activity lets learners practice intuitive questions, which may not have been covered in the course material. Also as shown in Fig. 4b, this is a screenshot of the answers for the intuitive questions. • Recommendations Activity: This activity, shown in Fig. 5, shows recommendations for quiz and intuitive test material for sensing/intuitive learners and active/reflective learners.

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Fig. 4 Intuitive test activity

(a) Intuitive Test Questions

(b) Intuitive Test Answer

7 Conclusion and Future Scope This paper helps in understanding the basic limitations that the existing teaching process in most parts of the world still suffers from. This work is an attempt at trying to help learners overcome these limitations when they wish to take a course to try and learn something new. Also, because of the learning styles this work takes into consideration a large number of learners, each having his/her own unique way of learning, will be able to find something suitable in order to make the most of the course material provided by the app. This app also provides tests and quizzes in order to allow the learners to perform a self-evaluation and track their learning progress. The learning style patterns that have been implemented in this work promise a brilliant future. With the knowledge of the learners’ learning styles, courses can be designed specifically to suit these particular learning styles which will benefit the learners in a big way. Also, with the latest boom of convertible personal device assistants like Samsung’s DeX dock feature recently launched with the Galaxy S8 or the Microsoft Surface Pro devices, this mobile learning application will not have to suffer from the limitation of learners not getting hands-on programming practice on the go while taking up courses in the app due to the lack of a physical keyboard.

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Fig. 5 Recommendations activity

Declaration Authors have taken required permission for the use of image/dataset in the work and take responsibility if any issues arise later.

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References 1. R.M. Felder, B.A. Soloman, Index of Learning Styles Questionnaire (North Carolina State University, USA) 2. B.A. Soloman, R.M. Felder, Index of learning styles questionnaire. [Online]. Available: http:// www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSpage.html 3. Kinshuk, T. Lin, Application of Learning Styles Adaptivity in Mobile Learning Environments (Massey University, Palmerston North, New Zealand, 2003) 4. M.C. Kinshuk, S. Graf, G. Yang, Adaptivity and Personalization in Mobile Learning (2009) 5. R.A. Tortorella, S. Graf, Personalized Mobile Learning via an Adaptive Engine (School of Computing and Information Systems, Athabasca University, Edmonton, Canada, 2012) 6. P. Melville, V. Sindhwani, Recommender Systems (Springer, Boston, MA, USA, 2010), pp. 829–838. [Online]. Available: https://doi.org/10.1007/978-0-387-30164-8_705 7. R.M. Felder, L.K. Silverman, Learning and Teaching Styles in Engineering Education (2002)

Privacy Sustaining Constant Length Ciphertext-Policy Attribute-Based Broadcast Encryption G. Sravan Kumar and A. Sri Krishna

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Ciphertext-Policy Attribute-Based Broadcast Encryption . . . . . . . . . . . . . . . . . . . . . . . . . 4 Implementation of CP-ABBE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

314 316 317 319 320 323 323

Abstract Cryptography-based data distribution is an advanced technique used to control the access of broadcasting data over cloud storage environment. A ciphertextpolicy attribute-based encryption (CP-ABE) technique encrypts the data in the cloud scenario, and the data is accessed only when the decryptor satisfies the encrypted access scheme. In this technique, each user is assigned with attributes in the recovering key, and the authorized user is allowed to process the data when their attributes match the keywords embedded in the access policy. Existing techniques used for providing privacy partially hides the access policy in the ciphertext. Therefore, it may leak private information about the user; also, the size of ciphertext is linear with respect to the sum of attributes. In this paper, an efficient privacy sustaining ciphertext-policy attribute-based broadcast encryption technique with AND gate and wildcard access scheme is proposed. The proposed technique provides security proof to sensitive data broadcasted over unsecured channel with hidden access policy. Further, this technique will achieve constant length ciphertext for any number of attributes. Moreover, the communication overhead and the computation complexity are significantly reduced in the proposed method.

G. Sravan Kumar (B) Acharya Nagarjuna University, Guntur, India e-mail: [email protected] A. Sri Krishna RVR & JC College of Engineering, Guntur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_30

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Keywords Cryptography · Data distribution · CP-ABE · Access policy Broadcast encryption

1 Introduction Data publishing has become a challenging task for the cloud storage environment as it is necessary to provide privacy to data publisher and authorized user. Cryptography is the process of providing privacy to sensitive information published over the Internet. Various cryptographic schemes are involved in protecting the privacy of published data like identity-based encryption (IBE) [1], attribute-based encryption (ABE) [2], key dispersion algorithm [3] and so on. Recently, ABE techniques are used by many authorities as it guarantee to provide data authentication [4], data confidentiality [5], data security [6], and data privacy [7] for an authorized user. The ABE techniques are classified as key-policy attribute-based encryption (KP-ABE) [8] and ciphertext-policy attribute-based encryption (CP-ABE) [2]. When the access scheme is embedded in user’s private key, the method is called as KP-ABE. In this method, the attribute set must satisfy the access scheme in order to decrypt the data. In CP-ABE technique, the access policy is embedded in the ciphertext and the user is assigned with an attribute set in his private key. The attribute set of the user should match the encrypted access policy in order to decrypt the sensitive data. However, CP-ABE technique provides the encryptor to control the access of data, whereas KP-ABE technique does not provide this advantage. Our proposed technique is constructed on the basis of CP-ABE technique. Sahai and Waters [1] introduced the first IBE scheme called fuzzy-IBE which uses biometrics as identity information. An access policy-based encryption technique called CP-ABE was used in [2, 9, 10]; in these techniques, the ciphertext size grows with respect to sum of attributes in the system. Herranz et al. [11] determined a constant length CP-ABE technique, which embeds the access policy in the ciphertext as original text. This may expose the secret information about the user, and it is critical to provide data anonymity. To overcome these limitations, partially hidden access policy-based CP-ABE methods [7, 12, 13] were proposed to hide the sensitive information in the ciphertext. Partially hidden access policy techniques hide the attribute values in the ciphertext, but the attribute names are not hidden. However, in some applications, the information in the attribute names is also sensitive. In such cases, it is efficient to provide hidden access policy that hides the whole sensitive information in the ciphertext. Several methods were proposed to gain constant ciphertext length along with hidden access policy. But, still it is difficult to obtain such method as the ciphertext length grows with reference to the sum of attributes. Emura et al. [14] identified constant-size CP-ABE that uses AND gate with multi-valued attributes as access policy. However, the length of ciphertext is reduced to a constant size in their technique, but the private key length is increased, and this technique does not guarantee users privacy. Odelu et al. [15] presented a pairing-based CP-ABE technique that

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offers constant-size ciphertext and constant private keys. Their scheme uses AND gate access structure and is proved to be effective under selective model. Further improvements are needed to extend the algorithm for other access policies that are fully secure against any adversary attacks. Related Work. Hidden AND gate with wildcard access scheme-based CP-ABE techniques was presented by the authors in [9, 16, 17, 22]. AND gate with wildcard access scheme needs three values each to represent positive, negative, and wildcard (do not care) attribute. In this, the user’s private information is fully hidden by wildcards. The user satisfying the wildcard attribute and either positive or negative attributes can recover the original text. Phuong et al. [9] constructed a CP-ABE scheme with hidden access control that uses AND gate with wildcards access policy. In this method, the access policy is hidden and the user’s anonymity is proved secured under DBDH assumption. Li et al. [16] suggested a multiple authority CP-ABE technique that uses AND gate with wildcards as access scheme. In both the techniques in [9, 16], the ciphertext length increases with respect to the sum of attributes. A wildcard-based hidden access scheme was introduced by Nishide et al. [17]. In their scheme, if the number of attributes used in encryption is larger, then the ciphertext length also becomes larger, and in such cases, it does not realize the wildcard functionality. A single authority system supporting hidden access policy with constant length ciphertext was implemented by Doshi et al. [18]. Obviously, their work is not applicable to applications involving multiple authorities. Recently, a computationally efficient CP-ABE scheme was estimated by Ohtake et al. [19]. This scheme reduces the key generation cost and the time needed for decrypting the text. But the system is differentiated into users requiring wildcard attributes and users’ not requiring wildcard attributes which makes it inconvenient for the key generator to use the system. Attribute-based broadcast encryption techniques were also presented in [8, 20–22] to obtain secure data transmission over broadcasting channel. Alomair et al. [4] presented a message authentication algorithm for broadcasting messages in mobile devices. In this, the broadcasting message is authenticated using block cipher-based encryption algorithm. The message can be decrypted by the user only after succeeding an integrity test. Kim et al. [23] formed an adaptively secured broadcast encryption. The limitation of these techniques [4, 23] is that the computational complexity linearly increases with total number of end users in the channel. The broadcasting scheme in [22] can express any kind of access policy, but it does not guarantee users privacy. Thus, in broadcast encryption-based data publishing systems, still there is a need of privacy sustaining scheme that offers constant ciphertext length for any number of users in the channel. Our Contribution. In this paper, privacy sustaining constant length ciphertextpolicy attribute-based broadcast encryption (CP-ABBE) technique is proposed. This technique uses hidden AND gate with wildcards as access control. Wildcard attributes are used to hide the sensitive information about the user, and the authorized user is allowed to access the message only when their attribute set matches the hidden access scheme. The main contribution of the proposed work is to achieve a constant length

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ciphertext with privacy sustaining hidden access policy and to provide collision-free data transfer over broadcasting channel. This technique is evaluated for a set of users over a broadcasting channel. Organization. This paper is organized as follows: Sect. 2 outlines the basics used in CP-ABBE technique. In Sect. 3, the proposed CP-ABBE algorithm is discussed and its implementation is presented in Sect. 4. Section 5 compares the CP-ABBE technique with the algorithm mentioned in [4]. Section 6 concludes the proposed work and determines the future work.

2 Preliminaries A. Attributes. Attributes are nothing but piece of information about the cloud storage data which are used as a key element in order to access authenticated the n ai , where n is data. The cloud storage system has group of attributes A  i1 the number of attributes in the broadcasting system. When a data accessing n user request a connection, the user is provided with an attribute set, S  i1 Si ,   where Si ∈ ai+ , ai− ·ai+ shows that the user has a valid attribute, and ai− denotes that the user does not have a valid attribute. B. Bilinear Mapping. The basic need for constructing CP-ABE technique is bilinear mapping. Bilinear mapping between multiplicative cyclic groups G 1 and G 2 of large prime order q is defined over a finite field Z f of an elliptic curve. The pairing e(G 1 × G 1 ) → G 2 is said to be bilinear if it follows the properties:   • Bilinearity. ∀(a, b) ∈ G 1 and (x, y) ∈ Z f , e(a x , b y )  e x a , y b • Non-degeneracy. e(g, g)  I; g is the generator of group G 1 • Computability. It is efficient to compute e(a, b) for any (a, b) ∈ G 1 C. Hidden AND gate access policy with Wildcards. Each takes three  +attribute − ∗ the , a , a values like positive, negative, and wildcard denoted as: a i i i +. Thus,  n − ∗ Pi , where P ∈ a , a , a AND gate access policy is initiated as: P  i1 i i i i . n ∗ The embedded access policy is of the form,  P  S ∪ i1 ai . The sensitive − + attributes in the access policy, i.e., ai , ai , were hide in the ciphertext. When the wildcard value is also hidden in the ciphertext, the decryptor has to evaluate 2n possibilities for identifying matching attributes during decryption. This will degrade the performance of the system; for this purpose, the wildcard value is not hidden in the ciphertext. Thus, it prevents the user from trying a large number of guesses over embedded access policies, thereby safeguarding the sensitive data against entity. The hidden access policy is defined as: nthe unauthorized P  P ∪ i1 ai∗ . The decryptor is allowed to access the text only when their attribute set matches the given access policy.

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D. CP-ABE Technique. The CP-ABE algorithm has the following basic steps: • Setup. Certified authority (CA) runs the setup phase with security parameter and attribute list as input and provides the public-key (PK) and master key (MK). The MK is controlled by the CA, and the PK is provided to the data publisher. • Key Generation. It is also run by the CA. The CA takes PK and MK as input and calculates the secret key (SK). The SK is then distributed to the data user. • Encryption. Using public-key, the data publisher encrypts the data with an access policy and publishes the encrypted data through the cloud storage environment. The encrypted data is called ciphertext which consists of access policy and data. • Decryption. The authorized user decrypts the data using the SK. The data is accessible by the end user only when the access policy matches the attributes in the SK.

3 Ciphertext-Policy Attribute-Based Broadcast Encryption A. Setup. Let us consider group of n attributes, A  {a1 , a2 , a3 , … an }. Since the attributes takes three values, the integer representation of ‘n’ number of the positive attributes is: ai+  {1, 2, . . . n}, negative attributes is: ai−  {n + 1, n + 2, . . . 2n} and wildcard attributes is: ai∗  {2n + 1, 2n + 2, . . . 3n}. The CA selects three random generators: g from G 1 and (∝1 & ∝2 ) from Z f and calculates the PK and the MK as given in Eqs. (1) and (2):   Public Key, PK  gi , g ∝1 . where, gi  g ∝2 , i  1, 2 . . . N , N + 2 . . . 2N ; N  3n.

(1)

Master Key, MK  (∝1 , ∝2 ).

(2)

i

2n B. Key Generation. SK of the users has set of attributes, S  i1 S(i),   + The . To determine SK, CA selects n random numbers from where S(i) ∈ ai , ai−  n Z f and calculates r  i1 r (i). Using Eqs. (3)–(5), the secret key is calculated as depicted in Eq. (6). K  g ∝1 r . n   ∝1 g S(i) Ki  · g ∝1 r (i) .

(3) (4)

i1

Fi 

n  

 gi (2n + i)∝1 · g ∝1 r (i) .

(5)

i1

Secret Key, SK  (K , K i , Fi ).

(6)

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C. Encryption. The encryptor will encrypt the message, M, using one-time encryption key obtained from block cipher encryption. The one-time key encrypted message is given by: M1  M ⊕ gi (i). Subsequently, the ciphertext is concatenated with hidden access policy, encrypted message, and header information. The hidden access policy contains positive and negative attributes. The ciphertext header provides information to predict the access policy during decryption. The ciphertext is obtained as shown in Eqs. (7)–(9). Hidden Policy, P  P ∩

n 

ai∗ .

(7)

i1

Ciphertext Header, Chdr  {Chdr1 , Chdr2 }.

where,

Chdr1  g1t ; t ∈ Z f Chdr2  (g ∝1 · g1 )t

(8)

.

  Cipertext, C  P, M1, Chdr .

(9)

D. Decryption. Since the attributes are hidden, the user will predict the access policy with the information in the header of ciphertext. For an authorized user, always the prediction is correct. If the access npolicy is an attribute symbol, §, Ppred  S(i). The authorized then the user predict the access policy as: i1 user decrypts the text using Eqs. (10)–(13). W0  e(gi , Chdr2 ). ⎧ ⎪ ⎨ e(K i , Chdr1 ), if Ppred ∈ S(i). n  W1  ⎪ ai∗ . ⎩ e(Fi , Chdr1 ), if Ppred ∈

(10) (11)

i1

W0 . W  W1 M  M1 ⊕ (gi(i) + W ).

(12) (13)

E. Privacy Model. The proposed system is designed to be safe under indistinguishable chosen plaintext attack (IND-CPA). Init. Consider two challenge access policies P1 and P2 , which are committed by the adversary. Setup. The challenger performs the setup phase, and the PK is submitted to the adversary. Phase 1. The adversary provides an attribute A, such that ( A  P1 ∩ A  P2 ) or ( A  P1 ∩ A  P2 ). The adversary submits the access policies to the challenger and gets the SK for his attribute set. This is repeated for a polynomial number of times.

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Challenge. The adversary gives two equal size messages to the challenger with a challenging access policy, P, such that none of the attributes given in Phase 1 match the attributes in the access policy. The challenger encrypts the messages with a random key, k, and the encrypted text is transmitted to the adversary. Phase 2. Since the access policy in Phase 1 is not identical to the attribute set, Phase 1 is repeated again to match the access scheme. Guess. The adversary predicts the random key k of k. In this attack, the adversary succeeds the game, if k  k. The profit gained by the adversary in this IND-CPA attack is P(k  k) − 21 . Definition. The proposed CP-ABBE algorithm is proved to be secure against the above attack if the polynomial time adversary in the above game produces a negligible advantage to the adversary.

4 Implementation of CP-ABBE The proposed CP-ABBE technique is implemented on the basis of algorithm constructed in Sect. 3. Consider a university broadcast system with students and faculties from CSE, ECE, EEE, and IT Departments. In this broadcasting system, the CA of the university holds the attributes, S  {CSE, ECE, EEE, IT, Student, Faculty}. In this system, initially, the number of attributes in the system is N  6. Since each attribute is represented by three values like positive (+), negative (−), and wildcard (§), N  3 × 6  18. The CA generates PK and MK, and both the keys are used to generate SK for each user in the broadcasting system. In the SK, the attribute set of the user is embedded and it is transmitted to the user. Since this is a multi-authority system, the users attribute set consists of a huge number of attributes either the authority has to follow or do not want to follow. For this purpose, for exact identification of authorized user, AND gate with wildcard access scheme is employed in our technique. If the university wants to broadcast message to all faculties and students of CSE department, the data publisher sets separate access policies for CSE students and CSE faculties. The AND gate access policy of faculties and students of various department takes the tree structure as shown in Fig. 1. In Boolean form it is expressed as: P  {(Faculty OR Student) AND ((CSE OR ECE) OR (EEE OR IT))}. Here, AND gate represents that the user must satisfy n-of-n attributes given in its child node and OR gate represents that the user must satisfy 1-of-n attribute given in its child node. The corresponding access policies of CSE students and faculties are P1 and P2 , respectively, as shown in Table 1. Alice, a faculty from CSE department, distributed a secret key having attribute list: {CSE, Faculty}, and her message is encrypted with access policy: {CSE, Student, Faculty, Name}. To decrypt the message, the SK of Alice should satisfy the policy: CSE AND Faculty. Thus, CSE and Faculty are positive attributes for Alice which she wants to satisfy necessarily. Though Alice is not a Student, the attribute Student is her negative attribute and her name is mentioned as wildcard attribute.

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Fig. 1 Tree structure of AND gate access policy Table 1 Access policy with wildcards (§—wildcard attribute) a1  CSE a2  Student a3  Faculty

Name

P1

a1+

a2+

a3−

§

P2

a1+

a2−

a3+

§

Table 2 Anonymized access policy with wildcards (*—sensitive attribute, §—wildcard attribute) a1  CSE a2  Student a3  Faculty Name P1

a1∗

a2∗

a3∗

§

P2

a1∗

a2∗

a3∗

§

An authorized user satisfying the positive attributes will definitely satisfy the wildcard attribute. In these access policies, the attributes resemble sensitive information which may leak private information about the users. To overcome this, the access policy is anonymized by hiding the sensitive attribute. The anonymized AND gate with wildcard access policy is shown in Table 2. The sensitive information in the access policies of CSE students and faculties such as P1 and P2 was hidden by the symbol *, and the anonymized access policies are P 1 and P 2 , respectively. The CA encrypts the message with anonymized access policy and transmits it to the broadcasting receivers. The authorized user can efficiently recover the message only when their attribute list matches the specified access policy.

5 Performance Analysis A. Theoretical Comparison. The performance of CP-ABBE method is theoretically compared with existing schemes based on some features as shown in Table 3. Existing CP-ABE schemes in [14, 15, 24] have produced constant ciphertext length, but the access policies used in this scheme are not hidden. The linear secret sharing access structure-based encryption techniques mentioned in

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Table 3 Comparison between different CP-ABE techniques Techniques Access policy Hidden policy

Ciphertext length

[4]

MAC

Threshold

No

Linear

[7]

CP-ABE

LSSS

Partial

–

[9]

CP-ABE

Yes

Linear

[11]

ABE

AND gate with wildcard Threshold

No

Constant

[14]

CP-ABE

No

Constant

[15]

CP-ABECSCTSK CP-ABE

No

Constant

No

Constant

No

–

Yes

Constant

[24]

[25]

DPU-CP-ABE

Ours

CP-ABBE

(a) Communication overhead

AND gate with multi-valued attributes AND gate AND gate with multi-valued attributes LSSS AND gate with wildcard

(b) Length of ciphertext

(c) Computation time

Fig. 2 Experimental analysis of the proposed CP-ABBE technique with MAC method in [4]

[7, 25] do not consider the size of ciphertext. Also, in [25] due to dynamic policy updation, the access structure is not hidden, and in [7], the access structure is partially hidden in the ciphertext. The access policy is not hidden in thresholdbased encryption concepts presented in [4, 11]. But in [11], the ciphertext size is constant and the method in [4] is used for short message encryption. Compared to all these techniques, the proposed CP-ABBE algorithm has achieved constant ciphertext length with hidden access policy. B. Experimental Comparison. The performance of the CP-ABBE method is evaluated in terms of communication and storage complexity, ciphertext length, and computational complexity. It is compared with an existing scheme discussed in [4]. Figure 2 shows experimental analysis of the proposed CP-ABBE technique. • Communication Complexity. The communication complexity of CP-ABBE method is compared with the scheme discussed in [4]. The communication com-

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plexity is measured in terms of size of message versus number of nodes. In our implementation, for efficient broadcast of messages to multiple authorities, i.e., for transmitting the messages to all CSE department students and faculties, the message is initially encrypted with one-time key and this is concatenated with hidden access policy and ciphertext header. But the message size is significantly reduced, thereby making it efficient to transfer for any number of receivers. However, the technique in [4] is used for short message transmission and the size of message is increased with respect to number of nodes. It is found that the communication overhead is significantly reduced in our method than the method in [4] as shown in Fig. 2a. • Storage Overhead. The secret keys of the users are distributed through the broadcasting channel when requesting a connection. Therefore, it is not necessary to store any key information in the ciphertext. Therefore, the storage overhead is also reduced during message transmission in our CP-ABBE method. Since each user is provided with individual secret key, the broadcasting channel remains collisionfree. • Length of Ciphertext. The ciphertext length is proportional to the number of attributes in the access policy. Therefore, it is advantageous to obtain constant length ciphertext even with any number of attributes. As shown in Fig. 2b, compared with ciphertext obtained from technique stated in [4], CP-ABBE has obtained a constant length ciphertext. Also, the ciphertext size remains constant for any amount of attributes in the system. • Computational Complexity. The performance of the proposed CP-ABBE algorithm and the MAC algorithm is evaluated on MATLAB platform in Windows Operating System. From Fig. 2c, it is clear that the CP-ABBE algorithm requires less time to perform calculations than the MAC algorithm in [4]. The computation time of CP-ABBE method is calculated based on amount of attributes involved in encryption purpose. It is clear that the proposed CP-ABBE technique can perform better with any number of attributes. Thus, the computational cost is reduced in our CP-ABBE technique. C. Proof of Privacy Model. Consider the proposed algorithm as β and encryption to be performed by adversary as δ. An adversary A, with privacy against β can construct an adversary B against δ. That is, if any adversary breaks the privacy of the proposed algorithm, then it will break the privacy for the encryption algorithm performed by the adversary. In this case, no sensitive information is attacked by the adversary. In our proposed work, the plaintext is encrypted in the ciphertext with one-time encryption key. The authorized user is allowed to access the data only when the one-time encryption key is successfully recovered by the user. This is possible only when the authorized user satisfies the hidden access policy encrypted in the ciphertext. Since it is not possible for the attacker to reveal the hidden access policy, our proposed method is secure against IND-CPA game described in privacy model given under Sect. 3.

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6 Conclusion In this paper, privacy sustaining CP-ABBE technique with constant length ciphertext is proposed. Compared to the previous methods, the experimental analysis shows that our method has produced better results. Obviously, the proposed scheme produces constant length ciphertext with hidden access policy. Also, it is clear that the communication overhead is reduced and the computation time is proportional to the number of attributes. The privacy policy of the proposed CP-ABBE scheme is found to be selective secure under IND-CPA security model. In our technique, to avoid ambiguity during decryption, the wildcard attributes are not hidden in the ciphertext. The future work of this paper is to support fully hidden access policy that is secure under adaptive adversary attacks.

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15. V. Odelu, A.K. Das, Y.S. Rao, S. Kumari, M.K. Khan, K.K. Choo, Pairing-based CP-ABE with constant-size ciphertexts and secret keys for the cloud environment. J. Comput. Stand. Int. 54, 3–9 (2017) 16. J. Li, Q. Huang, X. Chen, S.S. Chow, D.S. Wong, D. Xie, Multi-authority ciphertext-policy attribute-based encryption with accountability, in 6th ACM Symposium on Information, Computer and Communications Security (ACM, USA, 2011), pp. 386–390 17. T. Nishide, K. Yoneyama, K. Ohta, Attribute-based encryption with partially hidden encryptorspecified access structures, in ACNS 2008, vol. 5037, LNCS, ed. by S.M. Bellovin, R. Gennaro, A. Keromytis, M. Yung (Springer, Heidelberg, 2008), pp. 111–129 18. N. Doshi, D. Jinwala, Hidden access structure ciphertext policy attribute based encryption with constant length ciphertext, in ADCONS 2011, vol. 7135, LNCS, ed. by P.S. Thilagam, A.R. Pais, K. Chandrasekaran, N. Balakrishnan (Springer, Heidelberg, 2011), pp. 515–523 19. G. Ohtake, K. Ogawa, G. Hanaoka, S. Yamada, K. Kasamatsu, T. Yamakawa, H. Imai, Partially wild carded ciphertext-policy attribute-based encryption and its performance evaluation. IEICE 100(9), 1846–1856 (2017) 20. D. Boneh, C. Gentry, B. Waters, Collusion resistant broadcast encryption with short ciphertexts and private keys, in Crypto 2005, vol. 3621, LNCS, ed. by V. Shoup (Springer, Heidelberg, 2005), pp. 258–275 21. Y. Vidya, B. Shemimol, Secured friending in proximity based mobile social network. JECSE 1(2), 1–10 (2015) 22. Z. Zhou, D. Huang, On efficient ciphertext-policy attribute based encryption and broadcast encryption, in 7th ACM Conference on Computer and Communications Security (ACM, USA, 2010), pp. 753–755 23. J. Kim, W. Susilo, M.H. Au, J. Seberry, Adaptively secure identity-based broadcast encryption with a constant-sized ciphertext. IEEE Trans. Inf. Forensics Secur. 10(3), 679–693 (2015) 24. Y. Zhang, D. Zheng, X. Chen, J. Li, H. Li, Computationally efficient ciphertext-policy attributebased encryption with constant-size ciphertexts, in ProvSec 2014, vol. 8782, LNCS, ed. by S.S.M. Chow, J.K. Liu, L.C.K. Hui, S.M. Yiu (Springer, Cham, 2014), pp. 259–273 25. Z.Y. Jianfeng, J.Z. Cui, Adaptively secure ciphertext-policy attribute-based encryption with dynamic policy updating. J. Sci. 4, 019 (2016)

A Comprehensive Study of Challenges and Issues in Cloud Computing Shadab Siddiqui, Manuj Darbari and Diwakar Yagyasen

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Need of Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Cloud Service Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Cloud Deployment Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Challenges in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Security and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Data Storage Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Cultural Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Resource Exhaustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Performance and Bandwidth Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Monitoring Solutions in the Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Measures for Privacy and Data Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Cloud-Specific Solution to Storage Management, Access Control, and Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Cloud-Specific Solution to Resource Management and Task Scheduling . . . . . . 3.4 Study on Load Balancing Issues in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . 4 Cloud Computing Issues and Study in the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Cloud Data Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Data Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Cloud computing is a technology which provides the capability to use storage resources and computing services via Internet. Performance and resource S. Siddiqui (B) · M. Darbari Department of CS&E, BBD University, Lucknow, India e-mail: [email protected] M. Darbari e-mail: [email protected] D. Yagyasen Department of CS&E, BBDNITM, Lucknow, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_31

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management are a challenging task in cloud computing due to increase in demand of services by the user in a multitenant environment. Also, data privacy in cloud is a major issue because different organizations use various services of cloud. Security of cloud computing refers to a method used to protect data and various applications of cloud computing. In this paper, we have surveyed various issues in cloud computing related to resource management, load balancing, data privacy, energy consumption, data storage, etc. Moreover, the survey has also identified the solution to the existing problems in the literature to counter various issues in cloud environment. Keywords Access control · Cloud computing · Data storage Resource management · Task scheduling · Privacy Highlights • A detailed study on various issues of cloud computing has been discussed focusing on load balancing, resource management, privacy, and energy consumption. • The study has also identified the solutions to the issues discussed in the literature and the future work for the same.

1 Introduction Cloud computing is a widely used technology which works on Internet by providing various facilities to access common infrastructure and shared resources. According to NIST, “cloud computing is a method which enables sharing of resources easily and users can access on-demand resources provided by CSP” [1]. In cloud computing, the user is able to access data in a ubiquitous mode without worrying about management and maintenance of data. User is unaware of the physical location of the data and resources. Cloud computing follows the concept of pay as per use [2]. It uses cloud service providers which provide a platform for the customers to design Web services, and they have to pay for the services which they are using.

1.1 Need of Cloud Computing • Cloud computing helps the users to access in ubiquitous environment associated with physical storage. • Cloud avoids difficult recovery planning. • Cloud improves document control by allowing all files to be in one central location.

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1.2 Cloud Service Models 1.2.1

SaaS

This service focuses on applications that are delivered to the user by Web and managed by third-party vendor.

1.2.2

PaaS

It provides the user a platform to develop and test the applications. Using this service, user can easily deploy their applications.

1.2.3

IaaS

This service provides the users to rent any data center infrastructures like servers, storage, and networking devices. The uses of IaaS are also responsible for managing various applications, data, and runtime.

1.3 Cloud Deployment Models 1.3.1

Public Clouds

Public clouds are accessible by all users over the Internet. They are inexpensive to set up and provide best economy of sale. It is owned by a third-party cloud service provider, and in this users can access any data over the Internet.

1.3.2

Private Clouds

Private cloud is owned by a single organization. In this, users can access their own data through a proprietary architecture. They are owned by a private company to provide flexibility, scalability, and monitoring.

1.3.3

Hybrid Clouds

Hybrid clouds contain properties of both public and private clouds. Companies use a hybrid approach for maintaining control of internal private clouds while relying on the public cloud (Fig. 1).

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Fig. 1 Cloud computing service framework

The organization of the paper is as follows. After the brief introduction about cloud environment and services offered by cloud, Sect. 2 gives the issues in cloud environment. Section 3 presents the detailed comparative study of problems and its proposed solution in the literature review. Section 4 deals with study of issues of cloud computing as per the literature. The final section of the paper contains conclusion, future work, and references.

2 Challenges in Cloud Computing 2.1 Security and Privacy Cloud computing uses different technologies and mechanisms to provide services to customers [3]. The major and most important issue in cloud computing is security and privacy of data on cloud storage. Therefore, it should be taken into consideration for various organizations which are storing their data on cloud such that if the data gets leaked, then it will definitely affect various clients of the organization and will impact on the reputation of the organization. To overcome this problem, various techniques such as encryption, firewalls, and intruder detection systems are incorporated to track the unusual behavior across servers in the cloud environment. The graph in Fig. 2 is adopted from IDC in August 2008, and it summarizes about the issues which affect cloud computing.

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Fig. 2 Challenges/issues in cloud computing adopted from IDC in August 2008

2.2 Data Storage Issue It is another main challenge in cloud computing. Due to large volume of data on the cloud, organization the data is very important. The lack of control over the data can result in higher inconsistency and security risks.

2.3 Cultural Resistance It is also called organizational inertia, and main challenge is to share the data and also change the various ways of looking on it.

2.4 Resource Exhaustion The resources are available to users on pay as per use basis. Due to competition in the market, many cloud service providers are committing false promises with the

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users for providing resources, but they are unable to provide due to greater customer expectations.

2.5 Performance and Bandwidth Cost As the number of applications increases, the requirement of bandwidth also increases. Therefore, organizations need to spend more money for the bandwidth to deliver data over the network because of this many organizations are unable to provide cloud services to their users.

2.6 Resource Management In multitenant environment of cloud computing, the allocation of resources to users effectively is a complex task because of resource isolation; due to this, users are unable to get the required resources within time.

3 Monitoring Solutions in the Literature Review 3.1 Measures for Privacy and Data Classification Various authors have also given the solution for anomaly detection and classification of data. Lo’ai Tawalbeh et al. proposed a model of cloud computing which is secure and follows data classification. The authors have minimized the processing time and overhead to make data secure. Authors have made various security methods with different key sizes. The model gives a better result and proved to be efficient when tested with different encryption algorithms. In the future, the work can be extended by using automatic data classification and asymmetric encryption for better security. Pankaj Deep Kaur et al. have proposed a method to use cloud computing for healthcare services. They have designed intelligent care services. The sensors are used to collect the user-specific health data and store it in cloud for further classification and regression. The experimental results have shown that greater accuracy is achieved with this type of protocol. The result is cost-effective and covers many healthcare solutions. Bansidhar Joshi et al. studied security storage issues and techniques to mitigate them. The authors have classified the data as private and non-private by using machine learning approaches and probabilistic methods. The method will take parameters as input and the number of parameters alongwith its weights changes continuously for proper classification. In order to ensure authentication, one-time password should also be used. The authors have mainly focused on data storage

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issues, and this can be achieved by segregating the private data of the user from public data. In the future, different techniques of data segregation can be used for better results (Table 1).

3.2 Cloud-Specific Solution to Storage Management, Access Control, and Energy Consumption Some authors have also given the specific solution to problems of cloud computing. Keke Gai et al. proposed an energy-efficient method for current industry demands. The DECM method solves the problem of extra energy consumption in wireless communication by using dynamic cloudlets. The proposed model focuses on concept of green computing. It enables the users to accept IT in complicated environments. The authors proposed an environmental-friendly method for green computing. In the future, the main idea is to build strong connections for better communications between mobile devices and cloud servers. Thomas Pasquier et al. stated how information flow control (IFC) can be used with application-specific access control. The IFC technique offers data-centric access control. In order to ensure security, the application should be separated since the same data can be used by various applications. The access control should be data-centric for cloud in which data flows between applications. Authors have also identified how IFC can work with IOT architectures. The application logic should be separated from policy. Raju R et al. proposed EAMOCA by combining resource scheduling and echo localization for energy conservation, since cloud computing delivers high-end power computing so conservation of energy is a major factor to be considered. The proposed algorithm utilizes less energy and reduces time complexity. In the future, the work can be extended to make use of a hybrid approach by using salvation algorithms with EAMOCA. The proposed approach reduces complexity levels and increases the efficiency. Yibin Li et al. proposed Secure-Aware Efficient Distributed Storage model using intelligent cryptography. The proposed work partitions the file, and the data is stored in different servers of cloud. The authors have identified the problem of cloud data storage and provide a method to secure user’s sensitive data. The future work proposed by authors includes securing duplication of data to increase the level of availability in case of data center failure (Table 2).

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Table 1 Comparative study on privacy and data classification in cloud computing Work

Problem domain

Technique used

Features

Tawalbeh et al. [4]

Data privacy

Secure cloud computation using classification

Kaur et al. [5]

Data classification

Joshi et al. [6]

Data privacy

Tool and dataset used

Limitations

Future work

(1) The cloud Microsoft.Net model C# proposed by the author reduces the processing time and overhead using different security methods (2) The model was tested by various encryption algorithms and shows better efficiency

AES consumes more resources when the size of data is big

In the future, various data classification techniques and cryptographic algorithm can be used for more security of data

k-NN classifier, PCA, naïve Bayes

(1) The approach uses PCA for analysis of components and uses classification techniques for health status classification. (2) Experimental results demonstrate that 92.59% is achieved with the proposed system and better CPU usage

Predicting value of k is very difficult

In the future, various other approaches can be implemented to find better results

Probabilistic methods for data classifications

(1) The Hadoop authors separated the logical space by classifying the data in public and non-public mode (2) Authors have used probabilistic method of classifications (3) By using additional level of authorization in private data, it can be more secured

Relationship between dataset and time factor increases

(1) In the future work, different machine learning probabilistic methods can be used to obtain more accurate results (2) Better authentication techniques can be used to obtain good results

Amazon EC2, Weka (real-life dataset)

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Table 2 Comparative study on cloud-specific solution to storage management, access control, and energy consumption Work

Problem domain

Technique used

Features

Gai et al. [7]

Energy consumption

DECM

Pasquier et al. [8]

Granting access

Information flow control (IFC)

Tool and dataset used

Limitations

Future work

(1) Authors DECM-Sim have proposed DECM method. This model solves the problem of extra energy consumption by using dynamic cloudlet (DCL)-based model in wireless communications (2) Authors have contributed toward solving the problem of energy wastage in dynamic networking environment

(1) The given approach helps in saving more energy by defining long energy constraint

(1) In the future work, the research can be used to determine if DECM can work with multiple industries by providing different service requirements (2) The model can be used to construct structured connection to increase the strength of communication between cloud storage servers and mobile devices

In this paper, Directed the authors graphs have considered how IFC can be used with applicationspecific access control when constructing IFC access policies which arranges with data management limitation of cloud service providers and cloud users

The continuous use of IFC has a biggest challenge of developing policy specification with respect to application

In the future, the work can be extended for decoupling of policy specification and enforcement from application

(continued)

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Table 2 (continued) Work

Problem domain

Technique used

Features

Raju et al. [9]

Energy consumption

Energy-aware multiobjective chiropteran algorithm (EAMOCA)

Li et al. [10]

Secure data storage

(SA-EDS) model

Tool and dataset used

Limitations

Future work

(1) In this – paper, authors proposed a new algorithm (EAMOCA) by combining hibernating properties of scheduling resources and echo localization. The model also conserves energy (2) The authors have made use of performance metrics like SLA violation, energy consumption, and VM migration (3) The authors have also implemented the model by setting its own private cloud

(1) The proposed algorithm might work moderately in heavy workload conditions

(1) In the future, the existing algorithm can be compared with other energy salvation algorithms available and creating a hybrid approach by relating to EAMOCA

(1) In the – proposed method, the author partitions the file and stores the data on different cloud servers (2) The operation time is reduced by dividing the data into packets (3) The method used is efficient and secure, and it can prevent the threats from the cloud within computation time

The proposed approach takes more decryption time for both settings

In the future, the data duplications can be secured to increase the availability of data because if a data center is down it will lead to failure of data retrieval

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3.3 Cloud-Specific Solution to Resource Management and Task Scheduling Manish Verma et al. proposed a method by predicting the demand as per resources in a multitenant environment. In the proposed technique, the framework will give priority to those tenants whose demand for resources increases, thereby reducing the time needed for prediction. In this method, the virtual machines are allocated to the host using best-fit heuristic. In this approach, service tenants are added to match the virtual machines and then these virtual machines are allocated to host machines. In the future, the approach can be used for large datasets in different domains. Bhaskar Prasad Rimal et al. proposed a method for computing workflow in multitenant environment. The proposed technique will reduce the execution time, cost of workflow execution. This proposed technique is compared with many algorithms such as First Come First Serve (FCFS), Minimum Completion Time (MCT), backfilling for comparing the effectiveness and scalability of proposed solution. Authors have shown the result in which the performance of CWSA is much better as compared to conventional algorithms. Although multitenant environment provides better resource utilization, in the future the work can be extended to implement scheduling policies for considering resource failures and multitier applications. Gongzhuang Peng et al. proposed a method for analyzing and improving the performance of the system. Authors have made use of radial neural network function. The resource allocation process in multitenant environment is represented by mathematical model. It uses genetic algorithm with k-means for optimal resource allocation. In the future, the model can be extended for analysis of node price and priority weights. Yongkui Liu et al. proposed a method for scheduling multiple tasks simultaneously, and this method makes use of process model and other performance parameter. For timeconstrained task, the proposed strategy works well by successfully completing the task within time constraint. In the future, the model can be extended to consider task arriving continuously at different periods of time. Shubham Mittal et al. have proposed optimized task scheduling algorithm which enables the tasks to be completed on time by exploiting the resources available to ensure better efficiency. The output of the algorithm proves to be better in terms of throughput, make-span as compared to an existing scheduling algorithm. In the future, the work can be applied to real cloud environment for better scalability and efficiency (Table 3).

3.4 Study on Load Balancing Issues in Cloud Computing Marwa Gamal et al. proposed HBA and ACO method collectively called HBAC. The approach makes use of behavioral properties of ACO and ABC. The method monitors the load of virtual machine, and before allocating the process to the virtual machines it also does load balancing. The HBAC method has improved execution

Verma et al. [12]

Dynamic resource demand prediction and allocation framework

Resource allocation

Rimal et al. [11]

Technique used

Problem domain

Resource management in Cloud-based workflow multitenancy scheduling (CWSA) policy

Work

Tool and dataset used

(1) The authors have proposed a framework which helps in classification of tenants according to their increasing resource requirement (2) This framework will help in prioritization and prediction of tenants in which demands are increasing and time needed for prediction is decreasing

CloudSim

(1) The authors have CloudSim proposed a method for calculating the workflow in different applications in multitenant cloud environment (2) The method allots the resources in high speed so the length schedule of workflows increases easily

Features

Table 3 Cloud-specific solution to resource management and task scheduling Limitations

This approach might not work well for large datasets

It has not considered resource failures in multitier applications scaling which can affect different applications

Future work

(continued)

(1) In the future, the work can be extended to test and evaluate the proposed approach on larger datasets

In the future, the work can be extended for better optimization of CSWA and apply it to mobile cloud computing

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Problem domain

Resource allocation

Task scheduling and resource allocation.

Work

Gongzhuang et al. [13]

Yongkui Liu et al. [14]

Table 3 (continued) Technique used

Multitask scheduling model

Genetic algorithm and the K-means approaches

Features

Tool and dataset used

(1) The method proposed Microsoft Visual Studio by authors is used for 2010 determining efficiency of the system (2) The proposed method calculates the time required for computation and utilization of resources under heavy load conditions (3) The make-span time can be reduced by bigger workload task with higher priority time

(1) The authors have COSIM-CSP proposed a method for analyzing the system and improving the performance of system (2) The resource allocation process is constructed by a mathematical model in multitenant environment using priority-based parameter, computational cost, and load balance (3) Authors have proposed multi-objective genetic algorithm using k-means. The proposed framework provides effective multitenant requirements

Limitations

(1) The research has not worked upon continuous arrival of tasks at varying timings

(1) Large problems with complex constraints has less efficiency

Future work

(continued)

(1) In the future, the work can be extended by considering arrival of tasks continuously at different times (2) Intelligent algorithms in cloud can be used for optimizing task scheduling

(1) In the future, the effect of parameter node price and priority weight will be analyzed (2) The algorithm can be developed for large-scale applications with better performance

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Problem domain

Task scheduling and resource allocation

Work

Shubham Mittal et al. [15]

Table 3 (continued) Technique used Optimized task scheduling algorithm

Features

Tool and dataset used

(1) Authors have Java 7 Technology proposed optimized task scheduling algorithm by considering scalability and distribution of cloud resources (2) The proposed algorithm uses intelligent computation where according to situation the system automatically adapts optimized task scheduling scheme

Limitations (1) The research is tested on sample tasks rather than dynamic cloud environment

Future work (1) The work can be elaborated further to implement on CloudSim simulation toolkit for practical evaluations

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time, make-span, and response time. This method guarantees the load balancing of the system and results in increasing the utilization rate. Ibrahim Berkan Aydilek et al. proposed hybrid algorithm combining FA and PSO. The proposed method tries to search the local process by finding the best fitness values compared to other algorithms. Adam Slowik et al. proposed the concept of SI by combining the properties of PSO and ACO method. The proposed model is not analyzed mathematically. Future extension could be application of snooping. Mainak Adhikari et al. proposed an approach IaaS cloud. As per the tasks, the servers are arranged. The method used HBLBA. The proposed method has a strategy for finding suitable virtual machine. It leads to minimization of make-span. The comparison of algorithm is made with other algorithms by using various performance metrics. Shridhar Domanal et al. proposed an HBI algorithm for resource scheduling. It allocates the resources to virtual machines efficiently. The method makes use of modified particle swarm optimization to give task to virtual machine and use hybrid algorithm to allocate the resources. Ashish Gupta et al. proposed ACO algorithm in order to remove the problem of process scheduling in cloud environment. The method reduces the computation time and make-span. Ant colony optimization application finds local optimal solution. Bibhav Raj et al. proposed a meta-heuristic algorithm known as modified bat algorithm. For population generation, the method uses min-min, it also uses minmax and alpha–beta pruning algorithm, and for keeping the sequence of execution of task to minimum it uses bat algorithm. Authors have discussed the allocation of tasks in virtual machines and then compared their execution time. In the future, the work can be extended to identify more benefits from optimization methods. M. Vanitha et al. proposed a novel load balancing method making use of well organization of resources known as DWOLR. The algorithm distributes the load on virtual machines making use of genetic algorithm. The proposed method can attach multiple servers to virtual machine, thereby reducing the power consumption. Hui Wang et al. proposed a new variant of FA called FA with neighborhood attraction. In the proposed method, the fireflies are attracted toward the predefined neighbors of fireflies rather than the entire population of fireflies. Using attraction, the fireflies can better find new candidate solutions. If there are too many attractions, then it can lead to higher collisions so as to overcome this problem proposed method can be used. Aditya Narayan Singh et al. proposed the strategy for assigning virtual machine. The proposed method calculates weight factor according to physical memory, bandwidth, and number of processors. According to the calculation of weight, the virtual machine with highest weight is selected for execution of tasks (Table 4).

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Table 4 Load balancing issues in cloud environment Proposed work Focus

References

HBA and ACO

Load balancing

[16]

Hybrid FA and PSO, FA with neighborhood attraction

Optimization, load balancing

[17, 18]

PSO algorithm and ACO method

Swarm intelligence and load balancing

[19]

Heuristic-based load balancing algorithm, meta-heuristic approach

Load balancing

[20, 21]

Hybrid bio-inspired algorithm

Resource management

[22]

Weighted active monitoring, resource utilization Modified bat algorithm

Load balancing

[23, 24]

Task scheduling

[25]

4 Cloud Computing Issues and Study in the Literature 4.1 Resource Management The availability of resources is a major concern for customers. It refers to availability of resources to every customer on their need. Proper scheduling methods can be used for resource management.

4.2 Cloud Data Storage Due to centralization of data on the cloud, storage of data is a very important issue. Storage of data is done by resource pool. This resource pool can be accessed by any unauthorized user through virtual machines. So, proper methods must be used to prevent retrieval of data by any unauthorized user.

4.3 Data Privacy Privacy ensures that the data of user is not altered or modified by another user. Privacy of data is one of the major concerns of customer. If the data is accessed by authorized user only, then privacy of data is maintained; otherwise, methods to ensure data privacy should be incorporated.

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4.4 Energy Consumption Due to a large usage of data centers for resources, energy consumption is an important and key issue in cloud computing. So, proper energy conservation approaches should be applied to ensure proper working and maintenance of cloud data centers. Suitable optimization policies can be used to reduce energy consumption (Table 5).

Table 5 Summary of cloud computing issues and its study in the literature Category Issues Recommended solutions

References

Resource management

Job scheduling, scalability, pricing availability, energy management

Workflow scheduling, [26, 15, 27, pool manager, dynamic 28, 29, 30, resource demand 31, 32, 33] prediction, real-time resource scheduling, parallel computing, energy-efficient scheduling algorithm, resource allocation based on genetic algorithm

Cloud data storage

Data loss, leakage, data security, data breach, unavailability of data, access control, storage optimization

Secure cloud storage, [13, 14, 34, probabilistic methods for 35, 36, 37, data classification, 38, 39] compression, deduplication, throttling, intelligent cryptography for big data storage

Data privacy

Authentication, data protection, eavesdropping

Multifactor authentication, dynamic programming, privacy preservation record linkage, trust-based mechanism, role-based multitenancy access control, ant colony optimization

[40, 41, 42], [43, 38, 44, 47, 45, 39]

Energy consumption

Bandwidth and power optimization, time complexity, slow convergence

Dynamic energy-aware cloud computing, Amazon cloud watch, bandwidth optimization

[7, 10, 46, 37, 39, 33]

Load balancing

Optimization problem, task scheduling, allocation of resource, resource utilization

Hybrid bio-inspired algorithm, firefly algorithm with neighborhood attraction, weighted active monitoring, resource utilization, improvised bat algorithm

[16, 17, 19, 20, 22, 21, 25, 24, 18, 23]

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5 Conclusion and Future Work Cloud computing has become the latest technology in the IT world market because of its undaunted deliberations in terms of features and benefits. Performance is still a complex task in cloud and is a major issue of further exploration. Stored data is prone to risks so the security risks must be controlled in cloud environment. Authors have highlighted various issues that arise due to characteristics of cloud like performance, privacy of data, resource management, storage. Different researchers have proposed various methods to overcome the problem of resource management. Dynamic resource allocation and load balancing techniques can be used for fair allocation of resources in multitenant environment. Future work includes providing a better solution for the existing problems of cloud computing when dealing with multifactor optimization.

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Comparative Analysis of Major Jacobian and Gradient Backpropagation Optimizers of ANN on SVPWM Neeraj Seth, Ashish Ubrani, Sneha Mane and Faruk A. S. Kazi

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Space Vector Pulse Width Modulation (SVPWM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Training Algorithms for Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Resilient Backpropagation (RPROP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Conjugate Gradient Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Broyden–Fletcher–Goldfarb–Shanno Quasi-Newton (BFGS-QN) . . . . . . . . . . . . 3.5 Levenberg–Marquardt (LM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Bayesian Regularization (BR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Mean Squared Errors (MSEs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Time for Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Coefficient of Regression (R) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Total Harmonics Distortion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Analysis of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract This paper presents the comparative analysis of various training algorithms based on artificial neural networks (ANNs) to compute space vector pulse width modulation (SVPWM) to control the switching pulses of an inverter. SVPWM has a high mathematical and computational complexity. ANN helps in minimization of this complex computation in SVPWM. This paper presents basic concepts behind different ANN algorithms, its comparative analysis for the application of computing N. Seth · A. Ubrani · S. Mane (B) · F. A. S. Kazi CoE-CNDS, Electrical Engineering Department, VJTI, Mumbai, India e-mail: [email protected] N. Seth e-mail: [email protected] A. Ubrani e-mail: [email protected] F. A. S. Kazi e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_32

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SVPWM for a three-phase three-leg voltage-controlled inverter. Various Jacobian and gradient optimizers were used to train the ANN. Resultant network is tested on various parameters like mean squared errors (MSEs), time for training and total harmonic distortion (THD) of inverter. Keywords Space vector PWM (SVPWM) · Pulse width modulation (PWM) Artificial neural network (ANN) · Jacobian optimizers · Gradient optimizers Total harmonics distortion (THD)

1 Introduction Inverters are the heart of power electronics drives, and with the development of IGBT/MOSFET they are used for controlling of medium/high power drives [1]. There are various PWM techniques as mentioned in [2, 3]. One of the most optimistic techniques to generate required PWM is SVPWM because of its high utilization of DC link voltage as compared to sinusoidal pulse with modulation (SPWM). Problem with SVPWM is that it requires high computational power which takes higher time and limits its switching frequency. Switching frequency can be improved by using DSP having high computational power and by taking big size of lookup table (small lookup tables tend to reduce pulse width resolution) which again increases memory size. Artificial neural networks (ANNs), nowadays, are becoming highly immerging and are implemented in a vast area of power electronics field due to their inherent learning capabilities. One of the main advantages of ANN is that it will benefit the system where traditional computational methods are not enough to provide exact solution of the problem statement. A feed-forward ANN implements nonlinear input–output mapping. By using parallel architecture of network on application-specific integrated circuit (ASIC) chip, the computational time of this mapping can be neglected. SVM also looks feed-forward nonlinear input–output mapping where phase voltage is sampled at input and corresponding PWM generates at output; hence, ANN helps to improve switching speed when implemented on SVM. In this paper, a three-phase threeleg voltage-controlled inverter has been taken and gate pulses have been provided through SVPWM by using various ANN optimizers. The training of the network has been done by giving three-phase input voltages with fixed switching frequency. However, it can also be done for variable voltage and switching frequencies. A three-phase inverter is shown in Fig. 1. In order to generate desired three-phase output, switches 1–6 are triggered through gate pulses obtained from SVPWM by using ANN technique.

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Fig. 1 Three-phase inverter

2 Space Vector Pulse Width Modulation (SVPWM) SVPWM utilizes DC link better than SPWM technique. According to SVPWM theory, three-phase input voltage can be represented by a single vector having some magnitude and angle which rotates in the space and the tip of the vector makes hexagon in the space with six different sectors [4]. The reference voltage vector V ref is approximated with the use of eight different switching patterns. SVPWM can be realized by using these steps: Step 1: Finding the value of Vd , Vq , Vref and angle θ Step 2: Finding the value of time duration T1 , T2 , T0 Step 3: Finding the value of switching time of each IGBT/MOSFET (S1 to S6). Three-phase voltage has been taken for the reference. These three-phase voltages are then converted into two phase which is d-q frame by using park transformation as mentioned in (1) Vdq0  K s V ABC

(1)

⎡

⎤ 1 −1/2 −1/2 √ √ 2⎢ ⎥ Ks  ⎣ 0 3/2 − 3/2 ⎦ 3 1/2 1/2 1/2 The angle and magnitude of reference vector can be calculated from (2) and (3)  |Vref |  Vd2 + Vq2 (2) θ  tan

−1



Vq Vd

(3)

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Table 1 Switching pulses for all six sectors Sector 1 2 3

4

5

6

Upper switch

S1  T 1 + T 2 + T o /2 S3  T 2 + T o /2 S 5  T o /2

S1  T 1 + T o /2 S3  T 1 + T 2 + T o /2 S 5  T o /2

S 1  T o /2 S3  T 1 + T 2 + T o /2 S5  T 2 + T o /2

S 1  T o /2 S3  T 1 + T o /2 S5  T 1 + T 2 + T o /2

S1  T 2 + T o /2 S 3  T o /2 S5  T 1 + T 2 + T o /2

S1  T 1 + T 2 + T o /2 S 3  T o /2 S5  T 1 + T o /2

Lower switch

S 4  T o /2 S6  T 1 + T o /2 S2  T 1 + T 2 + T o /2

S4  T 2 + T o /2 S 6  T o /2 S2  T 1 + T 2 + T o /2

S4  T 1 + T 2 + T o /2 S 6  T o /2 S2  T 1 + T o /2

S4  T 1 + T 2 + T o /2 S6  T 2 + T o /2 S 2  T o /2

S4  T 1 + T o /2 S6  T 1 + T 2 + T o /2 S 2  T o /2

S 4  T o /2 S6  T 1 + T 2 + T o /2 S2  T 2 + T o /2

The vector can now be presented as −→ Vref  |Vref | θ where |Vref | is the magnitude and θ is the angle of reference vector which is rotating in space. In d-q reference frame, there are six sectors. Each sector is divided equally into sixty degrees. Basic vectors are V 1 , V 2 , V 3 , V 4 , V 5 , and V 6 . The trajectory of the space vector forms hexagon in space and is divided into six sectors with difference in magnitude and phase. From the calculation of reference vector angle (θ ), sectors can be calculated as mentioned below Sector I: 0 < θ ≤ 60◦ Sector II: 60◦ < θ ≤ 120◦ Sector III: 120◦ < θ ≤ 180◦ Sector IV: 180◦ < θ ≤ 240◦ Sector V: 240◦ < θ ≤ 300◦ Sector VI: 300◦ < θ ≤ 360◦ From the above calculations, the sector can be identified where the vector is located. Once the position of vector is known, then timings of the switches can be calculated from Eqs. (4) to (6) [5]. √  nπ 3TZ |Vref |   nπ cos θ − cos sin θ (4) sin T1  Vdc 3 3 √





3TZ |Vref | (n − 1)π (n − 1)π T2  − sin cos θ + cos sin θ (5) Vdc 3 3 T0  TZ − (T1 − T2 ) (6) where n is representing sector number, i.e., 1–6. The timings of the switches are different for different sectors and can be determined by using Table 1.

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3 Training Algorithms for Artificial Neural Networks Cost function is the measure of how bad a neural network is performing on a given dataset. After k iterations, it is given by E(k) 

M N 1

(h m (i) − ym (i))2 2M m1 i1

(7)

Here, ym (i) & h m (i) are target output and actual output of neuron i for training example m. M is the number of training examples, and N is the number of neurons in output layer. Objective of a training algorithm is to minimize this cost function. Minimizing cost function effectively translates to changing weights and biases to optimum values in order to achieve the minimum cost.

3.1 Gradient Descent Gradient descent computes new weights and biases of ANN by calculating gradient of cost function. In batch gradient descent, each step uses all the training examples. Weights and biases w(k) are updated as w(k + 1)  w(k) − x(k) ∂E x(k)  α × ∂x 3.1.1

(8) (9)

Gradient Descent with Momentum (GDM)

Gradient Descent with momentum is used to dampen oscillations while convergence, accelerate convergence and to avoid convergence at shallow local minima. Adjustment term is modified here as x(k)  m × x(k − 1) + α × (1 − m) ×

3.1.2

∂E ∂x

(10)

Gradient Descent with Adaptive Learning Rate (GDA)

Gradient descent with adaptive learning rate adjusts the learning rate dynamically. We have three predefined quantities in this case—E max , αinc , and αdec . If E(k) > E max , then

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α(k)  α(k − 1) × αdec x(k)  x(k − 1).

(11)

If E(k) < E max , then α(k)  α(k − 1) × αinc ∂E x(k)  α(k) × ∂x 3.1.3

(12)

Gradient Descent with Momentum and Adaptive Learning Rate (GDAM)

This algorithm combines both the algorithms (3.1.1) and (3.1.2); i.e., learning rate is adjusted dynamically according to (3.1.1), and x(k) is updated according to (3.1.2). If E(k) > E max , then α(k)  α(k − 1) × αdec x(k)  x(k − 1)

(13)

If E(k) < E max , then α(k)  α(k − 1) × αinc x(k)  m × x(k − 1) + α × (1 − m) ×

∂E . ∂x

(14)

3.2 Resilient Backpropagation (RPROP) Weight step—x(k) taken in gradient descent algorithms—is ‘blurred’ by unforeseen behavior of the partial derivative ∂∂ Ex . Resilient backpropagation (RPROP) was proposed to solve this problem by considering only the sign of partial derivatives [6] ∂E >0 ∂ xi j ∂E VT , drain current will become   1 2  W (VG S − VT )VDS − VDS . ID  k L 2

(1)

This equation is for linear region. Here drain to source voltage VDS is less than VG S − VT .   1  W (VG S − VT )2 (2) ID  k 2 L This is the case for saturation region. Here VDS is greater than or equal to (VG S − VT ). ID  0

(3)

This is the case for cutoff region. Here VT is threshold voltage at which MOSFET conducts, VG S is gate to source voltage, W /L defines the geometry of the device that can be combined with the factor k  . Here VG S is less than the threshold voltage VT .

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Fig. 1 Leakage current

Power loss on bus can be caused by three sources: 1. Leakage current occurs through the n-p junction diode, which is shown in Fig. 1. 2. Short circuit takes place when there is another path going to ground. 3. Charging and discharging of parasitic capacitance take place. Leakage component of power dissipation can be shown by Fig. 1, that is, the drain to source current leakage current is independent of the drain to source voltage VDS . The reduction in the losses gives better result to system [7].

1.2 Stuck-at Faults The circuit can be fixed to value due to short circuit that causes faults in the system. It may be permanent or temporary. The value may be zero or one. Non-repeated faults are also possible due to disturbance in the power supply. Due to this, the original information is lost. Such problem can be detected and can be corrected [8, 9]. The rest of the paper is organized as follows. In Sect. 2, the existing technique has been described. Section 3 gives the explanation of the proposed technique. Section 4 deals with result and analysis of different techniques. Section 5 is related with conclusion.

2 Existing Technique In digital communication, the digital data is sent over telephone lines using different binary codes [10]. During the transmission, because of noise signal, 0 may become 1 or 1 may become 0, and wrong information may be received at the destination [11]. This problem of communication is overcome by using error-detecting codes [12–14]. The error-detecting codes are: (i) Parity codes,

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Fig. 2 Even parity generator

Fig. 3 Odd parity generator

(ii) Block parity codes [13, 15], (iii) Linear block code, etc. [16, 17].

2.1 Even Parity Generator Even parity generator is a type of parity code shown in Fig. 2. Here there are sevenbit inputs which are fed to the XOR gate. It checks number of ones; if there is even number of ones, the output is zero, and if there is odd number of ones, the output will be one. This extra line goes onto the bus along with the original data. Through the extra line information, receiver will recognize the error present in information.

2.2 Odd Parity Generator Odd parity generator is a type of parity codes shown in Fig. 3. Here there are three-bit inputs which are fed to the XOR gate and XNOR gate. It checks number of ones; if there is odd number of ones, the output is zero, and if there is even number of ones, the output will be one. This extra line goes onto the bus along with the original data. Through the extra line information, receiver will recognize the error present in information.

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Apart from these techniques, we have block parity codes technique. In this technique, transmitter transmits the information as a block; each block consists of several binary words. Parity bits can be assigned to both rows and columns [13].

3 Proposed Technique One way of determining whether a combinational circuit operates properly is by applying to the circuit all possible input combinations and comparing the resultant outputs with either the corresponding truth table or a faultless version of the same circuit. Any deviation indicates the presence of some fault. Moreover, if a known relationship exists between the various possible faults and the deviations of output patterns, it is possible to diagnose the fault and to classify it at least within a subset of faults whose effects on the circuit outputs are identical. The proposed technique has been designed for seven-bit input. The design will vary depending on the requirement. Here a0, a1, a2 show the number of ones available at the inputs apart from generating the parity bits. Here a0, a1, and a2 show the weighted value. If we have input 0001111, then the output will be generated as 100, for 1001000, output will be 010 means 2. Here 2 say that there are two ones in the input lines. At the transmitting time, three extra lines are required. At the receiver end, original data will reach along with these extra lines. It informs the receiver about the number of ones/zeroes available in the input line apart from generating parity bits. This technique makes the analysis easier compare to even and odd parity generator. If there are any faults on the bus due to stuck-one or stuck-zero, we can easily able to analyze the input data. The design will be changed with a different number of inputs. Figure 4 shows the block diagram of “error detection using counting technique.” b0, b1, …, b6 are seven-bit inputs, whereas a0, a1, and a2 are outputs.

Fig. 4 Block diagram of error detection using counting technique

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4 Result and Analysis Figure 2 has been implemented using Cadence software, and output has been verified. The waveform shown in Fig. 5 is the output of the seven-bit inputs selecting only two input bits and output. It generates zero if there is even number of ones in the inputs else one. Figure 4 has been implemented using Cadence software and output has been taken. Figure 6 shows the waveform of the seven-bit inputs of error detection using counting technique circuit. From Fig. 6, number of zeros/ones can be observed. This technique has been analyzed using GDI technique. It gives the information available on the bus. It gives exact number of ones, which can be matched with original data. The different techniques have been implemented and compared with each other. The comparison is shown in Table 1 in terms of power and delay. Table 1 shows the comparative analysis of the error checkers using CMOS technology and GDI technology. From power and delay point of view, even parity generator is best approach, but the problem concerned with it is it cannot predict the exact

Fig. 5 Waveform of even parity generator using GDI technology Table 1 Comparison analysis of different techniques Parameters Power (mW) Error detection using counting 141.9 × 10−3 technique (GDI)

Delay (nS) 1.057

550.7 × 10−6

18.27 × 10−3

Even parity generator (CMOS) 20.68 × 10−3

530.8 × 10−3

10−3

313.5 × 10−3

Even parity generator (GDI)

Odd parity generator (GDI)

88.69 ×

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Fig. 6 Waveform of error detection using counting technique

number of errors. If someone wants to find exact number of ones and zeros available with the data, they should select the first approach which is nothing but error detection using counting technique. Since the power consumed by it in mW, it may not affect the system much, from delay point of view also it may be useful in finding and correcting the errors.

5 Conclusion Thus the paper has been designed using cadence software using Virtuoso tool. The different circuits have been analyzed. This paper presents a different technique, which is used for detection and correction of errors using counting technique. This improvement increases the efficiency and reliability of data transmission comparatively. In the future, one can find an application in which it may have helped to recognize the errors. There can be a plan of finding an exact location of the errors by using this technique. Acknowledgements Behind every achievement, there is unfathomable sea of gratitude to those who supported it and without whom it would ever have been a successful one. I am thankful to my Principal and Head of Department for their support and encouragement. This paper is dedicated to my beloved son.

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References 1. S. Fadnavis, An HVD based error detection and correction code in HDLC protocol used for communication. Int. J. Adv. Res. Comput. Commun. Eng. 2(6), 2349–2353 (2013) 2. A. Wang, N. Kaabouch, FPGA based design of a novel enhanced error detection and correction technique. IEEE 3(5), 25–29 (2008) 3. B.A. Forouzan, Data Communication and Networking 2nd edn. (Tata McGraw Hill) 4. B.A Forouzan, Data Communication and Networking, 4th edn. (Tata McGrawHill Publication) 5. D. Sinha, T. Sharma, K.G. Sharma, Prof. B.P. Singh, Design and Analysis of low Power 1-bit Full Adder Cell. IEEE (2011) 6. A. Anand Kumar, Fundamentals of Digital Circuits, PHI 2nd edn. 7. S. Sharma, V. Kumar, An HVD based error detection and correction of soft errors in semiconductor memories used for space application, in International Conference on Devices, Circuits and Systems (ICDCS), 2012, pp. 563–556 8. Using hierarchy in design automation: the fault collapsing problem, in 11th VLSI Design and Test Symposium Kolkata, 8–11 Aug 2007 9. Jump upˆ A. Veneris, R. Chang, M.S. Abadir, S. Seyedi, Function fault equivalence and diagnostic test generation in combinational logic circuits using conventional ATPG 10. S. Sharma, Digital Communication, 2nd edn. (SK Kataria & Sons Publication) 11. C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27, p. 418 (1948) 12. S. Lin, D.J. Jr. Costello, Error Control Coding: Fundamentals and Applications (Prentice-Hall, 1983). ISBN 0-13-283796-X 13. W.C. Huffman, V.S. Pless, Fundamentals of Error-Correcting Codes (Cambridge University Press, 2003). ISBN 978-0-521-78280-7 14. F.J. MacWilliams, N.J.A. Sloane, The Theory of Error-Correcting Codes (North-Holland, 1977), p. 35. ISBN 0-444-85193-3 15. J.H. van Lint, Introduction to Coding Theory. GTM. 86, 2nd edn. (Springer, 1992). p. 31. ISBN 3-540-54894-7 16. W.E. Ryan, S. Lin, Channel Codes: Classical and Modern (Cambridge University Press, 2009), p. 4. ISBN 978-0-521-84868-8 17. M. Greferath, An introduction to ring-linear coding theory, in Gröbner Bases, Coding, and Cryptography ed. by M. Sala, T. Mora, L. Perret, S. Sakata, C. Traverso (Springer Science & Business Media, 2009). ISBN 978-3-540-93806-4

Adaptive Sampling Rate Converter for Wireless Sensor Networks P. Swetha, S. Srinivasa Rao and P. Chandrasekhar Reddy

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Bridging Solutions in WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Hardware Support for the Gateway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Sampling Rate Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Implementation of ASRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract With the advent of technology, wireless sensor networks (WSNs) are becoming more preferable for diverse applications like in industrial settings for machine monitoring and in large buildings and bridges for structural integrity monitoring. These WSNs comprise different types of sensors based on the application. Each of these sensors transmits their information at different sampling rates to the central gateway (like IOT gateway) in the WSN. In this scenario, to meet the realtime rates, adaptive sampling rate converters (ASRCs) are necessary to the gateway for up/down converting the data collected from different sensors of the network. Therefore, this paper emphasizes the necessity of an efficient ASRC in wireless sensor networks (WSNs), and a case study for an audio is presented using different sampling rate converter structures. Keywords Sensors · Wireless sensor networks · Gateway Adaptive sampling rate converters P. Swetha (B) JNTUH, Hyderabad, Telangana, India e-mail: [email protected] S. Srinivasa Rao Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, India e-mail: [email protected] P. Chandrasekhar Reddy JNTUCE, Hyderabad, Telangana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_43

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1 Introduction WSN is defined as a wireless network consisting of sensor nodes, and each of these nodes is provided with a sensor which detects physical phenomena such as light, temperature, and pressure. WSN is considered as an innovative information congregation process to construct the information and communication system which will immensely enhance the accuracy and ability of an infrastructure system. On comparing the WSNs with the wired solution, the wireless sensor networks (WSNs) feature is easier organization and better flexibility of devices. Based on a survey of WSNs, it is stated that every sensor node operates at different sampling rates [1]. On considering a sensor sampling rate, a prototype is observed, and the applications with sampling rate below 1 Hz are the most popular; and the most frequently used high-frequency sampling rate ranges are 10–100 Hz and 10–100 kHz. The deployment count for the sampling rate range is shown in Fig. 1 [1]. These days, the conventional mobile communication network and the Internet are largely used for long-distance communication, while the WSNs are realized for communication among the objects placed over a short distance from each other forming an ad hoc wireless network. However, a difficulty arises in connecting the WSN with the mobile communication networks or the Internet as it lacks homogeneous standardization in sensing technologies and communication protocols used, and also because of the limitation of WSNs transmission protocols, data cannot be transmitted for long distance. Therefore, there is a need for a new form of network equipment which can settle the problem of heterogeneity among the various WSNs and mobile networks or the Internet. This equipment is referred as the Internet of things gateway. This equipment not only reinforces the organization of the wireless sensor network and its terminal nodes, but also makes the network communication easier between the sensor nodes and end-user by bridging the traditional communication networks with sensor networks. To list, the key issues encountered in the implementation of IOT gateway

Fig. 1 Deployment count versus sampling rate range

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system are addressing the heterogeneity of various sensor networks and the variety of protocols used in the traditional telecommunication networks and the WSNs.

2 Bridging Solutions in WSNs A wireless sensor network application mainly includes three layers, namely the collection layer, transmission layer, and the application layer, as shown in Fig. 2 [3]. In conventional WSNs, the sensor data from the collection layer will be forwarded to the sink nodes by means of short-distance transmission, and then the sink node forwards the data to the devices in the transmission layer through either the wired or wireless transmission. In wired transmission, the data usage is confined only to local applications. Hence, a need arises for a bridging solution in WSNs which is application specific [4]. It is the transmission layer where the bridging solutions are mainly applied [5]. In the transmission layer, once the collected data is received from the sink node, the WSN application transmits the data to the remote devices using specific bridging solutions rather than using the USB interface for transmitting data to the local base

Fig. 2 Architectures of gateways in WSN: WSN1 is an architecture without gateways; WSN2 uses hardware gateway-based solutions; middleware solutions are used in WSN3 [2]

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station. Thus, deployment of the bridging solutions allows sharing of the real-time sensor data between the WSN and other networks. Consider a WSN that uses transmission protocol as 802.15.4/Zigbee. Fixed IP addresses are not allocated to the devices in such networks, and a Zigbee interface is required in the gateway, so that it initiates the protocol conversion on receiving the collected data and later transfers the reorganized data to various networks using relevant transmission interfaces, which include 3G or 4G, Wi-Fi, and GPRS. The bridging solutions are broadly classified as hardware and middleware solutions. Each hardware solution has a different type of transmission interface with hardware implementation. On receiving the data from the collection layer, the hardware solutions process the protocol conversion and then forward the restructured data to a specific network which uses different transmission interfaces. Such solutions are stable and are also relevant for the networks that are already set up. These solutions require more hardware support or even specific hardware design.

3 Hardware Support for the Gateway Implementation of a central gateway (like an IOT gateway) hardware solution requires a multi-standard transceiver; as for a gateway, an important feature is its ability to manage several different standards. This transceiver should adapt its functionality according to the standard that is currently used. The advanced wireless transceivers should be able to support various sampling rates in either a receive chain or transmit chain in order to accommodate different system bandwidths or different operating bands. A basic block diagram of wireless transceiver is shown in Fig. 3. One element of accommodating diverse bandwidths, such as in baseband processing of a transceiver, is by the use of sampling rate conversion that converts one sampling rate of a signal to another. Such conversion can be performed at the output

Fig. 3 Basic block diagram of wireless transceiver

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of an ADC, at the input of a DAC, or any other section of baseband processing where conversion or adjustments of sampling rates are required. The main reasons of converting a digital signal from one sampling rate to the other can be listed as follows (1) Reduction in the computational cost, (2) to meet various applications which use different sampling rates, for example digital audio signal processing, digital image and video processing, [6] and SDR [7] use varied range of sampling rates and the data must be transmitted between these applications [8]. Thus, there is a need for an adaptive sampling rate converter that can be utilized for all the standards as well as support all the different applications which are encountered in a transceiver and in turn the gateway.

4 Sampling Rate Conversion The conventional sampling rate conversion (“resampling”) used in transceivers to accommodate different bandwidths or bands can include integer down-sampling (i.e. decreasing the rate at which a signal is sampled by an integer factor) or up-sampling (i.e. increasing a sampling rate of a signal by an integer factor) or fractional sampling (i.e. changing the sampling rate according to a predetermined fractional value). However, these conventional techniques cannot achieve sampling rate conversion for all required specific resampling ratios, but are restricted to the integer or fractional values. Hence, these conventional techniques do not have the ability to convert sampling rates to any other desired sampling rate. Accordingly, a need exists for more efficient and flexible adaptive sampling rate conversion with high-rate input sampling to ensure proper image rejection, but with less cost in terms of hardware and power consumption. Performance of sampling rate converters depends on the characteristics of the filter used. Few parameters are listed below: • • • • •

The length of the subfilters (prototype LPF). Technique used to design the prototype low-pass filter. Allowable pass band and stop band ripple in the prototype low-pass filter. Number of subfilters required. The order of the polynomial interpolation.

A varied range of filter algorithms used in the sampling rate converters are studied in the literature [9–11]. Sampling rate conversion can be of integer conversion or fractional conversion. For integer conversion, FIR-based HB and CIC filters with linear phase response are the efficient filter algorithms and which trade the implementation complexity to reduced throughput. For high values of interpolation (L) and decimation (M), the fractional conversion becomes more complicated. In this regard, compared to the conventional conversion that uses cascade operation of interpolator and decimator filter, first-order polynomial

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approximation implemented with farrow filter has been proved to be a good choice [10]. In order to support the integration of WSNs with the other standard networks, an efficient adaptive sampling rate converter is to be designed which will be a prominent element of the wireless transceiver and in turn the central gateway.

5 Implementation of ASRC An ASRC implementation can be broadly classified into two steps. The first step involves in finding the destined sampling rate based on the specific application or the end-user, and the second step involves in finding an efficient filter algorithm for designing an optimum sampling rate converter. In the second step, an ASRC can be implemented in a single stage or multiple stages which is explained in the form of a case study in the following section. The multiple stages involve a coarse sampling rate conversion stage followed by a fine conversion stage. But an interesting and practical issue concerning an adaptive sampling rate converter is how to adjust the sampling rate when target rate itself is unknown.

6 A Case Study A case study for sampling rate conversion of an audio signal from 8 to 44.1 kHz is considered and implemented using four different approaches, and its relevant simulations have been presented. To begin with, a polyphase approach of single stage is considered. In general, for an efficient implementation of multi-rate filters, the polyphase structures are considered. In this case, the overall interpolation and decimation factors are 441 and 80, respectively. The number of filters used in this approach is 2, where for one filter the interpolation factor is 147 and decimation factor is 80, and the other filter has only an interpolation factor of 3. In the polyphase approach, the number of coefficients to store is too large. One of the ways to overcome the problem of the need to store a large number of coefficients is by using polynomial-based filters. Farrow approach is efficient for implementation of such filters. In this regard, third- and fourth-order polynomials are considered. Using the third-order polynomials, the number of coefficients required is only 16, whereas the multiplications per input sample are 72. Considering the fourth-order polynomial, its low-pass response is better compared to the third-order polynomial. But, at the cost of an increase in number of coefficients (25) and multiplications per input sample (121). The frequency response of the polyphase approach and third- and fourth-order polynomial farrow structures of sampling rate converters are compared, and this is shown in Fig. 4.

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Fig. 4 Frequency response of the polyphase, third- and fourth-order polynomial farrow structure

Fig. 5 Frequency response of a hybrid structure

Similarly, a case study is done on the same audio frequency considering hybrid structure, i.e. cascade of farrow and polyphase structures. This hybrid structure takes the advantages of the two types of filters. Polyphase filters are best suited when the interpolation or decimation factor is an integer and also for fractional rate conversions, whereas farrow structures are used in the effective implementation of arbitrary rate change factors. For the implementation, a cascade of half-band (HB) filters and cubic Lagrange polynomial-based filter is used. The cascading of these two filters gives the overall filter. In this hybrid structure, the number of coefficients is relatively low, i.e. 36, and the total multiplications per input sample are 92. The frequency response of this hybrid structure is shown in Fig. 5.

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Table 1 Comparison of all the methods S. No. Parameter Polyphase approach

Polynomial farrow structure

Hybrid structure

Third order Fourth order

Polyphase

Polynomial

1

Multiplications per input sample

95

72

121

28

18

2

Additions per input sample

90

60

99

25

15

3

Number of coefficients Number of states

1774

16

25

20

16

30

3

4

18

3

4

Fig. 6 Frequency responses of single- and multi-stage designs

The comparison of all the above-discussed methods using various parameters is as shown in Table 1. Overlaying of the frequency response of single- and multi-stage designs is as shown in Fig. 6. In Fig. 7, the computational expenditure of the discussed structures is compared with respect to multiplications and additions per input sample.

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Fig. 7 Comparison of the four implemented techniques with respect to multiplications and additions per input sample

7 Conclusions This paper focuses on the need for integrating the WSNs and other network using adaptive sampling rate converters (ASRCs). Depending on the way of implementation, the bridging solutions are categorized as hardware and the middleware solutions. Here, in this paper our point of interest is the hardware solution. With respect to that, the need for an efficient adaptive sampling rate converter is emphasized. As a case study, four sampling rate converters (single-stage and multi-stage) are considered for sampling rate conversion of an audio signal from 8 to 44.1 kHz. Their performances have been compared, and a conclusion is drawn that the hybrid structure performance is better than the other approaches. We aim to design an efficient adaptive sampling rate converter which would be a prominent part in integrating the WSNs and other networks using different standards and applicable to all types of information like audio, video and text.

References 1. G. Strazdins, A. Elsts, K. Nesenbergs, L. Selavo, Wireless sensor network operating system design rules based on real-world deployment survey. J. Sens. Actuator Netw., pp. 509–556 (2013) 2. W. Yue, Y. Zhang, Z. Qin, M. Zhu, C. Jin, L. Wang, L. Shu, C. Chen, Gatewaying the wireless sensor networks, in IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, pp. 61–66 (2013) 3. R.C. Shah, S. Roy, S. Jain, W. Brunette, Data mules: modeling a three-tier architecture for sparse sensor networks. Ad Hoc Netw. J. (2003) 4. P. Mohanty, A framework for interconnecting wireless sensor and ip networks, in IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications. PIMRC 2007. IEEE, pp. 1–3 (2007)

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5. K. Emara, M. Abdeen, M. Hashem, A gateway-based framework for transparent interconnection between wsn and ip network, in EUROCON 2009, EUROCON’09. IEEE, pp. 1775–1780 (2009) 6. D.Q. Dai, T.M. Shih, F.T. Chau, Polynomial preserving algorithm for digital image interpolation. EURASIP Sig. Process. 67, 109–121 (1998) 7. T. Hentschel, M. Henker, G. Fettweis, The digital front-end of software radio terminals. IEEE Pers. Commun. Mag., pp. 6–12 (1999) 8. G. Evangelista, Design of digital systems for arbitrary sampling rate conversion. Sig. Process. 83(2), 377–387 (2003) 9. T. Hentschel, G. Fetnveiss, Sample rate conversion for software radio. IEEE Commun. Mag. 38(8), 142–150 (2000) 10. L. Lundheim, T. Ramstad, An efficient and flexible structure for decimation and sample rate adaptation in software radio receivers, in ACTS Mobile Communications Summit, Sorrento, Italy, pp. 663–668 (1999) 11. E. Hogenauer, An economical class of digital filters for decimation and interpolation. IEEE Trans. Acoust. Speech Sig. Process. 29(2), pp. 155–162 (1981)

Improvement of Signal-to-Noise Ratio for MST Radar Using Weighted Semi-parametric Algorithm C. Raju and T. Sreenivasulu Reddy

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Problem Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Review of SPICE Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Weighted SPICE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The Indian mesosphere–stratosphere–troposphere (MST) radar is the prominent atmospheric radar that provides the atmospheric movements’ information. The radar data is analyzed to obtain the wind parameter that requires the power spectral estimation. At higher altitudes, the estimation of Doppler spectrum is found to be unsatisfactory using both parametric and nonparametric methods for spectral estimation. In this article, the hyperparameter-free, weighted sparse iterative covariance-based estimation (SPICE) method has been considered. Unlike existing SPICE method, a different hyperparameter–free, weighted SPICE method has been derived using a gradient approach with different step sizes. The two versions of SPICE algorithm, i.e., SPICEa and SPICEb , are applied to the practical MST radar data collected at National Atmospheric Research Laboratory, Gadanki (13.5°N, 79.2°E). The obtained results are evaluated with the existing atmospheric data processor results which use the basic periodogram method. The proposed method shows the significant enhancement in signal-to-noise ratio even at elevated heights. Keywords Doppler spectrum · MST radar · SPICE · Gradient approach

C. Raju (B) · T. Sreenivasulu Reddy Department of Electronics and Communication Engineering, SVU College of Engineering, Tirupati, Andhra Pradesh, India e-mail: [email protected] T. Sreenivasulu Reddy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_44

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1 Introduction The detection and classification of hard targets along with the soft or distributed targets like earth’s atmosphere can be done by employing radars. These radars typically meant for atmospheric observations are called clear-air radars. These radars are operated in very high frequency (VHF) around 30–300 MHz and in ultrahigh frequency (UHF) around 300 MHz–3 GHz. It allows to exploit total mesosphere–stratosphere–troposphere domain by a high-power frequency backscatter that operates just about 50 MHz. MST radar is the state-of-the-art device capable of giving estimates of the atmospheric parameters with an exceptionally high resolution on the constant basis that are necessary in the studies of various dynamical progressions in the atmosphere. The information regarding mesosphere, stratosphere, and troposphere regions are observed by MST radars which are operated in 53 MHz. Several MST radars are being resoluted in meteorology and radio astronomy research. The Indian MST radar is situated at Gadanki (130.47 N, 790.18 E) in Andhra Pradesh, India. It has the phased antenna array which contains two orthogonal sets with each one of them having 1024 3-element Yagi–Uda antennas set in the 32 × 32 matrix over 15,000 m2 area. In the matrix grid, the spacing between the elements is 0.7λ. Scanning angles of about 20° from zenith will be allowed. The beam can be steered between 20° in either side of the two orthogonal planes, viz. east–west and north–south. The functional block diagram of various processing stages involved in the extraction and estimation of atmospheric parameters is shown in the schematic Fig. 1. The current algorithm used in the atmospheric data processing (ADP) [1] is known as the classical processing that is able to estimate the Doppler up to particular heights. This fails at elevated heights and even at the lesser heights when the data is corrupted due to clutter, interference, etc. The bispectral-based estimation [2], multitaper spectral estimation [3], and the adaptive estimate techniques [4] for the estimation of moments have been proposed. The filter bank implementation [5], wavelet denoising [6], and cepstral thresholding [7] have been implemented for radar data. Recently, a semi-parametric algorithm [8] has been put in for processing the MST radar data. There is a requirement for finer methods for precise spectral estimation. The paper is structured as follows. The data model and the problem formulation are stated in Sect. 2. The review of the SPICE algorithm is discussed in Sect. 3. The weighted SPICE algorithm is given in Sect. 4. In Sect. 5, the results are shown. Finally, the conclusion is given in Sect. 6. In this article, the lowercase and the uppercase boldface letters signify the vectors and the matrices, respectively. The scalars are represented using normal letters. Notations E(·), (·)T , (·)∗ , ·|·| denote the expectation, the transpose, the complex conjugate transpose (Hermitian transpose), and the Frobenius norm and modulus. Subscript [·]k denotes the kth element of the vector, and the identity matrix of size N is represented by I N .

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Fig. 1 Procedural steps for extraction of parameters

2 Problem Formation Let the complex data be y(tn ) which is derived by composition of the exponentials with frequencies {Ωr }rN1 [0, Ωmax ] y(tn ) 

C 

qr e jωr tn + e(tn )

(1)

r 1 N indicates the sampling time where C is a positive integer which is small and {tn }n1 that may be spaced nonuniformly. The magnitude linked with the rth frequency component Ωr be qr . The additive white Gaussian noise (AWGN) part e(tn ) corresponds to nth sampling instant. Subsequently, the data will be formed as

y(tn ) 

R  r 1

which is equivalent to

qr e jωr tn + e(tn )

(2)

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⎡

⎤⎡ ⎤ ⎡ ⎤ q1 e(t1 ) e jω1 t1 · · · e jω R t1 ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ . ⎥⎢ . ⎥ ⎢ . ⎥ ⎢ .. ⎥  ⎢ .. ⎣ . ⎦ ⎣ . . . . .. ⎦⎣ .. ⎦ + ⎣ .. ⎦ qR e(t N ) y(t N ) e jω1 t N · · · e jω R t N y(t1 )

⎤

⎡

(3)

The corresponding nonzero qr values will be likewise little, since C is small positive integer. Those values of r for which ωr {r }rN1 , qr values will be nonzero. Thus, the few components indicate the nature of sparsity in spectrum. In vector form, (3) can be rewritten as y

R 

d(ωr )qr + e

(4)

r 1

where ⎤ e jω1 t1 ⎥ ⎢ .. ⎥  d r d(ωr )  ⎢ ⎣ . ⎦ e jωr t N ⎡

(5)

Here y  [y(t1 ), y(t2 ), . . . , y(t N )]T ·|qr |2 is the power value that is to be estimated which is associated with the rth frequency component.

3 Review of SPICE Criterion The covariance matrix of the acquired signal is defined as

R  E yy

∗





R 



|qr |2 d r d r∗ + E ee∗

(6)

r1

R  D P D∗

(7)

D  [d 1 d 2 d 3 . . . d R I N ]  [d 1 . . . d R+N ]

E ee  diag(σ1 , σ2 , . . . , σ N )

P  diag |q1 |2 , |q2 |2 , . . . , |q R |2 , σ1 , σ2 , . . . , σ N

(8)

P  diag(m 1 , m 2 , . . . , m R+N )

(9)

where

∗

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 R+N R+N where {m i }i1 is the replacement for |qi |2 i1 . The matrix P is a diagonal matrix with initial R elements representing the spectral power that has to be calculated, and N elements show the variance of the noise. The SPICE algorithm deals with minimization of the function



2 f  R−1/2 y y∗ − R F

(10)

where R−1/2 denotes the Hermitian square root of R−1 . For the matrix X, the Frobenius norm is described as the trace(X ∗ X). The function f is calculated as follows f  tr





 y y∗ − R R−1 y y∗ − R .

(11)

Further simplification leads to f   y22 y∗ R−1 y + tr {R} + constant.

(12)

or equivalently, ∗

y R

−1

R+N 1  y+ wr m r , wr  d r 22 .  y22 r 1

(13)

We can rewrite SPICE criterion as ∗

min y R {m r }

−1

y+

R+N 

wr m r .

(14)

r 1

When d r 2 ≡ const., the weights in (14) can be substituted with 1s. The difficulty in (14) can be solved globally which is convex called as secondorder cone program (SOCP) [9]. The cyclic minimization (CM) will be preferred to SOCP that will monotonically decreases and globally converges at each iteration.   m r (i) d r∗ R−1 (i) y m r (i + 1)  (15) 1 wr2 where r  1, 2, . . . , R + N and R−1 (i)  Ddiag( P) D∗ is the ith iteration covariance matrix estimate.

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4 Weighted SPICE In this section, a more straightforward gradient approach is employed for the derivation of (15) than the cyclic minimization approach that is used previously [9–11]. This approach suggests many alternatives to (15) which are more flexible than the cyclic minimization approach [12, 13]. The derivative of (14) with respect to m r is − y∗ R−1

 2 ∂ R −1 R y + wr  − d r∗ R−1 y + wr ∂m r

(16)

Consequently, the (i + 1)th iteration of a simple gradient algorithm applied to (14) is stated as   2  (17) m r (i + 1)  m r (i) − ρr (i) wr −  d r∗ R−1 (i) y The step size ρr (i) must be the nonnegative integer, ρr (i) ≥ 0

(18)

By definition {m r (i + 1) ≥ 0}, we can choose ρr (i) such that: m r (i) ≥ 0 ⇒ m r (i + 1) ≥ 0

(19)

Let us select ρr (i) 

wr +

m r (i)  1/2  ∗ −1 wr d r R (i) y

(20)

which satisfies (18). The straightforward calculation yields m r (i + 1)

  2 1/2  m r (i)wr + m r (i)wr  d r∗ R−1 (i) y − m r (i)wr + m r (i) d r∗ R−1 (i) y   1/2  wr + wr  d r∗ R−1 (i) y   m r (i) d r∗ R−1 (i) y m r (i + 1)  (21) (SPICEa ) 1/2 wr and thus (19) is satisfied. When wr  d r 22 , (21) is the SPICEa algorithm in (15). The step length ρr (i) in (17) will be picked in various means than (20). The simple assumption that satisfies (18) is ρr (i) 

m r (i) wr

(22)

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that leads to   m r (i) d r∗ R−1 (i) y m r (i + 1)  (SPICEb ) wr

(23)

when wr  d r 22 ; hence, (23) is known as SPICEb which minimizes similar to (21). Inspite of sharing same stationary points, they exhibit various convergence rates. The step length in the (20) is lower than (22) when they are calculated employing the equal {m r (i)}. The periodogram method is used to initialize the algorithms SPICEa and SPICEb m r (0) 

ˆ r d r∗ Rd , r  1, 2, . . . , R + N d r 42

(24)

The termination of the algorithms is done if the following criterion is satisfied m(i + 1) − m(i)2 /m(i)2 < 10−3

(25)

Algorithm 1. 2. 3. 4. 5. 6. 7.

The time-series radar data is read. The algorithms are initialized using (24). Compute the covariance matrix R using (7). Update the power spectrum values using (21) for SPICEa and (23) for SPICEb . Update the step size using (20) for SPICEa and (22) for SPICEb . Repeat the steps 3 to 5 till (25) is satisfied. The output signal-to-noise ratio is computed by the method proposed in [14].

5 Results The data retrieved from the atmosphere through MST radar placed at Gadanki, Andhra Pradesh, India, is utilized for the implementation of the proposed algorithms. The data retrieved from NARL is of 15 scans, and every scan contains the information related to signal from six beam directions. Every beam has 147 bins with the resolution of 150 m, beginning from 3.6 to 25.6 km height. Every range of the bin consists of 512 samples of complex time-series data. This data is processed using atmospheric data processor, which uses the basic fast Fourier transform (FFT) for spectral estimation. Figure 2 shows the output signal-to-noise ratio calculated from power spectral estimation using periodogram, SPICEa , and SPICEb for the east and the west beams for the radar data retrieved on February 09, 2015.

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Fig. 2 Height profile of the SNR calculated using the periodogram, SPICEb , and SPICEa for the east beam and west beam

Fig. 3 Height profiles of the SNR computed using the periodogram, SPICEb , and SPICEb for the south and north beams

The output SNR estimated from power spectrum which is obtained using the periodogram, SPICEa and SPICEb for south and north beams is shown in Fig. 3. From Figs. 2 and 3, it is seen that the SPICEa and SPICEb algorithms are able to improve SNR values even at higher altitudes. This shows that the signal can be detected even at higher range of bins of the MST radar data. The average signal-to-noise ratio values (dB) compared for the six beams on February 09, 2015, for the three different algorithms are calculated and given in Table 1. The comparative assessment of average SNR values in all the six beams reveals that the SPICEa and SPICEb algorithms have given better SNR values compared to the existing algorithm. SPICEa provides the enhanced SNR value due to smaller step size in contrast to SPICEb .

Improvement of Signal-to-Noise Ratio for MST Radar Using … Table 1 Comparison of average SNR (dB)

475

Method

Periodogram

SPICEb

SPICEa

East West North South Zenith-X Zenith-Y

19.47 18.23 20.14 21.57 18.45 17.98

23.84 22.48 22.65 24.09 22.84 21.45

24.45 23.87 22.98 24.48 23.96 22.68

6 Conclusion In this study, the hyperparameter-free technique, weighted SPICE has been used for the estimation of signal-to-noise ratio for the MST radar data. Two versions of algorithms are derived based on the step sizes, and the version-a algorithm gives the better performance than the version-b algorithm. At higher altitudes, noise is reduced, and the radar returns are detected. From the observations, the SPICEa spectral estimation algorithm shows the significant improvement compared to the SPICEb and the periodogram method. The proposed method yields good results with difficulty in computations. This is the consequence of the repetitive method which involves in the calculation of matrix inversion. This can be achieved by using effective methods which results in less computational time. Acknowledgements We are thankful to the National Atmospheric Research Laboratory (NARL), Gadanki, for giving the radar data, and the Centre of Excellence, Department of ECE, SVU College of Engineering, SV University, for providing resource and assistance.

References 1. V.K. Anandan, V.K. Anandan, Atmospheric Data Processor—Technical and User Reference Manual, NMRF (DOS Publication, Gadanki, 2002) 2. V. Anandan, G.R. Reddy, P. Rao, Spectral analysis of atmospheric radar signal using higher order spectral estimation technique. IEEE Trans. Geosci. Remote Sens. 39(9), 1890–1895 (2001) 3. V. Anandan, C. Pan, T. Rajalakshmi, G.R. Reddy, Multitaper spectral analysis of atmospheric radar signals. Ann. Geophys. 22(11), 3995–4003 (2004) 4. V. Anandan, P. Balamuralidhar, P. Rao, A. Jain, A method for adaptive moments estimation technique applied to MST radar echoes, in Proceedings of Program Electromagnetic Research Symposium, pp. 360–365 (1996) 5. T.S. Reddy, G.R. Reddy, Spectral analysis of atmospheric radar signal using filter banks polyphase approach. Digit. Sig. Proc. 20(4), 1061–1071 (2010) 6. S. Thatiparthi, R. Gudheti, V. Sourirajan, MST radar signal processing using wavelet-based denoising. IEEE Geosci. Remote Sens. Lett. 6(4), 752–756 (2009) 7. T. Reddy, G.R. Reddy, MST radar signal processing using cepstral thresholding. IEEE Trans. Geosci. Remote Sens. 48(6), 2704–2710 (2010)

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8. N.I. Eappen, T. Sreenivasulu Reddy, G. Ramachandra Reddy, Semiparametric algorithm for processing MST radar data. IEEE Trans. Geosci. Remote Sens. 54(5), 2713–2721 (2016) 9. P. Stoica, P. Babu, J. Li, SPICE: a sparse covariance-based estimation method for array Processing. IEEE Trans. Sig. Process. 59, 629–638 (2011) 10. P. Stoica, P. Babu, J. Li, New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data. IEEE Trans. Sig. Process. 59(1), 35–47 (2011) 11. P. Stoica, P. Babu, SPICE and LIKES: two hyperparameter-free methods for sparse-parameter estimation. Sig. Process. 92, 1580–1590 (2012) 12. C. Raju, T. Sreenivasulu Reddy, Sparse iterative covariance based estimation for atmospheric radar data. Int. J. Eng. Technol., pp. 232–236 (2018) 13. P. Stoica, D. Zachariah, J. Li, Weighted SPICE: a unifying approach for hyperparameter-free sparse estimation. Digit. Sig. Process. (2014) 14. P.H. Hildebrand, R. Sekhon, Objective determination of the noise level in Doppler spectra. J. Appl. Meteorol. 13(7), 808–811 (1974)

A Robust DCT-SVD Based Video Watermarking Using Zigzag Scanning K. Meenakshi, K. Swaraja and Padmavathi Kora

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Discrete Cosine Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Watermark Concealing and Extraction Algorithm . . . . . . . . . . . . 3.1 Watermark Concealing Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Watermark Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Imperceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In this paper, a hybrid non-blind video watermarking based on discrete cosine transform (DCT) and singular value decomposition (SVD) is proposed. The DCT coefficients of each frame in the host video are reordered in a zigzag fashion and mapped into four blocks. These four blocks represent four frequency bands of low–low (LL), low–high (LH), high–low (HL), and high–high (HH) bands. Later, SVD is individually applied to each block. The singular values in each block are then modified by the singular values of the DCT transformed visual watermark to get watermarked video. This algorithm computes robustness in terms of Normalized Cross Correlation (NCC) between original and extracted watermarks from four bands. The

K. Meenakshi (B) · K. Swaraja · P. Kora Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India e-mail: [email protected] K. Swaraja e-mail: [email protected] P. Kora e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_45

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proposed algorithm is compared with recent works. and the experimental results confirm that the proposed method is more resilient to attacks and is transparent. Keywords Zigzag scanning · DCT · SVD · PSNR · NCC

1 Introduction With the advancements in microelectronics, very large scale integration (VLSI), development of multimedia technologies and Internet, the usage of video-based applications—video conferencing, video chatting, telemedicine, Internet video, and wireless video—are increasing day after day. The consequence of such increased usage results in malicious copying and reproduction of the digital video [1]. To rectify this and to provide authentication, a watermark is concealed into the multimedia document. Generally, concealed watermarks should be imperceptible, robust to malicious attacks [2]. Imperceptibility means the distinction between marked and unmarked video must be negligible. Robustness refers the withstanding of watermarking scheme against attacks such as averaging, scaling, HEVC compression. Video watermarking is carried in spatial, transform, and compressional domains. In the former, watermark is concealed by altering individual pixels in the frames of video. Reference [3] used spatial domain technique to embed watermark in 3D meshes. But, a simple cropping can erase the watermark. The watermark is embedded in frequency coefficients of transform domain. In Ref. [4], a video watermarking is proposed on hybrid combination of DCT, DWT [5], and SVD. The authors proposed that the method is highly invisible. The bottleneck of it is that they are not performed any type of robustness test. In another work Ref. [6], a video watermarking is proposed on DCT and SVD using hash function. Though the authors show there is increased capacity, the authors fail to present the other two issues of watermarking- transparency and robustness. In our previous work [7], a low complexity video watermarking is proposed with CS-SCHT. Experimental results of this scheme maintain perpetual transparency and are robust to attacks high-efficiency video coding (HEVC) compression, scaling, histogram equalization (HE). This method requires less hardware compared to DFT-based algorithm. In another work of author [1], watermarking is performed based on slant transform using human visual system. These blocks are modified with weight matrix of HVS in slant domain using quantization index modulation. The slant transform is recursive parameter. The NCC approaches 1 when threshold parameter in quantization index modulation is varied. This property is explored to design a robust watermarking scheme in Ref. [1]. These works are based on uncompressed domain. Compressed video watermarking is used in Ref. [8]. The paper is organized as follows: Sect. 2 describes the properties of the SVD and DCT. Section 3 illustrates the watermark concealing and extraction using DCT-SVD. Extensive experiments are conducted, and results are shown in Sect. 4. Conclusion is given in Sect. 5.

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2 Methods and Materials In this algorithm, DCT and SVD are used in proposed video watermarking scheme. The brief description of SVD and DCT is given in Sects. 2.1 and 2.2.

2.1 Singular Value Decomposition The SVD of an N × N matrix A is defined as A = PSQT

(1)

where A is the frame of video in matrix format. P, Q are the left and right singular vectors of the decomposed matrix, and S is the singular value. The values of S are less affected by inserting watermark bits. This property is utilized in the proposed video watermarking scheme.

2.2 Discrete Cosine Transform DCT has high information packing in few coefficients. After DCT is applied, the transformed image is divided into four quadrants in order to apply SVD to each block. All the quadrants will have the same number of DCT coefficients. For example, if the spatial resolution of frame is 144 × 176, the number of DCT coefficients in each block will be 72 × 88. The first encountered 72 × 88 coefficients in zigzag scanning are taken as block B1. The next encountered 72 × 88 coefficients are taken as block B2; the next encountered 72 × 88 coefficients are taken as block B3, and remaining coefficients constitute block B4. These blocks serve as LL, LH, HL, and HH bands. Embedding logo in all frequency bands will protect watermark from all types of attacks. The logo in LL band is resistant to one set of attacks and HH band are resistant to another set of attacks. The watermark strength is iteratively adjusted until the correlation coefficient of all the extracted watermarks is one under no attacks. In this paper, different watermark strengths are used for various bands. If the same watermark is embedded in the scene, it is easy for an attacker to extract the logo by comparing and averaging the frames. Independent watermark used for different scenes can prevent the attacker from colluding frames with frames to extract the watermark.

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3 Proposed Watermark Concealing and Extraction Algorithm Concealing the mark in low-frequency components enhances the resistance against attacks such as averaging, lossy compression H.264, and geometric distortion, whereas concealing it in the mid- and high-frequency components is robust against small geometrical deformation of the image, but is more robust to Gaussian, salt-andpepper noise, and histogram equalization. Therefore, the goal of this method is to conceal watermark in all the frequency bands so that it can withstand against all types of attacks. It increases the difficulty of erasing the mark from all the four bands.

3.1 Watermark Concealing Algorithm INPUT : Cover video, a gray scale logo, watermark strengths α1, α2, α3, α4. OUTPUT : Signed video. The cover video is partitioned into frames. For each frame perform: 1. Transform RGB to YUV where Y represents luminance and U, V represent the color information. To improve imperceptibility, luminance layer Y is used for watermark embedding and chrominance layers are untouched. 2. Apply 2D DCT to the luminance component of frames in original video. 3. Apply forward zigzag scan to the DCT transformed image. 4. Divide the DCT transformed image into four blocks as described in Sect. 2.2. 5. Apply SVD to each band to obtain Ck = Pck ck QckT ,

(2)

k = 1, 2, 3, 4 represents different bands such as B1, B2, B3, and B4. 6. Compute the singular values of the cover image by taking the diagonal elements of ck λkc = diag(ck ) (3) 7. Apply DCT and then SVD to visual watermark, w. W = Pw w QwT ,

(4)

8. Obtain the singular values of watermark by taking the diagonal elements of w λw = diag(w)

(5)

9. Modify the singular values in each quadrant of the frame with the singular values of the mark to obtain the singular values with modification.

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481 k λk i = λi + αk λw

(6)

10. Obtain ik by taking the diagonal entries of λi k ik = diag(λk i )

(7)

11. Obtain inverse SVD to obtain modified coefficients. Ck = Pck ck QckT

(8)

12. Apply inverse zigzag scanning to restore the coefficients in original position. 13. Apply inverse 2D DCT to obtain the watermarked frame. 14. Concatenate all the frames to obtain watermarked video.

3.2 Watermark Extraction Algorithm The inputs to the watermark extraction are watermarked video and original video, and the output of extraction process is the watermark obtained from four quadrants. The original video and watermarked video are converted into frames. For each frame do: 1. Transform each frame color standard from RGB to YUV. 2. Extract luminance of original and watermarked video, and apply DCT and forward zigzag to divide into four blocks—B1, B2, B3 and B4 and B1W, B2W, B3W and B4W—as described in Sect. 2.2. 3. Apply SVD to four blocks of original and watermarked video. 4. Compute singular values of four blocks from frames of host and watermarked video, and obtain singular values of watermark. λkwi

  k λi − λki = αk

(9)

5. Apply inverse SVD to reconstruct watermarks. 6. Apply inverse 2D DCT to extract watermarks as shown in Fig. 1.

4 Simulation Results In this paper, several test video sequences of the Common Intermediate Format (CIF) of spatial resolution 288 × 352 and Quarter Common Intermediate Format (QCIF) of spatial resolution 144 × 176- Akiyo, Claire, car phone, bridge, container, and football are used and shown in Table 1. These are the test videos available in www.

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Fig. 1 a Frame of host video sequence foreman. b Watermark cameraman. c Frame of watermarked video sequence foreman. d Extracted watermark cameraman from four quadrants Table 1 (a) Frames of cover video Akiyo, car phone, bridge, Miss America, foreman. (b) Frames of watermarked video Akiyo, car phone, bridge, Miss America, foreman

(a)Frames of host videos

(b)Frames of watermarked videos

xiph.org. The grayscale logo used is cameraman. Foreman is the frequently used test video sequence in watermarking. So attacks are applied for the watermarked foreman. The watermarking strengths iteratively adjusted till the correlation coefficient of all bands is nearly ‘1’. Different watermarking strengths are used for different bands. For B1, the watermarking strength used is 0.5, and for other bands B2, B3, and B4, watermarking strengths of 4.6, 4.8, and 9.5 are used. The peak signal-to-noise ratio gives the measure of imperceptibility.

4.1 Imperceptibility PSNR is used to measure the watermark invisibility. If PSNR is more, then the distinction between original and watermarked image is less. 

2 Imax PSNR = 10 log 10 MSE

 (10)

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45 40 35

PSNR

30 25

Akiyo carphone Bridge MissAm Foremann

20 15 10 5 0

DWT+DCT+SVD[4]

Hash based DCT+SVD{5}

Proposed

Method

Fig. 2 Comparison of proposed watermarking schemes with watermarking schemes proposed in Refs. [4, 5]

where Imax is the maximum intensity of image and MSE is the mean square error between frames of signed and host video. As watermark strength factor is increased, the invisibility improves, but the robustness against attacks is reduced. Higher the PSNR, better the invisibility. As shown in Fig. 2, the PSNR of different video sequences used in experimentation for the proposed algorithm is compared with Refs. [4, 5] and the high value of PSNR shows the proposed algorithm is highly invisible.

4.2 Robustness The robustness of the proposed method is assessed by applying different attacks on watermarked video sequences. These attacks include low pass filtering (LPF), rescaling (RS), vertical flipping (VF), ripple attack (RA), Gaussian noise (GN), rotation (RO), speckle noise (SN), Laplacian (LP), and also the combination of these attacks. For each watermark, we extracted four watermarks from four bands. The attacks are applied to frames of foreman, and from all the four bands, the watermark is extracted. Then, the NCC between original and extracted watermark is computed. The average NCC of the proposed video watermarking scheme is compared with Refs. [4, 5] in Fig. 3. The higher values of NCC show that the algorithm is more robust to attacks. The NCC of extracted watermark with the original watermark from the four bands is tabulated in Table 2, and the best one is recorded with the bold.

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Ref.[4] Ref.[5] Proposed

0.9 0.8 0.7

NCC

0.6 0.5 0.4 0.3 0.2 0.1 0

NA

LPF

RS

VF

RA

GN

RO

SN

La

Attacks

Fig. 3 Comparison of proposed watermarking schemes with watermarking schemes proposed in Refs. [4, 5] under various attacks Table 2 NCC of best extracted watermarks from four bands, and the best one is represented in bold Attacks B1 B2 B3 B4 No attack LPF RS VF RA GN RO SN La

0.9 0.88 0.88 0.92 0.94 0.95 0.65 0.92 0.85

0.845 0.78 0.76 0.87 0.89 0.82 0.78 0.84 0.95

0.64 0.75 0.74 0.92 0.67 0.75 0.94 0.72 0.98

0.53 0.64 0.58 0.8 0.52 0.62 0.74 0.69 0.88

5 Conclusion A robust video watermarking is designed with DCT and SVD using zigzag scanning. The results show that it is robust to attacks of LPF, Gaussian, ripple noise and more invisible than recent works reported in the literature.

References 1. K. Meenakshi, C. Srinivasa Rao, K. Satya Prasad, A scene based video watermarking using slant transform. IETE J. Res. 60, 276–287 (2014) 2. K. Meenakshi, C. Srinivasa Rao, K. Satya Prasad, A fast and robust hybrid watermarking scheme based on schur and SVD transform. Int. J. Res. Eng. Technol. 3, 7–11 (2014)

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3. E. Praun, H. Hoppe, A. Finkelstein, Robust mesh watermarking, in Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques (ACM Press/AddisonWesley Publishing Co., New York, 1999), pp. 49–56 4. S. Mawande, H. Dakhore, Video watermarking using DWT-DCT-SVD algorithms, in 2017 International Conference on Computing Methodologies and Communication (ICCMC) (IEEE, New York, 2017), pp. 1161–1164 5. S.A. Patil, N. Srivastava, Digital video watermarking using DWT and PCA. IOSR J. Eng. 3, 45–49 (2013) 6. A.V. Dabhade, Y.J. Bhople, K. Chandrasekaran, S. Bhattacharya, Video tamper detection techniques based on DCT-SVD and multi-level SVD, in TENCON 2015-2015 IEEE Region 10 Conference (IEEE, New York, 2015), pp. 1–6 7. K. Meenakshi, K.S. Prasad, C.S. Rao, Development of low-complexity video watermarking with conjugate symmetric sequency-complex hadamard transform. IEEE Commun. Lett. 21, 1779–1782 (2017) 8. K. Swaraja, Y.M. Latha, V. Reddy, The imperceptible video watermarking based on region of motion vectors in p-frames. Adv. Comput. Technol. 3, 335–348 (2010)

Digitization and Parameter Extraction of Preserved Paper Electrocardiogram Records Rupali Patil and Ramesh Karandikar

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Background Grid Removal Using Sauvola’s Thresholding . . . . . . . . . . . . . . . . . . 3.3 Vertical Scanning for ECG Signal Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Parameter Extraction Using Pan Tompkins Method . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Parameter Extraction from Digitized ECG Signal . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Electrocardiogram (ECG) paper records are used commonly for diagnosing heart abnormalities. The stored paper ECG records may be blurred or affected by noise. Enhancement of such blurred paper ECG records is done using a low-pass Wiener filter and background grid removal using adaptive thresholding, signal extraction, and parameter extraction. In this work, clinically important parameters such as heart rate, R-peak, RR-interval, and S-peak are extracted from the enhanced digitized ECG signal, and these extracted parameters are then compared with printed parameters on original paper ECG records. The average absolute error between extracted parameters and original paper ECG parameters is 0.02 with an average accuracy of 97.66%. The extracted digitized signal from stored ECG records may be useful for retrospective analysis of cardiac abnormalities, using automated diagnosis software. Keywords Paper ECG records · Digitization · Digital signal · Signal analysis R. Patil (B) Department of Electronics and Telecommunications, Rajiv Gandhi Institute of Technology, Mumbai, India e-mail: [email protected] R. Karandikar Department of Electronics and Telecommunications, K. J. Somaiya College of Engineering, Mumbai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_46

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1 Introduction As computers are an integral part of the medical field, developing an application for rapid and easy diagnosis is essential. For cardiac abnormalities, electrocardiogram (ECG) is generally used by doctors for diagnosis. The ECG waveform is printed usually on paper, and the printed ECG is preserved by patients and produced when asked by a doctor. The problem encountered while reading the data from documented ECG paper is the damage caused by paper tearing or the disappearance of the signal traces because of various types of noise deposition. To overcome this problem, the ECG paper record is converted into digital time series signals through the ECG digitization process. Ultimately, electronic conservation of the preserved or documented ECG records is crucial for ECG signal preservation in single uniform format, which can be used by already existing automated diagnosis software for retrospective analysis [1]. In this paper, we propose a digitization scheme which enhances and digitizes preserved paper ECG records to extract clinically important information. First enhancement of scanned paper ECG records is done using a low-pass Wiener filter [2] where adaptive Wiener method is used which is based on local statistics. Second step is dedicated for removal of background grid using Sauvola’s thresholding [3] which selects threshold adaptively. Next, as a third step, the resultant black-and-white image from step two is vertically scanned [4] to identify signal pixels and converts twodimensional (2D) image into one-dimensional signal vector. The complexity of the proposed algorithm is reduced by employing vertical scanning, which significantly reduces the number of iterations in the scanning process. Finally, tracing, profiling, and sharpening of the extracted digitized ECG signal are done using Pan and Tompkins [5] algorithm. Pan Tompkins algorithm is also used for detection of peaks and intervals in scanned paper ECG record. For validating the proposed method, clinically important features like heart rate, RR-interval, R-peak, and S-peak of 108 (9 ECG with 12 leads each) paper ECG records are extracted. The digitized signal and extracted parameters from preserved paper ECG records using the proposed method will help automated diagnosis and analysis. The paper is organized as follows. Section 2 presents the literature survey. Methodology is described in detail in Sect. 3 and finally conclusions are drawn in Sect. 4.

2 Literature Survey Preserved ECG records are printed on preprinted thermal graph papers with grid. During scanning of such records, noise is introduced because of paper quality and the type of machine used. The different sources through which noise is introduced to preserved paper ECG records are: the background grid lines which interfere with the ECG trace, and dust particles leading to salt-and-pepper noise [6–8]. Hence, grid removal is necessary for retrieving the ECG signal from ECG printouts. Many

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researchers have proposed different methods for grid removal from paper records: (1) mathematical morphology [9] to dilate the grid lines, (2) Hough transform [10] to find the lines in all directions, (3) projection of lines [11, 12] for estimation of full grid from printed graph, (4) histogram is used to distinguish the signal trace [13] from the background grid. The accuracy is affected when it is required to fix anchor points [14] for detection of ECG peaks. The ECG waveform is extracted, and parameters are retrieved using K-means method [15]. The extracted ECG signal [16] is interfaced with patient’s record using optical character recognition (OCR). Grayscale thresholds [17] are used to separate the ECG trace from the background grid lines. However, all the above works address the issue of one-dimensional time series signal from recent and clean paper records. The proposed algorithm considered stored paper ECG records, recorded at different time durations (2014–2016).

3 Methodology The proposed methodology for enhancement of scanned paper ECG records, thresholding-based background grid removal, signal and parameter extraction is illustrated in Fig. 1 and fully described in this section.

3.1 Preprocessing For preserved paper ECG records, an enhancement stage is required for the noise removal, clear separation of background grid pattern and foreground signal. All the above goals are efficiently handled using a low-pass Wiener filter. As shown in Fig. 2a, the RGB to grayscale converted scanned paper ECG record is enhanced to the Weinerfiltered grayscale image I as shown in Fig. 2b according to the following formula: I  μ + (σ 2 − υ 2 )(Is − μ)/σ 2

Enhancement using Weiner filter

Adaptive Sauvola’s thresholding

scanned paper Pre-processed image Is(x,y) grayscale image Ir(x,y)

Vertical scanning

(1)

Parameter extraction

B/W image after Digitized signal, thresholding B(x,y) x(t)

Fig. 1 Adaptive digitization and parameter extraction of paper ECG records

Final extracted parameters

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Fig. 2 Enhancement: a scanned paper ECG image; b Wiener filtered image

Fig. 3 Sauvola’s adaptive thresholding for grid removal a preprocessed image, b ECG signal image

where μ and σ 2 are mean, variance with localized window surrounding every pixel and υ 2 is the normal of all expected variances for every pixel in the region.

3.2 Background Grid Removal Using Sauvola’s Thresholding Background grid removal is very important step in ECG digitization. The ECG signal mixed with background grid can create problem in ECG signal extraction, like sometimes grid pixel can be wrongly detected as signal pixel. In this step, a rough estimation of foreground (ECG signal) regions is done. For this case, adaptive Sauvola’s thresholding approach for k  0.4 is used as shown in Fig. 3. Blackand-white image (B(x, y)) is extracted from preprocessed image I(x, y) for rough estimation of foreground signal regions.

3.3 Vertical Scanning for ECG Signal Extraction After scanning, preprocessing, and thresholding, equivalent 2-D digital image is available. Further, this 2-D image is vertically scanned for high-intensity pixels of signal. For extracting the ECG signal which is recorded horizontally, row-wise scanning is not appropriate as it gives multiple equal gray valued pixels. And then this row-wise scanning requires iterative process for correct detection of ECG signal pix-

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els. Vertical scanning helps to avoid this iterative process, to extract 1-D ECG signal from 2-D binarized image obtained from Sauvola’s thresholding.

3.4 Parameter Extraction Using Pan Tompkins Method For parameter extraction, it is necessary to detect peaks and intervals of signal x(t). For that the signal, x(t) is first passed through high-pass and low-pass filters, derivation, squaring, and integration steps as shown in Fig. 4. To remove effect of muscle noise and the power line interference, signal x(t) is filtered using low-pass and highpass filters as shown in Fig. 5b, c. The differentiation step (Fig. 5d), finds high slopes that separate the QRS complexes from other ECG waveforms. Next, as shown in Fig. 5e, f, the resultant signal obtained by differentiation is passed through squaring and averaging operation making the result positive and considers large differences resulting from QRS complexes. The small differences arising from P and T waves are suppressed. Enhancement of the high-frequency components in the signal related to the QRS complex is done further. The moving window integrator then processes the squared waveform (Fig. 5g) where it adds the area under the resultant squared waveform over a suitable interval, advances one sample interval, and integrates the new predefined interval window. Finally, signal peaks are detected as shown in Fig. 5h.

3.5 Parameter Extraction from Digitized ECG Signal The R-peaks which have maximum and S-peaks have minimum amplitude in the ECG signal are extracted by finding maximum and minimum values of vector X. The number of sample values between consecutive R-peaks is used to find RR-interval and heart rate (HR) as follows, Heart Rate 

60 R R interval

(2)

The database consists of recorded or documented 108 (9–12 lead paper ECG) records obtained from Gautami Hospital. ECG signal parameters like heart rate (HR), RR-interval, R-peak, and S-peak are printed on these records as shown in Fig. 6. Extraction of clinically important parameters like HR, R-peak, S-peak, and RR-

x(t)

LPF and HPF

Derivation

Squaring and Averaging

Integration

Fig. 4 Parameter extraction from digitized ECG signal x(t)

Detection of signal peaks and intervals

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Fig. 5 a Input ECG signal, b low-pass filtered signal, c high-pass filtered signal, d ECG signal after derivative, e squaring of ECG signal, f ECG signal after averaging, g integration of ECG signal, h R- and S-peak detection

interval and comparing them with parameters printed on paper at center top validate the proposed algorithm. Table 1 shows heart rate, RR-interval, R-peak, and S-peak extracted from paper ECG records. ECG1-ECG3, ECG4-ECG6, and ECG7-ECG9 are the records taken for 2014, 2015, and 2016, respectively. Absolute error (E) is calculated between parameters extracted from digitized signal (Pd ) and original paper ECG parameters (Po ). E

|Po − P d | Po

(3)

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Table 1 Comparison of extracted parameters with printed parameters, on paper ECG records Sample Parameter Po Pd E % Accuracy ECG1

ECG2

ECG3

ECG4

ECG5

ECG6

ECG7

ECG8

ECG9

HR R-peak

84 1.22

81 1.26

0.035 0.032

96.5 96.8

RR-interval S-peak

712 −0.95

740 −1.0

0.04 0.05

96 95

HR R-peak

61 1.24

64 1.20

0.05 0.03

95 97

RR-interval S-peak

981 −0.58

937 −0.60

0.045 0.034

95.5 96.6

HR R-peak

85 0.67

81 0.69

0.047 0.028

95.3 97.2

RR-interval S-peak

704 −1.10

740 −1.05

0.05 0.05

95 95

HR R-peak

60 2.57

59 2.61

0.02 0.02

98 98

RR-interval S-peak

1007 −1.90

1022 −1.84

0.01 0.03

99 97

HR R-peak

62 2.58

64 2.58

0.03 0

97 100

RR-interval S-peak

971 −1.88

937 −1.88

0.03 0

97 100

HR R-peak

90 1.26

92 1.23

0.02 0.02

98 98

RR-interval S-peak

667 −1.09

652 −1.15

0.02 0.05

98 95

HR R-peak

80 1.67

81 1.70

0.01 0.02

99 98

RR-interval S-peak

748 −1.07

740 −1.05

0.01 0.02

99 98

HR R-peak

47 1.68

46 1.68

0.02 0

98 100

RR-interval S-peak

1274 −0.58

1304 −0.6

0.02 0.03

98 97

HR R-peak

74 1.74

75 1.70

0.01 0.02

99 98

RR-interval S-peak

814 −1.06

800 −1.05

0.01 0

99 100

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Fig. 6 12-lead scanned paper ECG image (recorded at March 2016) with parameters displayed on top

4 Conclusion The focus of this paper is enhancement and digitization of stored paper ECG records. The proposed method digitized 108 ECG paper records, and clinically important parameters such as heart rate, R-peak, RR-interval, and S-peak are extracted from the enhanced digitized ECG signal. The average absolute error and the average accuracy of proposed algorithm are evaluated by comparing the extracted parameters from digitized ECG signal with manually read data from the paper ECG records. The average absolute error is 0.02 with an average accuracy of 97.66%. The digitized ECG signal obtained from preserved ECG records can be used in retrospective studies by research organizations.

References 1. G.S. Waits, E.Z. Soliman, Digitizing paper electrocardiograms: status and challenges. J. Electrocardiol. 50(1), 123–130 (2017) 2. S.P. Ebenezer, Noise reduction and comfort noise gain control using bark band weiner filter and linear attenuation. U.S. Patent No. 7,454,010. 18 Nov. 2008

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3. J. Sauvola, T. Seppanen, S. Haapakoski, M. Pietikainen, Adaptive document binarization, in 1997 Proceedings of the Fourth International Conference on Document Analysis and Recognition, vol. 1 (IEEE, New York, 1997), pp. 147–152 4. R. Patil, R. Karandikar, Digitization of documented signals using vertical scanning, in 2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE) (IEEE, New York, 2015), pp. 239–242 5. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985) 6. G.A. Story et al., The RightPages image-based electronic library for alerting and browsing. Computer 25(9), 17–26 (1992) 7. N. Premchaiswadi, S. Yimgnagm, W. Premchaiswadi, A scheme for salt and pepper noise reduction and its application for OCR systems. Wseas Trans. Comput. 9, 351–360 (2010) 8. J. Serra, Image Analysis and Mathematical Morphology (Academic Press, Inc., 1983) 9. J.N. Said, M. Cheriet, C.Y. Suen, Dynamical morphological processing: a fast method for base line extraction, in 1996 Proceedings of the 13th International Conference on Pattern Recognition, vol. 2 (IEEE, New York, 1996) 10. L. Xu, E. Oja, P. Kultanen, A new curve detection method: randomized Hough transform (RHT). Pattern Recogn. Lett. 11(5), 331–338 (1990) 11. H. Cao, R. Prasad, P. Natarajan, A stroke regeneration method for cleaning rule-lines in handwritten document images, in Proceedings of the International Workshop on Multilingual OCR (ACM, New York, 2009) 12. Z. Shi, S. Setlur, V. Govindaraju, Removing rule-lines from binary handwritten arabic document images using directional local profile, in 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, New York, 2010) 13. T.W. Shen, T.F. Laio, Image processing on ECG chart for ECG signal recovery, in Computers in Cardiology 2009 (IEEE, New York, 2009) 14. F. Badilini et al., ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files. J. Electrocardiol. 38(4), 310–318 (2005) 15. G. Shi, G. Zheng, M. Dai, ECG waveform data extraction from paper ECG recordings by K-means method, in Computing in Cardiology, 2011 (IEEE, New York, 2011) 16. L. Ravichandran et al., Novel tool for complete digitization of paper electrocardiography data. IEEE J. Trans. Eng. Health Medicine 1, 1800107–1800107 (2013) 17. W.T. Lawson et al., New method for digitization and computerized analysis of paper recordings of standard 12-lead electrocardiograms, in Computers in Cardiology 1995 (IEEE, New York, 1995)

Segmentation and Classification of CT Renal Images Using Deep Networks Anil Kumar Reddy, Sai Vikas, R. Raghunatha Sarma, Gurudat Shenoy and Ravi Kumar

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 DNN Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Deep Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Visualization Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Dataset Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Experimental Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Challenges with Original Renal Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 U-Net Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Complete Work Flow of Data Preprocessing on Renal Images . . . . . . . . . . . . . . . 3.5 Experimental Results on Renal Dataset Using Alexnet Architecture . . . . . . . . . . 3.6 Visualization Results on Renal Dataset Using Saliency Maps . . . . . . . . . . . . . . . . 4 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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We thank Sri Satya Sai Institute of Higher Medical Sciences for providing the dataset and the details about it. A. K. Reddy (B) · S. Vikas · R. Raghunatha Sarma · G. Shenoy · R. Kumar Sri Sathya Sai Institute of Higher Learning, Prashanti Nilayam, Anantapur, India e-mail: [email protected] URL: http://www.sssihl.edu.in/ S. Vikas e-mail: [email protected] R. Raghunatha Sarma e-mail: [email protected] G. Shenoy e-mail: [email protected] R. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_47

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Abstract The study of deep learning (LeCun et al. in Nature 521(7553):436, 2015 [1]) models, in particular, the convolutional neural networks (CNN), is playing a key role for various applications in medical domain since last decade. It has successfully demonstrated interesting results with higher accuracy which motivates sophisticated diagnosis tools in the Healthcare domain. We have done a study on using CNN models such as U-net and Alexnet on renal dataset for segmentation and classification of renal images. Data preprocessing on kidney images has been carried out using Unet architecture (Ronneberger et al. in U-net: convolutional networks for biomedical image segmentation. Springer, Berlin, pp. 234–241, 2015 [2]). A detailed study on fine tuning the hyper parameters that governs the model performance and test accuracy has been carried out. We achieved a dice coefficient of 83% in creating masks for renal data using U-net. We performed experiments on AlexNet and the best accuracy achieved is 94.75%. Finally, we have visualized the convolutional layers using saliency maps. Keywords Renal data · Segmentation · Classification · Convolutional neural networks · U-net · Alexnet · Tensorflow · Saliency maps · Visualization

1 Introduction Deep networks such as convolutional neural networks (CNN), deep belief networks (DBN), and recurrent neural networks (RNN) are being used in current research fields such as computer vision and natural language processing where all these architectures have produced very interesting results as compared to human experts [3]. In this paper, we are mainly interested in the experimental study for optimizing the hyperparameters of prominent CNN models such as U-net for segmentation and AlexNet for classification of the CT renal images. We have specifically focused on the axial dimension CT images to find whether the kidney has stones or not. The rest of the paper is organized as follows: Sect. 2 briefly describes the background of all the frameworks and the related work. Section 3 describes the experimental setup. In Sect. 4, we present and discuss the results. Section 5 concludes by giving the summary and immediate future directions.

2 Background and Related Work With the rapid proliferation of deep learning techniques, a number of deep neural networks (DNNs) such as fully connected neural networks (FCNs) and convolutional neural networks (CNNs) have been developed for various applications. In this work, we segmented the kidney from the renal images using U-net and classified the resultant images using AlexNet.

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2.1 DNN Training DNN training starts off with the forward propagation. The results from the forward propagation step are compared against the known label to calculate the error value. In backward propagation, error propagates back through the network’s layers and updates their weights using gradient descent. It is a very common approach to batch hundreds of training inputs and operate on them simultaneously during DNN training in order to prevent over fitting and more importantly, to reduce loading weights from GPU memory across multiple inputs increasing computational efficiency.

2.2 Frameworks We have worked with keras framework with TensorFlow as backend. TensorFlow [4] is an open-source software library for numerical computation using data flow graphs developed by Google. It is an ecosystem for developing deep learning models. Keras [5] is a high-level open-source neural network application program interface written in Python language. Instead of keeping all the functionalities within keras it runs on top of Theano, TensorFlow, and CNTK.

2.3 Deep Networks U-net U-net [2] is a convolutional network architecture for fast and precise segmentation of images. Till now it has outperformed the prior best method on the ISBI challenge for the segmentation of neuronal structures in electron microscopic stacks. We have added batch normalization after every convolution in the network and initialized the network with the glorot uniform [6] and kernel regularizer with l2_lambda of 0.0001. U-net comprises of a contracting path and an expansion path. Contracting path is same as the usual convolutional neural network architecture. In the expansion part of the architecture, each step of an upsampling procedure for the feature map is followed with a 2 × 2 convolution, which is known as up-convolution. After which there are two 3 × 3 convolutions and a ReLU. After all the convolutional layers, at the end there is a single convolution. So in total U-net architecture consists of 23 layers. Alexnet Alexnet [7] is the 2012 winner of ILSRVC image classification challenge. This work is widely regarded as the most influential publication in the field. AlexNet consists of five convolutional layers and three fully connected (FC) layers.

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2.4 Visualization Technique Saliency maps Saliency map is an image which shows the unique nature of each pixel. The main aim of the saliency map is to change the given image representation to a more meaningful one which helps in analysis. Saliency is a kind of image segmentation. The output of these maps are a set of contours extracted from the original image. Formally speaking, in a given image x, the class which it belongs to as c, and the classification network (in our case Alexnet) with class score function SC (x), we can rank the image pixels based on their influence on the class score. So if the class score function is piecewise differentiable, for any given image, we can construct these saliency maps MC (x) very easily by just differentiating MC (x) with the input x as shown in 1 [8]. MC (x) =

d SC (x) dx

(1)

These maps [9] are just the depictions of what exactly the convolutional network is doing in each layer. Some pixels in the image might seem to be scattered in a random fashion, but across the image they are central which shows us how the CNN is making decisions in each layer.

2.5 Dataset Description Renal dataset consists of computed tomography (CT) images in native format. These are then converted to JPEG format for the training purposes. This dataset is collected from the Sri Sathya Sai Institute of Higher Medical Sciences (SSSIHMS) hospital, Puttaparthi, Andhra Pradesh, India. This dataset is collected by us under the direct supervision of Dr. Gurudat, Department of Radiology. This dataset can be used for problems such as diagnosing whether kidney has stones or not, whether stones are present in urinary bladder, or anywhere in the human body parts between chest to the urinary bladder. Sample images that are healthy and with stone are shown in Fig. 1. We have collected healthy and non-healthy images of 1800 in total.

3 Experimental Results and Discussion 3.1 Experimental Setup We have conducted experiments on two different GPU cards, namely GeForce GTX TITAN X and Tesla K20c.

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Fig. 1 Coronal view a Healthy. b Stone Renal samples

3.2 Challenges with Original Renal Dataset While our goal is to classify a renal image as healthy or non-healthy based on the presence or absence of the stone, both the stone and the bone pixels have similar shade in the image corpus. This makes the classification task difficult for the CNN. As a result, when we trained Alexnet on those kidney images, we could only achieve around 50% accuracy on the test data. Given network with large depth, the ability to backpropagate the gradient to all layers was a concern. This can be combated by adding regularization terms and techniques like batch normalization and Xavier initialization. When we included these techniques in the CNN models, the accuracies were touching 60%. This accuracies are not worth speaking in medical domain, so we manually created the masks of the kidneys so that the bone part of the image is not accounted for. Masks for close to 1200 image were created just as it is done for ultrasound nerves images in [3].

3.3 U-Net Results We trained U-net architecture using Adam optimizer, with an initial learning rate of 0.001 on the 1200 images with batch size of 32 for 50 epochs and achieved dice coefficient close to 83%. We could achieve these accuracies by including the optimization techniques such as batch normalization and Xavier initialization. Then U-net has predicted the masks for all the remaining test images (600 images) with the aforementioned dice coefficient. Some of the images which are predicted by the network are shown in Fig. 2. So far we have only the shape of the kidney but not the information about it. Now the final step is to extract only the kidney information from the shape and that is achieved by applying the masked kidney on the corresponding image. The graphs in Figs. 3 and 4 show the training loss, training accuracy, dice coefficient for training, validation loss, validation accuracy, and validation dice coefficient.

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Fig. 2 Masks predicted by the U-net architecture for given test images

Fig. 3 Metrics a Train loss. b Training dice coefficient

Fig. 4 Metrics a Validation loss. b Validation dice coefficient

3.4 Complete Work Flow of Data Preprocessing on Renal Images Segmenting the kidneys from the renal image is effectively a data preprocessing step for the classification. Here we show the whole system design on how we achieved segmentation using U-net. Figure 5 sums up the whole process.

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Fig. 5 Complete work flow of data preprocessing on Renal images

3.5 Experimental Results on Renal Dataset Using Alexnet Architecture To attain higher levels of accuracies, we worked with Alexnet model. The model is trained from scratch for our experiment by first doing data preprocessing with U-net architecture as discussed previously. We implemented this model with added features such as Batch normalization, Dropout, Xavier initialization, he uniform and regularization terms to improve our results. We worked with images of size 224 × 224 × 1, where the training set is divided into 580 healthy and 580 stone images, whereas the validation set into 150 healthy and 150 stone images. To increase the dataset, we used image data generator function in keras. We conducted experiments by focusing only on single TitanX GPU which is different from [7]. We trained AlexNet model from scratch with Adam optimizer, ReLU non-linearity, initial learning rate of 0.001 and trained the model for 300 epochs.The accuracy we achieved is 0.9475 and loss of 0.2613 for test dataset. All the classification results are shown in Table 1. We can see false positive rate to be 0.0555 (9 out of 170 images are predicted as wrong). Using tensorboard, we calibrated the metrics related to renal data such as train loss, train accuracies, validation loss, and validation accuracies are shown in Figs. 6 and 7.

Table 1 Results of Alexnet with ReLU activation on test dataset Test_Loss

Test_Accuracy Sensitivity

FPR

Precision

Classification error

F1 score

0.2613

0.9475

0.0555

0.944

0.0524

0.9468

0.9506

Fig. 6 Metrics a Train loss. b Training accuracy

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Fig. 7 Metrics a Validation loss. b Validation accuracy

In the figures below, we can see the training and validation loss close to 0.30, whereas the train and validation accuracies going up to 94.75%.

3.6 Visualization Results on Renal Dataset Using Saliency Maps We attempted the visualizations of each convolutional layer in Alexnet architecture to see what exactly each layer does for a given image. For this, we constructed the saliency maps for each layer where the gradient is multiplied with the input. By doing this only the key regions are highlighted in the image which means that in the corresponding layer, those features have been identified. Figure 8 shows the different saliency maps that are produced at different layers. When dealing with image data, each pixel in the image is a feature. So in the above figures, we can see the input images and the corresponding attributions. The main aim of attribution is to output the original value for every feature with respect to a target neuron of interest. So when all the attributions of input image features are grouped together to get the similar shape as the input then we talk about attribution maps which are clearly shown in the above figures, where red indicates the features that contribute positively to the activation of the output, whereas the blue color represents the features that are having a suppressing effect on them. These maps also show us the features that contribute in getting the desired output image, the layers that are responsible for it and the main reasons for the misclassifications.

4 Conclusion and Future Work We performed segmentation and classification of renal images by experimenting with U-net and Alexnet. U-net is used for the prediction of kidney masks. We conducted series of experiments on Alexnet to tune the hyper parameters. We then experimented with saliency maps to visualize each layer’s output. In future, we would like to

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Fig. 8 Visualizations of first and second convolutional layers for the given a Healthy. b Non-healthy images

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consider sagittal and axial images of renal images to achieve a more general model. Along with segmentation of the stone, predicting the size and it’s severity can also be very useful.

5 Declaration The authors hereby declare that they have taken required permission from Sri Sathya Sai Institute of Higher Medical Sciences, Puttaparthi for the use of renal images in the work and take complete responsibility if any issues arise later.

References 1. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436 (2015) 2. O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, 2015), pp. 234–241 3. D.C. Ciresan, L.M. Gambardella, A. Giusti, J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images, in IN NIPS, 2012, pp. 2852–2860 4. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., Tensorflow: a system for large-scale machine learning, arXiv preprint arXiv:1605.08695 (2016) 5. F. Chollet, keras, https://github.com/fchollet/keras (2015) 6. X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics 2010, pp. 249–256 7. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, 2012, pp. 1097–1105 8. K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034 (2013) 9. D. Smilkov, N. Thorat, B. Kim, F. Viégas, M. Wattenberg, Smoothgrad: removing noise by adding noise, arXiv preprint arXiv:1706.03825 (2017)

A Novel Traffic Sign Recognition System Combining Viola–Jones Framework and Deep Learning Ajay Jose, Harish Thodupunoori and Binoy B. Nair

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traffic Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Viola–Jones Framework-Based Traffic Sign Recognition System . . . . . . . . . . . . 3.2 Deep Learning-based Traffic Sign Recognition System . . . . . . . . . . . . . . . . . . . . 3.3 Proposed Traffic Sign Recognition System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract An automated traffic sign detection system is an important aspect of advanced driver assistance systems (ADAS) as it significantly enhances the driver’s situational awareness. In this paper, a novel approach combining Viola–Jones framework and deep learning, to design of a camera-based traffic sign recognition system capable of detecting and identifying the traffic signs, is presented. The proposed system is designed and tested for its effectiveness under Indian road conditions. It is observed that the designed system is well capable of correctly detecting the correct traffic signs even in the presence of non-standardized traffic signs. Keywords Viola–Jones · Deep learning · Transfer learning · TensorFlow

A. Jose · H. Thodupunoori Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] H. Thodupunoori e-mail: [email protected] B. B. Nair (B) SIERS Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_48

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1 Introduction Automatic detection of road traffic signs is a very challenging problem. It has important practical applications, especially with respect to advanced driver assistance systems. Such automatic systems are ideal for car drivers traveling at high speeds since the driver’s field of view decreases from 100° to 30° when the car’s speed is increased from 40 to 130 km/h [1]. Automatic traffic sign recognition systems can be used to assist the driver by detecting and interpreting road signs for the driver without the driver having to take the focus off the traffic ahead. The problem of traffic sign detection and identification can be divided into two parts: detection (finding if the current video frame contains any traffic sign at all) and identification (identifying the specific traffic sign). Lillo-Castellano et al. [2] and John et al. [3] used HSI color segmentation to detect the color, and for shape classification, invariant geometric moments are used. Liu et al. [4] used genetic-based biological algorithm (GBA) to effectively preprocess the chosen dataset, feature selection (or dimensionality reduction), and instance selection. Others have proposed techniques such as group sparse coding [4], color segmentation, and shape matching [5] etc. Once the region of interest (RoI) is determined and a traffic sign is detected, it should be recognized using a predetermined database of all the traffic signs in the system. The recognition methods are typically, either template-based or classifierbased. Template-based systems approach the traffic sign identification problem as a pattern matching problem, e.g., in [6], and have demonstrated limited success. Classifier-based techniques are based on more sophisticated machine learning (ML) techniques which in general achieve more robust final results when the images are analyzed under uncontrolled environments and are hence considered in the present study. HOG features along with SVMs to classify traffic signs have been presented in [7]. Convolutional neural networks (CNNs) have been used in [8] for this problem. Zaklouta and Stanciulescu [9] got high accuracy rates by using K-d tree. Shopa et al. [10] used pattern matching based on normalized correlation. It is seen from the literature that ML-based techniques are being increasingly used for detection and identification of traffic signs. Deep learning techniques appear to be particularly suited for the purpose. In this study, a hybrid ML-deep learning approach to traffic sign detection that can detect Indian road signs in the presence of non-standardized road signs is proposed. Organization of the remainder of the paper is as follows: A brief description of the traffic signs and the datasets is presented in Sect. 2. A detailed description of the proposed system is presented in Sect. 3. Results are reported in Sect. 4 and the conclusions in Sect. 5.

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2 Traffic Signs Road signs are standardized via the Vienna Convention on Road Signs and Signals [11], which India has ratified, as well. Indian road signs have been standardized as per the code of practice for road signs [12]. Performance of the automatic traffic sign recognition system proposed in the present study needs to be validated first on a publicly available dataset. It was observed that the following publicly available datasets could be used for the purpose: (a) German TSR Benchmark (GTSRB) [8], (b) RUG Traffic Sign Image Dataset, (c) Laboratory for Intelligent and Safe Automobiles (LISA) Traffic Sign Dataset, (d) Swedish Traffic Signs Dataset, (e) Stereopolis Database, and (f) KUL Belgium Traffic Signs Dataset. GTSRB dataset is highly suited for validating machine learningbased traffic sign identification techniques since it contains around 50,000 + images taken under different conditions of traffic, lighting, etc. Hence, in the present study, the proposed system is first validated on GTSRB dataset and then tested for its effectiveness on Indian road signs. The standardization of road signs has been of great help to the original equipment manufacturers (OEMs) in developing a traffic sign recognition system, which could be used globally. However, in India, the standardization is not universally followed. An example is given in Fig. 1. The traffic signs shown in the figure are collected from different parts of India. Figure 1a, b indicates pedestrian crossing, but they are not identical. Same is the case with Fig. 1c, d that indicates the presence of a school ahead. Such non-standardization in the traffic signs makes the development of an automated sign detection system especially challenging. This study proposes a system that is also robust to such variations. The following section describes the methodology employed in the present study.

Fig. 1 Examples of Indian traffic signs

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3 System Description The proposed traffic sign detection and identification system are designed to work in three steps. The process flow is presented in Fig. 2. The first step in the process is to extract the frame from the video. Detection of the traffic sign in the frame is the next step. Once the presence of traffic sign is detected, ML is employed next, to identify the sign and the identified sign is communicated to the driver as an audio clip so that the driver does not have to take his/her eyes off the road. Three techniques for detection and identification of traffic signs are evaluated. The first technique employs Viola–Jones framework [13]. Though this framework has been traditionally used for detection of faces from images, we have attempted to employ the framework to detect and identify traffic signs from images. The second technique evaluated employs deep learning to combine steps 1–3 mentioned above into a single (CNN) model. The third technique combines Viola–Jones framework and deep learning into a composite traffic sign detection and identification system. A brief description of each of these techniques is presented in the following sections.

3.1 Viola–Jones Framework-Based Traffic Sign Recognition System The Viola–Jones framework [13] has been successfully employed for detection of faces in an image. A few studies have also attempted to employ this framework for detection of traffic signs as reported [14, 15]. In the present study too, the Viola–Jones framework-based traffic sign detection system forms the baseline system against which the other two systems considered in the present study are evaluated. Steps in the Viola–Jones framework-based system are given below: Feature extraction: Histogram of oriented gradients (HOG), since they appear to be widely used, e.g., in [8, 15]. Cell size: 8 × 8 pixels. Filter for gradient orientation: 3 × 3 Sobel. Number of histogram bins per cell: nine. Cell block size: 2 × 2 cells. Classification is carried out with decision stumps and Adaboost ensemble learner as

Video Camera

Extract Frame

Detection of traffic sign

Yes Traffic Sign detected ? No

Fig. 2 Proposed traffic sign detection and recognition system

ML based symbol Identification

Output Traffic Symbol Information to Driver

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(a)

(c)

511

(b)

Detected Road Sign

(d)

Fig. 3 Example output from Viola–Jones framework-based traffic sign detector for Indian road signs (a, b) and for GTSRB dataset (c, d)

Speed limit sign correctly identified

Vodafone® logo detected as a speed limit i

Fig. 4 Detection example using Viola–Jones-based detection system on a GTSRB video frame

per the classical Viola–Jones framework, cascaded over a maximum of 20 possible stages. Figure 3 shows an example result of the Viola–Jones framework-based traffic sign detection system for Indian and GTSRB road signs. The accuracy obtained is 71.56% for GTSRB signs and 84% for Indian road signs. It is observed that false positives for both datasets are mainly in identifying speed limit signs. An example is shown in Fig. 4, where in for a frame drawn from the GTSRB dataset, the speed limit sign is correctly detected, but the logo of telecom company Vodafone® is detected as speed limit sign as well.

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Actual Speed limit 30 Misclassified as 70

Fig. 5 Example frame for CNN-based traffic sign detection system

3.2 Deep Learning-based Traffic Sign Recognition System Deep learning-based systems have become extremely popular recently, primarily due to their ability to significantly outperform conventional ML-based techniques, primarily because of their ability to handle extremely large datasets and elimination of the need for feature engineering. In the present study, CNN [16] is the deep learning technique chosen due to its ability to handle complex image classification problems. In the present study, the TensorFlow library from Google is used for CNN implementation. It is also observed that rather than training a CNN from scratch, better accuracy and lower training time could be obtained using transfer learning. Transfer learning [17] is a machine learning technique, where knowledge gain during training in one type of problem is used to train in other similar type of problem. In this study, Inception-v3 model [18] which has been shown to achieve state-of-the-art accuracy for recognizing general objects with 1000 classes is used as the pre-trained model on which transfer learning is carried out using the images from the GTSRB and Indian road sign databases. The CNN training parameters used are as follows: learning rate  0.01, maximum number of training steps  4000, training batch size  100, training data percentage  80%, testing data percentage  10%, validation data percentage  10%. It is observed that transfer learning-based technique works exceedingly well when only the traffic signs are presented to the system. An accuracy above 92% was obtained for both the datasets when only the signs (not the entire video frame) was presented to the system. However, when the entire video frame like the one shown in Fig. 5 is fed into the system, it generates very poor classification accuracy. In Fig. 5, the actual speed limit is 30; however, it is detected as speed limit  70 (with a score of 0.34271).

3.3 Proposed Traffic Sign Recognition System It was observed that while the Viola–Jones framework-based traffic sign recognition system presented in Sect. 3.1 is able to detect the presence of traffic signs with a very high degree of accuracy, the correct identification of the detected sign is

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an issue. On the other hand, the CNN-based detection system described in Sect. 3.2 demonstrated good accuracy at identifying the traffic signs but had difficulty detecting the traffic signs from a video frame. Hence, a cascade approach is proposed, wherein the Viola–Jones framework issued to find the potential ROIs which may contain the traffic signs from the entire image, instead of finding a specific sign in an image. These ROI’s are then given to a CNN-based sign identification system, which attempts to correctly classify the signs present in these ROI’s. The parameters for Viola–Jones framework and the CNN are identical to the parameters detailed in Sects. 3.1 and 3.2, respectively. The detailed results are discussed in the following section.

4 Results All the three systems described above were validated on the GTSRB dataset and Indian road sign dataset. The number of signs and their description are presented in Tables 1 and 2.

Table 1 GTSRB dataset classes and description Class ID Description Class ID Description

Class ID

Description

1

Speed limit 20

16

No traffic both ways

31

Snow

2

Speed limit 30

17

No trucks

32

Animals

3

Speed limit 50

18

No entry

33

Restriction ends

4

Speed limit 60

19

Danger

34

Go right

5

Speed limit 70

20

Bend left

35

Go left

6

Speed limit 80

21

Bend right

36

Go straight

7

Restriction ends 80 Speed limit 100

22

Bend

37

Go right or straight

23

Uneven road

38

Go left or straight

9

Speed limit 120

24

Slippery road

39

Keep right

10

No overtaking

25

Road narrows

40

Keep left

11

No overtaking (trucks)

26

Construction

41

Roundabout

12

Priority at next intersection

27

Traffic signal

42

Restriction ends (overtaking)

13

Priority road

28

Pedestrian cross

43

Restriction ends (overtaking (trucks))

14

Give way

29

School crossing

15

Stop

30

Cycles crossing

8

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Table 2 Indian road sign dataset classes and description Class ID Label Class ID Label

Class ID

Label

1

Speed limit 20

11

Left-hand curve

21

Men at work

2

Speed limit 40

12

Right-hand curve

22

Cross road

3

Speed limit 50

13

Narrow bridge

23

Gap in median

4

Speed limit 80

14

Uneven road(Bump)

24

Horns prohibited

5

Speed limit 100

15

Keep left

25

Guarded level crossing

6

Bus stop

16

Keep right

26

Major road ahead

7

Stop

17

No overtaking

27

Side road right

8

No parking

18

Pedestrian crossing

28

Side road left

9

Traffic signal ahead Traffic circle ahead

19

School ahead

29

Y-intersection

20

Right reverse bend 30

T-intersection

10

There are 43 signs (classes) considered from the GTSRB dataset and 30 classes from the road sign dataset in the present study. The effectiveness of the proposed system is illustrated using the following example. The selection of RoIs that could possibly be road signs, using the proposed system for a frame extracted from the GTSRB dataset, is presented in Fig. 6. As can be seen, three possible RoIs are detected, two of which are actual road signs (both indicating: speed limit 100) while third is a cloud. These potential RoIs are then given to the CNN trained using technique described in Sect. 3.2. The CNN-based sign identification system generates the following scores for the three RoIs: Sign on the left of the frame (in Fig. 6): Actual sign: Speed limit 100. Identification system predicted sign and the corresponding scores (only the top three classes in terms of the score are presented): (speed limit 100, score  0.94105), (speed limit 50, score  0.03886), (speed limit 30, score  0.00946). As can be seen, the predicted class with highest score is the ‘speed limit 100’ class, indicating that the system is able to correctly identify the sign. Similarly, for the sign on the right of the frame, the actual sign is speed limit 100. The predicted classes and the respective scores are (speed limit 100, score  0.99610), (speed limit 50, score  0.00278), (speed limit 60, score  0.00081). In this case too, we can see that the predicted sign with the highest score is the sign: ‘speed limit 100’, indicating that the system is able to correctly classify the sign. Now for the third RoI, which is actually a part of cloud in the sky seen on the image, the scores are: (go left, score  0.39424), (danger, score  0.21439), (bend left, score  0.09884). It can be seen here that the identified sign with the highest score is ‘go left’, but it has a score of only 0.39424, which is much lesser than the scores obtained for the other two correctly classified RoIs. This low value of score is used as an indicator that the RoI may actually not contain a sign and needs to be

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Fig. 6 RoIs selected as possible road signs in a GTSRB image

(a)

(b)

Fig. 7 RoI selected as possible road sign in Indian road images

discarded. Using trial and error, it was found that setting a threshold score of 0.5 was useful in eliminating the detection of false positives to a large extent. Similarly for the Indian road signs, an example of the RoIs detected is presented in Fig. 7a, b. In Fig. 7, both (a) and (b) have a road sign each, indicating pedestrian crossing. For both (a) and (b), the proposed system correctly detects the RoI and the corresponding classification scores are: for Fig. 7a: (pedestrian crossing, score  0.97003), (school ahead, score  0.01597), (side road right, score  0.00531) and for Fig. 7b, the classification scores are: (pedestrian crossing, score  0.96865), (side road left, score  0.00827), (left-hand curve, score  0.00648). As can be seen, even when the two road signs are dissimilar (but denoting the same sign), the proposed system is able to detect the signs with high accuracy. Overall accuracy for the three techniques is presented in Table 3. Detailed performance metrics for the proposed system for the two datasets considered in this study are presented in Table 4. Results reported in Table 4 are arrived at using macro-averaging each performance measure over all classes considered.

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Table 3 Traffic sign detection accuracy Method

GTSRB dataset (%)

Indian dataset

Viola–Jones framework-based system

71.56

84

Deep learning (CNN)-based system

22

34

Proposed system

90

92

Table 4 Performance measures for the proposed system

Parameter

GTSRB dataset

Indian dataset

True positive rate

0.9298

0.9233

False negative rate

0.9006

0.9233

False positive rate

0.0024

0.0026

Prevalence Precision F1-score Accuracy

0.0233 0.8677 0.8875 0.9

0.0333 0.928 0.9234 0.92

The results as reported in Tables 3 and 4 indicate that the proposed traffic sign identification system significantly improves the accuracy of identifying a road sign in a traffic video frame.

5 Conclusions A novel system that combines Viola–Jones framework and deep learning for detection and consequent identification of traffic signs is presented in this study. Three different methods for traffic sign detection and recognition are considered and their performance is validated. It can be concluded from the results obtained that the proposed system significantly outperforms both the Viola–Jones framework-based traffic sign detection system and the stand-alone deep learning-based system. It is also observed that the proposed system is capable of detecting multiple signs in the same frame as well being robust to variations in the road signs. Hence, it can be said that the proposed system could be considered as a suitable candidate for accurate detection of traffic signs.

References 1. S.B. Wali, M.A. Hannan, A. Hussain, S.A. Samad, Comparative survey on traffic sign detection and recognition: a review. Przegl˛ad Elektrotechniczny 91, 38–42 (2015) 2. J.M. Lillo-Castellano, I. Mora-Jiménez, C. Figuera-Pozuelo, J.L. Rojo-Álvarez, Traffic sign segmentation and classification using statistical learning methods. Neurocomputing 153, 286–299 (2015)

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3. A.A. John, B.B. Nair, P.N. Kumar, Application of clustering techniques for video summarization—an empirical study, in Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing ed. by R. Silhavy, R. Senkerik, Z. Kominkova Oplatkova, Z. Prokopova, P. Silhavy (Springer, Cham, 2017), pp. 494–506 4. H. Liu, Y. Liu, F. Sun, Traffic sign recognition using group sparse coding. Inf. Sci. (Ny) 266, 75–89 (2014) 5. H. Li, F. Sun, L. Liu, L. Wang, A novel traffic sign detection method via color segmentation and robust shape matching. Neurocomputing 169, 77–88 (2015) 6. G. Sivan, M. Dhanya, Automated traffic sign recognition system for Indian roads. Int. J. Appl. Eng. Res. 10, 35618–35626 (2015) 7. S. Salti, A. Petrelli, F. Tombari, N. Fioraio, L. Di Stefano, Traffic sign detection via interest region extraction. Pattern Recognit. 48, 1035–1045 (2015) 8. J. Stallkamp, M. Schlipsing, J. Salmen, C. Igel, Man versus computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Networks 32, 323–332 (2012) 9. F. Zaklouta, B. Stanciulescu, Real-time traffic sign recognition in three stages. Rob. Auton. Syst. 62, 16–24 (2014) 10. P. Shopa, N. Sumitha, P.K.S. Patra, Traffic sign detection and recognition using OpenCV, in Proceedings of International Conference on Information Communication and Embedded Systems (ICICES) (IEEE, Chennai, 2014), pp. 1–6 11. UNECE: Road Traffic and Road Signs and Signals Agreements and Conventions 12. IRC: Code of Practice for Road Signs. (2012) 13. P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, Hawaii, 2001), pp. 511–518 14. S. Houben, J. Stallkamp, J. Salmen, M. Schlipsin, C. Igel, Detection of traffic signs in real-world images: the German traffic sign detection benchmark, in Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN) (IEEE, Dallas, TX, 2013), pp. 1–8 15. S.K. Berkaya, H. Gunduz, O. Ozsen, C. Akinlar, S. Gunal, On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67–75 (2016) 16. Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998) 17. TensorFlow: how to retrain inception’s final layer for new categories 18. C. Szegedy, V. Vanhoucke, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, Seattle, 2016), pp. 2818–2826

Detection of Cardiac Arrhythmia Using Convolutional Neural Network Padmavathi Kora, K. Meenakshi and K. Swaraja

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Myocardial Infarction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Preprocessing of ECG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Classification of ECG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Convolutional Neural Network (CNN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Cardiac arrhythmia (abnormal heart rhythm), which may even life threatening sometimes. An automatic diagnosis system is required to identify the cardiac arrhythmia at early stages for immediate precaution and treatment. With the use of novel machine learning algorithms, we classified different types of cardiac diseases. Detection of heart arrhythmia requires preprocessing, feature extraction and classification steps. Feature extraction step plays a major role in accurate detection of arrhythmia, as feature extraction methods provide us a way of reducing computation time, increasing prediction performance, and provides a detailed understanding of the disease. Discrete wavelet transform (DWT) is used as feature extraction technique, and extracted features are classified using SVM and KNN. We applied the feature extraction and classification techniques on the standard MIT-BIH datasets of (myocardial infarction) to demonstrate the applicability of feature extraction techniques for the detection of abnormal heart rhythm. Keywords ECG · Myocardial infarction · Atrial fibrillation · Bundle branch block · Dual-tree wavelet transform · Convolutional neural network classifier

P. Kora (B) · K. Meenakshi · K. Swaraja GRIET, Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_49

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1 Introduction In the present generation, coronary heart disease (CHD) is the pre-eminent reason of death in India. According to the statistics form World Health Organization (WHO), greater than 80% of heart failure is due to ischemic and stokes. WHO reports predict that nearly 2.6 million Indians will cause morbidity and death by 2020 due to CHD. Having a healthy heart is the necessary condition for a healthy body. In recent years, with the busier life of human being, the incidence of heart diseases has increased annually. Heart diseases have become one of the diseases that seriously threaten human life. Owing to the rapid proliferation of computer technology, digital research on heart disease has gradually developed. In normal circumstances, the rhythm of the heartbeat is regular. However, an arrhythmia will be produced when the heart segments irregularly. Arrhythmia can be divided into several categories, including some types of arrhythmia which are extremely dangerous and often imply some potentially serious heart disease. Without proper prompt treatments, it would cause very serious consequences and even sudden death. Therefore, it is important to confirm the type of arrhythmia and select targeted treatment as early as possible to prevent and monitor heart disease and improve the working efficiency of doctors. The rhythm (morphology) of ECG wave changes due to the irregularities present in the heart. These changes can be observed by measuring heights (amplitudes) and intervals (durations) of PQRST signal (ECG beat). But for the doctors it is a very cumbersome process to manually analyze the ECG patterns when the occurrence abnormal segments are occasional (diseases like MI). Sometimes, continuous monitoring of ECG is required, so the computer-based analysis helps the doctors to reduce their workload. Many coronary heart diseases can be detected by analysing ECG signal. In this paper one disease was discussed, namely MI.

1.1 Myocardial Infarction Myocardial infarction (MI) [1] occurs when blood flow to an area of the heart muscle (myocardium) is interrupted. These interruptions are caused by the blockage of the coronary arteries due to acute rupture of cholesterol-rich atherosclerotic plaques. The affected myocardium stops functioning, and if left untreated, it may lead to chronic heart failure. This is responsible for many deaths around the world. It has been appraised that around 750,000 Americans suffer heart attack every year [2]. Among these, 210,000 have recurrent heart attacks. It is challenging and essential to detect the initial stage of MI so that early correct treatment can be instituted, which increases life expectancy. A system needs to be designed that is able to diagnose the MI accurately and rapidly and yet is robust. Two different types of MI can be observed from ECG; they are ST elevation (type 1) and ST depression (type 2). In this paper, we considered type 2 MI signal. There are mainly three stages (steps) in the ECG signal classification ((i) preprocessing; (ii) feature extraction; (iii) classification). Feature extraction step

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plays a major role in the detection of cardiac arrhythmia. Different types of feature extraction techniques are available in the literature due to the availability of data with a larger number of variables (features). Recent spectral estimation-based feature extraction methods, for example, continuous wavelet transform (CWT); discrete wavelet transform (DWT); magnitude-squared coherence (MSC); wavelet coherence (WTC) using Physionet database, yielded a collection of huge feature set. Here, in this paper, we have used dual-tree complex discrete wavelet transform (DWT) as a feature extraction technique as it offers benefits over the crucially sampled wavelet transform for image, signal and video analysis. The DWT is realized using two different filter banks approximated and wavelet coefficients.

2 Background The wavelet has been successfully applied to solve many engineering problems like image processing, denoising , feature extraction. Even though the standard DWT is a powerful tool, it suffers from three main drawbacks: (a) shift sensitivity, (b) poor directionality and (c) absence of phase information. Lee et al. [2] used three statistical methods for the detection of AF and tested on AFDB and NSR datasets. The results suggested that MSC applied to surface ECG was used to quantify rhythm organization. Sumathi et al. [3] used wavelet transform with an adaptive neuro-fuzzy system for detecting dangerous arrhythmias. KalaiSelvi et al. [4] used DWT and four other features with SVM classifier to detect cardiac arrhythmia. Engin et al. [5] proposed neuro-hybrid neural network for the classification of ECG features. ECG features were extracted using the wavelet transform and AR modeling and tested with MIT/BIH arrhythmia database. Significant performance enhancement was observed from this method. Ding-Fei et al. [6] explored the ability of multivariate autoregressive model to extract the features from the ECG signals in order to classify six cardiac arrhythmias. The classification was performed using a quadratic discriminate function (QDF). The results showed that multivariate autoregressive coefficients produced the best results among the four EGG representations, and this modeling was a useful classification and diagnosis tool. Kutlu et al. [7] made an experimental study of using WT for extracting relevant features and KNN-based classifier for the detection of BBB. In addition to these features, different features obtained from the relations of cumulants were also used. Simulation results showed that features obtained from the relations among cumulants were more discriminative than the cumulants. Kora et al. [8–12] used different feature extraction and optimization techniques to classify cardiac arrhythmia.

3 Preprocessing of ECG Signal The initial step in preprocessing mainly concerns in removing the noise from the signal with filters. The next step in preprocessing is segmentation of ECG file of

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duration 10 s. The segmented signals that are extracted from each type of arrhythmia (MI) contain non-uniform samples (because of different sampling rates for different data type). The non-uniform ECG segments are converted into uniform segments using resampling MATLAB command. The data was gathered from Physionet database. Sample ECG signals from Physionet database are sinus rhythm (N). For the detection of MI, the MIT-BIH PTB database is considered. It consists of 52 normal individuals and 148 MI patients (sampling rate of 1000 Hz). From the PTB database, we have used six normal, seven MI files of duration of 30 min.

4 Feature Extraction In this study, we have developed two spectral methods like wavelet transform modeling and DWT for the detection of MI. The WT coefficients characterize the features of ECG. The coefficients (using WT) which are measured for ECG segment are taken as features. WT and DWT techniques are compared using three different classifiers for the detection of normal and abnormal signals.

4.1 Wavelet Transform Wavelet transforms are efficiently useful for the identification of abrupt changes in the signal. Wavelet is a rapidly decaying wave-like oscillations that has zero mean. Unlike the sinusoids which extend up to infinity, a wavelet exists for finite duration. Wavelets are in different sizes and shapes. The availability of wide range of wavelets is the key strength of wavelet analysis. Scaling and shifting are the two important concepts of wavelets. For each ECG segment, coefficients of wavelets d( j, n) and scaling functions c(n) are calculated via f (t) =

∞ 

c(n)φ(t − n) +

n=−∞

∞  ∞ 

d( j, n)2 j/2 ψ(2 j t − n)

(1)

j=0 n=−∞

∞ c(n) =

f (t)φ(t − n)dt

(2)

−∞

∞ d( j, n) = 2

j/2

f (t)ψ(2 j t − n)dt

(3)

−∞

We have extracted six approximated coefficients A1 to A6 and six detailed coefficients D1 to D6.

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5 Classification of ECG 5.1 Convolutional Neural Network (CNN) A convolutional neural network contains more than one layer and then followed by neural network layers [13, 14]. The structure of a CNN is designed to take benefit of the 1D (signal) or 2D (image) anatomical structure of the input. The two main operations of CNN are: (i) Convolution. (ii) Pooling. Convolution operation with weights followed by pooling, which performs optimization of invariant features as shown in Fig. 1. With obtained features, it is easier for the neural network to train the CNN. In this section, we will discuss the architecture of a CNN and the back propagation neural network to compute the gradient. In this work for the classification of arrhythmia, CNN was used.

6 Results The data has been collected from the MIT-BIH AF database consisting normal sinus rhythm database (18 patients, 128 Hz) and the PTB data base, 6 normal and 7 MI files of duration 30 min has been used. Ten-second ECG waveform during MI was considered along with its detailed coefficients of levels D1 to D6. As the sampling rate of MI signal is 1000 Hz, this signal contains 10,000 samples. The features of MI are apparent in the wavelet domain, especially in the detail coefficients. The present research work proposed three efficient approaches for ECG classification. The XWT coefficients, WT coefficients and DWT coefficients (spectral estimation) are used for feature extraction: These features are classified using KNN, SVM and CNN classifiers. The detection accuracy of AF using WT coefficients is

Fig. 1 CNN architecture

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Table 1 Classification of MI using WTC using SVM, KNN and CNN classifiers Method Sen Spe acc XWT+KNN XWT+SVM XWT+CNN WT+KNN WT+SVM WT+CNN

79.5 81.3 90.5 69.5 86.3 99.6

78.3 82.5 87.3 68.3 76.5 98.1

77.5 81.4 92.2 67.5 79.4 99.1

found to be 99.1%. The detection accuracy of MI using the DWT technique in combination with CNN classifier is 98.9%. The detection accuracy of the DWT technique in combination with CNN classifier is 99.3% as depicted in Table 1. It is clear that the DWT technique in combination with CNN has given fast and accurate results compared to the other techniques in the literature survey. We have selected 9193 normal segments 6068 MI segments user for classification. The specificity is defined as the fraction of correctly classified abnormal segments to the total number of abnormal segments. The sensitivity of an arrhythmia is defined as the fraction of correctly identified normal segments to the total normal segments. The overall accuracy is the division of the total ECG segments correctly classified to the total number of segments used for the classification. The performance of feature extraction methods is compared with three classifiers: KNN, SVM and CNN. The experimental results state that the proposed DWT features with CNN classifier have greater accuracy for the detection of MI than other the classifiers. In this research work, an effort was made to increases classification rate of diseases (AF, BBB and MI). The overall achievement is estimated by the parameters specificity, sensitivity and accuracy in terms of ROC performance curves shown in Fig. 1. Table 1 shows the performance of different feature extraction techniques in terms of sensitivity, specificity and accuracy.

7 Conclusion The present research work proposed three efficient approaches for ECG classification. The DWT coefficients and DWT coefficients (spectral estimation) are used for feature extraction: These features are classified using KNN, SVM and CNN classifiers. It is clear that the DWT techniques in combination with CNN have given fast and accurate results compared to the other techniques in the literature.

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The main conclusions of this paper are: • To perform preprocessing of the ECG signal. – To remove the noise from ECG signals using filters, obtained from MIT-BIH database. – ECG files are segmented into 10 s duration. • To develop feature extraction and classification using spectral estimation techniques (DWT) and convolutional neural network. In the spectral estimation process, the coefficients characterize the features of ECG segment in the frequency domain. The WTC techniques measure the similarity between two signals (normal and abnormal) in the frequency domain. These features are classified using SVM, KNN and CNN classifiers. • To implement the feature extraction and classification using spectral estimation techniques and convolutional neural network. It has been observed that the performance of the classifier is improved with the help of the extracted features than raw features. Among the three classifiers, CNN has the best accuracy. The results show that modified DWT techniques can be more effectively used for the detection of heart arrhythmia than some other techniques proposed in the literature survey. Ethical Approval Ethical approval was not required for this work as no new empirical data were collected.

References 1. P. Kora, K. Krishna, Myocardial infarction detection using magnitude squared coherence and support vector machine, in International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom) (IEEE, New York, 2014), pp. 382–385 2. J. Lee, Atrial fibrillation detection using an iPhone 4S. IEEE Trans. Biomed. Eng. 60(1), 203– 206 (2013) 3. S. Sumathi, H. Lilly Beaulah, R. Vanithamani, A wavelet transform based feature extraction and classification of cardiac disorder. J. Med. Syst. 38(9), 98 (2014) 4. A. KalaiSelvi et al., Complex wavelet transform based cardiac arrhythmia classification 202, 34–37 (2016) 5. M. Engin, ECG beat classification using neuro-fuzzy network. Pattern Recogn. Lett. 25(15), 1715–1722 (2004) 6. G.E. Ding-Fei, H.O.U. Bei-Ping, X.-J. Xiang, Study of feature extraction based on autoregressive modeling in EGG automatic diagnosis. Acta Automatica Sinica 33(5), 462–466 (2007) 7. Y. Kutlu, D. Kuntalp, M. Kuntalp, Arrhythmia classification using higher order statistics, in IEEE 16th IEEE Conference on Signal Processing, Communication and Applications Conference, 2008 SIU 2008 8. P. Kora, S.R. Kalva, Hybrid bacterial foraging and particle swarm optimization for detecting Bundle Branch Block. SpringerPlus 4(1), 481 (2015) 9. P. Kora, S.R. Kalva, Improved Bat algorithm for the detection of myocardial infarction. SpringerPlus 4(1), 666 (2015) 10. P. Kora, ECG based myocardial infarction detection using hybrid firefly algorithm. Comput. Methods Programs Biomed. 152, 141–148 (2017)

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11. P. Kora, A. Annavarapu, P. Yadlapalli, N. Katragadda, Classification of sleep Apnea using ECG-signal sequency ordered Hadamard transform features. Int. J. Comput. Appl. 156(14) (2016) 12. A. Annavarapu, P. Kora, P. Yadlapalli, An Enhanced SCHT based Alamouti scheme over multipath channels (2006) 13. B. Zhao, H. Lu, S. Chen, J. Liu, D. Wu, Convolutional neural networks for time series classification. J. Syst. Eng. Electron. 28(1), 162–169 (2017) 14. J. Gu et al., Recent advances in convolutional neural networks, arXiv pp. 1–14 (2015)

Dual-Function Radar-Communication Using Neural Network K. S. Anjali and G. Prabha

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neural Adaption in Phased Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Phased Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Artificial Neural Network (ANN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Multilayer Back-Propagation Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Mathematical Modeling of Received Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Direction of Arrival Estimation Using MUSIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Optimization Using Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Estimation of DOA Using MUSIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Beam Formation Using Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 BER Plot of Communication Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract A novel approach for dual-function radar-communication (DFRC), by making use of frequency diversity in waveforms, is proposed in this paper. This is an approach to make use of an available band of spectrum (2.38–2.42 GHz) for both radar and communication. Unlike the previous approaches, the system can process both radar and communication signals simultaneously. Digitization of receive signals at the element level gives a way to deliver maximum flexibility. The use of neural network is to minimize the BER and for optimizing beam formation. MUSIC algorithm is applied to phased array to precisely estimate the arrival directions of signals. Keywords DFRC · Frequency diversity · MUSIC algorithm · Neural network K. S. Anjali · G. Prabha (B) Department of Electronics and Communication, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] K. S. Anjali e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_50

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1 Introduction The main aim of wireless technologies today is to reduce the hardware and installation costs and radio-frequency (RF) spectrum exploitation. Researches have been carried out on DFRC systems by using waveform diversity techniques to obtain good performance and spectrum sharing. The approach of unification of both radar and communication platforms originates mainly from [1], in which the system acts in communication mode or radar mode at a time. Data insertion into the radiation of MIMO radar using frequencyhopping waveforms is proposed in [2]. In a DFRC system, during the pulse repetition intervals, radar waveforms which are orthogonal are repeatedly transmitted from different elements and are used for embedding symbols [3]. In phase-modulation-based scheme for insertion of data into the radar emission, at the time of each radar pulse, one phase symbol is put into the radar signal radiation toward the needed direction of communication [4]. Comparing different signaling strategies, phase modulation provides the best BER performance [5]. In [6], time-modulated array (TMA) is used for DFRC. TMA is able to perform adaptive beam forming with the benefit of using a single RF front end. This method is able to perform radar operation in the main lobe, while communication in the side lobe. A transceiver that find application in intelligent transportation infrastructure for joined functions of radio communication and radar sensing is developed in [7]. Here, both radar and communication functions are integrated well balanced in a time-division platform which finds application in dedicated short-range communication (DSRC) which uses very narrow bandwidth. The recent approach to DFRC is information embedding using waveform diversity along with side lobe controls [8]. In this method, two responses of the same array with the same main beam were formed to attain distinct special transmit radiation patterns outside the main beam which is designated for radar. The receiver retrieves the bits on a particular waveform either as 0 or 1 depending on the case that whether the first or second weight vectors are used for radiating waveform over the transmit beam link. Adaptive algorithms have a major role in beam formation, and error is found to be less when algorithms are cascaded [9]. The conceivable outcomes of optimization of the synthesis problem for the printed antenna arrays with neural network are studied in [10]. There are noteworthy difficulties in radar programmed information handling emerging from poor adaptability of known calculations and low computational limit of conventional PC gadgets. Neural systems can help the radar designer to overcome these difficulties because of computational energy of neural parallel equipment and versatile abilities of neural calculations [11]. Unlike giving primary and secondary priorities for both the signals, the aim of this project is to design a phased array receiver antenna which can receive and process both radar and communication signals simultaneously. Both the signals of different frequency are considered to be arrived. That is, a communication signal of 2.39 GHz and radar signal of 2.41 GHz are received by the array. A single-phased array is divided to two subarrays, and each subarray is designated for each function, that

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is, either communication or radar based on the band-pass filter used to filter out each signal to each element in the array. Direction of arrival (DOA) of signals is found using MUSIC algorithm. By making use of neural network for optimization, element-level digital beam forming is performed based on the target signal obtained from DOA.

2 Neural Adaption in Phased Arrays 2.1 Phased Array Phased array antennas (PAAs) are antenna arrays that consist of multiple feeds, or radiating elements, that collectively form a beam in the far field of the antenna. Early, antenna arrays had fixed beam-pointing and utilized mechanical rotation to steer the beams. But now, electronic beam steering is used in phased arrays [12]. In elementlevel digital phased array, every element of the array possesses a separate digital receiver exciter [13]. Adaptive digital beam forming (ADBF) with degrees of freedom extending to the element level is the main benefit of element-level digitization [14, 15], but cost of production is a challenge. An antenna generates uneven electromagnetic waves. The strength of radiation varies in different directions. A plot of field strength versus direction is the radiation pattern of antenna. It applies to both transmitting and receiving antennas. An electromagnetic wave deliberated at a given point which is far away from the antenna is comparable to the sum of the radiation emitted from all elements of the antenna. These waves are summed constructively to get a gain at some other direction, and they are summed destructively to get a loss [16]. The antenna’s gain depends on number of elements on the antenna and spacing between elements. For a linear phased array, the array factor, F, is given as: F  1 + e jkd cos θ+β + e jk2d cos θ+β + · · · + e jk(N −1)d cos θ+β . 

N 

e j(n−1)φ

n1

Where, φ  kd cos θ + β

(1)

And k  2π/λ, ‘d’ represents the distance between elements in array, β be the phase shift at each element, and θ is the angle of arrival of signal with respect to array. Directivity of beam can be increased by increasing the number of elements in an array. And as distance between elements increases, beam width gets reduced. But when distance is increased to 1λ, grating lobes appear, which will increase the probability of false target detection. Therefore, greater the space between elements, the main beam gets narrower, but the chance of generating grating lobes is high.

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2.2 Artificial Neural Network (ANN) ANNs are architectures for data processing inspired from behavior and structure of the neurons in brain. They are composed of basic units which are called neurons and are interconnected in such a way that no two neurons in the same layer are connected but every neurons of each layer will be connected to that of next layer, and likewise it forms a network. An artificial neuron communicates with adjacent layer neurons by weights. Every neuron has its own particular activation function which scales the output. Many sigmoid activation functions including hyperbolic tangent function and logistic function are used in neurons. Sigmoid function is defined by the following formula: Y 

1 1+e

−x

.

(2)

A single neuron can be represented as shown in Fig. 1. Here, ‘I1 , I2 , . . . , In ’ is the information entering to a neuron and ‘w1 , w2 , . . . , wn ’ are their weights, respectively. And bias is a vector which is added along with the transformed information. Bias is used for translation of every point to a specified direction by a constant distance. Mathematically, input to a neuron can be formulated as: X

n 

wi Ii + b.

(3)

i1

‘I i ’ represents the information entering, ‘wi ’ represents weights, and ‘b’ defines bias. The variable ‘i’ varies from 1 to ‘n’ where ‘n’ be the number of neurons in the input layer.

Fig. 1 Single neuron

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Fig. 2 Multilayer neural network

2.3 Multilayer Back-Propagation Neural Network A multilayer neural network consists of an input layer, which transforms the input data, an output layer containing the desired output number neurons, and a flexible number of layers in between them called ‘hidden’ layers as shown in Fig. 2. The antenna array architecture can be related to a neural network by considering the input layer neurons as the antenna elements in antenna architecture, hidden layers as weight segments, and data from output layer of neural network as output from antenna architecture which will be an optimized, noiseless signal. The algorithm used for this learning process is back-propagation learning algorithm. Here, all the weights are randomly chosen initially. For each input in the training data, output of ANN is observed. This output is compared with the desired output, and the error found is passed again to the previous layer and the weights get updated accordingly so as to reduce the error. Until the output error is less than a predetermined threshold, this process is repeated. ANN can execute a task that a linear program cannot perform. It has a capability of deriving optimal solution from an imprecise data.

3 Methodology 3.1 Mathematical Modeling of Received Signal The signal received V (t) can be expressed in matrix form as:

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V  A ∗ S + N .

(4)

The steering vector matrix is given as ‘A’, signal sources are represented as ‘S’ and ‘N’ represents the total noise added during transmission of signals. The ‘Q’ signal sources can be depicted in matrix form as:  T S  S1 (t), S2 (t), . . . , S Q (t)

(5)

And steering vector matrix ‘A’ is represented as: A  [a(Θ0 ),a(Θ1 ), . . . , a(Θ Q )]

(6)

where for ‘D’ number of elements,   a(θk )  1 e− j(2πd1 /λ) sin(θk ) . . . e− j(2πd D−1 /λ) sin(θk )

(7)

And ‘k’ is in the range of 1 to ‘Q’ number of signal sources.

3.2 Direction of Arrival Estimation Using MUSIC A method, which can detect more number of signal sources than the elements of the array, is given in [17]. Eigenvalue decomposition of covariance matrix of the signal received is the basic approach in MUSIC algorithm. The signal and the noise subspaces are orthogonal to each other, and they were computed using the matrix algebra. The orthogonality property of signal and noise subspace matrix is exploited in this algorithm to separate the signal and noise subspaces. The covariance matrix of received signal is represented by ‘Rj ’, and it is the expectation of received signal matrix and its Hermitian equivalent.   Rj  E V V H

(8)

Substituting the expression for received signal matrix from Eq. (4) in Eq. (8) gives:   R j  E (AS + N )(AS + N ) H      AE SS H A H + E N N H  A R j AH + RN

(9)

This correlation matrix is decomposed, and it results in ‘D’ eigenvalues; among them, larger ‘F’ eigenvalues correspond to signal sources and the remaining minor eigenvalues correspond to the noise subspace. When this correlation matrix is decomposed, it results in ‘D’ number of eigenvalues; among them, larger ‘Q’ values conform

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the signal sources and the remaining smaller eigenvalues correspond to the noise subspace. The larger ‘Q’ eigenvalues can be represented in matrix format as ‘Vλ ’, and the orthogonality relation of noise and signal subspace remains true. That is a H (θ )Q N  0

(10)

The noise subspace is given as ‘QN ’. The spectral estimate plot in which peaks are obtained at direction of arrival of signal sources can be obtained by: PMUSIC 

1   a(θ ) 1 − Vλ Vλ )a(θ ) H

(11)

where the DOA of signal source is represented in terms of incident source and noise subspace is: ΘMUSIC  arg mina H (θ )Q N Q NH a(θ )

(12)

The above equation gives exact angle of direction of arrival of signal, and Eq. (11) gives high peaks in the spectrum at the exact angle of arrival.

3.3 Optimization Using Neural Network From the DOA estimated using MUSIC algorithm, a predetermined data is assigned as target of a multilayer neural network. The input data that has to be optimized is fed to the neural network, and the initial weights are taken as randomly generated numbers. After the propagation process, in back-propagation error ‘ej ’ is calculated by:   e j  Y j − G 

(13)

‘Y j ’ represents the obtained output in ‘j’ th iteration, and ‘G’ represents the target output. The obtained error is used to adjust the bias value delta ‘d j ’’ by gradient descent algorithm as:    dj  Yj ∗ 1 − Yj ∗ ej

(14)

The next error of back propagation is updated by: e j−1  w j∗ d j

(15)

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Fig. 3 MUSIC spectrum with peaks at angles of arrival

And weights of neural network are restored by:   wj  wj− r ∗ dj ∗ X

(16)

where ‘r’ represents the learning rate and ‘X’ represents the input.

4 Results and Discussion 4.1 Estimation of DOA Using MUSIC To implement the methodology proposed for estimation of DOA, analog signals of two different frequencies from two different signal sources such a way that it is received by each element along with noise are taken as input data. A MATLAB code was generated for MUSIC algorithm and found the DOA of signals. The simulation for signal sources corresponding to arrival angles for communication signal at 30° and radar signal at 80° is shown in Fig. 3. Spectrum showing DOA of signals with number of elements equal to 16 and 32 is plotted. In the plot, peaks are obtained at the angles in the direction of source signals. The impact of varying number of elements in an array is that the resolution of estimation of DOA is more when elements in an array are increased.

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Fig. 4 Radiation pattern of antenna array

4.2 Beam Formation Using Neural Network Using a band-pass filter (BPF), the signals arriving at each element of the array from both the sources are filtered out such that particular element can receive either radar signal or communication signal corresponding to the center frequency of BPF attached to it. For implementing the methodology of beam forming using neural network, antenna array of 32 elements is related to 32 neurons in input layer in which 16 are fed by communication signals and the other half by radar signal. The target radiation pattern is obtained from the DOA estimated by MUSIC algorithm. A MATLAB code is generated for generating and training the ANN. The ANN has two hidden layers of 16 and 8 neurons, respectively, which converges to an output data which is used to form the beam pattern. Dual beams, in which one beam at 30˚ designated for communication and the other main beam at 80˚ designated for radar, are obtained as in desired radiation pattern after optimization using neural network as shown in Fig. 4. There is a 0.5 dB difference in the main lobe designated for radar from desired radiation pattern. Lesser the beam width, more the directivity for radar antenna. Half-power beam width (HPBW) is calculated for comparing the beam width of desired and obtained beams. The HPBW of obtained main beam for radar signal is calculated by measuring the width of main beam from 3 dB below the peak. HPBW (desired)  83.3◦ − 76.7◦  6.6◦ HPBW (obtained)  82.9◦ − 77.15◦  5.75◦

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Fig. 5 MSE versus iterations in beam formation using ANN Table 1 Mean square error of neural network after particular epochs

Epochs

MSE (in dB)

5000 10,000

−59.7866 −66.7191

15,000

−70.7741

30,000

−77.7059

50,000

−82.8143

70,000

−86.179

75,000

−86.869

The pattern obtained is having a narrow main beam as in desired radiation pattern, and side lobe level is at −12 dB which is at same level as desired pattern and close to the SVR algorithm method suggested in [18]. The performance of this approach for beam formation is analyzed by finding the mean square error by increasing the number of iterations which is shown in Fig. 5. Compared to the method proposed in [19], this approach finds better MSE. And from Table 1, it can be inferred that values converge only after many iterations.

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Fig. 6 BER versus SNR by varying number of iterations

4.3 BER Plot of Communication Signal The communication signal received here is a QPSK signal along with AWGN noise. The signal is passed through a neural network for obtaining noiseless, distortionless output. The BER versus SNR plot by varying number of iterations is as shown in Fig. 6. As iterations increase, considerable change in BER can be seen. BER is decreased as iterations increase. Also as SNR increases, BER is getting reduced which verifies that the process undergone is correct. The result obtained is better than that is obtained using the method proposed in [6]. At different epochs with fixed SNR, the training errors are given in Table 2. Errors at 100, 500, and 1500 epochs are tabulated at 10, 20, and 30 dB SNR. Training error is getting reduced as epochs increase, and for higher SNR, training error is comparatively lesser than that for lower SNR. Once learning of neural network is done, there will be no significant change in errors as iterations are increased.

5 Conclusion In this paper, we studied the possibilities of modeling a DFRC system using neural network. This study was developed in order to solve problem of receiving both communication and radar signals simultaneously. The results obtained for beam formation and the BER estimated for communication signal are satisfactory, and that shows interest of neural network in modeling a DFRC system.

538 Table 2 Total error while training neural network after particular epochs

K. S. Anjali and G. Prabha SNR (dB)

Epochs

Total error

10

100 500 1500 100 500 1500 100 500 1500

0.35587 0.35586 0.35585 0.29533 0.29532 0.29531 0.29067 0.29065 0.29063

20

30

This ensures the possibilities of optimizing, learning, approximating, and modeling of the nonlinear models. The flexibility and nonlinear nature of neural networks clearly show the electromagnetic radiation behavior of the antenna array. The results of antenna array synthesis obtained by using neural networks are almost same as desired results. However, the learning process of artificial neural networks is timeconsuming, and once it is finished, the network gives precise data.

References 1. R. Cager, D. LaFlame, L. Parode, Orbiter Ku-band integrated radar and communications subsystem. IEEE Trans. Commun. 26, 1604–1619 (1978) 2. A. Hassanien, B. Himed, B.D. Rigling, A dual-function MIMO radar-communications system using frequency-hopping waveforms, in Radar Conference (RadarConf) (IEEE, New York, 2017), pp. 1721–1725 3. E. BouDaher, A. Hassanien, E. Aboutanios, M.G. Amin, Towards a dual-function MIMO radarcommunication system, in Radar Conference (RadarConf) (IEEE, New York, 2016), pp. 1–6 4. A. Hassanien, M.G. Amin, Y.D. Zhang, F. Ahmad, Phase-modulation based dual-function radar-communications. IET Radar Sonar Navig. 10(8), 1411–1421 (2016) 5. A. Hassanien, M.G. Amin, Y.D. Zhang, F. Ahmad, Signaling strategies for dual-function radar communications: an overview. IEEE Aerosp. Electron. Syst. Mag. 10, 36–45 (2016) 6. J. Euziere, R. Guinvarc’h, M. Lesturgie, B. Uguen, R. Gillard, Dual function radar communication time-modulated array, in 2014 International on Radar Conference (Radar) pp. 1–4 (2014) 7. L. Xie, X. Yin, L. Yang, C. Lu, H. Zhao, Multifunctional communication transceiver with distance measurement capability, in 2014 Asia-Pacific Microwave Conference (APMC) (IEEE, New York, 2014), pp. 405–407 8. A. Hassanien, M.G. Amin, Y.D. Zhang, F. Ahmad, Dual-Function radar-communications: information embedding using Sidelobe control and waveform diversity. IEEE Trans. Signal Process. 64(8), 2168–2181 (2016) 9. B. Sridhar, I.A. Sheriff, K.N. Kutty, S.S. Kumar, Comparison of cascaded LMS-RLS, LMS and RLS adaptive filters in non-stationary environments, in Novel Algorithms and Techniques in Telecommunications and Networking (Springer, Dordrecht, 2010), pp. 495–499 10. L. Merad, F.T. Bendimerad, S.M. Meriah, S.A. Djennas, Neural networks for synthesis and optimization of antenna arrays. Radioeng.-Prague 16(1), 23 (2007)

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11. A.L. Tatuzov, Neural network methods for radar processing, in Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP’02, vol. 4 (IEEE, New York, 2002), pp. 1718–1722 12. R.A. Monzingo, T.W. Miller, Introduction to Adaptive Arrays (Scitech publishing, 1980) 13. J.S. Herd, M.D. Conway, The evolution to modern phased array architectures. Proc. IEEE 104(3), 519–529 (2016) 14. A. Hassanien, M.G. Amin, Y.D. Zhang, F. Ahmad, Signaling strategies for dual-function radar communications: an overview. IEEE Aerosp. Electron. Syst. Mag. 36–45 (2016) 15. S.H. Talisa, K.W. O’Haver, T.M. Comberiate, M.D. Sharp, O.F. Somerlock, Benefits of digital phased array radars. Proc. IEEE 104(3), 530–543 (2016) 16. L. Huang, Y. Zhang, Q. Li, J. Song, Phased array radar-based channel modeling and sparse channel estimation for an integrated radar and communication system. IEEE Access 5, 15468–15477 (2017) 17. G. Prabha, G.S. Sundaram, Estimation of DOA using a cumulant based quadricovariance matrix, in 10th European Conference on Antennas and Propagation (EuCAP) (IEEE, New York, 2016), pp. 1–5 18. G.C. Lin, Y.A. Li, B.L. Jin, Research on uniform array beamforming based on support vector regression. J. Mar. Sci. Appl. 9(4), 439–444 (2010) 19. H.H. Chen, S.C. Chan, Adaptive beamforming and DOA estimation using uniform concentric spherical arrays with frequency invariant characteristics. J. VLSI Signal Process. Syst. Signal Image Video Technol. 46(1), 15–34 (2007)

Patient Nonspecific Epilepsy Detection Using EEG Sandeep Banerjee, Varun Alur and Divya Shah

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

542 543 543 545 546 546 547 547

Abstract The main objective of this paper is to establish a novel method for patient nonspecific epilepsy detection. The proposed method is applied on two different publicly accessible datasets. Features like mean, variance, skewness, kurtosis, line length, energy, and band power were calculated and then used for classifying the datasets. Classification algorithms used are KNN and RUSBoost which classify the EEG signals, and their respective performances are evaluated by the measurement of specificity, sensitivity, and accuracy. The results of all the classifiers are compared, and out of these RUSBoost shows the best performance. An overall accuracy of 94–99% is achieved which proves the success of this method. Keywords Epilepsy · EEG · Artifacts · Statistical features · Ensemble learning KNN · RUSBoost

S. Banerjee (B) · V. Alur · D. Shah Ramrao Adik Institute of Technology, Sector 7, Phase I, Nerul, Navi Mumbai 400708, India e-mail: [email protected] V. Alur e-mail: [email protected] D. Shah e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_51

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1 Introduction World Health Organization has said that 50 million people of this world suffer from epilepsy, and out of these 40 million live in the developing countries. According to another report, there are about 10 million people in India who suffer from epilepsy [1, 2]. International League against Epilepsy defines epilepsy as a condition when there are multiple seizures [3]. Epilepsy can be divided mainly into two types. This classification is dependent on which areas of the brain are involved during the seizure. When there is an involvement of the entire brain, it is known as generalized seizures while focal seizures occur in a specific part of the brain. During an epileptic seizure, there can be a loss in consciousness [4, 5]. Conventionally, an electroencephalogram is used to assess a patient with a seizure. It measures the patient’s brain waves for the duration of about 20 min. The problem with very small duration of EEG recording is that the actual events may not be recorded. The solution is EEG recording of a patient which is done continuously for at least 24 h. The recordings can then be analyzed for seizures that may have happened during the recording session. But the problem with analyzing a long-term EEG recording is that it is a time-consuming process which can last for up to several days. So, automatic detection of epilepsy can help to speed up the process. The difficulty of developing a system that can automatically detect seizures is that there are a lot of EEG patterns that can describe a seizure, like low-amplitude waves, multiple spikes, rhythmic waves for a wide assortment of frequencies, and amplitude waves [5]. There are mainly two types of detection—one is patient-specific and the other is patient nonspecific. Many approaches [6–8] have been tried out in both types of detection and have yielded good results. But out of the two, patient nonspecific is much more complex and harder than patient-specific detection because of the variations of EEG among different people. Another problem in the detection of epilepsy is the noise that is present in the EEG signals. The noise may be due to the movement of muscle, blinking of eyes, etc. Sometimes the noise may be similar to a seizure, so it becomes even harder to detect epilepsy in patients. Research in detection of epilepsy started in the 1970s, and the algorithms were based on two approaches: The first is to examine the EEG signal just before a seizure to find features like spikes in the waveforms which only occur before a seizure, and the second is analysis of nonlinear spatial-temporal evolution of EEG signals to find features which tells when the system moves from a seizure-free state to a seizure state. Different patterns in the EEG signal like increase in amplitude [9], continuous rhythmic activity, [6, 10] or flattening of EEG [11] are found out by algorithms for detecting epilepsy and then features are extracted from it. Then these features are used to train classifiers like nearest neighbor [12], decision trees [13], support vector machines [14], and artificial neural networks [15]. Gotman developed the earliest patient nonspecific seizure detectors in the year 1982 [6]. His algorithm searched for the sustained rhythmic activity, and when the degree of rhythmicity spanned a threshold on two channels or more and continued for 4 s it was declared a seizure. When the Gotman algorithm was tested on 652 h of scalp EEG which consisted of 126 seizures from 28 patients showed an accuracy

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of 50% to detect test seizures. Another patient nonspecific seizure detector was developed by Wilson [7] known as Reveal seizure detector. The Reveal algorithm uses matching pursuit algorithm to decompose 2-s EEG epoch into time-frequency atoms and then uses a neural network for classification. It was tested on a dataset containing 426 individuals with epilepsy and reported an accuracy of 76%. Saab also developed a patient nonspecific seizure detector [16]. He extracted the features by applying wavelet transform to the EEG signal and estimated the probability of the signal being a seizure or not. It reported an accuracy of 78% when tested on 652 h of scalp EEG with 126 seizures from 128 patients.

2 Proposed Method Initially, the Bonn university dataset was considered and since it was already noisefree, there was no need for any noise removal technique. The results on this dataset became the baseline for the further experiments. To begin with, features like line length, energy, mean, variance, skewness, kurtosis, and band power from each row of the dataset were extracted. These features were then used to train two classifiers: KNN and RUSBoost. CHB-MIT database was successively considered. The records of first, third, fifth, and the eighth patient were used. The reason for choosing these patients was that they had enough seizure intervals to be used for the classification purposes. As the CHB-MIT database contains noise, an artifact removal technique developed by Shoeb in his paper [8] was applied. In Shoeb’s noise removal technique, eight bandpass filters are used in the frequency range of 1–24 Hz. The 2-s EEG epoch is passed through them, and then features were extracted from it. After this (i.e., the filtering was done for the first 2 second EEG epoch), the same procedure was done for the previous 2 s EEG epoch and then again for the previous 2 s EEG epoch and then they were grouped together. Then similar procedure like with the case of Bonn University dataset was carried out on the CHB-MIT database. Figure 1 shows the block diagram of the proposed method.

2.1 Dataset In this paper, two datasets were used: The first is from the CHB-MIT EEG database [17], and the second is taken from the Epitology Department at the Bonn University, Germany [18]. CHB-MIT database consists of extended EEG recordings from pediatric patients suffering from intractable seizures. There are a total of 23 subjects in the database out of which there are 5 males in the age group of 3–22 years and 17 females in the age group of 1.5–19 years. The EEG signals were first sampled at 256 Hz and at 16-bit quantization, and then it was recorded. Also, the EEG data in this dataset is not clean, i.e., it contains a lot of artifacts. chb01, chb03, chb05, and chb08 cases are used for the study.

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Fig. 1 Block diagram of proposed method

Fig. 2 Seizure-free EEG signal of a patient

The Bonn university EEG dataset had no artifacts as the signals were analyzed by experts who removed the noise from it by carefully examining it. There are five sets in the dataset (i.e., set A, set B, set C, set D, set E), where each set consists of 100 EEG segments of 23.6 s duration each. The EEG recordings during the seizure also called as ictal period are stored in set E. To record the EEG signals, a 128-channel amplifier system is used and using a 12-bit analog-to-digital converter, the data is digitized at 173.6 samples per second. Figures 2 and 3 show the EEG waveforms of seizure-free signal and a seizure signal of a patient.

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Fig. 3 EEG signal of a patient suffering from a seizure

2.2 Features The various features of the EEG signal used are: Line length: The distance between successive samples of the signal is known as line length [19]. It represents the amplitude-frequency characteristic of the EEG signal. Energy: Energy of the signal is defined as [9] E

N 

X i2 ,

(1)

1

where X i  sample points and N  total number of samples and i goes from 1 to N. Mean: There are many variations of the meaning of mean in mathematics but in case of datasets, the mean is nothing but the average of the data. The central value of number of values is the addition of those values divided by the number of values. Variance: Variance can be a useful tool where there is a need to statistically analyze a dataset. Variance can be informally defined as how far the random values are spread out from their mean value (mean). It is the squared deviation of random variables from its mean value. Skewness: Skewness shows the asymmetric nature of the statistical distribution of the data. There are two types: positive and negative skew. Positive skew means that the data results at the extreme are large and thus the mean is increased more than the mode whereas in case of negative skew it is exactly opposite. Kurtosis: Kurtosis describes the distribution of the observed data centered on the mean of the data. It is also known as the volatility of volatility. Band power: Band power is the average power of the given data in a given specific frequency band.

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2.3 Classifiers In this study, two classifiers have been used—KNN and RUSBoost. KNN classifier was already used by Tessy [20] in the case of Bonn university dataset which was noiseless. In this paper, KNN classifier [15] was used to see how well it can perform in the case of noisy dataset. In a KNN classifier, the classification of a new sample is done by taking into consideration the whole training set. The distance between the sample and its K nearest neighbors is calculated. The class with shortest distance from the test sample is chosen. Euclidean distance metric was chosen for the classification as all the samples were of the same type; that is, they all represented the amplitude of the EEG signal. The value of K was chosen to be 3 after experimenting with different values for K. RUSBoost is a technique to overcome the problem of class imbalance [21], that is, when one class contains more training samples than the other class. In long-term EEG recordings, the seizure-free signals are in greater quantities than the seizure signals which can cause the classifier to skew in the direction of seizure-free signal. RUSBoost applies random undersampling (RUS) which is a technique that randomly gets rid of examples from the majority class which in this case is healthy EEG signal class. RUSBoost then introduces data sampling into AdaBoost algorithm to classify the test samples. RUSBoost thus reduces the time required to develop a model.

3 Results The results are shown in Tables 1 and 2. The results of the proposed method are compared with well-known methods of patient nonspecific seizure detection. From Table 1, it can be seen that in case of Bonn university dataset an accuracy of 94.4% was achieved with KNN but the sensitivity was quite low while RUSBoost has the highest accuracy and sensitivity with, 99.2% and 96.43%, respectively. In both the cases, a specificity of 100% was achieved. Then in the case of CHB-MIT dataset with Shoeb’s noise removal technique applied, the accuracy of KNN is quite high but the sensitivity has decreased to quite an extent. But even in this case RUSBoost is a clear winner as it has high accuracy, sensitivity, and specificity which is desirable in a real-time setting.

Table 1 Results of the proposed method

Dataset

Classifiers

Bonn University

KNN 94.4 RUSBoost 99.2

Accuracy

Sensitivity Specificity 90.28% 96.43%

Dataset CHB-MIT Database

KNN 92.47 RUSBoost 98.94

51.39 90.28

100 100 96.79 99.85

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Table 2 Comparison of results of the proposed methodology with other well-known patient nonspecific seizure detection methods Method Sensitivity (%) Proposed method

90.28

Gotman’s algorithm

50

Reveal algorithm

78

Saab’s algorithm

76

4 Conclusion In this study, the importance of automated seizure detection along with the various methods to implement it was seen. The result obtained from the RUSBoost in the case of CHB-MIT dataset after applying the noise removal technique is similar to the accuracy of the RUSBoost of the Bonn University dataset which already had noise removed from it by professionals. Thus this method can be used in hospital setting to detect seizure with high accuracy. In future work, the proposed method can be implemented on an FPGA or a DSP processor and see how well it performs in case of real-time seizure detection. Other feature extraction methods coupled with different classifiers can also be used to increase the sensitivity.

References 1. Geneva: World Health Organization; WHO. Neurological Disorders: Public Health Challenges (2006) 2. N.S. Santhosh, S. Sinha, P. Satishchandra, Epilepsy: Indian perspective. Ann: Indian Acad. Neurol. 17(Supply 1), S3–S11 (2014) 3. W.A. Hauser, L.T. Kurland, The epidemiology of epilepsy in Rochester, Minnesota, 1935 through 1967. Epilepsia 16, 1–66 (1975) 4. B. Litt, J. Echauz, Prediction of epileptic seizures. Lancet Neurol. 1, 22–30 (2002) 5. K. Lehnertz, F. Mormann, T. Kreuz, Seizure prediction by nonlinear EEG analysis. IEEE Eng. Med. Biol. Mag. 22(1), 57–63 (2003) 6. J. Gotman, Automatic recognition of epileptic seizures in the EEG. Electroencephalogr. Clin. Neurophysiol. 54(5), 530–540 (1982) 7. S. Wilson, M. Scheuer, R. Emerson, A. Gabor, Seizure detection: evaluation of the Reveal algorithm. Clin. Neurophysiol. 10, 2280–2291 (2004) 8. A. Shoeb, Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, Massachusetts Institute of Technology, September (2009) 9. J. Gotman, Automatic detection of seizures and spikes. J. Clin. Neurophysiol. 16(2), 130–140 (1999) 10. W.R.S. Webber, R.P. Lesser, R.T. Richardson, K. Wilson, An approach to seizure detection using an artificial neural network (ANN). Electroencephalogr. Clin. Neurophysiol. 98(4), 250–272 (1996) 11. G.W. Harding, An automated seizure monitoring system for patients with indwelling recording electrodes. Electroencephalogr. Clin. Neurophysiol. 86(6), 428–437 (1993)

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12. H. Qu, J. Gotman, A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device. IEEE Trans. Biomed. Eng. 44(2), 115–122 (1997) 13. K. Polat, S. Gunes, Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast 12 Computational Intelligence and Neuroscience Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007) 14. B. Gonzalez-Vellon, S. Sanei, J.A. Chambers, Support vector machines for seizure detection, in Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (ISSPIT’03), pp. 126–129 (2003) 15. A. Subasi, A. Alkan, E. Koklukaya, M.K. Kiymik, Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Networks 18(7), 985–997 (2005) 16. M.E. Saab, J. Gotman, A system to detect the onset of epileptic seizures in Scalp EEG. Clin. Neurophysiol. 116, 427–442 (2005) 17. A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.Ch. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000) 18. R.G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, C.E. Elger, Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64, 061907 (2001) 19. L. Guo, D. Rivero, J. Dorado, J.R. Rabunal, A. Pazos, Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural network. J. Neurosci. Methods 191, 101–109 (2010) 20. E. Tessy, P.P.M. Shanir, S. Manafuddin, Time domain analysis of epileptic EEG for seizure detection, in International conference in IEEE Next Generation Intelligent Systems (ICNGIS) vol. 10, pp 1–4 (2016) 21. C. Seiffert, T.M. Khoshgoftaar, J. Van Hulse, A. Napolitano, RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE Trans. Syst. Man Cybern.—Part A: Syst. Humans 40(1), 185–197 (2010)

Power Efficient PUF-Based Random Reseeding True Random Number Generator Anirudh Siripragada, R. Shiva Prasad and N. Mohankumar

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 True Random Number Generator (TRNG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Physical Unclonable Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 TRNG Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 PUF Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 PUF-Based TRNG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 NIST Test Analysis for Randomness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In the present scenario, the major threat faced by electronic system designers is protecting the device or a circuit from the attackers and Hardware Trojans. Though there is a drastic advancement in the field of cryptography, attackers are finding out the alternative ways of attacking the device. One possible solution of protecting the device is through the usage of random numbers. The true random number generator (TRNG) is used for the generation of random sequence which does not follow any particular pattern or sequence which is a major advantage over pseudo-random number generators (PRNGs). Though the usage of TRNG is highly secure, there are other methods like reverse engineering which may be used by an attacker to break into device. In order to avoid such attacks, this paper proposes the PUF-based design which acts as an irreversible function and is included in the TRNG module. The paper mainly focuses on LFSR-based random seeding which makes the A. Siripragada · R. Shiva Prasad · N. Mohankumar (B) Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] A. Siripragada e-mail: [email protected] R. Shiva Prasad e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_52

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design truly random and produces an unpredictable sequence. An additional level of security to the device is provided by adding PUF. This work provides an irreversible function which is hardly possible to re-engineer, and it provides a completely random sequence, which is evident from the NIST test results. Keywords True random number generator (TRNG) Pseudo-random generator (PRNG) · Nonlinear-feedback shift registers (NFSRs) Physical unclonable function (PUF) · Hardware security · NIST

1 Introduction Designing hardware with security concerns and overcoming the security vulnerabilities are difficult tasks. Every device or a circuit should be protected from unwanted malware and access from the external sources. The name hardware security conveys a basic security which is to be provided to every device. Simple circuit architectures consume less area and power which makes the TRNG module suitable for invasive method in design for system security. One of the traditional ways of protecting the hardware is by using the random numbers. Random number generators are of two types, namely pseudo-random number and true random number. The pseudo-random number generators [1] make use of an algorithm or a mathematical formula and generate the sequence. So the periodicity of the generated sequence is high, and hence, the sequence is repetitive. The PRNG is used to generate a sequence which is predictable. The main drawback of this linear feedback shift register (LFSR) PRNG is if a same seed or input is provided to this pseudo-random numbers, it will generate an identical sequence which is a repetition of the previously generated sequence and it is easy to identify the generation pattern. This makes the device vulnerable to hardware attacks.

1.1 True Random Number Generator (TRNG) In order to overcome this security issue from PRNG, the true random number generator which is truly random in nature is used. This type of random number makes use of natural phenomenon such as noise from the CPU fan, speed of air, etc, to produce an unpredictable sequence. One can measure the current speed of the air, but it is difficult to predict the speed of air for the next coming hour. Even though these methods are secured, it is vulnerable to attackers due to reverse engineering. This necessities the need for a PUF module.

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1.2 Physical Unclonable Function Physical unclonable function (PUF) is a module used in the design, which cannot be analyzed based on the generated pattern of sequence or implement a reverse engineering technique by an attacker [2]. Reverse engineering is a technique used to extract the device information by analyzing the output of a device. PUF module restricts the attacker to implement reverse engineering technique to analyze the generated sequence. In this design, enable, clock, and output of PUF change for every instance of the given time interval which makes the prediction impossible. This unique feature makes this module to find its place in many cryptographic methods and encryption techniques [3]. To this entire design, a stream of random bits is provided as a seed or initial input. The n-bit random output from the TRNG is given as an input to the PUF module. This PUF module takes the sequence generated from the TRNG module and makes it more secure.

2 Literature Survey Due to the globalization, the ICs are exposed to various attacks while designing, manufacturing, and post-manufacturing. To provide a better security, a PUF-based random number generator with nonlinear-feedback shift register whose inputs will be nonlinear which depends upon the past state is discussed [2]. The PUF model is attached to ring oscillator module where the tap points are given to the PUF module. This paper makes use of nonlinear functions to produce random sequence. In random seeding based TRNG proposed by Shiva Prasad et al. [4], the 8-bit LFSR module is used, and the tap points are taken by using XOR gate in the design. Abhranil Maiti et al. proposed the area reduction technique which is implemented and achieved through combining TRNG and PUF modules [5]. The design which includes two pseudo-random number generators is discussed by Blum [1]. This design comprised of two pseudo-random sequence generators which produces a random sequence. The pseudo-random generators are designed in such a way that the sequence of thefirst random sequence generator is predictable and the sequence of the second one is unpredictable. The design mainly concentrated on producing forward as well as backward sequences and also even ‘jump’ from one point to other sequences. The use of physical unclonable function as a source of randomness is discussed [6]. Manoj Reddy et al. proposed a technique to insert exclusive signatures by using IC validation in [7], where the IC can be protected from being pirated or overproducing. The design mainly focused on security of the hardware [8]. Siam U. Hussain et al. proposed a scheme for the online evaluation of true random generators, and PUF is proposed, which mainly focuses on providing the online assessment to the random sequence and monitors the security in [3]. The device securing and generation of the secret key which is used for encryption are discussed in [9]. The random number

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generation is done on the basis of thermal noise. Under various temperature changes, the randomness from PUF random number generation is achieved in [10]. The true random sequence generator is secured by including the physical unclonable function to its output. In this paper, true random generator comprised of an 8-, 16-, and 32-bit LFSR is implemented. In the initial process, the LFSR is designed with a random seed generator which produces a random seeding to the linear feedback shift register. The seed generator is designed by using SR latch due to its unique metastability nature [4]. When the inputs are given as ‘0’ then the output will be ‘1’, but as soon as the input changes from ‘0’ to ‘1’ then it is very difficult to predict whether the output is ‘0’ or ‘1’. This provides truly occurring sequence to the true random sequence generator.

3 Proposed Design The 8-, 16-, and 32-bit LFSR module with PUF is proposed and validated in this paper. The design uses a TRNG module with linear feedback shift registers. The input to the design will be linear input, and XOR gate is being used as a tap point at the feedback which generates a polynomial function. The seed generator is used for initializing the input to the module. Random output is taken from the TRNG module and fed to the PUF module. The clock is given to the PUF from the common clock signal. The input which is given to the PUF will go through the first block, i.e., arbiter-based switch module which comprises of multiplexers. As the design of PUF depends on the functional logic, the arbiter-based module is designed with muxes in it. The series of switches are connected in a sequence. The generated sequence is then sent to the second block, i.e., arbiter PUF module. The arbiter block acts as a bus which comprises of switch components. The final result is collected as random output. The model overview of the design is shown in Fig. 1.

3.1 TRNG Module TRNG module comprises of a clock signal block, seed generator block, and 1 … n/2- 8-, 16-, and 32-bit LFSR blocks. The initial block of the design, i.e., ‘seed generator,’ in the module itself is secured. As the true random generators make use of truly occurring events, the seed generator is designed with latches which make

LFSR based TRNG Module

Fig. 1 Overview of the proposed design

Arbiter PUF

Random output 110010...010111...

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use of metastability phenomenon. For every feedback input from the LFSR, seed generator produces an unpredictable seed which is fed as input to the module. The seed generator produces an 8-bit seed value which is provided as input to the 8-bit LFSR. The TRNG module produces an n-bit random output which is given to PUF module.

3.2 PUF Module The physical unclonable function is connected to the TRNG block. This block acts as a one-way function and restricts the attacker to perform reverse engineer technique to extract the device information. Most of the PUF models are based on delay change in the system, so basically PUF will be designed with arbiter [2]. PUF module comprises of arbiter-based switch component with mux and arbiter PUF module with series of switch components connected in sequence. The input to the PUF module is given through the tap points from the TRNG module. The PUF module receives the nbit random output from the TRNG module and secures the output which is highly random in nature.

3.3 PUF-Based TRNG The main advantage of the previous simple TRNG model to the design proposed in this paper is including the physical unclonable function module. Generally, the PUF is used to hide the behavior or the functionality of the output. The PUF is designed in such a way that it will produce a random output which changes for every specified time interval. By using PUF, the functionality of the output is hidden which prevents the attacker to implement reverse engineering technique. Figure 2 shows the block diagram of the proposed design.

4 Results and Analysis For perfect verification, the TRNG and PUF modules are individually simulated and the results are compared. In addition to the simulation results, the comparison of area, power, and delay of TRNG and PUF-based TRNG is found out. The comparison is shown in Table 1. The above table represents the area and power comparison between the TRNG module and PUF TRNG module. The comparison of the results shows the power efficiency of the proposed design. The variation has been gradually decreased from 8-bit PUF TRNG to 32-bit PUF TRNG. Even though there is increase in area, the power consumption is less. This proves the power efficiency of the proposed design.

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Clock Signal

PUF Module

TRNG Module Seed Generator

8 Bit LFSR 1

8 Bit LFSR 2

Clock divider with different taps

n-bitrandom o/p

Arbiter based Switch component comprise of mux

Arbiter PUF

Random o/p

8 Bit LFSR n/2

Fig. 2 Block diagram of the proposed design Table 1 Area and power comparison of proposed 8-, 16-, 32-bit design 8 bit 16 bit 32 bit Area (µm2 ) Total power Area (µm2 ) Total power Area (µm2 ) Total power (µW) (µW) (µW) TRNG 201.91 module PUF-based 438.02 TRNG % Variation 116.9%

53.5

403.89

107.72

809.42

215.55

63.9

1062.20

113.79

2078.00

225.68

19.4%

156.9%

5.6%

162.7%

4.6%

The functional verification of the proposed 8-, 16-, and 32-bit designs is done. Figures 3, 4, and 5 illustrate the schematic view of the proposed design. The diagram consists of two top modules—Top 1 and Top 2. Top 1 is TRNG module, and Top 2 is PUF module. The clock source is similar to the two modules. The output from the TRNG module is fed as input to the PUF module. The output is taken from the PUF which is found to be a random output. The functional verification of the design is implemented in Xilinx tool. The waveforms are extracted as shown in Fig. 6. The functionality consists of clock to excite the circuit, whenever the clock changes from ‘low’ to ‘high’ or ‘high’ to ‘low’ according to the time period provided the seed value changes constantly. The seed generator of TRNG generates the initial seed input. The tap points will be supplied to the PUF module as the input, then the resultant (actual) output of the circuit is obtained and analysed. Figure 6 represents the functional waveform of the design. The circled area represents the change in the clock, seed, and tap points. From (i) and (ii) regions from circled area in Fig. 6, we can observe that whenever the clock is changing periodically, the seed values changes from ‘high’ to ‘low’ and accordingly the tap points

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Fig. 3 Schematic of 8-bit synthesized design

Fig. 4 Schematic of 16-bit synthesized design

also will change. The functional changes in waveforms can be observed from circled area.

4.1 NIST Test Analysis for Randomness As the design proposed in this paper is based on random number generation, it is essential to verify its randomness through standard tests. Generally, the randomness can be verified with the NIST standards. The proposed design is tested with the following NIST tests.

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Fig. 5 Schematic of 32-bit synthesized design

Fig. 6 Functional waveforms of the proposed design

T1: Frequency Mono-bit Test: The main aim of this test is to find out whether the number of ones and zeros in the resulted sequence are nearly same. If the randomness of the result is above 0.01, then the result is a random one.

Power Efficient PUF-Based Random Reseeding … Table 2 NIST results for 8-, 16-, and 32-bit PUF-based TRNG 8 bit 16 bit

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32 bit

Test names

Sequence 1a–4a

Sequence 1b–4b

Sequence 1c–4c

Frequency mono-bit test Frequency test within a block Run tests Longest run of ones within a block Overlapping template matching

0.1096

0.1549

0.3108

0.3917

0.8808

1.0000

0.5008 0.0000

0.6617 0.0000

0.6861 0.0000

0.8999

0.8999

0.9030

Non-overlapping template matching

0.0000

0.0000

0.0000

Cumulative sums test Spectral test

1.4000 0.1238

1.4456 0.1483

1.6459 0.1626

T2: Frequency Test Within A Block: The main aim of this test is to find out whether the accuracy of ones in a sequence in a block is nearly equal to half of it. If the randomness of the result is above 0.01, then the result is a random one. T3: Run Test: The main aim of this test is to clear whether the occurrence between the bits is fast or slow. If the value of the randomness of the result is above 0.01, then the result is a random one. T4: Longest Run of Ones in a Block Test: The main aim of this test is to find out whether the period of the bits in a tested sequence is equal to the period of the longest ones. If the randomness of the result is equal to zero, then the result is a random one. T5: Overlapping Template Matching Test: The main aim of this test is to check whether it is producing the repeated sequences in a specified period. If the randomness of the result is above 0.01, then the result is a random one. T6: Non-overlapping Template Matching Test: The main aim of this test is to check whether in a given time period, the result producing repeated sequence is too large or small. If the value of the randomness of the result is zero, then the result is a random one. T7: Cumulative Sums (Cusum) Test: The main aim of this analysis is to check whether the partial sum of the bits in a tested sequence is large or small. If the value of the randomness of the result is above 0.01, then the result is a random one. T8: Spectral Test: The main aim of this test is to check whether the number of excitations is crossing 95% threshold. If the value of the randomness of the result is above 0.01, then the result is a random one. The above tests are carried out for 8, 16, and 32 bit by reseeding the LFSR with different random keys. The results are as shown in Table 2. Inference: For each test, there will be a prescribed cutoff value of randomness to verify that the sequence is random or not. The frequency mono-bit test, frequency

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test within a block, run tests, overlapping template matching, cumulative sums test, and spectral tests are verified as the value of the randomness is greater than cutoff value, i.e., 0.01. According to the NIST standards, the longest run of ones within a block and non-overlapping template matching should be ‘0’, and the generated sequence is random as the value generated is 0.

5 Conclusion In this paper, the PUF-based TRNG is designed in 8, 16, and 32 bits. This design provides advanced security by including PUF module to the TRNG module. The 8-, 16-, and 32-bit designs are compared with different parameters like area and total power and are verified. When compared to the other larger circuits or devices, the power requirement of the proposed design is very low and effective. The proposed PUF-based TRNG produces a random sequence output with a large periodicity which is verified from the eight standard NIST tests. Mainly due to the power efficiency, this PUF-based TRNG design is suitable for incorporation into cryptographic applications like digital signatures, electronic money, and hardware security-based applications.

References 1. L. Blum, M. Blum, M. Shub, A simple unpredictable pseudo random number generator. SIAM J. Comput. (1986) 2. A. Sadr, M. Zolfaghari-Nejad, Physical unclonable function (PUF) based random number generator. Adv. Comput. Inte. J. (ACIJ) 3(2) (2012) 3. S.U. Hussain, M. Majzoobi, F. Koushanfar, A built-in-self-test scheme for online evaluation of physical unclonable functions and true random number generators. IEEE Trans. Multi-Scale Comput. Syst. 2(1) (2016) 4. R. Shiva Prasad, A. Siripagada, S. Selvaraj, N. Mohankumar, Random seeding LFSR based TRNG for hardware security applications, in Second International Conference on Integrated Intelligent Computing, Communication & Security (ICIIC) (January 2018). https://doi.org/10. 1007/978-981-10-8797-4_44 5. A. Maiti, R. Nagesh, A. Reddy, P. Schaumont, Physical unclonable function and true random number generator: a compact and scalable implementation, in The 19th ACM Great Lakes Symposium on VLSI (2009) 6. S.S. Zalivako, A.A. Ivanuik, The use of physical unclonable functions for true random number sequences generation. Autom. Control Comput. Sci. 47(3), 156–164 (2013) 7. D.M. Reddy, K.P. Akshay, R. Giridhar, S.D. Karan, N. Mohankumar, BHARKS: built-in hardware authentication using random key sequence, in 4th International Conference on Signal Processing, Computing and Control (ISPCC), pp. 200–204 (2017). https://doi.org/10.1109/ ispcc.2017.8269675 8. D.K. Karunakaran, N. Mohankumar, Malicious combinational hardware trojan detection by gate level characterization in 90 nm technology, in 5th International Conference on Computing Communications and Networking Technologies (ICCCNT), pp. 1–7 (2014). https://doi.org/10. 1109/icccnt.2014.6963036

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9. G.E. Suh, S. Devadas, Physical unclonable functions for device authentication and secret key generation, in Proceedings of Design Automation Conference (2007) 10. D.C. Ranasinghe, D. Lim, S. Devadas, D. Abbott, P.H. Cole, Random numbers from metastability and thermal noise. Electron. Lett. 41(16), 13–14 (2005)

Edge Cut Dual-Band Slot Antenna for Bluetooth/WLAN and WiMAX Applications J. Rajeshwar Goud, N. V. Koteswara Rao and A. Mallikarjuna Prasad

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antenna Construction and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Snapshots of Simulated Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Screenshots of Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract A novel edge cut dual-band microstrip slot antenna and dual-band slot antenna are presented. The presented antennas find applications in Bluetooth/WLAN and WiMAX. These antennas use microstrip feed; in dual-band slot antenna, the lower band is considered from about 2.38 to 2.42 GHz, and the upper band is considered 2.59–2.64 GHz, whereas edge cut dual-band slot antenna, the impedance bandwidth of lower band is 2.37–2.43 GHz and the impedance bandwidth of upper band is 2.71–2.76 GHz. For dual-band antenna, the center frequency for lower band is 2.4 GHz and for upper band is 2.61 GHz, whereas for edge cut dual-band slot antenna, center frequency for lower band is 2.4 GHz and for upper band is 2.73 GHz which is assumed. The antenna simulations are carried out using HFSS, and a comparison among simulation and measured results is presented in this paper. Keywords Edge cut · Dual-band · Slot antenna · HFSS · Microstrip antenna WLAN · WiMAX J. Rajeshwar Goud (B) ECE Department, St. Martin’s Engineering College, Secunderabad, Telangana, India e-mail: [email protected] N. V. Koteswara Rao ECE Department, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] A. Mallikarjuna Prasad ECE Department, University College of Engineering, JNTUK, Kakinada, Andhra Pradesh, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_53

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1 Introduction In recent times, the advancements in antenna designs are required for wireless communication technology due to limited spectrum. Modern wireless gadgets require compact and low-power antennas. For these applications, dual-band slot antennas are required which have remarkable reasons for interest in light of its easy construction. These antennas are fed by the microstrip line which has favorable circumstances, for example, low profile, lightweight, basic structure, and simple manufacture. By adjusting the dimensions of slot width and length, the bandwidth can be increased. Bigger space demonstrates bigger emanating region and a lower Q factor, which infers a large bandwidth [1]. With appropriate construction of antenna, to accomplish a wide bandwidth different shapes of slot antennas were proposed, which included rectangular, triangular, circular [2], elliptical [3], triangles [4], and fractal [5]. Now and again, the feed line works as a monopole antenna; it gives additional bandwidth in addition to slot [6–9]. More resonant modes can be obtained by using wide slot, and wider bandwidth can be achieved by merging two adjacent modes [10, 11]. Nonetheless, these outlines require wide area for slot; consequently, it is not appropriate for handheld gadgets. For WLAN/WiMAX applications, some dual-band slot antennas are designed by utilizing a rectangular slot [12] and parasitic element [13]. Additionally, to conform to different WLAN conditions, an antenna should be fit for working at quad frequency bands [14, 15]. However, these dual-band slot antennas have bigger dimensions and are bad contender, so compact antennas are required. A novel design of an edge cut dual-band slot antenna was proposed, and dual-band slot antenna was also presented. The proposed antenna has favorable circumstances, for example, basic structure, reduced size, and easy fabrication. By cutting the edges of all sides of microstrip patch antenna, electrical size of the antenna is decreased, good impedance matching is achieved, and performance of radiation is increased. The proposed edge cut dual-band slot antenna and the presented dual-band slot antenna are suitable for the Bluetooth, WLAN, and WiMAX applications. Design details of these antennas are portrayed, and the results of same antennas such as VSWR, return loss, and directivity are explained in this paper.

2 Antenna Construction and Design The structure of dual-band slot antenna is shown in Fig. 1, and the configuration of the proposed edge cut dual-band slot antenna is shown in Fig. 2. The dual-band slot and edge cut dual-band slot antennas are designed with its patch length (L) and width (W ); square slot having area 21.5 mm × 21.5 mm is etched from the patch, using a substrate FR4 epoxy having height of 1.6 mm with loss tangent 0.02 and dielectric constant of 4.4. A 50  microstrip line is used for feeding, having width of 10 mm and length of 25 mm. Copper material is used for

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Fig. 1 Structure of dual-band slot antenna

Fig. 2 Structure of edge cut dual-band slot antenna

patch as well as ground plane. These antennas are fabricated using photolithographic method. The antennas are designed using HFSS simulation software, which is based on the integral equation ‘Method of Moment.’ The constructed design dimensions are as follows:

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Fig. 3 Fabricated dual-band slot antenna

Design dimensions of a dual-band slot antenna are patch length (L)  51 mm, patch width (W )  56.89 mm, slot length (L S )  21.5 mm, slot width (W S )  21.5 mm, feed line length (L f )  25 mm, and feed line width (W f )  10 mm. Design dimensions of an edge cut dual-band slot antenna are patch length (L)  51 mm, patch width (W )  57.8 mm, slot length (L S )  21.5 mm, slot width (W S )  21.5 mm, edge cut length (a)  6 mm, edge cut width (b)  6 mm, feed line length (L f )  25 mm, and feed line width (W f )  10 mm. In our study, antenna becomes compact due to cutting the edges of the patch and also the electrical size of the antenna is decreased. The first resonant mode can easily excite the second resonant mode to obtain dual-band operation because they are very close to each other in square slot antenna. Even though there is slight change in resonance frequency of upper band due to the cutting of length and width of the patch, good impedance matching is achieved. These antennas will provide the better return loss and VSWR, which are suitable for intended applications.

3 Results and Discussion The physically realized antenna model of dual-band slot antenna is shown in Fig. 3, and edge cut dual-band slot antenna is shown in Fig. 4. In dual-band slot antenna, the first band impedance bandwidth is from 2.38 to 2.42 GHz with −10 dB, return loss is −17 dB as shown in Fig. 5, and VSWR is 1.32 as shown in Fig. 6 at 2.4 GHz; the second band impedance bandwidth is from about 2.59 to 2.64 GHz with −10 dB, return loss is −19 dB as shown in Fig. 5, and VSWR is 1.22 as shown in Fig. 6 at 2.61 GHz. In edge cut dual-band slot antenna, the first band impedance bandwidth is from 2.37 to 2.43 GHz with −10 dB, return loss is −40 dB as shown in Fig. 7, and VSWR is 1.02 as shown in Fig. 8 at 2.4 GHz; the second band impedance bandwidth is from about 2.71 to 2.76 GHz with −10 dB, return loss is −19 dB as shown in Fig. 7,

Edge Cut Dual-Band Slot Antenna for Bluetooth/WLAN …

Fig. 4 Fabricated edge cut dual-band slot antenna

Fig. 5 Return loss (S11) of a dual-band slot antenna

Fig. 6 VSWR of a dual-band slot antenna

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Fig. 7 Return loss (S11) of an edge cut dual-band slot antenna

Fig. 8 VSWR of an edge cut dual-band slot antenna

and VSWR is 1.2 as shown in Fig. 8 at 2.73 GHz. The simulation results of these antennas are analyzed by using HFSS.

3.1 Snapshots of Simulated Results See Figs. 5, 6, 7 and 8.

3.2 Screenshots of Experimental Results The return loss and VSWR were measured using vector network analyzer. The measured values of dual-band slot antenna are return loss which is −15.58 dB at 2.4 GHz and −19.98 dB at 2.75 GHz as shown in Fig. 9 and VSWR which is 1.39 at 2.4 GHz

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Fig. 9 Return loss of dual-band slot antenna

Fig. 10 VSWR of dual-band slot antenna

and 1.23 at 2.75 GHz as shown in Fig. 10, and the measured values of edge cut dual-band slot antenna are return loss which is −24 dB at 2.4 GHz and −17 dB at 2.8 GHz as shown in Fig. 11 and VSWR which is 1.19 at 2.4 GHz and 1.37 at 2.8 GHz as shown in Fig. 12. A return loss of minimum −15 dB is obtained for both the bands of edge cut dual-band antenna and dual-band slot antenna. The return loss in the lower-band center frequency of edge cut dual-band slot antenna is better as compared to dual-band slot antenna with difference of about 23 dB. The peak directivity of a dual-band slot antenna is 1.45, and an edge cut dual-band slot antenna is 2.64. These antenna parameters meet the requirements for Bluetooth, WLAN, and WiMAX applications.

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Fig. 11 Return loss of edge cut dual-band slot antenna

Fig. 12 VSWR of edge cut dual-band slot antenna

4 Conclusion An edge cut dual-band slot antenna and dual-band slot antenna are designed and analyzed using HFSS simulation software. An awesome comprehension among the simulation and experimental results is obtained. These antennas have lower-band impedance bandwidth from about 2.38 to 2.42 GHz which finds application in Bluetooth and Wi-fi, whereas upper-band impedance bandwidth of an edge cut dual-band slot antenna is from about 2.71 to 2.76 GHz and of dual-band slot antenna is from about 2.59 to 2.64 GHz which can be used for WiMAX applications. Edge cut dualband slot antenna’s return loss, VSWR, and peak directivity are good compared to

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dual-band slot antenna. These antennas are very useful for Bluetooth/WLAN and WiMAX applications.

References 1. K. Sharma, L. Shafai, N. Jacob, Investigation of wide-band microstrip slot antenna. IEEE Trans. Antennas Propag. 52(3), 865–872 (2004) 2. S.-W. Qu, J.-L. Li, J.-X. Chen, Q. Xue, Ultrawideband strip-loaded circular slot antenna with improved radiation patterns. IEEE Trans. Antennas Propag. 55(11), 3348–3353 (2007) 3. P. Li, J. Liang, X. Chen, Study of printed elliptical/circular slot antennas for ultrawideband applications. IEEE Trans. Antennas Propag. 54(6), 1670–1675 (2006) 4. W.-S. Chen, F.-M. Hsieh, Broadband design of the printed triangular slot antenna, in Proceedings of the IEEE APS International Symposium, vol. 4, pp. 3733–3736 (2004) 5. W.-L. Chen, G.-M. Wang, C.X. Zhang, Bandwidth enhancement of a microstrip-line-fed printed wide-slot antenna with a fractal-shaped slot. IEEE Trans. Antennas Propag. 57(7), 2176–2179 (2009) 6. S. Cheng, P. Hallbjörner, A. Rydberg, Printed slot planar inverted cone antenna for ultrawideband applications. IEEE Antennas Wireless Propag. Lett. 7, 18–21 (2008) 7. Y.F. Liu, K.L. Lau, Q. Xue, C.H. Chan, Experimental studies of printed wide-slot antenna for wide-band applications. IEEE Antennas Wireless Propag. Lett. 3, 273–275 (2004) 8. L. Dang, Z.-Y. Lei, Y.-J. Xie, G.-L. Ning, J. Fan, A compact microstrip slot triple-band antenna for WLAN/WiMAX applications. IEEE Antennas Wireless Propag. Lett. 9, 1178–1181 (2010) 9. P. Liu, Y. Zou, B. Xie, X. Liu, B. Sun, Compact CPW-fed tri-band printed antenna with meandering split-ring slot for WLAN/WiMAX applications. IEEE Antennas Wireless Propag. Lett. 11, 1242–1244 (2012) 10. J.-Y. Jan, J.-W. Su, Bandwidth enhancement of a printed wide-slot antenna with a rotated slot. IEEE Trans. Antennas Propag. 53(6), 2111–2114 (2005) 11. N. Behdad, K. Sarabandi, A wide-band slot antenna design employing a fictitious short circuit concept. IEEE Trans. Antennas Propag. 53(1), 475–482 (2005) 12. S.Y. Lin, K.L. Wong, A dual-frequency microstrip-line-fed printed slot antenna. Microwave Opt. Technol. Lett. 28, 373–375 (2001) 13. T. Morioka, S. Araki, K. Hirasawa, Slot antenna with parasitic element for dual band operation. Electron. Lett. 33, 2093–2094 (1997) 14. W.C. Liu, Broadband dual-frequency meandered CPW-fed monopole antenna. Electron. Lett. 40, 1319–1320 (2004) 15. A.A. Eldek, A.Z. Elsherbeni, C.E. Smith, Square slot antenna for dual wideband wireless communication systems. J. Electromagn. Waves Appl. 19(12), 1571–1581 (2005)

J. Rajeshwar Goud working as Assistant Professor in the Department of Electronics and Communication Engineering, St. Martin’s Engineering College, Hyderabad, Telangana, India. He awarded UGC-NET Lectureship in 2013. He is currently working toward the Ph.D. in the Department of Electronics and Communication Engineering, JNTUK, Kakinada, Andhra Pradesh, India. His current research interest is in planar antennas. Dr. N. V. Koteswara Rao is Professor and HOD, Department of ECE, CBIT, Hyderabad. He is also Chairman, Board of studies, ECE under Autonomous and also Coordinator for TEQIP-II. He is best teacher awardee and best paper awardee in an international conference. Presently, he is

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working on a project from DRDO and one in-house project on reconfigurable microstrip antennas. He is guiding Ph.D. research scholars. He is associated with Center for Excellence in Microwave Engineering of Osmania University, Hyderabad. Dr. A. Mallikarjuna Prasad is Professor and Vice Principal, UCEK, JNTUK, Kakinada. He obtained his Ph.D. from Andhra University. He worked as HOD, Department of ECE, UCEK, JNTUK. He worked as Controller of Examinations in JNTUK. His research interests include wireless communications, biomedical instrumentation, and microwave antennas.

A Complete End-to-End System for Iris Recognition to Mitigate Replay and Template Attack Richa Gupta and Priti Sehgal

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Enrollment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Performance with Respect to Replay Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Performance with Respect to Template Database Attack . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Widespread use of iris biometric-based authentication makes it vulnerable to several attacks like template attack, replay attack, print attack. Several approaches have been proposed to mitigate each attack individually but nothing could be found, in the literature, that handles them collectively. A complete end-to-end system is required that shall be capable to handle these attacks together rather than just focusing on a particular type of attack. In this paper, we propose a system, which is capable of handling replay attack and template-based attack and paves a path to the evolution of a complete secured system. A non-deterministic approach for iris recognition, based on robust regions, proposed earlier (Gupta and Sehgal in Pattern Anal Appl 1–13 (2018), [1]) has been used to mitigate template-based attack along with replay attack. Biometric-based key generation is one of the techniques to evade the templatebased attack. It requires a key generation to authenticate the user. The robust regions are further shown here to be effective in iris key generation as well. This eludes the necessity of saving iris template and the use of biometric keys for user authentication. The entropy of our system is calculated as 57 bits which shows the effectiveness of the proposed approach. R. Gupta (B) · P. Sehgal University of Delhi, Delhi, India e-mail: [email protected] P. Sehgal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_54

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Keywords Iris recognition · Key generation · Replay attack · Local binary pattern Security

1 Introduction Iris is considered to be the most stable biometric during the entire life span of a human. Iris textures are high on randomness and uniqueness, which makes it one of the most accurate biometric [2]. Its capture also involves non-intrusive ways, which requires a distant camera, and makes it quite hygienic for use [3, 4]. With the advent of technology, issues related to its security have also arisen. Ratha et al. [5] were first to propose several attack points in a biometric authentication system. Since then, exploring techniques to handle and mitigate attacks at different points have been extensively studied. Richa and Priti [3] provide a comprehensive survey on such attack points and the literature survey of approaches to mitigate these attacks on iris biometric. The iris biometric being a physiological attribute, if revealed, can be useless for the entire life of the person. Smarter ways to handle this limitation need to be developed which can mitigate the attacks at all points of the system collectively, rather than focusing on each attack point individually. To our best of knowledge, no technique has been developed which could aim at different attack points simultaneously and provide a complete end-to-end secured system. Existing approaches that have been proposed to mitigate the attacks focus on only one point of attack. Most of the techniques make use of a deterministic approach to iris recognition, wherein the complete and same repetitive set of features are used for authentication. Any revelation of these features renders the biometric, useless for the future. This monotonous flow of information is at a severe risk of being hacked. In our previous paper [1], we proposed a non-deterministic approach to mitigate replay attacks on iris biometric system. The interception of communication channel between sensor and the system by an impostor to obtain biometric template is known as replay attack. This interception can be used to gain access to it without the knowledge of the user. On the other hand, the attack in which the impostor gains access to the templates stored in the database and reveals the user’s biometric identity is known as a template-based attack. Both these attacks make the biometric identity of the user completely inept for any further use. Biometric cryptosystem (BC) is a widely used approach to handle template-based attacks. BCs are further categorized into key generation and key binding schemes [6–13]. In this paper, we extend our previous approach [1] and propose an integrated solution by using a non-deterministic approach to handle replay attack and templatebased attack simultaneously. The key generation has been used to handle attacks on template database. The major contributions of this paper are: determination of robust iris regions for each eye, using a subset of robust regions to mitigate replay attack on iris recognition system between the sensor and the system and using the subset of robust regions for key generation and user authentication to mitigate template-based

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attack on iris recognition system and experimental assessment on the feasibility of the proposed system.

2 Related Work Replay attack which poses a severe threat to identity of the user has been least studied area under any biometric recognition system. Some of the few works found in the literature include a research by Czajka and Pacut [14] who proposed the use of randomized iris sectors to generate new iris template for each authentication. Hämmerle-Uhl et al. [15] suggested watermarking the iris images to mitigate replay attack. Shelton et al. [16] suggested the use of genetic algorithm for feature extraction from biometric sample and employ a non-deterministic method for feature extraction. In other works, Richa and Priti [17] suggest the use of reversible watermarking to ensure the performance of iris recognition is not compromised, yet mitigating replay attacks. A recent work in the field of face recognition has been done by Smith et al. [18]. They proposed a dynamic challenge—response technique to mitigate replay attack on handheld devices. They suggest the use of watermarking the face images based on reflections on the screen and mitigate replay attack on videos. In our previous approach [1], we propose the use of robust iris regions for authenticating the user. The set of regions to be used is dynamically calculated. The set and ordering of the regions, both play an important role in successful user authentication. Biometric cryptosystem that is used to handle template-based attacks has been based widely explored. The key generation-based scheme was first pioneered by Davida et al. [19]. They proposed the use of private template scheme, wherein they use error correction bits as part of helper data. However, no experimental results were given. Wu et al. [13, 20] proposed key generation on iris biometric where they use 256-dimensional textural iris pattern and Reed Solomon codes to translate it to a cipher key. The FRR at FAR of 0% was found to be nearly 5%. Later, Rathgeb and Uhl [21] proposed context-based iris key generation where they employed best bits of an IrisCode to generate stable keys. They employ two different algorithms by Ma et al. [22] and Maesk [23] to generate IrisCodes and evaluate the performance of their approach on both these techniques.

3 Proposed System The proposed system is mainly comprised of three components as given in Fig. 1 and detailed in following subsections. 1. Enrollment 2. Training 3. Verification.

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Fig. 1 Working of the proposed system

3.1 Enrollment This phase involves preprocessing and feature extraction from different samples of iris biometric. Preprocessing involves image segmentation and normalization. The normalized samples are averaged around center pixel [1] and then divided into 64 equal sized regions of size 16 × 32. This partitioned image is further passed to feature extraction module, which extracts the iris feature code f 1 , f 2 , . . . , f n for each user. The iris features are extracted using local binary patterns (LBP) using the method detailed in our previous paper [1]. The feature code is comprised of the LBP code of all regions of a sample iris. The LBP configuration for our approach is described in [1].

3.2 Training This phase analyses the enrolled iris samples for each user and extracts individual contextual information. This contextual information can be categorized into two parts: robust regions determination and check bits. They both are combined and saved in database as helper data, which is further used during user authentication. (a) Robust Region Determination The traditional approaches to iris recognition, which use entire feature vector to authenticate a person are quite accurate but suffer a serious drawback of consistency. They repeatedly use the same portion of information to authenticate a user. This approach lacks the dynamicity and once revealed, poses a serious threat to the further usage of that biometric as a unique identification tool. Moreover, it affects all the applications, which uses that biometric as an authentication tool. This demands dynamicity in the feature set used for authentication. In our previous approach [1], we presented a way to uniquely identify robust iris regions for each user and use a subset of them to authenticate the

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user. This non-deterministic iris recognition approach is capable of mitigating replay attacks on the system. The determination of robust iris regions has been carried out in detail in [1]. The identified robust regions for each user is marked and labeled as l1 , l2 , . . . , ln . This set of regions is encrypted and used for user authentication further. (b) Check Bits Generation The template information, if stored in raw form in the database is hazardous to the security of database. These templates contain the user’s identification information and if revealed to an impostor can breach the security of the entire system. A solution to this kind of an attack is biometric cryptosystems that binds or generates a unique key to/from a biometric and avoids storage of raw templates to the database. In this paper, we propose biometric-based key generation as a solution to biometric template protection. This requires the generation of check bits to assist in the key generation process. The helper data is comprised of two parts: encrypted locations of robust iris regions L  l1 , l2 , . . . , ln  (derived previously in subsection (a)) and the check bits (C) comprising of error-correcting codes. The error correction check bits are used to handle the variance in biometric samples from the same user and are saved as second part of the helper data [21]. For this purpose, we calculate the mean of each robust region for each user from the ALBP derived iris feature code f 1 , f 2 , . . . , f n (Sect. 3.1). The mean of each block for the specified ALBP configuration (P  16, R  4) falls in the range [0.65535] which is normalized such that it falls in the range of [0.7]. This is denoted by m 1 , m 2 , . . . , m n . It is further encoded using Reed Solomon Code (RS Code). RS codes are block-based error-correcting codes with its wide application in iris biometric cryptography [3, 7, 24]. It is denoted as RS(n, k) where, there are k data symbols of s bits each, and encoder adds parity to make an n symbol codeword. The number of parity bits appended to k data symbol is n − k bits; i.e., it can correct up to (n − k)/2  t data symbols [25, 26]. The configuration used in proposed approach has n  82 and k  40. It is capable of correcting up to 21-bit errors, with 42 error-correcting check bits (C). The key generated from RS code is hashed using SHA-256. This hashed version, the cipher key, is saved in the database for user authentication. The check bits C generated during this process are saved as a part of helper data.

3.3 Verification The user authentication needs a shift from a typical approach of authenticating a user based on the complete set of biometric identity to a dynamic system that can handle each user differently. In the proposed approach, we use only a subset of iris features and authenticate the user based on it. The user authentication proposed in this paper is capable of mitigating replay attack as well as template database attack.

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The existing approaches to authentication use predictable information for user authentication; i.e., they are deterministic in nature. The proposed approach is nondeterministic; i.e., it uses a different set of information for authentication on each access. This has been explained with the help of Fig. 2. The helper data comprising of encrypted robust iris locations (L) and check bits (C) previously calculated in training phase is stored in database and used during authentication process. The authentication involves the following steps: Step 1: The user sends his claimed identity (ID) to the system. Step 2: The system validates the user’s identity, extracts, and decrypts the robust iris locations (L) for the claimed user, from the database (saved as part of helper data). Step 3: The fetched robust iris locations are permuted, and the feature code for these regions is requested in particular order from the sensor by sending a feature extractor FE (Eq. 1). This feature extractor contains robust iris locations that are encrypted using public key RSA cryptography. This overcomes the limitation to our previous approach [1], where the interception of these messages was still a threat to the secured system. F Esi  IDi , n, r1 , r2 , . . . , rn 

(1)

where ID is the unique ID for the user, n is the number of regions requested, and r1 , r2 , . . . , rn are the encrypted region numbers chosen for authentication. Step 4: The FE received is decrypted using user’s private key and feature code sent in response to the above query is: FCsi  IDi , n, f 1 , f 2 , . . . , f n 

Fig. 2 Message sequence for the proposed approach

(2)

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where ID is the unique ID for the user, n is the number of regions requested, and f 1 , f 2 , . . . , f n are the feature codes for the corresponding regions as specified by r i . Step 5: The obtained feature codes are again re-arranged according to the locations as specified by L (robust iris locations) and sent to the matcher for verification. Step 6: Matcher calculates the mean of the feature codes and appends the ECC check bits C to the obtained byte stream. This byte stream is then RS decoded, and hash key (k’) is generated. The authenticity of this key is verified by matching with the one saved in the database. If the variance of captured biometric is within the permissible threshold, i.e., RS decoding is capable of correcting the errors and regenerating the same hash key, the user is validated, otherwise rejected.

4 Experimental Results The performance of the proposed approach is evaluated on CASIA-Iris-Interval v3 [27, 28] database. This is a freely available database, rich in iris texture, which is suitable to our requirement. Moreover, it is the most commonly used database for results evaluation that makes it easy to evaluate and compare the performance of the system with other approaches. The database consists of 249 subjects, with a total of 2639 images of 320 * 280 resolution. As we consider each eye from each subject unique, the database is considered to have 395 subjects for comparisons including both left and right irises. After evaluating the system based on the prerequisite as mentioned in Sect. 3.1, the database is reduced to 373 subjects with 2376 images. The performance of the proposed approach is evaluated with respect to replay attack and template attack individually. We also compare the performance of the biometric cryptosystem with other existing works in the literature.

4.1 Performance with Respect to Replay Attack Replay attack, in terms of messages can be termed as interception of M4 (refer to Fig. 2) and its replay to the system. The working of the system with respect to replay attack has been discussed previously in [1]. The limitation of our previous approach [1] was when the impostor knows all three messages M1, M2, and M4, it reveals useful information. We overcome the drawback here by using public key encryption algorithm RSA to encode M2 and allay man-in-the-middle attack.

578 Table 1 Performance at FAR  0.1%

Table 2 Performance with key length 40

R. Gupta and P. Sehgal GAR (%)

CRR (%)

Replay attack

97.38

99.85

Replay attack + BC

87.78

99.71

Check bits

FAR (%)

GAR (%)

CRR (%)

42 44 46

0.1 0.4 0.9

87.78 92.19 94.93

99.71 99.48 98.97

4.2 Performance with Respect to Template Database Attack Template database attack is hacking the stored iris templates from the database. Since the proposed approach does not save any templates as such, it is secured with respect to this attack. The performance of the proposed system with respect to this attack is evaluated in terms of false acceptance rate (FAR), genuine acceptance rate (GAR), and correct recognition rate (CRR) which measures the percentage of correctly identified users. The GAR of the proposed approach at FAR  0.1% is estimated. The region threshold as described in detail in [1] is considered to be 40. Table 1 details the performance of the system at FAR  0.1%. The decrease in the GAR of the proposed approach can be clearly seen, but this is at the cost of increased security. As compared to our previous approach, which takes care of only replay attack, the proposed technique presents a complete solution—mitigating replay as well as template database attack. The decrease in GAR which implies an increase in false reject rate (FRR) means that a genuine user will have to present his biometric sample multiple times to authenticate himself. This should be acceptable at the cost of increased security. We can see a decrease in GAR, but a stable CRR suggests the efficiency of the proposed approach in correctly identifying the genuine users. The performance is also evaluated at key length 40 and number of check bits varying by 2. The statistics are listed in Table 2. An increase in the key length improves the GAR, but at the cost of a rise in FARs. This is due to the fact that increase in key length increases the number of check bits and hence the number of errors that can be corrected. (a) Performance with respect to incorrect key ordering The verification of user using key requires the key to be generated using same block order as was used to train the system. The impact of incorrect ordering of regions on the performance of BC is presented in Table 3, which shows a clear decline in performance. The true positives (TP) should reduce under the proposed scenario as users with incorrect ordering shall not be allowed, as it might be replayed message. Also, false positives (FP) shall also reduce as impostors with incorrect ordering shall not be allowed as well. The former

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Table 3 Performance at key length 40 with incorrect ordering of blocks Check bits FAR (%) GAR (%) CRR (%) 42 44 46

0.18 0.43 1.04

0.78 1.17 2.73

98.74 98.49 97.91

Fig. 3 Performance with incorrect ordering at key length 40

scenario is reflected by a steep decrease in GAR whereas the latter can be seen by stable FAR, which implies that system is discarding impostors. This has been depicted in Fig. 3 which shows the ROC curve with incorrect and correct ordering of regions. (b) Entropy of the proposed system An important criterion in evaluating the performance of BC lies in entropy of the key. The key entropy relates to the possibility of recovering the key without the availability of genuine user [29]. It tells the number of average guesses an impostor will have to make to correctly reconstruct the key without the user’s knowledge [30]. In the proposed approach, the helper data P is comprised of robust iris locations (L) and the check bits (C) for overcoming the variance in biometric samples of the same user. The normalized mean of the robust regions, which forms the key to biometric cryptosystem has a range of [0.7] as explained in Sect. 3.2. This implies each element of biometric key has a possibility of guessing 8 values. Further, the proposed configuration RS(82, 40) gives a possibility of recovering total of (82 – 40)/2  21 erroneous key elements. It leaves with 40 – 21  19 key elements that need to be correctly guessed. Hence, the entropy of the proposed approach is 819 ≈ 257 , i.e., 57 bits of entropy for the key length 40, which is quite high. Using a similar approach, entropy of the system proposed by Rathgeb and Uhl [21] evaluates to 50 bits for key length 70. For each 7-bit block, their system is capable of correcting 2-bit error; i.e., they are left with 7 − 2  5 bits to be guessed. This evaluates to 810.5 ≈ 50 bits of entropy, which is relatively small as compared to our proposed approach.

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Table 4 Comparison with other techniques Key length

Entropy

Rathgeb and Uhl [21]

70

50 bits

Wu et al. [20]

1024

596 bits

Proposed

40

57 bits

Similarly, the approach by Wu et al. [20] use RS(256, 470) with four such blocks of data. This calculates to 24.149 ≈ 596 bits of entropy for the given key length of 1024 bits, which is relatively low. The comparison of other existing biometric key generation-based techniques on iris with the proposed technique is presented in Table 4 as follows.

5 Conclusion The increasing use and demand of iris-based biometric recognition have given way to several security issues. These issues have been studied and addressed by various researchers. In the proposed approach, we provide a complete end-to-end system, which tackles attacks at the sensor system interface level and at the stored database level; i.e., it combines and provides a complete solution to mitigate replay attack and stored template attack simultaneously. The system achieves CRR as 99.71% @ FAR  0.1%, which proves the efficiency of the system. We also achieve a high entropy as 57 bits for the aforementioned key length of 40, which proves its effectiveness. We explored all the possible scenarios of attack and overcome the limitation to our previous approach as well. The system has also been compared with other existing techniques with respect to biometric cryptosystem. Future work shall explore the idea of robust iris regions in combining yet another attack to provide a foolproof solution.

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Tampering Detection in Digital Audio Recording Based on Statistical Reverberation Features Tejas Bhangale and Rashmika Patole

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Reverberation Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Thresholding-Based Tampering Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Data and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Audio authentication has become a challenging task with an increase in easily available manipulation tools which can be used to forge the audio recordings. This paper focuses on audio tampering detection which is a task that involved in the area of audio forensics. Reverberation-based acoustic features are considered to distinguish between original recordings and their tampered versions. Two types of feature sets have been considered which depend on the decay rate distribution of the signal in each frequency band. The statistical features of the decay distribution and that of the Mel-frequency cepstral coefficient matrix of the reverberant component have been used. A threshold-based technique that considers the percentage error between these statistical parameters of the original audio recordings and their tampered versions has been employed. The methodology has been tested on synthetically created data set which consisting of original recordings and their tampered versions recorded in different acoustic environments. Keywords Audio authentication · Tampering detection · Reverberation Decay rate distribution T. Bhangale (B) · R. Patole College of Engineering, Pune, India e-mail: [email protected] R. Patole e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_55

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1 Introduction Digital multimedia is a perfect example for the extent of penetration of digitization. Digital media has fenced the entire human race with digital images, audio, video, paperless transactions, etc. This digital age has also led to the development of tools that can easily manipulate the digital data. There are plenty of such easy-to-use tools available free of cost which forces us to question the genuineness or authenticity of digital media. If the digital media is an audio recording whose integrity has to be identified in connection with a legal investigation, the forensic analyst has to face a number of challenges to authenticate the recording because of the advent of manipulation tools that can forge the recording without leaving any visible traces. The term audio authentication refers to investigate whether an audio recording is original or has been tampered with. Audio authentication detects tampering or altering done to an audio recording and decides whether a recording is a precise portrayal of the sound occasions/events that happened when the recording device was recording audio. Audio authentication may involve identification of the recording environment, type of recording equipment, location and type of tampering, etc. This paper aims on extracting acoustic parameters/artefacts from the recording which can help in identifying whether the recorded audio has been tampered. The statistics extracted from the reverberation component present in the audio recording along with some other standard statistical features have been used to detect whether tampering or forgery has been done on the audio recording.

2 Previous Work The previous work carried out in the field of the audio forensics for authentication purpose is diverse. Audio authentication techniques are broadly classified on the basis of the type of analysis, namely container analysis and content analysis. Container analysis: the recording analysis is on the data—other than signal itself. These include the analysis of the file format, header of the file, HASH check and hex data [1]. In each of these methods, an exhaustive analysis about the data is done for the traces of any irregular information, inconsistencies and possibilities of editing mark of the editing software. Content analysis: this basically involves the analysis of the data in the recording for integrity. These include ENF comparison, signal power and spectrum analysis, waveform analysis for the abnormalities, reverberation consistency etc. In [2], author proposed a distinct component in digital audio recording based on the electric network frequency (ENF). ENF is the frequency of the main power supply in distribution network or the power grid. ENF value for the Europe and Asia is 50 and 60 Hz in the USA. The recording device picks up the ENF and is added as a background sound. The ENF has same frequency over the entire frequency range

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and has random frequency variations. ENF is same at all points in an interconnected network. Based on above two criteria, various methods are proposed to establish a suitable way for audio authentication. In [3], the author proposes the use of ENF in the field of forensics for audio and video recordings. In [4], author proposed a method for extraction of ENF based on frequency demodulation. The ENF content was approximated to an observable bandwidth which was embedded in the digital audio recording. In [5], the ENF component is extracted from the recording and the discontinuities in the phase are detected. The abrupt changes in the phase of the extracted ENF component aid visually for edit points in the recording. The author also proposed a method to quantify the discontinuity that allows automatic decision regarding the integrity of the audio recording. In [6], a method is proposed based on the fact that butt-splicing leaves discontinuities in audio signal. It starts with modelling a discontinuity in time domain and taking first difference of audio signal. The discontinuity model is compared with the audio by obtaining the cross-correlation coefficient. The SNR is studied with increasing order of differencing, and second-order differencing is found to be optimum. Optimum length of the model in terms of samples is also found. Butt splices cannot be detected at lower sampling rates. In [7], author proposes a method for detecting common forgery technique, splicing. In splicing, some part of the audio recording is replaced with part/full audio from another recording. The method proposed detects the abnormalities in local noise levels in the audio recording. The kurtosis of the audio signal is constant in band-pass domain, and the paper exploits this property of audio signals. Defining an objective function for the noise variance and then minimizing it to perceive the inconsistency within the recording and detect the location and length of the tampered part. In [8], author presents about the reverberation component extraction from the signal by designing an IIR filter whose coefficients are basically the estimation of the room impulse response. Once the filter block response is derived, the reverberation component is extracted. This paper takes into account the early and late reflection in the reverberation extraction by the considering the rate of decay. In [9], a method to detect tampering in an audio recording has been proposed. The method for authentication is based on the reverberation present in the recording. A threshold-based clustering technique has been used to identify tampering in the given audio file. Audio samples in the same cluster are from the same recording environment. Tampering is detected based on the number of clusters. Three steps are undertaken for this purpose: estimation of room impulse response, estimation of reverberant component and threshold-based tampering detection. This method is able to detect insertion type of attacks, but exact location of insertion cannot be found. It considers the source and recorder to be stationary. There has been a lot of research done in the area of audio tampering detection. But tampering detection using acoustical features of the reverberant component has not been much explored. This paper tries to explore the variation in the statistical features (including Mel coefficients) extracted from the reverberation component between original and tampered audio recording.

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3 Methodology This paper describes a method to authenticate digital audio recording with the help of reverberation embedded in the digital audio recording. To extract the reverberation from the signal, below steps were applied on the audio recording.

3.1 Reverberation Extraction Reverberation is the persistence of sound after the source that has terminated. It occurs because of multiple reflections of sound from the surfaces of the room. STFT and Transformation to Mel Scale. The digital audio recordings are time domain signals. The first step is to transform the signal to the frequency domain. For this purpose, the signal is divided into overlapping frames of short duration and the short-time Fourier transform is computed on each frame. Then the log magnitude of the resulting spectrum is computed after which it is transformed to the Mel scale. The reason for this transformation is to reduce the dimensionality, as the Mel-scale transformation reduces the frequency bands. Also, the Mel scale is a perceptual scale that considers the human auditory response and is found to increase the recognition rate in many speaker recognition applications. Decay Rate Estimation. If for a given frequency band, the decays are estimated, it essentially computes the reverberated signal in that frequency band. Therefore, the next step is to estimate the decays in each frequency bands obtained in the previous step. In [10, 11], the reverberation component, i.e. the decays were extracted manually which is obviously computationally sluggish. For this purpose, this paper uses an automatic decay rate estimation procedure which estimates the decay in each frequency band and therefore the reverberation component. In order to estimate the decays in each frequency band, first the energy profile is calculated as in [12]. Now for the energy profile in each frequency band, the decay start and stop points are detected; refer Fig. 1. The peaks are detected by comparing the current sample to the previous and next samples, and by keeping a valid threshold, valid peak points in each frequency band are estimated. For estimating the stop point, we start from the valid peak and traverse up to the sample where the signal monotonically decreases for a given threshold. The signal between the start and the stop points provides the decay in each frequency band and thus provides the reverberant component in each band.

3.2 Feature Extraction The next step is to extract the features from the reverberant component extracted in the previous step. Following features have been extracted:

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Fig. 1 Block diagram for reverberation extraction

Mel-Frequency Cepstral Coefficients (MFCC). The MFCCs are extracted from the reverberant component for each frequency band. Statistical Features from the MFCC matrix. The statistical features like mean, variance and skewness are extracted from the MFCC matrix of the reverberant component. Statistical Decay Rate Distribution (DRD) Features. After the decay start and stop points have been estimated for each frequency band, the slope of this decay is computed using least square fitting. This provides the slope distribution over time per frequency band. The statistics applied on this decay rate are further extracted as features for audio tampering detection. Following features are extracted from the decay rate distribution [13]. Suppose there are M frequency bands, a vector consisting of peak decays for each band is defined as Pj (where j  1, 2, …, M and N p is number of peaks).  Np ( j) m t ( j)

P j (k) ( j  1, 2, 3, . . . , M and Np is number of peaks). Np ( j)

k1

(1)

Decay in each frequency band is now defined in terms of the mean which represents the mean of the decay distribution for each band over time as shown in (1). Now, the statistical features like mean, variance, standard deviation, skewness and kurtosis of this vector are computed. Out of all the statistical features, the skewness features showed a significant difference in values for original and tampered version of the recording. Skewness of MFCC vector is computed, and comparison of skewness feature was calculated as shown in (2). 2 1 M j1 (m t ( j) − m b ) Nb (2) sb   3/2 . 2 1  Nb j) − m (m ( ) t b j1 Nb

3.3 Thresholding-Based Tampering Detection The features extracted in the previous step belong to two different categories: features from the MFCCs and features from DRD.

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A simple threshold-based tampering detection technique based on the above features has been used to detect tampering in an audio recording. A vector consisting of percentage error between the statistical features of the original and its tampered version is computed. A threshold is set based on experimentation which provides the necessary distinction between tampered and original recordings. The detailed description is given in Sect. 4.1.

4 Experimental Data and Results 4.1 Experimental Data The data set used consists of 12 reverberated recordings of different acoustic environments which are created synthetically using the anechoic speech and audio signals and the room impulse responses [14]. These recordings are further edited using audacity. So the data set consists of the original recordings and their respective tampered versions. The types of tampering attacks that considered are: Insertion. Where an audio clip recorded in a different environment has been inserted in the original recording. Deletion. Where a portion of the audio recording has been deleted. These editions change the reverberation properties of the extracted reverberation component. The frame length is taken 20 ms. The threshold for decay start–stop points is taken to be 3 ms. The signal is filtered using 32 Mel filter banks giving 32 frequency bands on which the above-mentioned operations are made.

4.2 Results As discussed in the previous section, different statistical features have been extracted from the Mel matrix as well as directly from the decay distribution for each frequency band. Tables 1 and 2 show skewness extracted after extracting the Mel-frequency cepstral coefficients of the reverberant component for both the original recording and its tampered version. The tables show the features for six different recordings and their tampered versions. The percentage error between the skewness of the original and tampered recordings is also shown in fourth column. The type of editing done in Table 1 is insertion and that for Table 2 is deletion. Similarly, Tables 3 and 4 show the skewness computed on the decay rate distribution of the original and tampered recordings. The percentage error is also shown in the last column. Now in order to quantify the difference in the features of the original and tampered recordings, a threshold is employed as discussed previously. It is evident that if the percentage error is more, the original and the tampered versions can be easily

Tampering Detection in Digital Audio Recording Based … Table 1 Skewness from MFCC matrix for insertions S. No. MFCC skewness Original Tampered 1 2 3 4 5 6

0.18907 0.23866 0.17791 −0.30046 −0.14029 0.30987

−0.00324 0.16692 0.05359 −0.19724 −0.2379 0.04781

Table 2 Skewness from MFCC matrix for deletions S. No. MFCC skewness Original Tampered 1 2 3 4 5 6

0.18906 0.23866 0.17791 −0.3004 −0.1402 0.30987

0.11261 0.17341 −0.13007 −0.03106 0.00495 −0.41173

Table 3 Skewness from DRD matrix for insertions S. No. DRD skewness Original Tampered 1 2 3 4 5 6

−0.4832 −1.3442 −0.3740 −0.5598 −0.4721 −1.1685

−0.4632 −0.8133 −0.3728 −0.6541 −0.4631 −1.1122

Table 4 Skewness from DRD matrix for deletions S. No. DRD skewness Original Tampered 1 2 3 4 5 6

−0.4832 −1.3442 −0.3740 −0.5598 −0.4721 −1.1685

−0.3674 −0.7984 −0.2548 −0.5641 −0.3905 −1.1581

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Error 98.285 30.060 69.873 34.352 69.580 84.570

Error 40.435 27.339 26.888 89.663 96.468 32.869

Error 4.137 39.498 0.3142 16.850 1.9085 4.8134

Error 23.964 40.603 31.873 0.7719 17.272 0.8858

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InserƟon 200

% Error

Fig. 2 Percentage error in skewness of MFCC (solid) and DRD (dashed) for recordings with insertion

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100 0 1

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Audio Recordings MFCC_Skeww

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Fig. 3 Percentage error in skewness of MFCC (solid) and DRD (dashed) for recordings with deletion

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distinguished. A threshold of 3% error is used. Figures 2 and 3 show percentage error between all original and their tampered versions for insertions and deletions. From the graphs shown in Figs. 2 and 3, it can be clearly seen that MFCC skewness features can undoubtedly distinguish original and tampered versions for both types of editions, insertions and deletions. In case of insertions, the decay rate skewness fails in two cases. Similarly, when the type of edition is deletion, the decay rate skewness fails only in one case. Here also, MFCC skewness is a reliable feature that can be used for tamper detection.

5 Conclusion In this paper, the method proposed for audio authentication is based on the reverberation component embedded in the audio recording. The statistical features of the MFCC and the decay rate distribution are used for tampering detection. The skewness of MFCC features and the skewness of DRD features are computed for both the original and tampered version of the recordings. The absolute percentage error is calculated, and threshold is applied on it for tampering detection. It can be seen that the MFCC features of the reverberant component show a significant difference compared to DRD features to edition in the recording for both insertions and deletions. The future work direction can be towards increasing the features that clearly give an indication of tampering. Also, identifying the type of tampering attack can be an extension of the present work by modifying the threshold-based technique.

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References 1. M. Zakariah, M.K. Khan, H. Malik, Digital multimedia audio forensics: past, present and future. Multimedia Tools Appl. 77(1), 1009–1040 (2008) 2. C. Grigoras, Digital audio recording analysis–the electric network frequency criterion. Int. J. Speech Lang. Law 12(1), 63–76 (2005) 3. C. Grigoras, Applications of ENF analysis method in forensic authentication of digital audio and video recordings, in Audio Engineering Society Convention 123 (Audio Engineering Society, 2007) 4. L. Dosiek, Extracting electrical network frequency from digital recordings using frequency demodulation. IEEE Signal Proc. Lett. 22(6), 691–695 (2015) 5. D.P.N. Rodríguez, J.A. Apolinário, L.W.P. Biscainho, Audio authenticity: Detecting ENF discontinuity with high precision phase analysis. IEEE Trans. Inf. Forensics Secur. 5(3), 534–543 (2010) 6. A. Cooper, Detecting butt-spliced edits in forensic digital audio recordings, in 39th International Conference: Audio Forensics: Practices and Challenges (Audio Engineering Society, 2010) 7. X. Pan, Z. Xing, L. Siwei, Detecting splicing in digital audios using local noise level estimation, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2012) 8. G.A. Soulodre, About this dereverberation business: a method for extracting reverberation from audio signals, in Audio Engineering Society Convention 129 (AES, 2010) 9. R. Patole, G. Kore, P. Rege, Reverberation based tampering detection in audio recordings, in Audio Engineering Society Conference: 2017 AES International Conference on Audio Forensics (Audio Engineering Society, 2017) 10. H. Malik, H. Farid, Audio forensics from acoustic reverberation, in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (IEEE, 2010), pp. 1710–1711 11. U.A. Chaudhary, H. Malik, Automatic recording environment identification using acoustic features, in Audio Engineering Society Convention 129 (Audio Engineering Society, 2010) 12. H. Malik, Acoustic environment identification and its applications to audio forensics. IEEE Trans. Inf. Forensics Secur. 8(11), 1827–1837 (2013) 13. M. Markovi´c, G. Jürgen, Reverberation-based feature extraction for acoustic scene classification, in IEEE International Conference on. Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2017) 14. MARDY (Multichannel Acoustic Reverberation Database at York) Database Speech and Audio Processing Laboratory, https://www.commsp.ee.ic.ac.uk/_sap/resources/mardymultichannelacoustic-reverberation-database-at-york-database/

Acoustic Scene Identification for Audio Authentication Meenal Narkhede and Rashmika Patole

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Conversion to Frequency Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Estimation of Decay Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Data and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Data Set Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Feature Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract This paper discusses acoustic scene identification which is a part of audio authentication. The work focuses on extracting different acoustical parameters embedded in the recording that can aid in identifying the claimed environment in a legal investigation. Four different feature sets have been used. Out of these, two feature sets have been extracted from the original speech recording and the other two are based on the reverberation component embedded in the recording. Also, the methodology has been tested on two different classifiers. The methodology has been tested on synthetically created speech recordings in seven different environments. The paper gives a comparative study of classification accuracy obtained with different feature sets and different classifiers. Keywords Acoustic scene identification · Acoustic reverberation Audio authentication M. Narkhede (B) · R. Patole College of Engineering, Pune, India e-mail: [email protected] R. Patole e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_56

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1 Introduction Digital multimedia has evolved a lot in the last few years and can be easily obtained nowadays. The use of digital multimedia in the court of law as evidence in legal investigations has become very common. In this digital age, it has also become very easy and effortless to tamper the available digital media with the advent of freely available, easy-to-use manipulation tools. It is therefore important to prove the authenticity and integrity of the digital media for it to be considered as an evidence in the court of law. Digital media can be available in different formats like audio, image, video or text. Significant research has been done in the image forensics area [1]. However, audio forensics area is relatively less developed. In a forensic investigation, the recordings that are submitted as evidence may contain conversations between speakers and other types of audio signals related to the environment in which it was recorded. Before presenting the evidence in the court of law, the forensic analyst has to perform a number of tasks such as identifying the speakers from the recording, identifying the environment or the recording device and verifying the integrity of the audio evidence. This paper focuses on acoustic scene identification (ASI) for the purpose of audio authentication. ASI deals with extracting acoustic parameters from the recording which can help to identify the acoustic environment of the crime scene in which the questioned audio was recorded. Once the environment is identified, these results can be used to justify whether the audio was recorded in the claimed environment. The acoustic signatures embedded in the audio recording under question can provide clues regarding the acoustic environment in which the audio clip was recorded. Reverberation is one such feature that can provide information about the recording environment. The statistical features from the reverberation component have been extracted which are used as features for the classification of the acoustic scenes. The previous work related to the domain of audio authentication is discussed in Sect. 2. Section 3 explains the acoustic reverberation-based feature extraction and classification. The experimental data and results are discussed in Sect. 4. Section 5 gives the main conclusions and inferences of the presented work.

2 Literature Review Based on the previous work in the domain of audio authentication, a broad categorization of various authentication techniques has been discussed in [2]. Audio authentication techniques can be broadly classified into content-based and container-based techniques. Container-based techniques exploit the audio file structure and its description for authentication. HASH-based analysis, analysis of MAC timestamps and file format analysis can be done for forgery detection [2].

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Content-based techniques use content present in recorded signal for authentication. These include electric network frequency (ENF)-based, recording device signature-based and recording environment signature-based authentication. ENF-based authentication: The ENF signal which is the main power line signal with frequency 50 or 60 Hz is naturally embedded in many audio recordings. The authentication techniques based on ENF exploit this embedded signal for forgery detection. One such method used is to extract ENF and its features from an audio recording and use them to detect forgery. In [3], three broad categories for methods of ENF extraction are explained: spectrographic, spectrum on short-time windows and zero crossings. There are ENF databases maintained by researchers, which can be compared to the extracted ENF and find out the time of recording or the timestamp for tampering. The comparison can be done visually or automatically. In [4], the phase discontinuities are detected in the ENF signal in the questioned audio recording. The abrupt phase changes help to locate the edit points in the recording and also give a clue about the type of editing (insertion or deletion). In [5], an absolute-error-map (AEM) is obtained between the ENF signal from audio recording to be examined and ENF database to detect the location and type of tampering (insertion, deletion or splicing). It is a challenge to maintain a database for the power grids all over the world. Such techniques may not be applicable for battery-powered devices located far away from the power line and the presence of any compression algorithm [6]. ENF-based authentication techniques could be reliably used if the database of ENF values exists. Recording device signature-based authentication: The recording device introduces some artefacts in the recording signal. These artefacts can be used to determine the microphone used, which has its applications in audio forensic analysis. In [7], the microphone classification is done by proposing a statistical framework. The artefacts modelled using nonlinear function are captured, and microphone classification is done. In [8], Fourier coefficient histogram is extracted to be used as a feature set and six different classification algorithms are used. The accuracy of these methods depends on the training and testing database and the type of classifiers used. Recording environment signature-based authentication: When an audio is being recorded in a room, there are two components recorded at the recorder: the direct sound component and the reverberant component. The reverberant component is a characteristic of that room and can be used for extracting acoustic artefacts for the identification of recording environment or tampering detection. In [9], a method of inverse filtering has been proposed to extract the reverberant component from the given audio recording. In [10], tampering in the questioned audio recording is detected by extracting the reverberant component. Inconsistencies in the reverberation are observed in a recording, and tampering is said to be present if the recording environments are different. This paper deals with insertion type of tampering attack.

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In [11], a statistical technique to estimate the amount of reverberation and background noise variance in an audio recording is presented. The energy-based voice activity detection method is used for automatic decaying tail selection in order to find reverberation. In [12], temporal peaks are detected and decaying tail is obtained. This gives information about reverberation. A feature set is obtained from this reverberation and used for acoustic scene classification. In [13], reverberation is estimated based on inverse filtering and spectral subtraction. Background noise is estimated by the method of particle filtering. Features are extracted from this obtained reverberation and background noise for the purpose of classification. Audio authentication by recording environment identification is a growing area of research. The reverberation component embedded in the recordings is a known acoustic parameter that can be used in identifying an acoustic scene. This work explores the use of statistics on this reverberation component for acoustic scene identification.

3 Methodology The aim of this paper is to identify the environment of the questioned recording using acoustic signatures present in it. Reverberation-based acoustic clues have been extracted from the recording which provide information regarding the recording environment. Reverberation is the persistence of sound after the source has terminated. It occurs because of multiple reflections of sound from the surfaces of the room, and it is the characteristic of the acoustic environment [9]. Figure 1 shows the overview of steps involved in ASI for extracting features from the reverberant component. The steps involved in the methodology have been explained in the subsequent sections. The present work is an extension of the work done in [12, 14].

Fig. 1 Overview of steps involved in ASI

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3.1 Conversion to Frequency Domain The audio recording in time domain is first converted to frequency domain using short-time Fourier transform (STFT). The result of this step is a matrix representation of the input time domain signal, where rows represent frequency and columns represent frames. The log magnitude spectrum of the resulting spectrum is obtained which is later converted to perceptual scale by applying mel filterbank. Conversion to the mel scale is a frequently used step in speaker recognition because it depends on the human auditory response and gives better recognition rate. The result of this step is the log magnitude spectrum in different frequency bands N b [12].

3.2 Estimation of Decay Rate For each frequency band, the peaks and their decay rates have to be determined. The signal after each peak that decays exponentially represents the reverberation component for that frequency band. Therefore, the first step is to estimate the peaks. The decay rate has been estimated for each frequency band along the temporal direction. The estimation of decay rate can be broken down into three steps: estimation of energy envelop, estimation of decay start and stop points and linear least squares fitting [12, 14]. Estimation of Energy Envelop. An energy estimator has been used to obtain the decay fragment along the temporal direction in each frequency band. The energy is estimated based on (1). e[k]  α . x[k − 1]2 + (1 − α) . x[k]2 .

(1)

where x is the log magnitude spectrum in perceptual scale for each frequency band, α estimates the weight between current and previous samples and is called the forgetting factor; it is set to 0.2, and e is the estimated energy envelop. The obtained envelop has been approximated using a root mean square detector. This approximated envelop detector is based on (2). y[k] 



β . y[k − 1]2 + (1 − β) . e[k]2 .

(2)

where y is the approximated detected energy envelop, e is the estimated energy −1 envelop obtained from (1), and β  e τ . Fs . Here, Fs is the sampling frequency of the input speech signal and τ is the time constant of the energy detector and is set to 5 ms. Estimation of Decay Start and Stop Points. The next step is to search out those peak points after which the decay starts. The current sample from the signal y[k] has been entitled as a local peak after comparing it with the immediate previous

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and the next sample. Later, only those peaks have been selected as valid peak points after which the signal is monotonously decreasing for some defined threshold. The threshold is set to 3 ms. The end point of the monotonously decreasing signal is the decay stop point. Linear Least Squares Fitting. Finally, the decay rate for each frequency band has been obtained by fitting a line between the decay start and stop point in least squares sense. The decay rate is the slope of the fitted line.

3.3 Feature Extraction The system has been tested using four sets of features. First is the standard Mel Frequency Cepstral Coefficient (MFCC) features which are obtained by applying mel filterbank on the input speech recording and statistics like mean, standard deviation, skewness and kurtosis applied to the obtained MFCCs. The second set of feature vector has been obtained by combining the features like short-time energy, spectral roll-off, spectral centroid and spectral flux directly from the framed speech signal [15]. For obtaining the short-time energy, the signal has been framed with a suitable window size and framewise energy vector is obtained. The spectral roll-off is that frequency below which about 95% signal energy is confined. The spectral centroid has been calculated by dividing the average frequency weighted by sum of amplitudes by the sum of amplitudes. The spectral flux vector has been obtained by converting the framed signal in frequency domain and then taking the second normal of the difference between consecutive frames. Standard deviation has been applied to each of the obtained vectors to get a single value. All these four values have been combined to form a feature vector. Third set consists of a feature vector obtained by applying statistics over the decay rates in different frequency bands. This feature vector consists of two parts. The first part of the third set of features consists of mean of the decay rates in each frequency band as shown in (3).  N p ( j) m t ( j) 

D j (i) , N p ( j)

i1

j 1, 2, . . . , Nb

(3)

where D j is the decay rate for each frequency band, and N p is the number of decays obtained in one frequency band. The result of (3) is a vector whose length is equal to Nb . This vector is combined with the second part that is mean and skewness of the vector m t thus giving a vector of length Nb + 2. The fourth set consists of mean, standard deviation, variance, skewness and kurtosis of the vector obtained with the vector m t giving a vector of length Nb + 5.

Acoustic Scene Identification for Audio Authentication

599

4 Experimental Data and Results 4.1 Data Set Used The methodology has been tested on speech recordings taken in different acoustic environments from the same speaker. Two anechoic speech recordings of the same speaker have been convolved with the impulse responses available from Multichannel Acoustic Reverberation Database at York (MARDY) [16] database as well as an inhouse database created by changing the source and microphone positions. Seven different environments are considered for the purpose of classification. There are five recordings for each environment with different source and microphone positions. There are total of 70 reverberated speech recordings. For STFT, a periodic Hamming window is used with a window size of 20 ms. The perceptual filterbank used has 32 filters; i.e. the signal is divided into 32 frequency bands. The window size chosen for framing the speech recording for obtaining the statistical features is 20 ms with an overlap of 50%.

4.2 Feature Set As discussed in Sect. 3.3, the first set of features consist of standard set of MFCC features and their mean, standard deviation, skewness and kurtosis. Thirteen MFCC features have been extracted for the given speech recording, and the statistics on these 13 MFCCs in all gives a feature vector of 52 elements. The second set of features consists of standard deviation applied to short-time energy, spectral roll-off, spectral centroid and spectral flux. This feature set is of length 4. The third set of features have been extracted from the reverberated component that is estimated from the decay rate distribution for each frequency band. The feature set consists of mean of the decay rates obtained in each frequency band which is a vector of length 32. This vector is combined with the statistics applied to the decay rates thus resulting in a vector of length 34. The fourth set consists of mean, standard deviation, variance, skewness and kurtosis applied to the mean of the decay rates obtained for each frequency band thus resulting in a vector of length 37.

4.3 Classifier For classification, the entire data set of 70 speech recordings has been divided into training set and testing set. The training set consists of 46 recordings, and the testing set consists of the rest 24 recordings. The classifier used is support vector machine (SVM) with pairwise classification for multiclass classification. The system has been tested on SVM with a linear kernel. Two methods for obtaining the separating hyper-

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plane have been considered: linear squares (LS) and sequential minimal optimization (SMO). A model has been trained based on the features extracted from the available training data. The same features have been extracted from an unknown audio recording from the testing database. The unknown audio is classified into the best matching class based on the trained model and features extracted. Artificial neural network (ANN) is also used as classifier so that its results can be compared with SVM. In ANN, there are two main steps involved in the classification. Firstly, the training data features and target class labels are specified to the neural network model. Then, the testing data features are fed to the model and the model predicts the class in which the data belong. The accuracy of the model is dependent on the number of hidden neurons and the number of samples kept for training and testing. The model is trained for a number of iterations until the validation accuracy is achieved and the one with maximum accuracy is considered. The implementation of both the classifiers has been done in MATLAB.

4.4 Results Table 1 shows the accuracy of classification of different environments for the four different feature sets using different classifiers. The feature sets (third and fourth) computed on the decaying signal for each frequency band are essentially the features computed on the reverberant component in each frequency band. From the above table, it can be concluded that the feature set considering features extracted from the reverberant component shows an improved accuracy than the standard MFCC features and statistical features. Also, if additional statistical features are added (as in the fourth feature set), the accuracy shows slight improvement with the SMO method. Also, ANN shows a considerable increase in the classification accuracy for all feature sets except the second feature set.

Table 1 Testing accuracy for different classifiers for feature sets S. Feature set Length of Accuracy No. feature vector LS method SMO method (%) (%) 1

ANN (%)

Standard MFCC features Statistical features on speech recordings

52

83.33

83.33

81.8

4

83.33

83.33

63.63

3

Decay rate features

34

87.50

83.33

90.9

4

Decay rate features

37

87.50

87.50

90.9

2

Acoustic Scene Identification for Audio Authentication

601

5 Conclusions In this paper, a method for acoustic scene identification by exploiting the properties of the reverberant component of the audio recording is presented. The standard statistical features applied to MFCCs and from the reverberant component (decay rates) have been extracted. A comparative analysis in terms of testing accuracy has been done between four feature sets. Different classifiers have been used for testing, and their results have been compared. The performance of this system is evaluated on speech recordings in different environments. Future work can be done by considering more number of classifiers to increase the accuracy. Different features of the reverberant component can also be explored which can provide additional information about the acoustic scene.

References 1. H. Farid, Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009) 2. M. Zakariah, M.K. Khan, H. Malik, Digital multimedia audio forensics: past, present and future. Multimedia Tools Appl. 77(1), 1009–1040 (2018) 3. C. Grigoras, D. Rappaport, J.M. Smith, Analytical framework for digital audio authentication, in Audio Engineering Society Conference: 46th International Conference: Audio Forensics, Audio Engineering Society (2012) 4. D.P.N. Rodríguez, J.A. Apolinário, L.W.P. Biscainho, Audio authenticity: detecting ENF discontinuity with high precision phase analysis. IEEE Trans. Inf. Forensics Secur. 5(3), 534–543 (2010) 5. G. Hua, Y. Zhang, J. Goh, V.L. Thing, Audio authentication by exploring the absolute-error-map of ENF signals. IEEE Trans. Inf. Forensics Secur. 11(5), 1003–1016 (2016) 6. C. Grigoras, Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis. Forensic Sci. Int. 167(2–3), 136–145 (2007) 7. S. Ikram, H. Malik, Microphone identification using higher-order statistics, in Audio Engineering Society Conference: 46th international conference: Audio Forensics. Audio Engineering Society (2012) 8. R. Buchholz, C. Kraetzer, J. Dittmann, Microphone classification using fourier coefficients, in International Workshop on Information Hiding (Springer, Berlin, Heidelberg), pp. 235–246 9. G.A. Soulodre, About this dereverberation business: a method for extracting reverberation from audio signals, in Audio Engineering Society Convention 129 (Audio Engineering Society, 2010) 10. R. Patole, G. Kore, P. Rege, Reverberation based tampering detection in audio recordings, in Audio Engineering Society Conference: 2017 AES International Conference on Audio Forensics (Audio Engineering Society, 2017) 11. H. Malik, Acoustic environment identification and its applications to audio forensics. IEEE Trans. Inf. Forensics Secur. 8(11), 1827–1837 (2013) 12. M. Markovi´c, J. Geiger, Reverberation-based feature extraction for acoustic scene classification, in International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2017), pp. 781–785 13. H. Zhao, H. Malik, Audio recording location identification using acoustic environment signature. IEEE Trans. Inf. Forensics Secur. 8(11), 1746–1759 (2013)

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14. P. Murgai, M. Rau, J.M. Jot, Blind estimation of the reverberation fingerprint of unknown acoustic environments, in Audio Engineering Society Convention 143 (Audio Engineering Society, 2017) 15. shodhganga.inflibnet.ac.in/bitstream/10603/107558/8/08_chapter%202.pdf 16. MARDY (Multichannel Acoustic Reverberation Database at York) Database Speech and Audio Processing Laboratory, https://www.commsp.ee.ic.ac.uk/~sap/resources/mardy-multichannelacoustic-reverberation-database-at-york-database/

Retinal Blood Vessel Extraction Using Morphological Operators and Kirsch’s Template Jyotiprava Dash and Nilamani Bhoi

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Segmentation Using Kirsch’s Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

604 605 605 607 608 608 610 610

Abstract Retinal imaging is a foremost pointer for analysis of various ophthalmological diseases, which has driven the development of different vessel segmentation approaches. So, in this study, we have introduced a morphological approach for retinal vein extraction. The technique presented takes in different image processing techniques for vessel extraction, viz image enhancement and smoothening with contrast-limited adaptive histogram equalization (CLAHE) and anisotropic diffusion filter, respectively, segmentation by Kirsch’s template and morphological cleaning to get the final segmented image. The enactment of the given technique is appraised by means of openly accessible digital retinal images for vessel extraction (DRIVE) database and attains an accuracy value of 0.951. Keywords Retinal imaging · Kirsch’s template · Morphological cleaning

J. Dash (B) · N. Bhoi Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha, India e-mail: [email protected] N. Bhoi e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_57

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1 Introduction The sensual portion of the eye is retina that includes photoreactors, fovea, macula, and optical disk. Innumerable major diseases manifest in the retina which require medical diagnosis by the optometrists [1]. Hence, vasculature extraction of fundus images is a crucial work for verdict different ophthalmological ailments [2] like diabetic retinopathy, cataract, glaucoma, hypertension, and cardiovascular diseases. So, different techniques have been brought together for extraction of blood vessels which can be categorized as: matched filter methodology, morphological methodology, vasculature tracing methodology, supervised methodology, and unsupervised methods [3]. In matched filter method, the original image is filtered and thresholded for the identification of blood vessels, allowing for the fact that vessel’s cross section can be shown by way of a Gaussian function [4]. In [4], Zhang et al. make known to a matched filtering technique aimed at identification of retinal vasculature by means of matched filter with first-order Gaussian derivative. In [5], an algorithm is elaborated by the author for retinal vasculature segmentation by optimizing the matched filter’s parameter based on genetic algorithm. In [6], the author introduced a concoction of ant colony and matched filter for fundus vessel segmentation. In morphological method, some vascular shape features are known a priori and then by means of morphological operatives the vessels are passed through the filter for ultimate segmentation [7]. In [8], the author used a morphological and topological-based method for identification of veins from fundus imageries. In [9], a mechanized tactic using morphological bit plane slicing is used for segmenting the vein. In vessel tracking method, initially, interested vessel pixels are located for tracing followed by vessel segmentation based on some local image features. In [10], Can et al. proposed an automated tracing algorithm for vessel extraction using the direct exploratory algorithm. In [11], the author proposed an instinctive model-based vessel locating technique for vessel identification. Supervised method marks the pixels either as vessel or nonvessel, and here the classifiers are proficient with the data from manually segmented images [12]. In [7], Marin et al. extract the blood vessels from fundus images with gray level and moment invariants-based features. In [13], the author detects the venules by means of a Bayesian classifier through class-conditional probability density function. In [14], Staal et al. explain a modus operandi for venules extraction grounded on vivisection of image edges that concur almost with venules centerline. Unsupervised process is a mechanism of abstraction of inferences from data groups where data is encompassed deprived of characterized retorts and the information are accumulated in altered classes [15]. In [16], Zhao et al. proposed an unsupervised technique for veins extraction with level set and region growing method. In [17], Kande et al. introduced a novel technique to detect vessels by means of Fuzzy Cmean clustering. In [18], a way that segments the retinal veins by using B-COSFIRE filter is elaborated. In [19], Minar et al. recommended an automatic technique of blood vessel extraction using adaptive filters. From the above-collected works, it is clear that many approaches have been applied for vivisection of venules from ophthalmoscope imageries. However, there

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are certain limitations such as loss of connectivity and detection of thin vessels that need to be improved. Therefore, this study brings a morphological-based method by using Kirsch’s template to overcome the above limitations.

2 Methodology Here, a morphological approach for extraction of retinal veins by using Kirsch’s template is employed. The process of extraction of blood veins in distinction to the fundus imageries can be carried out by the following stages: (1) preprocessing of the original RGB images for vessel enhancement, (2) vessel extraction using Kirsch’s edge detection method, and (3) post-processing for removal of unwanted isolated pixels using morphological cleaning. From fundus image analysis, we know that the green band unveils superlative contrast, whereas the red band is saturated and has nethermost contrast, and blue band is noisy and agonizes deprived dynamic range. So, for better segmentation performance, green band is taken. Whole scheme is represented in Fig. 1.

2.1 Preprocessing For image quality upgradation, preprocessing is an important step before any vivisection course. So, this step consists of following steps: (i) vessel enhancement using CLAHE, (ii) morphological opening to eradicate the brighter strip, (iii) smoothening by using anisotropic diffusion filter (ADF), and (iv) deletion of optical disk.

2.1.1

CLAHE

CLAHE actuates on petite territories in the image, baptized pantiles, contrary to the entire image. Adaptive HE enumerates the contrast transmute function for apiece pantile separately. Individual pantile’s contrast is upgraded such that the histogram of the yield territories almost contests the histogram stated by the distribution limit.

Fig. 1 Graphic diagram of the presented technique

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The immediate pantiles are then joint by means of bilinear interpolation to abolish affectedly cajoled frontiers. The contrast, exclusively in consistent zones, can be bounded to evade intensifying any clamor that might exist in the image [20].

2.1.2

Morphological Opening

The filtration of enhanced image by means of a morphological opening action exploiting a three-pixel diameter disk defined in a square grid by means of eight connectivity, as structuring element.

2.1.3

Anisotropic Diffusion Filter (ADF)

Before the segmentation process, to smooth the image, ADF is applied on the morphological image. The mathematical equation for ADF is given for instance [16, 21], ∂I  div(D(x, y, t))∇ I  ∇c . ∇ I + c(x, y, t)I ∂t

(1)

D(x, y, t) represents the diffusion parameter that directs the degree of diffusion and is commonly chosen as a function of the gradient of the image to uphold boundaries, ∇ denotes the gradient, and  denotes Laplacian operator. D(x, y, t) is a non-negative monotonically decreasing function.   2 D(x, y, t)  G ∇ I  k

(2)

If ∇ I is lesser, the ADF will smooth the image else be apt to preserve the boundary.

2.1.4

Deletion of Optical Disk

The intense portion of the retina is the optical disk that intrudes the vivisection course as the veins exist in this zone may cause misidentification of pixels belonging to veins as optical disk [22]. So, to avert this difficulty, optic disk should be evicted. This is accomplished by subtracting enhanced result from the smoothed result. The output of the preprocessing steps is depicted in Fig. 2.

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Fig. 2 a Input image, b green band image, c enhanced image, d morphologically opened, e smoothed image, and f optical disk evicted image

2.2 Segmentation Using Kirsch’s Template Kirsch edge revealing process is initially hosted by Kirsch in 1971. This process utilizes a sole mask of size 3 × 3 and revolves the mask in 45° rises through entire eight directions as follows: 5 5 5 0° −3 −3 −3 180°

−3 0 −3

−3 −3 −3

−3 0 −3

5 5 5

−3 5 5 45° −3 −3 −3 225°

−3 0 5

−3 −3 −3

5 0 −3

5 5 −3

−3 −3 5 90° 5 −3 −3 270°

−3 0 5

−3 −3 5

5 0 −3

5 −3 −3

−3 −3 −3 135° 5 5 −3 315°

−3 0 5

−3 5 5

5 0 −3

−3 −3 −3

The extreme value found by the convolution of distinct mask with the image is demarcated as edge magnitude. The mask that forms the supreme magnitude is well defined as direction [23, 24].

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Fig. 3 Results of different images of DRIVE databases: a, b input image, c and d corresponding output of (a) and (b)

2.3 Post-processing Thereafter receiving the upshot from subdivision course, some undesirable pixels will be marked in the upshot which may be mismarked as vessel. So, to get away from this misrecognition, a morphological cleaning action is exploited which eliminates the unsolicited pixels. Figure 3 depicts the final segmentation images of the DRIVE database.

3 Results and Discussion The fallouts of the segmentation tactic are studied by means of freely suggested DRIVE database which includes a total of 40 images out of which 20 images are from test set and 20 images are from training set [25]. The manual segmentation result given by the former viewer is considered as ground truth images. To analyze the system enactment, the achieved yield image is compared with the ground truth image. The enactment calculation is done with the help of sensitivity (Sen), specificity (Spe), precision (pre), negative predictive value (npv), false discovery rate (fdr), Matthews’s correlation coefficient (mcc), and accuracy (acr). Sen is the quantity of authentic vessels which are suitably acknowledged as it is. Spe is the quantity of nonvessels which are precisely marked as itself. Pre and npv are the degree of vessels and nonvessel significances inside the finding procedure that are true vessel and true nonvessel outcomes correspondingly. The degree of type-I error in null hypothesis analysis during multiple comparisons is called as fdr. The mcc is used as a proportion of degree of binary labeling. Degree of similarity amid the final output to the manually segmented image is termed as accuracy [26]. The performance matrices are computed for apiece images of the database which provides average values of Sen, Spe, pre, npv, fdr, mcc, and acr of 0.703, 0.985, 0.810, 0.962, 0.187, 0.656, and 0.951. The system recital is also compared with other obtainable procedures in terms of Sen, Spe, and acr which is shown in Table 1. From Table 1, we can see that the presented technique outperforms the existing methods by giving high performance values. In Fig. 4, we have compared our segmented image with other images of

Retinal Blood Vessel Extraction Using Morphological … Table 1 Performance comparisons of different approaches Technique acr Sen

609

Spe

Zhang et al. [4]

0.938

0.712

0.972

Fraz et al. [23]

0.943

0.715

0.976

AI-Rawi et al. [5]

0.942

–

–

Cinsdikici et al. [6]

0.929

–

–

Marin et al. [7]

0.945

0.706

0.980

Rossant et al. [8]

0.943

–

–

Fraz et al. [9]

0.942

0.730

0.974

Soares et al. [13]

0.946

–

–

Zhao et al. [16]

0.947

0.735

0.978

Kande et al. [17]

0.891

–

–

Azzopardi et al. [18]

0.944

0.765

0.970

Presented Technique

0.951

0.703

0.985

Fig. 4 Final segmentation output of a RGB image, b manually segmented image, c Cinsdikici et al. [6], d AI-Rawi et al. [5], e Zhang et al. [4], f presented technique

different authors. From the images, we can see the offered technique can classify together thick and tinny veins precisely.

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4 Conclusion Accurate vessel extraction from fundus images is a crucial task for analysis of various ophthalmological syndromes. This work presents a morphological technique for identification of retinal blood veins from retinal images using Kirsch edge detection method. The advantage of the method is that it can identify the vasculature devoid of any tiny information. The presented approach performs better than other methods by giving an average value of 0.703, 0.985, and 0.951 for Sen, Spe, and acr correspondingly. This approach gives better result in upholding connectivity among the vessels and can be employed simply.

References 1. M.D. Abràmoff, M.K. Garvin, M. Sonka, Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010) 2. R. Panda, N.B. Puhan, G. Panda, New binary Hausdorff symmetry measure based seeded region growing for retinal vessel segmentation. Biocybernetics Biomed. Eng. 36(1), 119–129 (2016) 3. J. Dash, N. Bhoi, Detection of retinal blood vessels from ophthalmoscope images using morphological approach. ELCVIA Electron. Lett. Comput. Vision Image Anal. 16(1), 1–14 (2017) 4. B. Zhang, L. Zhang, L. Zhang, F. Karray, Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 40(4), 438–445 (2010) 5. M. Al-Rawi, H. Karajeh, Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Comput. Methods Programs Biomed. 87(3), 248–253 (2007) 6. M.G. Cinsdikici, D. Aydın, Detection of blood vessels in ophthalmoscope images using MF/Ant (Matched Filter/Ant Colony) algorithm. Comput. Methods Programs Biomed. 96(2), 85–95 (2009) 7. D. Marín, A. Aquino, M.E. Gegúndez-Arias, J.M. Bravo, A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011) 8. F. Rossant, M. Badellino, A. Chavillon, I. Bloch, M. Paques, A morphological approach for vessel segmentation in eye fundus images, with quantitative evaluation. J. Med. Imaging Health Inf. 1(1), 42–49 (2011) 9. M.M. Fraz, A. Basit, S.A. Barman, Application of morphological bit planes in retinal blood vessel extraction. J. Digit. Imaging 26(2), 274–286 (2013) 10. A. Can, H. Shen, J.N. Turner, H.L. Tanenbaum, B. Roysam, Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inf. Technol. Biomed. 3(2), 125–138 (1999) 11. K.K. Delibasis, A.I. Kechriniotis, C. Tsonos, N. Assimakis, Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput. Methods Programs Biomed. 100(2), 108–122 (2010) 12. J. Dash, N. Bhoi, A thresholding based technique to extract retinal blood vessels from fundus images. Future Comput. Inf. J. 2(2), 103–109 (2017) 13. J.V. Soares, J.J. Leandro, R.M. Cesar, H.F. Jelinek, M.J. Cree, Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006) 14. J. Staal, M.D. Abràmoff, M. Niemeijer, M.A. Viergever, B. Van Ginneken, Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)

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Wire Load Variation-Based Hardware Trojan Detection Using Machine Learning Techniques N. Suresh Babu and N. Mohankumar

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 KNN Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Detection of malicious form of hardware is commonly referred to as hardware Trojan and had become a major challenge. Especially when we go down to deep submicron technology, it becomes really difficult to detect the presence of Trojan using conventional testing approaches. Logic testing proves to be effective only when the number of inputs to trigger the Trojan is of small number. Further using sidechannel approaches, the complexity of triggering the entire Trojan circuit is reduced because partial activation of Trojan will cause a considerable change in the measured parameter that is used to differentiate between the original circuit and circuit infected with Trojan. This work provides a non-invasive hardware Trojan detection methodology which uses side-channel power of the circuit to detect the presence of the Trojan. Moreover, the proposed method deviates from other existing techniques by accounting the change in side-channel power, due to interconnects that is the distributed resistance and capacitance of the wire connecting different standard cells in the circuit, by taking wire load variations into consideration. The power profile from Trojan-infected and Trojan-free circuits is used for training the machine. The machine predicts with much accuracy whether the circuit consists of Trojan or not based on data we have trained, therefore effectively categorizing the circuits and thus eliminating the errors caused due to manual intervention. The proposed work is validated by using ISCAS 85 and ISCAS 89 benchmark circuits. N. Suresh Babu · N. Mohankumar (B) Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] N. Suresh Babu e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_58

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Keywords Wire load · KNN machine learning algorithm · Side-channel analysis Hardware security · Hardware Trojan

1 Introduction The malicious circuit can be included in any phase of the VLSI design flow. Since to design a typical SoC today, there is no need for the designers to start from the elementary level, they can procure the IPs from the third parties which are readily available in the market so this reduces the time and also cost. Several approaches for detecting the Trojan have been proposed till date, but there is no single technique which can detect all classes of Trojan. Trojans can be mainly inserted in two forms—Trojans that can be inserted by means of hardware and other is by means of software. Conventional fault detection techniques cannot be employed for detecting the Trojans because there are numerous kinds of Trojans that are not utilitarian and cannot be activated using normal fault detection techniques and also the Trojans are not active all the time. For the detection of the Trojan, many approaches are proposed. Logic testing [1] is one such approach for detecting the circuit containing Trojan. In this, the entire part of the trigger should be activated so as to pass the malicious logic to the primary outputs. Because for logic testing, we need to compare outputs for the corresponding inputs with the golden reference values and if there is any mismatch, then there is the possibility that the Trojan might have been inserted and it was activated and propagating malicious logic. There are many problems in logic testing like if the number of inputs to the trigger is more then it becomes difficult to activate the Trojan. Similarly, several other approaches for detecting hardware Trojan are described by Thakur et al. in [2]. Side-channel analysis is another technique which measures the circuit parameters and verifies whether there is any change in the measured parameter if there is deviation of the measured parameter from the golden reference then we can say that it has some malicious module present in circuit which is causing this change. Many side-channel approaches were proposed in the literature for the detection of hardware Trojans by measuring various side-channel parameters of the circuit like power, current, temperature, and electromagnetic profile for both Trojan-infected and Trojan-free circuits; hence, if any difference is present then there is enough evidence of Trojan being inserted. In decision making, there are problems in setting the threshold; as a solution to this, techniques like principal component analysis (PCA) and many soft-computing techniques are available. The use of these techniques provides effective classification based on the side-channel parameters under consideration. The trust of the IPs is to be cross-checked and needs sophisticated testing procedures for detecting the presence of Trojan. Several mechanisms [3] have been proposed to evaluate trust by hardware duplicating by procuring similar functional IPs from different company vendors and running the process simultaneously. This process utilizes additional blocks, so it leads to an increase in area overhead and

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power. Some follow modular redundancy. Further, the adversary tries to insert the Trojan in such a way it evades the detection process and will show or manifest during the real field when the device is running. The adversary can insert the Trojan with many intends like he may include just for disrupting the functionality of the circuit or to leak confidential information like cryptographic keys. Principal component analysis is also used for classifying the infected circuits from the original circuits. Gate-level characterization is used for detecting hardware Trojans as proposed in [4]. In this method, the with the help of the circuit’s leakage power a linear equation is formulated , the gate coefficients were computed and further the set of obtained equations are solved using linear programming. Wire load which is used for interconnecting different standard cells serves as an important variable in timing and power analysis [5]. Illustrates the effect of wire load on power and timing of the design and deviation in power consumption while estimating power profile of the circuit at various levels of the design. Golden reference-free technique was proposed in [5] where modules were excited individually at different time slots, so that the module’s parameter variations can be used for detecting the Trojan. Kulkarni et al. in [6] addressed many communication attacks by Trojans like spoofing and resolved them by using SVM, K-Nearest Neighbors (KNN), and decision tree (DT) algorithms, respectively. Iwase et al. in [7] converted the obtained power from time-domain to frequency-domain using DFT and support vector machine (SVM) is used for detection by training the frequencydomain results. Literature has reported various schemes using virtual intelligence [8] by using virtual instrumentation for detecting the presence of Trojan by delay fingerprint. Machine learning algorithms are accurate and are widely used in many critical classification applications like military and defense where safety is needed irrespective of cost. Several methods involving delay, power, GLC, and techniques like CRC, voting, virtual intelligence, and virtual instrumentation are available to detect hardware Trojans [9–12]. This paper includes variations due to wire loads also.

2 Proposed Design The side-channel analysis for detecting Trojan does not account for variations due to interconnects that is the distributed resistance and capacitance of the wire connecting different gates in the circuit. Generally, there will be a minor change in measured power of the circuit for different wire loads. The adversary can use different wire loads for inserting the Trojan one such possible way is to use wire load which reduces power consumption, and the reduced power due to wire load can be exploited for inserting small Trojans typically of order two gates for disrupting the functionality of the circuit. The variations in power due to wire load have to be taken into account even though the change in power is minimal. Because when testing using power analysis without exiting circuit, it can cause considerable change. In this work, the effect of interconnect on power consumption of the circuit using different wire load models

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present in standard Synopsys cell library is estimated and detected the presence of Trojan using power analysis. Further, machine learning algorithms were used to predict whether the circuit consists of Trojan or not depending on the training we give to the machine. Figure 1 shows the proposed Trojan detection methodology. The design flow starts by taking the circuit which is to be tested in the form of RTL without any Trojans in it, since its attributes serve as golden references for training the machine. Now these circuits are compiled using different wire loads present in Synopsys to estimate the effect of wire loads on power of the circuit, because the wire load that is used for interconnecting the standard cells is an important variable in analyzing the power profile of the circuit. The obtained power values from original circuits using different wire loads by including variations due to wire loads are labeled as “NO Trojan” or “0” and for power values obtained from circuit inserted with different Trojans are labeled “TROJAN” or “1”, respectively. When training the data set (Power values) can also be labeled accordingly, to know the impact and size of the Trojan on original circuit like medium, small and large size Trojan. The obtained power values are further screened if there exists any redundant data in power values using principal component analysis (PCA). The obtained screened values are used for training the machine. After training the machine with the set of power values as training inputs. As testing pattern when the new values of power is given to the machine it predicts with accuracy whether it consists of Trojan or not depending on the training. The effectiveness of this depends on training the machine, the more the training data we give better is the result. The relative error in power due to each wire load is also estimated, since power obtained at pre-layout level will vary after post-layout for the same circuit even though the same wire load is used. This can be reported by analyzing the circuits using wire load instead of layout parasitics, 2.5% variation in power is observed during analysis. This is also included in training set of machine to eliminate errors in detection due to variations in each wire loads. So variations due to different wire loads and due to same wire loads on power are taken into account for detection.

3 Results and Analysis ISCAS 85 and ISCAS 89 benchmark circuits are considered and power profile is extracted from them using different wire loads (like tc8000000 and tc540000). Later, the benchmarks are inserted with Trojans of various sizes. The size of the Trojan depends on logic that is used to implement the Trojan. If the goal is to just malfunction the circuit, it might need less gates; whereas if the attacker intends to gain access to data or replace the contents, it may require more number of gates and also he should incur some logic exclusively for hiding the Trojan. The Trojans are inserted in rare triggered or less toggled nets, since Trojan inserted in these sites evade the functional testing. To find rare triggered nodes, the circuit is excited using all set of possible combinations of inputs and the nets which toggle less number of times

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Circuit in RTL form with different types of Trojan Inserted in it

Original circuit in RTL form

Compile the circuits and report the power

Select different wire load present in that library

Extract power by selecting each wire load

Estimate the variations wire load on Power profile

Remove redundant values and label accordingly for training

Train the machine using power values

Trojan free

Machine

Trojan Infected Fig. 1 Proposed hardware Trojan detection approach

New power values for verification

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Table 1 Dynamic power extracted from ISCAS 89 benchmark circuits ISCAS 89 Original circuit Trojan-1 Trojan-2 benchmarks dynamic power dynamic power dynamic power circuits (µW) (µW) (µW) s1238 s1196 s953 s832 s820 s520 s510 s444 s420 s400 s382 s344 s298

62.7852 63.9817 58.6273 22.3865 22.7502 26.6229 16.1934 32.6271 20.7565 29.7744 35.4519 22.9765 26.7009

64.8857 65.5295 61.8344 24.9764 24.0432 28.6512 19.9427 34.9021 23.0256 31.9956 37.8404 25.0919 28.8446

65.2799 67.3878 63.7142 26.8723 26.4521 30.705 20.6734 37.9467 24.8655 33.6858 40.0021 27.6405 29.9516

Trojan-3 dynamic power (µW) 68.053 69.2374 65.3121 27.4521 27.6636 32.042 22.186 39.0141 27.898 36.1646 42.0969 29.1537 31.9905

are considered as rarely toggled nets. Now the benchmark circuits with Trojans are synthesized and power profile is extracted using different wire loads. The power values are tabulated for different benchmark circuits (ISCAS 85 and ISCAS 89). The benchmarks are inserted with three types of Trojans (NAND type, counter based Trojan) and the power values of the circuit due to each Trojan is taken using tc8000 wire load. Table 1 shows the dynamic power, and Table 2 shows leakage power profile of the original circuit and circuit with Trojan for ISCAS 89 sequential benchmarks circuits. The s1238 shown in Table 1 has the power variation of maximum difference up to 6 µW when Trojan 3 is inserted clearly showing that the circuit is malfunctioned. For the analysis purpose in this work, we have shown three types of Trojan. We have considered other types of Trojans and successfully detected using this proposed methodology. Table 3 describes dynamic power extracted from the ISCAS 85 combinational benchmarks with Trojan inserted in them showing the difference in power consumption between the original and Trojan-inserted circuits. Table 4 shows the dynamic power of the circuit using different wire loads present in library. The wire loads varies with the technology node. There will be a minor variation in the circuit-measured power which is captured and tabulated. Below, we have shown the extracted power for c880 benchmark circuit of ISCAS 85. The dynamic power using each wire load is extracted, and there will be a slight variation since the library that we selected for our analysis has global operating voltage of 0.7 V. Table 5 shows the total power (Dynamic Power + Leakage Power) obtained using different wire loads in c880 circuit. The variations is Total power is shown using different wire load for the same design, considering three different Trojan designs.

Wire Load Variation-Based Hardware Trojan Detection … Table 2 Leakage power extracted from ISCAS 89 ISCAS 89 Original circuit Trojan-1 leakage benchmark leakage power power (µW) circuits (µW) s1238 s1196 s520 s510 s444 s420 s400 s382 s344 s298

6.6704 6.5871 5.9298 3.5927 6.546 3.8642 5.4505 5.337 4.5093 5.2301

7.737 7.6942 6.9949 4.6428 7.6751 4.9503 6.5762 6.4801 5.6257 6.3581

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Trojan-2 leakage power (µW)

Trojan-3 leakage power (µW)

7.7501 7.7031 7.0359 4.9621 7.6707 4.9782 6.5143 6.4757 5.5999 6.3498

7.8235 7.6738 7.0317 4.8742 7.9901 4.977 6.5333 6.4119 5.5958 6.3207

Table 3 Dynamic power and leakage power extracted from the ISCAS 85 ISCAS 85 Original circuit Trojan-1 Original circuit benchmarks dynamic power dynamic power leakage power circuits (µW) (µW) (µW) c7552 c1908 c1355 c880 c499 c432

805.6118 182.6524 212.342 90.8294 221.285 36.376

809.1041 186.287 217.912 97.8201 239.462 39.6405

2.896 6.3291 7.2401 103.8264 6.9414 2.1962

Trojan-1 leakage power (µW) 3.9964 7.4238 8.3396 114.9497 8.0432 3.268

Table 4 Dynamic power from the c880 benchmark circuit using different wire loads Original circuit Trojan 1 Trojan 2 Trojan 3 Wire load

Total dynamic power (µW)

Total dynamic power (µW)

Total dynamic power (µW)

Total dynamic power (µW)

tc8000000 tc4000000 tc2000000 tc1000000 tc540000 tc280000 tc140000 tc70000 tc35000 tc16000 tc8000

90.5456 90.5327 90.5484 90.5427 90.5385 90.5421 90.3247 90.6125 90.7122 90.8221 90.8294

97.6008 97.5922 97.8291 97.817 97.8123 97.8075 97.8029 97.7992 97.824 97.8747 97.8201

103.4156 103.2231 103.4432 103.4152 103.2354 103.6252 103.6784 103.6995 103.7234 103.7741 103.8264

114.6162 114.5926 114.7254 112.7398 114.7387 114.7291 114.7642 114.8231 114.8671 114.9231 114.9497

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Table 5 Total power extracted from c880 benchmark circuit using different wire loads Wire loads Original circuit Trojan 1 Trojan 2 Trojan 3

tc8000000 tc4000000 tc2000000 tc1000000 tc540000 tc280000 tc140000 tc70000 tc35000 tc16000 tc8000

Total power (µW)

Total power (µW)

Total power (µW)

Total power (µW)

95.3496 95.3367 95.3524 95.3467 95.3425 95.3461 95.1287 95.4165 95.5162 95.6261 95.6334

102.4388 102.4302 102.6738 102.6621 102.6574 102.6526 102.648 102.6443 102.6691 102.7198 102.6651

108.3476 108.1552 108.3772 108.3573 108.1684 108.5612 108.6158 108.631 108.3775 108.7109 108.7785

119.7403 119.918 119.9468 117.9763 119.9801 119.8743 120.0907 120.1105 120.0545 120.1472 120.0921

The variations that occur in total power for the same design using different wire loads present in the library are shown by selecting each wire load model separately. Thus, it is evident that power varies for different wire loads for the same design.

3.1 KNN Machine Learning Algorithm In this algorithm, the machine is trained with attributes like dynamic, total power, and labeled the values as Trojan and No Trojan for the respective power values. Now a new test data is provided to classify whether it is Trojan infected or Trojan free. It shows the K (K value is square root of trained samples or it can be arbitrarily chosen) closest values to trained samples from the test data by computing the Euclidean distance from the test data to all other trained data. Ranking is done to distances in ascending order and maximum type of respective K label ranks decides whether circuit contains Trojan in it or not. Rank indicates how close the testing is corresponding to trained samples. For instance, if the training data set is 100 samples then the K values become 10. So it computes Euclidean distance and shows 10 closest samples near to it and based on 10. The decision on presence or absence of Trojan is taken based on the number of instances the corresponding wireload is infected. If in 10 top ranks 9 are Trojan type power values and 1 No Trojan values then it is concludes as containing Trojan in it. To improve the confidence level, we can increase the K value and check for more close data. The first two columns in Table 6 show the attribute’s dynamic power and total power of circuit c800 with variations included. The attributes of the circuits are taken to train the machine, and labeling column represents the status of the circuit corresponding to the attributes.

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Table 6 Attributes of c880 circuit (total dynamic power and total power) taken as training set for machine and KNN machine learning analysis for sample test data (total dynamic power 90.7102, total power 95.4211) Total dynamic Total power Labeling for KNN analysis for sample test data power (µW) (µW) SVM

90.5456 91.1327 91.2484 90.5427 90.5385 90.5421 91.3247 90.6125 91.2122 90.0221 91.2194 97.5922 97.8291 97.8170 97.8123 97.8075 97.8029 97.7992 97.8240 97.8747 97.8201 103.2231 103.4432 103.4152 103.2354 103.6252 103.6784 103.6995 103.7234 103.7741 103.8264 114.5926 114.7254 112.7398 114.7387 114.7291 114.7642

95.3496 95.3367 95.3524 95.3467 95.3425 95.3461 95.1287 95.4165 95.5162 95.6261 95.6334 102.4388 102.4302 102.6738 102.6621 102.6574 102.6526 102.6480 102.6443 102.6691 102.7198 102.6651 108.3476 108.1552 108.3772 108.3573 108.1684 108.5612 108.6158 108.631 108.3775 108.7109 108.7785 119.7403 119.918 119.9468 117.9763

−1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Labeling for KNN

Rank

Euclidean distance

NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN NO TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN TROJAN

2 6 8 3 5 4 10 1 7 11 9 12 13 19 18 16 15 14 17 21 20 22 25 23 24 27 26 29 30 31 28 32 33 34 36 37 35

0.179459 0.430848 0.542567 0.18328 0.188836 0.184072 0.68052 0.097808 0.510929 0.717988 0.551685 9.829041 9.990306 10.15422 10.14258 10.13586 10.12921 10.12334 10.13809 10.19135 10.18929 14.4585 18.14452 17.98817 18.02058 18.27956 18.18428 18.47658 18.53223 18.57867 18.43646 27.33108 27.47999 32.81351 34.31424 34.32809 32.97472 (continued)

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Table 6 (continued) Total dynamic Total power power (µW) (µW)

114.8231 114.8671 114.9231 114.9497

119.9801 119.8743 120.0907 120.1105

Labeling for SVM

1 1 1 1

KNN analysis for sample test data Labeling for KNN TROJAN TROJAN TROJAN TROJAN

Rank 39 38 40 41

Euclidean distance 34.41768 34.37317 34.56666 34.59942

Table 6 shows the machine learning analysis, and the sample testing data is given to the machine, and the machine computes the Euclidean distance from the sample data to all the trained data which is shown in last column. Depending on the distance, the ranks are given in ascending order and the K closest value labels are looked and the machine decides whether it consists of Trojan or not depending on the labels of closest values. Then for the analysis shown the test data (power value) with total dynamic power 90.7102, total power 95.4211 is taken for which the machine should predict whether it is circuit with Trojan or not. To start with the KNN machine learning analysis, the K value is chosen. In our case for carrying out an analysis, a small set of training data of 41 samples, so K value will be 6 or 7 (K is square root of trained samples) we went for the worst value that is 7 and in that all values closest K values have status as no Trojan (that is ranks 1–7 has status as no Trojan). So the circuit does not have Trojan in it. For the taken sample, first 7 closest values are labeled as NO Trojan, so the test data given has no Trojan. Further using other machine learning techniques like SVM, the training data is labeled as ‘−1’ for circuit with Trojan and ‘1’ for Trojan-free circuit. In SVM, it selects the hyperplane which classifies the Trojan-free and Trojan-infected class, when we give test data it classifies and separates them using a plane. Neural networks are also trained, and the circuits are classified successfully using these networks. Labeling is also done in another way depending on the size of Trojan by looking its power difference from original power, so that the size of the Trojan can also be known during testing. For example, if there is change in power profile in the range of 5 µW or below, it can be labeled as consisting of small Trojan and with 10 µW variation in power from original circuit it can be labeled as consisting of medium-size Trojan. Using this KNN machine learning algorithm, we have achieved 84.34% detection accuracy.

4 Conclusion The proposed work shows the classification of circuits with and without Trojan using machine learning algorithms considering the effect of wire loads (interconnect) on circuit’s power. This method is capable of detecting Trojans independent of size,

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nature or type of Trojan like leaking of information and circuit malfunctioning. The method addressed the Trojans that can be inserted by exploiting the wire load and detecting them successfully. The proposed approach is much more accurate since artificial intelligence is employed for classifying the circuits eliminating the errors caused. The proposed model is efficient and accurate since it does not need human intervention. Since manual approach may cause some errors in classification, machine detects with much more accuracy, further using other machine learning techniques various attributes of the circuit like delay, current, and other fingerprints can also be trained and classified.

References 1. R.S. Chakraborty, F. Wolff, S. Paul, MERO: a statistical approach for hardware Trojan detection, in Proceedings of Cryptographic Hardware and Embedded Systems Workshop (2009), pp. 396–410 2. A. Thakur, P. Sivaraj, R. Periasamy, N. Mohankumar, Hardware Trojan detection-a survey, in 4th National Conference on Recent Trends in Communication Computation and Signal Processing (2013), pp. 99–102 3. X. Zhang, M. Tehranipoor, Case study: detecting hardware Trojans in third-party digital IP cores, in HOST (2011) 4. D. Karunakaran, N. Mohankumar, Malicious combinational hardware Trojan detection by gate level characterization in 90 nm technology, in Proceeding of International Conference on Computing, Communications and Networking Technology (2014), pp. 1–7. https://doi.org/10. 1109/icccnt.2014.6963036 5. P.K. Maneesh, M. Nirmala Devi, Power based self-referencing scheme for hardware Trojan detection and diagnosis. Indian J. Sci. Technol. 8(24) (2015) 6. A. Kulkarni, Y. Pino, T. Mohsenin, Svm-based real-time hardware Trojan detection for manycore platform, in 2016 17th International Symposium on Quality Electronic Design (ISQED) (2016) 7. T. Iwase, Y. Nozaki, M. Yoshikawa, T. Kumaki, Detection technique for hardware Trojans using machine learning in frequency domain, in 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) (2015), pp. 185–186 8. S. Kamala Nandhini et.al., Delay-based reference free hardware Trojan detection using virtual intelligence, in 4th International Conference on Information System Design and Intelligent Applications, (India–2017) (Vietnam, 2017)https://doi.org/10.1007/978-981-10-7512-4_50 9. V.R.R. Koneru, B.K. Teja, K.D.B. Reddy, M.V. GnanaSwaroop, B. Ramanidharan, N. Mohankumar, HAPMAD.: hardware-based authentication platform for malicious activity detection in digital circuits, Advances in Intelligent Systems and Computing, vol. 672 (Springer, Singapore, 2018). https://doi.org/10.1007/978-981-10-7512-4_60 10. R. Bharath et al., Malicious circuit detection for improved hardware security, in SSCC 2015. Communications in Computer and Information Science, vol. 536 (2015). https://doi.org/10. 1007/978-3-319-22915-7_42 11. G. Aishwarya, H. Revalla, S. Shruthi, V.P. Ananth, N. Mohankumar, Virtual instrumentationbased malicious circuit detection using weighted average voting, in Electromagnetics and Telecommunications, Proceedings of ICMEET 2017, vol. 471, p. 423 (2018). https://doi.org/ 10.1007/978-981-10-7329-8_43 12. N. Mohankumar, M. Jayakumar, M.N. Devi, MCRC-based hardware Trojan detection for improved hardware security, in Electromagnetics and Telecommunications. Lecture notes in electrical engineering, vol. 471 (Springer, Singapore, 2018). https://doi.org/10.1007/978-98110-7329-8_39

A Neural Network Approach for Content-Based Image Retrieval Using Moments of Image Transforms D. Kishore, S. Srinivas Kumar and Ch. Srinivasa Rao

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 CBIR Using the Transform Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Regional or Boundary Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Retrieval Based on Improving Color Features Over Existing Moments by Counting the Number of Objects in RGB Plane . . . . . . . . . . . . . . . . . . . . . . . . . 3 Algorithm for CBIR Based on Moments of Transforms and Color Objects and Color Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Algorithmic Steps for the Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Due to ever-increasing explosion of multimedia and storage devices available, accessing large image databases becomes inevitable. Furthermore, the availability of high-speed Internet has raised drastically the level of multimedia exchange by users across cyberspace every second. Hence, content-based image retrieval is gaining importance day by day. Therefore, this work proposes contentbased image retrieval based on moments in edge map of different transforms such as Walsh–Hadamard transform (WHT), discrete cosine (DCT), and discrete wavelet transform (DWT). The first- and second-order moments of edges of these transform coefficients are combined with moments of color and color objects to improve the average retrieval efficiency. It has been shown that DWT exhibits better average retrieval efficiency compared to FWHT and DCT on Corel data base. D. Kishore (B) Aditya College of Engineering and Technology, Surampalem, India e-mail: [email protected] S. Srinivas Kumar ECE, JNTUA, Ananthapur, India e-mail: [email protected] Ch. Srinivasa Rao ECE, JNTUK, Vizianagaram, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_59

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Keywords Color moments · Color objects · CBIR · DWT · DCT · FWHT

1 Introduction Content-based image retrieval (CBIR) [1] is widely used to arrange digital image archives by features or content of image [2]. The rapid digitization of multimedia gave thrust to new methods for archiving and accessing the data. The key problem in CBIR is to address the semantic gap between the low-level features and the higher abstraction of understanding the image. To narrow the semantic gap, a CBIR is proposed based on moments of transform coefficients and color moments. In images, high-frequency information is present in edges. Therefore, first- and second-order moments in transform domain are computed for high-frequency region. The images used in the database are color sensitive, and the images captured in the same class are different. Hence, color moments in database are combined with objects in color plane to improve retrieval efficiency.

2 Methods and Materials In the literature, there are several algorithms existing on CBIR. Xingyuan and Zongyu [3] proposed a CBIR method integrating the local textural features that are fused with HSV color quantization. The spatial correlation of color and texture is well addressed by structural element histogram. The method provides good retrieval efficiency. But the features directly depend on the statistical and textural features of image. But the bottleneck of this method is that it has not considered the shape features in retrieval. Gonde et al. [4] proposed another work based on modified curvelet-based transform (MCT). This transform is based on Gabor wavelets, and a trous wavelet transform is used for subband decoding. Later, the generated feature vectors are indexed by vocabulary tree. This work has shown improved performance in weighted average precision, average precision, average retrieval rate, and average rank. In the proposed work, an improvement in accuracy is obtained by considering firstand second-order moments of the high-frequency components of transforms such as DCT, WHT, and DWT. For shape information, they are fused with MajorAxisLength, MinorAxisLength, area, and perimeter. For color information, RGB color moments are fused with the color objects and later neural network is used to train and test the data.

2.1 CBIR Using the Transform Domain The idea of using transformation is to de-correlate the data so that most of the information is packed in few coefficients [5]. Therefore, in transform domain, there is a compact representation of image information. The kernels of DCT, WHT, and

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DWT are given in Eqs. 1, 2, and 3. M N 1  Vπ Uπ C(U, V ) = cos(2n + 1) . f (m, n) cos(2m + 1) αk m=0 n=0 M N

where αk is equal to

√1 N

for K = 0 and

√1 2N

(1)

when K is other than 0.

M n N  1  W (U, V ) = √ f (m, n) (−1)[bi (x)bn−1−i (u)+bi (y)bn−1−i (v)] N m=0 n=0 i=0

(2)

where f (m, n) is the original image of size M × N , M and N are the spatial resolutions of the image, and W (U, V ) is the Walsh–Hadamard transform coefficients of the image. And forward DWT is given by φs,k (t) =

1 φ 2s



t − k2s 2s

 (3)

where s is the scale parameter and k is the shift parameter, both which are integers.

2.2 Regional or Boundary Representation Image representation may be based on regional or boundary detection [6]. Regional and boundary representations are used for extracting internal and external features. In finding number of objects, boundary descriptors are used. To obtain the number of objects, the color plane is converted into binary and boundary is formed by eight connected components of pixels, and later the number of objects is computed. The total number of color features used in the proposed algorithm is twelve.

2.3 Retrieval Based on Improving Color Features Over Existing Moments by Counting the Number of Objects in RGB Plane In object identification and extraction from a scene, color is a used as powerful descriptor [6]. Generally, the color shows more robustness to image spatial resolution and orientation in the presence of noise [7]. The proposed technique uses first-, second-, and third-order moments in RGB color plane [8]. The color image is partitioned into three planes, and in each plane, the above-mentioned moments are computed. Further, the number of objects in each color plane is fused with the color moments to improve the retrieval efficiency in the proposed algorithm.

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3 Algorithm for CBIR Based on Moments of Transforms and Color Objects and Color Moments The algorithmic description of proposed algorithm is shown in Fig. 1.

3.1 Algorithmic Steps for the Proposed Algorithm The steps of the proposed CBIR algorithm are given as follows. 1. Decompose each image into four bands of transformed image. The transformation is either DCT, FWHT, or DWT. 2. Compute mean and standard deviation of edge map in transformed domain. 3. Compute color moments in R, G, and B planes. 4. Binarize the images in database. Compute the number of objects in each plane. 5. Form feature vector based on moments of transforms, color objects, and color moments of all images in the database as shown in Table 1. 6. For shape features, the area, perimeter, MajorAxisLength, MinorAxisLength of largest blob are computed. 7. Apply query image and calculate the feature vectors as given in steps 1, 2, 3, and 4.

Fig. 1 Algorithmic flow of proposed content-based image retrieval

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Table 1 Feature data set of proposed algorithm Feature Dimension Color moments Objects in color plane Transform moments (mean, SD) shape features (area, perimeter, MajorAxisLength, MinorAxisLength)

9 3 2 4

Fig. 2 Feedforward neural network with 18 inputs and 35 hidden layers

8. Apply standard normal density to obtain normalized feature vector. 9. Form feature vector by assigning class label as the last column. Rose, kangaroo, horses, buildings, and mountains are labeled with class 1–5. 10. Classify whole feature database into training set and testing set. 11. Train the feedforward neural network with the given inputs and output, and model is developed with 35 hidden neurons as shown in Fig. 2. 12. Test the given inputs and retrieve the class information predicted labels. 13. Plot the confusion matrix between true class and predicted class. 14. The diagonal elements of confusion matrix give the classification accuracy. 15. Note precision and recall from confusion matrix of three works. 16. Retrieve all images from the image database relevant to query image based on minimum weighted distance.

4 Results and Discussion Retrieval performance of the proposed CBIR algorithm is verified by conducting experiments on Corel database. Corel is a database of color images containing ten classes as shown in Fig. 3. This corresponds to 100 subjects/images per class/object resulting in a total of 1000 images. All images in the database are of size 256 × 324. All the images in the database are scaled to a size of 256 × 256.

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Fig. 3 Corel database

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For creating the feature database, each image is decomposed in the DCT, FWHT, and DWT mean and standard deviation for high-frequency band comprising two features. Color moments of first order, second order, and third order contribute nine features. Besides these, the number of objects in each color plane contributes three features. The fusion of these features contributes a feature vector of 18. Experiments are conducted using MATLAB 13 with Pentium-IV, 3.00 GHz computer using basic MATLAB. The motive of image retrieval is to extract images similar to the query image. The performance of image retrieval is based on two factors: precision and recall. Precision is the number of relevant images retrieved by the system divided by the total number of images retrieved (i.e., true positives plus false alarms). Recall is the number of relevant images retrieved by the system divided by the total number of images in the database (which should, therefore, have been retrieved). The purpose of our neural network-based model is to enhance results provided by the initial retrieval model. The data set is divided into two parts: training set and test data set. The training and testing sets are 50 and 50%. The confusion matrix of wavelet transform is determined. The sum of diagonal entries of confusion matrix by total elements in testing data set gives classification accuracy. Similarly, precision for class n is computed as sum of elements in the column of class ‘n’ by diagonal entry of class n and recall for class ‘n’ is computed as diagonal entry of class n by sum of elements in the row of n. The precision and recall of Subrahmanyam et al. [9] and the proposed CBIR schemes based on WHT, DCT, and DWT are shown in Figs. 5 and 4. The best 20 retrieved images of query horse with DWT-based CBIR scheme are shown in Fig. 6. The experimental results of DWT-based CBIR validate that the proposed algorithm is efficient in terms of average precision and recall.

Fig. 4 Retrieval precision results based on each method

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Fig. 5 Retrieval recall results based on each method Fig. 6 The best 20 retrieved images of class horse with DWT

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5 Conclusion The tremendous improvements in data storage and image acquisition systems have enabled the need for maintenance of large database. Therefore, we need to build efficient retrieval systems to efficiently manage such collection. To address this, content-based image retrieval systems are used which retrieve images similar to the query sample from large database. In this work, color moments and objects in color plane and transform moments of FWHT, DCT, and wavelet transform of highfrequency coefficients are used as features. This method emphasizes that selection of transform is decisive in feature selection. It has been shown that the retrieval average precision and recall of proposed method based on moments of wavelet transform are better than DCT, FWHT2D, and Subrahmanyam et al. [9].

References 1. F. Kuang, W. Xu, S. Zhang, A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl. Soft Comput. 18, 178–184 (2014) 2. C.A. Hussain, D.V. Rao, S.A. Masthani, Robust pre-processing technique based on saliency detection for content based image retrieval systems. Procedia Comput. Sci. 85, 571–580 (2016) 3. X. Wang, Z. Wang, A novel method for image retrieval based on structure elements descriptor. J. Visual Commun. Image Represent. 24, 63–74 (2013) 4. A.B. Gonde, R. Maheshwari, R. Balasubramanian, Modified curvelet transform with vocabulary tree for content based image retrieval. Digital Signal Process. 23, 142–150 (2013) 5. L. Bolc, Z. Kulpa, Digital image processing systems: proceedings 6. R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital image processing using MATLAB 7. C.S. Rao, Content Based Image Retrieval Fundementals and Al (LAP LAMBART Publishing, 2012) 8. M. Huang, H. Shu, Y. Ma, Q. Gong, Content-based image retrieval technology using multifeature fusion. Optik-Int. J. Light Electron Opt. 126, 2144–2148 (2015) 9. M. Subrahmanyam, Q.J. Wu, R. Maheshwari, R. Balasubramanian, Modified color motif cooccurrence matrix for image indexing and retrieval. Comput. Electr. Eng. 39, 762–774 (2013)

Probe-Fed Wideband Implantable Microstrip Patch Antenna for Biomedical and Telemetry Applications Komal Jaiswal, Ankit Kumar Patel, Shekhar Yadav, Sweta Singh, Ram Suchit Yadav and Rajeev Singh

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Antenna Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract A probe-fed comb-shaped wideband implantable microstrip patch antenna operates at MICS band (403–405 MHz), 433.1–434.8, 868–868.6, 902.8–928.0 MHz, and 2.4–2.48 GHz (ISM band). An antenna with dimension (12 × 7.5 × 0.25) mm is designed in meander shape and comb shape for biomedical application and is aimed to be utilized in human tissue such as skin and muscles as boundaries. Antenna characteristics within the tissue like VSWR, return loss, radiation efficiency, and radiation pattern with their comparative simulated results are presented. Keywords Biomedical applications · Implantable antenna · MISC · ISM Biotelemetry

K. Jaiswal · A. K. Patel · S. Yadav · S. Singh · R. S. Yadav · R. Singh (B) Department of Electronics and Communication, University of Allahabad, Allahabad, Uttar Pradesh, India e-mail: [email protected] K. Jaiswal e-mail: [email protected] A. K. Patel e-mail: [email protected] S. Yadav e-mail: [email protected] S. Singh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_60

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1 Introduction Recent researches on biomedical applications of microstrip antenna are focused to facilitate better diagnosis and treatment of patients suffering from chronic diseases. The antenna used for biomedical applications is basically wearable antenna and is used in body implantable devices [1–4]. Implantable antennas transmit data to external base station through bidirectional telemetry operations; however, it is an arduous task to design human implantable antenna, and in order to implant antennas within human body, it is important to understand the properties of human tissues. It is desirable that the antenna parameters should not be affected by the physiology and anatomy of the human body or its other parameters and the antenna shall not harm the human body; therefore, such antennas are designed using biocompatible materials. Materials like Duroid, ceramic, alumina, and silicon are reported [5] to have been used as implantable materials. High tissue conductivity, low power requirements, impedance matching, antenna miniaturization are the major factors in designing of an antenna. There are several techniques to reduce size of the antenna which includes loading wide range of slots in the radiating patch or ground plane, loading stubs, loading shorting pins, employing slits, and embedding edge tails [6, 7]. The different patch shapes like, square, circular, loop, spiral are used in biomedical implantable antennas [8–11]. Implantable device often communicate in the medical implant communications service (MICS) band (402–405 MHz) or in industrial, scientific, and medical (ISM) bands (2.4–2.48 GHz), but in some countries, bands such as 433.1–434.8 MHz, 868–868.6 MHz and 902.8–928.0 MHz are also used for biotelemetry [2]. In this paper, wideband implantable meander- and comb-shaped miniaturized antenna is designed with a dimension of (12 × 7.5 × 0.25) mm. It radiates at wide frequency band ranging from 0.188 GHz to 4.96 GHz with percentage bandwidth of 185.39% which is useful in MICS band, ISM band, and 433.1–434.8 MHz, 868–868.6 MHz, and 902.8–928.0 MHz frequency band applications. The proposed antenna is simulated with different human tissues like muscle and skin as a boundary, and its performance has been analyzed. The antenna exhibits broad impedance bandwidth, which enables implantable antenna to work in different applications of biomedical and telemetry.

2 Antenna Design The proposed antenna comprises of a meander-shaped radiating patch printed on Rogers 6010LM as a dielectric substrate (dielectric constant  10.2) with dimensions of (12 mm × 7.5 mm × 0.25 mm) as shown in Fig. 1a. To prevent contact of patch and human tissue, a layer of super substrate material Rogers 6010LM is placed on the patch as depicted in Fig. 1b. Supersubstrate helps in preserving the biocompatibility and robustness. The patch dimension is taken as (10.6 mm × 6.5 mm), and the three

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Fig. 1 a Geometrical top view of the proposed antenna. b Side view Table 1 Parameters of substrate and boundary Material Dielectric permittivity Air Muscle Skin Rogers 6010LM

1 57 38 10.2

Conductivity 1 0.8 0.6 –

rectangular notches of variable length (L 1  5.35 mm, L 2  5 mm, and L 3  5.35 mm) with equal width of 0.5 mm are loaded on the patch to design meander shape and comb shape. The meander- and comb-shaped patch helps in minimizing the size of radiating patch by increasing the flow of current and reducing the antenna resonant frequency. The proposed antenna is fed at (−5, −1) position by probe feeding as it is compatible and advantageous for lower frequencies. The performance of the proposed antenna has been observed for muscles and skin as a region around the radiating patch in place of air and compares its variation within the body (Table 1).

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3 Results and Discussion The antenna design, simulation, and optimization are done with the help of HFSS 13.1 version. Figure 2 illustrates simulated result of return loss vs. frequency plots of the proposed antenna in air, muscle, and skin as a boundary. The proposed antenna in air boundary resonates from 0.25 to 2.9 GHz with impedance bandwidth of 168.25%. When antenna is simulated within human tissue like skin as a boundary, it is observed that antenna resonates from 0.188 GHz to 4.96 GHz with percentage bandwidth of 185.39%. Further, the antenna is placed within muscle, and it is observed that it resonates from 0.199 to 4.8 GHz frequency range with impedance bandwidth of 153.3%. The resonating bands for air, skin, and muscle boundaries cover five important frequency bands, viz. MICS (403–405 MHz), (433.1–434.8 MHz), (868–868.6 MHz), (902.8–928.0 MHz), and ISM Band (2.4–2.48 GHz). Comparison of return loss and VSWR of the proposed antenna for air, skin, and muscle boundary is shown in Tables 2, 3 and 4, respectively. It is depicted from Fig. 3 that VSWR value at MICS (403–405 MHz), (433.1–434.8 MHz), (868–868.6 MHz), (902.8–928.0 MHz), and ISM Band (2.4–2.48 GHz) is below 1.5 for muscle and skin boundary and below than 2 for

Fig. 2 Return loss |S11 | in dB versus frequency for proposed antenna for air, muscle, and skin boundaries Table 2 Antenna characteristics in air boundary (|S11 | and VSWR at various frequencies) Return loss VSWR MICS 403–405 MHz 433.1–434.8 MHz 868–868.6 MHz 902.8–928.0 MHz ISM 2.4–2.48 GHz

−14.8 −15.2 −17.7 −17 −13.27

1.52 1.41 1.29 1.29 1.2

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Table 3 Antenna characteristics in muscle boundary (|S11 | and VSWR at various frequencies) Frequencies Return loss VSWR MICS 403–405 MHz 433.1–434.8 MHz 868–868.6 MHz 902.8–928.0 MHz ISM 2.4–2.48 GHz

−14.8 −15.2 −17.7 −17.8 −18

1.44 1.41 1.29 1.29 1.2

Table 4 Antenna characteristics taking skin as boundary (|S11 | and VSWR at various frequencies) Frequencies Return loss VSWR MICS 403–405 MHz 433.1–434.8 MHz 868–868.6 MHz 902.8–928.0 MHz ISM 2.4–2.48 GHz

−15.8 −16.3 −21.9 −22.2 −21.8

1.38 1.35 1.17 1.16 1.17

Fig. 3 Variation of voltage standing wave ration (VSWR) versus frequency

air boundary. Figure 4 shows the radiation efficiency of the proposed antenna in air, muscle, and skin boundaries. It is observed that when the antenna is implanted into muscle and skin tissues, its radiation efficiency is improved very high (above to 80%) as compared to air boundary. The radiation pattern according to total radiated power of the proposed antenna for far-field region is plotted in Fig. 5 and for near-field region is depicted in Fig. 6, and it is observed that the radiation pattern of antenna in air boundary is omnidirectional for both far-field and near-field region. For the far-field region, antenna radiates maximum power at   120° in skin and muscle boundary as depicted in Fig. 5. In the near-field region, the proposed antenna radiates omnidirectional in air, skin, and

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Fig. 4 Radiation efficiency versus frequency plot

Fig. 5 Far-field radiation pattern of the proposed antenna at 2.4 GHz, a for φ  0°, b for φ  90°

muscle boundaries for both E-plane (φ  0°) and H-plane (φ  90°) as shown in Fig. 6 which are suitable for biomedical applications.

4 Conclusions A miniaturized meander- and comb-shaped implantable antenna with wideband is proposed for biomedical applications. Simulation is performed within boundaries of air and human tissue such as skin and muscle. The proposed antenna achieves five useful frequency bands, viz. MICS (403–405 MHz), 433.1–434.8 MHz,

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Fig. 6 Near-field radiation pattern of the proposed antenna at 2.4 GHz, a for φ  0°, b for φ  90°

868–868.6 MHz, 902.8–928.0 MHz, and ISM Band (2.4–2.48 GHz). When the antenna is simulated taking tissue boundries like muscle and skin, the radiation efficiency of 80% is observed which is reasonably high and good for such applications.

5 Declaration The authors hereby declare that the experiment reported in this work is a simulation work, and we declare that no human body, tissue, skin or muscles have been directly and indirectly used in the present work.

References 1. A. Kiourti, S.N. Konstantina, Miniature scalp-implantable antennas for telemetry in the MICS and ISM bands: design, safety considerations and link budget analysis. IEEE Trans. Antennas Propag. 60, 3568–3575 (2012) 2. J. Kim, Y. Samii, Implanted antennas inside a human body: simulations, designs, and characterizations. IEEE Trans. Microw. Theor. Tech. 52, 1934–1943 (2004) 3. P. Soontornpipit, A dual band compact microstrip patch antenna for 403.5 MHz and 2.45 GHz on body communication. Procedia Comput. Sci. 86, 232–235 (2016) 4. A. Kiourti, S.N. Konstantina, A review of implantable patch antennas for biomedical telemetry. IEEE Antennas Propag. Mag. 54, 210–228 (2012) 5. N. Mahalaxmi, A. Thenmozhi, Design of hexagon shape bow tie patch antenna for implantable bio-medical applications. Alexandria Eng. J. 56, 235–239 (2017) 6. S. Bhattacharjee, S. Maity, Performance enhancement of implantable medical antenna using different feed technique. Eng. Sci. Technol. Int. J. 19, 642–650 (2016)

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7. Z.J. Yang, S. Xiao, L. Zhu, B.Z. Wang, A circularly polarized implantable antenna for 2.4-GHz ISM band biomedical applications. IEEE Antennas Wirel. Propag. Lett. 16, 2554–2557 (2017) 8. P. Merih, Compact bioimplantable MICS and ISM band antenna design for wireless biotelemetry applications. Radioengineering 26, 917–923 (2017) 9. I. Gani, H. Yoo, Multi-band antenna system for skin implant. IEEE Microwave Wirel. Compon. Lett. 26, 294–296 (2016) 10. J.A. Costa, A broadband implantable and a dual-band on-body repeater antenna design and transmission performance. IEEE Trans. Antennas Propag. 62, 2899–2908 (2014) 11. P. Soontornpipit, C.M. Furse, Y.C. Chung, Design of implantable microstrip antennas for communication with medical implants. IEEE Trans. Microwave Theor. Tech. 52, 1944–1951 (2004)

Mapping Urban Ecosystem Services Using Synthetic-Aperture Radar (SAR) Images from Satellite Data for Rural Microgrids in India Prem Raheja, Surmeet Kaur Jhajj, Purva Jhaveri and Jignesh Sisodia

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Digital Surface Model (DSM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Edge Correction and Geometric Rectification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Decision Boundary Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Evaluation and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Microgrids, which make use of renewable resources, are a low-cost alternative to conventional methods of electricity generation for villages in India. Correctly identifying the suitable microgrid setup for an area according to the availability of local resources is extremely important to be able to get desired results and maximum profits. In order to make this process easier, we propose an algorithm which uses SAR images of the targeted area, captured over a period of time, to extract data about environmental factors of that area (solar heat, groundwater flow, erosion). This data is used to calculate the energy efficiency of each setup type and compare the economic viability of all, hence suggesting the best option. Thus, the system aims P. Raheja · S. K. Jhajj · P. Jhaveri · J. Sisodia (B) Department of Information Technology, Sardar Patel Institute of Technology Mumbai, Mumbai, India e-mail: [email protected] P. Raheja e-mail: [email protected] S. K. Jhajj e-mail: [email protected] P. Jhaveri e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_61

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to ameliorate the decision-making process of microgrid setups in underdeveloped areas. Keywords SAR images · Power generation · Microgrids · Image processing

1 Introduction With the decrease in conventional energy resources, there has been raising awareness of the advantages of renewable resources and various countries are turning to new methods of electricity generation. One such proposed method is the setting up of low-cost microgrids in rural India, which has the potential to provide better quality of living and economic opportunities for people residing in these areas. These microgrids can be sustained by locally available resources and are of different types according to the primary resource intended to be used [1]. However, there are multiple “operational challenges in developing a functional commercial model” [2]. One of them is the inability to ascertain the viability of the setup in terms of the type of microgrid most suitable for a selected area according to the availability of renewable resources in that area. Though an important step, one on which the financial returns of the project depend on, it is a taxing process which can be shortened with use of information retrieved from synthetic-aperture radar (SAR) images [3]. SAR images provide high-resolution and weather-independent images which capture “dynamic processes on the Earth surface in a reliable, continuous and global way” [4] and, hence, can be used to suggest the most feasible type of microgrid for a particular area. In this paper, we propose a system to calculate the total efficiency of a potential microgrid setup in rural villages of India, based on the information retrieved from SAR images of that area. We present the design of the system in Sect. 2 and the methodology of data extraction from the images and its analysis in Sect. 3. Experimental results of the demonstration and simulation performed on a village in India are explained in Sect. 4. Sections 5 and 6 conclude the findings of the same and throw light upon the future scope of this study.

2 Design As seen in Fig. 1, the system allows the user to select the suggested region for the proposed microgrid setup and provides the wasteland, groundwater, and solar SAR images for the respective location. The user interface interacts with the control system which segregates and passes the images to the image processing unit. The image processing unit is the heart of the system; here is where the image processing takes place to extract the required attributes. The detailed working and methodology of the same are explained in the next section. Once these attributes have been obtained,

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Fig. 1 System design

they are further processed by a statistical unit to calculate the power generated at a particular location by a certain type of microgrid, hence allowing the user to carry out an analysis and comparison of the predicted results.

3 Methodology 3.1 Image Acquisition For this research, SAR images for 20 rural areas across India are obtained from “Bhuvan—the Indian Geo-Platform of Indian Space Research Organisation (ISRO)” [5]. Four types of images are considered for each area—wasteland, solar heat, soil erosion, and groundwater. The wasteland images as displayed in Fig. 2a highlight the unoccupied regions in that area and are hence used to identify potential locations for the setup. The groundwater and solar images each undergo image processing to extract the flow of groundwater (depth in meters and yield in LPM) and the solar heat temperature (in °C) in the area, respectively. Erosion images give the locations affected by erosion, the type of erosion (sheet, rill and gully erosions) as well as ravines and dunes (stabilized, partially stabilized, and unstabilized dunes) in the area. Multiple images of each type may be collected over a large period of time for a more accurate evaluation.

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Fig. 2 a Original wasteland SAR image. b Scale of wasteland image. c Wasteland to grayscale image. d DSM model of wasteland image (after mapping on erosion images). e Edge detection and correction. f One of the templates after contour separation. g Mapping resulting contour templates onto groundwater images. h Mapping resulting contour templates onto solar images

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3.2 Digital Surface Model (DSM) The wasteland SAR image is color-coded to represent barren stretches of land which may be available for the setup. This image is represented as the digital surface model (DSM), which is generally used to “represent the mean sea level (MSL) elevations of the reflective surfaces of trees, buildings, and other features elevated above the bare Earth” [6]. Hence, we are able to identify and highlight the locations where setup is possible and eliminate the rest as in Fig. 2c. Locations affected by detrimental environmental factors such as soil erosion are also eliminated using erosion images. The final locations are represented as contours in the image as displayed in Fig. 2d. Highlighting these contour structures will allow us to focus on these land sectors only and eventually determine the most economic location for the microgrid setup.

3.3 Edge Correction and Geometric Rectification The resulting wasteland image undergoes edge detection and correction as shown in Fig. 2e. Canny’s Algorithm is used here, due to its efficiency in giving sharper results with distinct edges and borders of the required areas. The various stages of the algorithm are [7]: 1. Noise Reduction: Use of 5 × 5 Gaussian filter to remove the noise in the images and make them more suitable for further processing. 2. Finding Intensity Gradient of the Image: Sobel operator is used to find the first derivative in horizontal direction (Gx ) and vertical direction (Gy ), from where the edge gradient and direction for each pixel are obtained.  Edge Gradient (G)  G 2x + G 2y (1) Angle(θ )  tan−1



Gx Gy

 (2)

3. Non-maximum Suppression: To remove pixels which are not forming the edge, a full scan of the image is done. If the pixel is the local maximum in its neighborhood, it is considered as an edge pixel, else it is given the value zero. The result image is a binary image with “thin edges” [7]. 4. Hysteresis Thresholding: Based on two threshold values (minimum and maximum) of intensity gradient and connectivity, we eliminate lines which are not edges. Lines with intensity gradient more than the maximum value are sure to be edges, and those below the minimum value are sure to be non-edges and are hence discarded. Lines lying between the two thresholds are checked for connectivity to the edge pixels; if they are connected to “sure-edge” pixels, they are considered to be edge pixels [7].

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Further, closing operations were performed on the radar imagery, which distinguishes the boundaries of the contours (representing the aforementioned locations extracted from the wasteland and erosion images in DSM). The contours are then separated into various templates, as seen in Fig. 2f, for further mapping to the solar and groundwater images in order to highlight the corresponding locations in the respective images.

3.4 Decision Boundary Feature Extraction Using content-based image retrieval (CBIR) [8] with color and texture fused features, parameters such as water flow from groundwater SAR images are retrieved. Mapping is as shown in Fig. 2g. The steps followed for CBIR are as follows: 1. After specifying the contours on each image, the area of each contour is calculated using Green’s formula and scaled according to requirements. 2. A color image detection algorithm is used to detect the different colors in the contours. 3. The corresponding values for the identified colors are extracted from the MongoDB [9] database, which is used to store the value–color mappings retrieved from the Bhuvan’s portal (as seen in Fig. 2b). These steps are repeated for solar images, as seen in Fig. 2h, to extract the solar heat received at the region (in °C). Results obtained from multiple images of each type for every location are averaged to find mean value at each location throughout the year.

3.5 Evaluation and Analysis The power generated by solar microgrid, as well as the groundwater microgrid, in each region is calculated for further analysis. Factors affecting the amount of power generated include panel efficiency (for solar microgrids with 12–20% efficiency [10]), turbine efficiency (for groundwater) (80–85% efficiency [11]) are considered. The formulae used are as follows: • Groundwater: Power = Head × Flow × Gravity

(3)

where power is measured in Watts, head in meters, flow in liters per second, and acceleration due to gravity in meters per second [12].

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• Solar: E  A × r × H × PR

(4)

where E is the energy measured in kWh, A is the total solar panel Area in m2 , the solar panel yield or efficiency (r) is given as a percent, H is the annual average solar radiation on tilted panels, and PR denotes performance ratio, coefficient for losses (range between 0.5 and 0.9, default value  0.75) [13]. We also consider the irradiance:  Irrhourlyi Irraveragei  (5) n where Irraveragei is average hourly irradiance for the ith hour of the day, Irrhourlyi is the hourly irradiance for the ith hour of the day and n denotes number of days in given month [14]. Hence, the power generated by both types of microgrids (solar and groundwater) can be predicted for a particular location. A detailed analysis of these outputs will be beneficial in determining the most feasible location for the microgrid plant as well as the type of microgrid most beneficial to the project. Python language is used for the processing of images and also the generation of bar graphs which provide a visual representation of the outputs. The power generated is represented by the Y -axis, and the prospective locations are plotted on the X-axis. Two parameters are considered for each of the powers generated by the solar plant and the hydro-plant. This graph will give a clear comparison of the predicted power outputs. The final result is inferred from this graph, and the most optimum region and type of microgrid are obtained. The proposed algorithm: i. Wasteland image of the area along with the solar, groundwater, soil erosion images are retrieved. ii. Using DSM, color normalization is performed on the wasteland image. iii. Mapping on erosion images is carried out to eliminate affected areas. iv. Edge enhancement and color space conversion for image specification using Canny’s and closing operations are performed. v. The wasteland image is then mapped over groundwater and solar images to identify the locations available for microgrid setup in the selected area. vi. Features of the area are extracted using content-based image retrieval technique using color models. vii. Calculation of power generated. viii. Bar graph generation for each location. ix. The results obtained in the above step are compared to determine the most beneficial setup.

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4 Experimental Results Given below are the results of a test case carried out for the village Erraballe, Telangana, in India. Wasteland, erosion, groundwater, and 60 solar images of the area were collected from Bhuvan’s portal. A large number of solar images were used for an accurate evaluation—solar heat is subject to large changes on a daily basis. All images cover a surface area of 193.916 km2 with a scale of 1 cm  1 km. The system was run over these images, all saved in the same directory. Using four 1.80 GHz processors, the system displayed energy generated by each type in each location in 12.09 min. Along with this, the best location, type of microgrid, and predicted energy generated are also displayed, as shown in Fig. 3. Intermediate images formed during the processing were saved for the purpose of this study and, as shown in Fig. 2, included the DSM model, edge-corrected image, contour templates, and the corresponding mappings over solar and groundwater images. Given in Table 1 are the outputs generated for each location in the selected area. The area selected for this simulation is a remote village with ample scrub wasteland locations where setup will be possible. Hence, we have obtained a high number

Fig. 3 Output as displayed on command prompt Table 1 Outputs for ten locations in village Erraballe, India Location Power by solar microgrid (in kW) Power by groundwater (in kW) Location 1 102.1190955 Location 2 8.10225737 Location 3 113.33292016 Location 4 97.107671 Location 5 3189.81690 Location 6 581.404678 Location 7 310.649945573 Location 8 588.0928747 Location 9 381.2978081967 Location 10 217.45541819

173.42136 113.75088 154.99288 98.53312 3025.17256 661.987119999 413.6396 595.17311999 389.68104 236.9472

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Fig. 4 Power versus location graph as plotted according to obtained outputs

of contours (ten possible locations) for an area of 193.916 km2 , after scaling and consideration of only the locations having surface area greater than 1 km2 . For the same surface area value, a wasteland image of a populated area would yield lesser number of locations. Hence, the surface area of the wasteland locations is an important factor handled by the system. It can be observed from the results that Location 5 gives the highest amount of both energies for both types of microgrids. This is due to Location 5 having the highest surface area of usable land as well as highest resource availability, which automatically results in greater energy generation. The results are plotted on a bar graph as shown in Fig. 4, for further analysis.

5 Conclusion This paper aims to improve the process of microgrid setups by providing an alternative to the current methods of data collection. Use of SAR images for the same will reduce the time and cost requirements for the initial stages of the setup. Information retrieved about the solar heat coverage and groundwater flow is used to predict the power generated by solar and hydro-microgrids at separate locations in the targeted area. Along with this, possible environmental repercussions are considered during calculation of the power generated to ensure least harm to the ecosystem. This is done with the help of erosion and geology SAR images which provide us with information about the locations under erosion and also the land structures. Finally, an analysis of the derived outputs will provide stakeholders of the microgrid project

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a convenient insight into the benefits and limitations of the proposed setup, helping them make informed decisions for the same. The system will be a viable solution to major economic and operational challenges faced in the process of microgrid setups and will result in the near-ideal models of microgrids, ensuring maximum revenue generation with minimum costs.

6 Future Scope The proposed system gives effective results for comparison between solar and groundwater microgrids. There is further scope for analysis and comparison of wind and biogas microgrids. Along with this, hybrid microgrid systems and their analysis may be explored. Besides the erosion factor, others such as geology of the area, mine locations, salt effects, croplands, vegetation types, drought, and flood vulnerability and more may be taken into account. It should also be noted that the system is implemented using SAR images obtained over a short period of time due to the restriction of time frame, whereas images over a larger time span will allow the system to predict power generation of the plant according to the changing attributes of natural resources.

References 1. The Rockefeller Foundation, Smart Power for Environmentally-sound Economic Development (2011) 2. Energetica India, Solar Micro-grids in India; a case study, pp. 4–6 (2014), http://www. energeticaindia.net 3. J. Haas, Y. Ban, Mapping and monitoring urban ecosystem services using multitemporal highresolution satellite data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(2), 669–680 (2017) 4. A. Moreira, P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, K.P. Papathanassiou, IEEE Geosci. Remote Sens. Mag. 6–43 (2013) 5. Bhuvan—Indian Geo-platform from ISRO, http://bhuvan.nrsc.gov.in 6. Intergovernmental Committee on surveying and Mapping, ICSM Guidelines for Digital Elevation Data, pp 19 (2008) http://www.icsm.gov.au 7. Z. Xu, B. Xu, G. Wu, Canny edge detection based on OpenCV, in 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (IEEE Press, China, 2017), pp. 53–56 8. J. An, S.H. Lee, N.I. Cho, Content-based Image retrieval using color features of salient regions, in International Conference on Image Processing (ICIP) (IEEE Press, France, 2014) 9. MongoDB website, https://www.mongodb.com 10. Qualitative Reasoning Group, Northwestern University, http://www.qrg.northwestern.edu/ projects/vss/docs/power/2-how-efficient-are-solar-panels.html 11. The Electropaedia Battery Knowledge Base, http://www.mpoweruk.com/hydro_power.htm 12. Reuk- The Renewable Energy Website, http://www.reuk.co.uk/wordpress/hydro/calculationof-hydro-power/

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13. Photovoltaic software for calculating Solar Energy, http://photovoltaic-software.com/PV-solarenergy-calculation.php 14. J. Hurtt, D. Jhirad, J. Lewis, Solar resource model for rural microgrids in India, in PES General Meeting | Conference and Exposition (IEEE Press, USA, 2014), p. 2

Analysis of Denoising Filters for Source Identification Using PRNU Features Nadia Siddiqui, Syeda Shira Moin and Saiful Islam

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Denoising Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Analysis of Wiener Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Analysis of Total Variation-Based Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Analysis of Gaussian-Based Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In Digital Image Forensics, one of the important techniques is Source Camera Identification (SCI) that attempts to identify the source camera of captured images. The sensor patterns of captured images are used for identification. The most regular pattern noise is photo response non-uniformity (PRNU) that can be generated by sensor defects during manufacturing process. These noises are distinguishable due to different sensor vendors of devices. In this work, identification is based on mobile camera that is from which mobile model a given image is captured. Here the analysis of three different denoising filters (Wiener, Total Variation and Gaussian) are done, to get the best result in our dataset. For classification, support vector machine (SVM) classifier is used and validation is done using 10-fold cross-validation technique. N. Siddiqui (B) · S. S. Moin · S. Islam Department of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, Uttar Pradesh, India e-mail: [email protected] S. S. Moin e-mail: [email protected] S. Islam e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_62

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Keywords Photo Response non-Uniformity · Wiener · Total variation · Gaussian

1 Introduction In the digital era, one of the principal means of expression is digital documents. Unfortunately, the authenticity of the digital documents has been questioned due to the use of inexpensive and easily available editing software tools that manipulate the images. Because of this manipulation and image forgery, the digital image forensics (DIF) comes with most useful tools in order to unhidden the forged images [1]. One of the most popular and important forensic tool is source identification, in this DIF deals with lot of questions regarding the authenticity of image as: • Is this image captured by device X or Y? Or • Was device model X belongs to same vendor as Y model? Or • Is this image is original or manipulated one? And so on. These are all questions that are faced routinely by investigator and law enforcement, which are answered by forensics by knowing the intrinsic characteristics of device. The forensics verified the authenticity of the image by knowing its source and can be done by estimating the distinguishable characteristics from the images [2, 3]. In this paper, it focuses on one of the questions above, that is given an image can we determine the source camera that captured this image. For such a work, the image captured by a specific device exhibits the source information that is recorded during acquisition phase of camera [4]. For the estimation of photo response non-uniformity (PRNU) noise, denoising filters are always used to get residuals. Experiments are conducted using three filters for analysis work to come up with best decision regarding PRNU estimation. The rest of the paper is organized as Sect. 2, briefly presents the literary work that reviewed some of the prior-related works in support of source identification. The technique is presented in Sect. 3. Next section begins with the dataset that is used in the experiment, results of experiment followed by the discussion regarding the analysis of various techniques. Finally, Sect. 6 summarizes the whole work.

2 Literature Survey Source camera identification methods commonly use the defects in a lens [5, 6], noises from images due to sensors [7, 8], chromatic aberration, CFA interpolation/demosaicing artifacts and image statistical features [9, 10]. However, one of the suitable approaches for source identification is photo response non-uniformity (PRNU) noise [11], as this is one of the distinguishable characteristics of each camera sensors [12, 13]. The correlation-based identification in [7] that uses the sensor

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pattern noise. This noisy image of particular camera considered to be a reference pattern that can be estimated by taking mean of multiple residual images captured from that camera. Finally, for source identification the correlation between estimated PRNU and reference pattern of different cameras are computed, and the value with highest correlation is considered to be the source of the give image [14]. Total variation-based denoising is another field for noise removal without blurring an edges [12]. This method of denoising for PRNU estimation is considered to be speedy technique in a sense of noise extraction without losing accuracy. Another approach that uses the noise is fixed pattern noise from charge-coupled device (CCD) also called dark currents [8]. A feature is extracted from the noisy images considered to be a suitable approach for source identification, where each image is represented as numerical feature vector [15]. These features are extracted from wavelet domain using DWT decomposition, while some are extracted from spatial domain [16]. A technique that uses both frequency domain and spatial domain features of residuals in order to get better identifier accuracy. Most of the correlation-based techniques are having limitations of working with same image size and highly affected by random noise component of estimated PRNU. This work focuses on the analysis of denoising filters to have an effective tool for source camera identification (SCI). The method builds a statistical model consisted of PRNU noise extracted by using different denoising filters, and results are analyzed to get better discrimination among different mobile cameras.

3 Methodology The camera sensor exhibits the fingerprints on captured images. These fingerprints can be estimated as PRNU (photo response non-uniformity) by using denoising filters. Figure 1 shows the block diagram for PRNU estimation. The PRNU noise can be estimated by extracting the residual from images as shown in Eq. (1), the denoised image I(denoised) subtracted from original image I(original), that gives residual image I(residual). I (residual)  I (original) − I (denoised)

Denoising Filter

Residual Computation

Fig. 1 Block diagram for PRNU estimation using a denoising filter

(1)

PRNU Estimation

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Three level wavelet decomposition (db8)

HL (horizontal) LH (vertical) HH (diagonal)

Mean variance, skweness, &kurtosis

Fig. 2 Block diagram of feature extraction in frequency domain

3.1 Denoising Filters Wiener Filter. The Wiener filter is the most favorable denoising filter in terms of minimizing mean square error in noise smoothing and inverse filtering process. It gives logical step-by-step esteem of original image. Total Variation-based denoising. It is a well-known method for digital restoration. It implicitly preserves the edges that allow the qualitative improvement at corners for anisotropic model. Reduction of total variation of signals subject being a close to match the original signal while preserving important details such as edges. Gaussian-based denoising. It is well-known method for blurring an image by Gaussian function. This denoising filter uses a Gaussian function (which also expresses the normal distribution in statistics) for computing the transformation to apply to pixel in the image.

3.2 Feature Extraction For estimation of PRNU noise, several denoising filters can be used and each gives different results for source identification. Three denoising filters: Wiener, Gaussian and Total variation are used for estimating PRNU. Figure 2 shows block diagram for feature extraction in frequency domain. In spatial domain, mean and variance are computed from noisy and original image in each channel separately.

4 Experimental Setup The experiment is conducted on implemented algorithm in MATLAB running on Intel Core i3 processor with 4 GB RAM. For classification, SVM classifier is used and results are verified using 10-fold cross-validation.

4.1 Dataset The following experiments are conducted by using the images taken by 6 different mobile cameras. Details of dataset listed in Table 1 that shows the mobile camera

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Table 1 List of camera models with sensor type used in experiment Camera Notation Sensor Lens size Resolution Image model format

Number of images

ISOCELL Sony ExmorRS IMX315 ISOCELL Samsung S5K2L1

(1/2.9”)

4032 × 3024

JPEG

400

(~ 1/2.5”)

4032 × 3024

JPEG

400

BSI Samsung S5K3L2 ISOCELL Samsung S5K2P8

(1/3.06”)

4208 × 3120 2560 × 1440

JPEG

400

JPEG

400

MG5

ISOCELL Sony IMX260/IMX362

(~ 1/2.55”) 4032 × 3024

JPEG

400

Lumia

BSI Sony IMX091PQ

(1/1.5”)

3072 × 1728

JPEG

400

iPhone 6s

iPhone

Samsung S7

S7

Samsung J7 Lenovo K4

J7

Moto G5 plus Nokia Lumia

Lenovo

(1/2.6”)

model from which images are taken and its corresponding resolution, sensor type and size and number of images taken from each mobile model.

5 Experimental Results 5.1 Analysis of Wiener Filter In this wiener filter is used for PRNU estimation, since these features are extracted and then classification results are evaluated using the group of two camera models as: 1. iPhone 6s and Samsung Galaxy S7 (group 1): Different sensor vendor and sensor model. 2. Lenovo K4 note and Samsung Galaxy S7 (group 2): Same sensor vendor but different model. 3. Lenovo K4 note and iPhone 6s (group 3): Different vendor but almost same rear (K4) and front (iPhone6s) camera sensor models. 4. Lenovo K4 note and MotoG5 (group 4): Different vendor but almost same sensor model. Table 2 shows the classification results of two camera groups. The average accuracy of 99.2, 98.85, 98.15 and 97.35% is obtained. The bar graph shown in Fig. 3 depicts the accuracy of the classifier for each class group when using Wiener filter for residual estimation. It gives the average accuracy of about 98.38%.

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Table 2 Classification results of two camera model using Wiener filter iPhone (%)

S7 (%)

iPhone S7

99.7 1.86 Lenovo (%)

0.25 98.1 iPhone (%)

Lenovo iPhone

98.1 1.80

1.90 98.2

Lenovo (%)

S7 (%)

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Avg Accuracy: 98.38%

Binary classes Fig. 3 Classification accuracy of four different class groups using Wiener filter Table 3 Classification results of two camera model using total variation-based denoising iPhone (%)

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5.2 Analysis of Total Variation-Based Denoising Table 3 shows the results of two camera groups using total variation-based denoising. The average accuracy of 98.0, 96.11, 96.05, 92.65% is obtained. The bar graph shown in Fig. 4 depicts the accuracy when using total variationbased denoising for residual estimation. It gives the average accuracy of about 95.70%.

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Accuracy

Total VariaƟon based denoising 100.00% 98.00% 96.00% 94.00% 92.00% 90.00% 88.00%

Avg Accuracy: 95.70%

Binary classes Fig. 4 Shows classification accuracy of four different class groups using total variation-based denoising Table 4 Classification results of two camera model using Gaussian-based denoising iPhone (%)

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Fig. 5 Shows classification accuracy of four different class groups using Gaussian-based denoising

96.00% 95.00% 94.00% 93.00% 92.00% 91.00%

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5.3 Analysis of Gaussian-Based Denoising Table 4 shows the classification results of two camera groups using Gaussian-based denoising. The average accuracy of 94.25, 95.80, 95.60 and 93.24% is obtained for Gaussian-based denoising. The bar shown in Fig. 5 depicts the accuracy when using a Gaussian filter. It gives an average accuracy of about 94.72% that is very low as compare to Wiener and total variation-based denoising.

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6 Conclusion Source camera identification is one of the important technique of DIF that can be deal by estimating the PRNU noise from the images by using denoised filters. The features are computed from the estimated PRNU noise and these features are used by classifier for source camera identification. Some experiments are performed on mobile camera images of different models. Binary classes are used in which classification result shows that for group 1 which contains the features of two devices of different sensor brand gives better result as compared to remaining groups which include images of same sensor vendors. By carrying out experiments, three different denoising filters are used that provides absolutely different result in each case. The analysis of these filters depicts that wavelet-based denoising gives better result in our dataset, while the other two denoising filters shows less probability of detection for correct device labeling.

References 1. Z. Geradts, J. Bijhold, M. Kieft, K. Kurosawa, K. Kuroki, N. Saitoh, Methods for identification of images acquired with digital cameras, in Enabling Technologies for Law Enforcement and Security (2001) 2. X. Kang, J. Chen, K. Lin, P. Anjie, A context-adaptive SPN predictor for trustworthy source camera identification. EURASIP J. Image Video Proc. 2014(1), 19 (2014) 3. R. Lukac, K. Plataniotis, Secure single-sensor digital camera. Electron. Lett. 42, 627 (2006) 4. J. Zhao, Q. Wang, J. Guo, L. Gao, F. Yang, An overview on passive image forensics technology for automatic computer forgery. Int. J. Digit. Crime Forensics 8, 14–25 (2016) 5. H. Farid, Digital doctoring: how to tell the real from the fake. Significance 3, 162–166 (2006) 6. K. Choi, E. Lam, K. Wong, Automatic source camera identification using the intrinsic lens radial distortion. Opt. Express 14, 11551 (2006) 7. J. Luka, J. Fridrich, M. Goljan, Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1, 205–214 (2006) 8. Y. Hong, Analysis and comparison of several pattern recognition methods in source camera identification. J. Electron. Meas. Instrum. 26, 367–371 (2013) 9. P. Wighton, T. Lee, H. Lui, D. McLean, M. Atkins, Chromatic aberration correction: an enhancement to the calibration of low-cost digital dermoscopes. Skin Res. Technology 17, 339–347 (2011) 10. L. Xiaolin, Based on wavelet transform plane principal component inspection application research of image denoising algorithm. Int. J. Signal Proc. Image Proc. Pattern Recogn. 8, 19–28 (2015) 11. M. Chen, J. Fridrich, M. Goljan, J. Lukas, Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3, 74–90 (2008) 12. F. Gisolf, A. Malgoezar, T. Baar, Z. Geradts, Improving source camera identification using a simplified total variation based noise removal algorithm. Digit. Invest. 10, 207–214 (2013) 13. J. Janesick, M. Blouke, Scientific charge-coupled devices: past, present, & future. Opt. Photonics News 6, 16 (1995) 14. X. Kang, Y. Li, Z. Qu, J. Huang, Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 7, 393–402 (2012)

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15. C.-T. Li, Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5, 280–287 (2010) 16. U. Venkata, S. Sugumaran, R. Naskar, K-unknown models detection through clustering in blind source camera identification. IET Image Proc. 12(7), 1204–1213 (2018)

Advanced Protection for Automobiles Using MSP430 C. Ravi Shankar Reddy, V. Siva Kumar Reddy, T. Vinay Simha Reddy and P. Sanjeeva Reddy

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Basic Block Diagram and Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract As the demand for automobiles rises for everyone, there has been a continuous increase in the number of car thefts. This paper presents a new mechanism for increasing the security for automobiles by employing fingerprint-based biometric security system. Drunken drivers will not be in stable condition, and so the rash driving is the inconvenience for other road users and also the question of life and death. This paper also presents a new automatic engine-locking system which detects the alcohol content on the driver, and the ignition system automatically turns off if the alcohol content of the driver is more than the threshold level. The entire security system, alcohol detection system, and auto-braking system by detecting sudden and any fast moving objects on roads have been implemented using MSP430. The greater advantage of MSP430 is that consumes low power which can increase battery life.

C. Ravi Shankar Reddy · V. Siva Kumar Reddy · T. Vinay Simha Reddy (B) · P. Sanjeeva Reddy MallaReddy College of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] C. Ravi Shankar Reddy e-mail: [email protected] V. Siva Kumar Reddy e-mail: [email protected] P. Sanjeeva Reddy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_63

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The RISC architecture employed by MSP430 is user-friendly and provides greater scope for new innovations. Keywords MSP430 · RISC · HCSR · LM35 · GPS · GSM

1 Introduction It is a known fact that the demand for automobiles is increasing day by day, and proportionally there is continuous increase in the number of automobile thefts. Generally, theft of many vehicles is majorly observed in big metropolitan cities. The major reason for increase in theft is due to the presence of manual mechanical key-based lock system present in the vehicles. It is quite easy for people to break the mechanical locking system or starting the vehicle with any other duplicate key. Hence, there is necessity to develop a new security-based locking system which can authenticate the owner or the known person. Here we present biometric fingerprint-based ignition lock system which can reduce vehicle thefts drastically. The biometric fingerprintbased ignition lock system has a fingerprint scanner which reads the pattern of ridges and valleys on the finger. Fingerprint authentication comes under biometrics and is a way of comparing the fingerprints by matching with the known fingerprints. The starting of vehicle or opening biometric fingerprint-based ignition lock is as simple as to keep finger of the known person (owner/driver) on the fingerprint scanner. The scanner scans the pattern of ridges and valleys on the finger and matches with the fingerprints that are already present in the memory of microcontroller. If there is a match between the stored fingerprint and the print of the scanned finger that is placed on the fingerprint scanner, then the ignition lock opens or else the ignition lock refuses to open.

2 Related Work Few techniques and mechanisms were addressed earlier to improve security and reduce the risk associated with collisions and accidents. The mechanisms discussed in [1] are aimed to improve the security by employing fingerprint-based security and accident avoidance unit, and further tracking of vehicle by using Bluetooth and GPS. However, it works well for short distances as the range of the Bluetooth is restricted of around 100 m. The other technique discussed in [2, 3] tries to provide safety to pedestrians by extracting the features related to driver behavior. This technique works on the traffic data collected by video recordings at accident-prone regions. A similar sort of driver attention distraction is considered in [4] where extraction of face and eyes features was done based on probabilistic principal component analysis and support vector machine classifier.

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Wlodarczyk et al. [5] and Stan et al. [6] present extraction of head pose using precise localization of face landmark points to determine person’s gaze direction or its facial expression. A same sort extraction of head pose is obtained by mixing 2D and 3D camera images of the driver [7]. As driver fatigue is one of the major causes of accidents, a methodology for detection of eyestrain, in different lighting conditions, is presented in [8] by analyzing the blink rate using computer vision techniques. The [9] does not discuss anything related to accident avoidance. The majority of earlier work discussed the estimation of drivers fatigue which is a major cause for accidents, and moreover, majority of the techniques considered above were extracting the features of drivers based on image processing techniques which require high computation time involving complex programming strategies. All the above-discussed techniques aimed to reduce either accidents rate or tried to increase the security. However, the proposed mechanism is capable of producing high security as well as it can reduce accident rate and thereby reduces life risk.

3 Proposed System The proposed system is implemented using MSP430 which claims to have lower power consumption as compared to any other microcontrollers in the present world; this helps to improve battery life and power conservation, which is the motto of the present world. The analysis and implementation of the proposed system are discussed in the three sections. The first section discusses the block diagram and the specifications of different modules employed to build the proposed system. The second section discusses software which is used to make the system intelligent, and the third section discusses implementation and results of the proposed system. Sections four and five discuss the conclusion and future scope.

3.1 Basic Block Diagram and Specifications The block diagram of the proposed system is given in Fig. 1. The heart of the proposed system is MSP430 microcontroller. MSP430 consumes less power as compared to any other microcontroller and thereby increases the battery life. The microcontroller has integrated read and write memory for data storage, flash memory for permanent storage. This works at minimum clock speed as little as 32 kHz which is quite suitable for many real-time applications. The major advantage of using MSP430 lies with its ability to consume less power. It works in five power-saving modes; typically, the maximum amount of power consumed is at its active mode. The maximum amount of current consumed during active mode is 230 µA at 1 MHz, 2.2 V, and the minimum amount of current consumed during standby mode is 0.5 µA. This MSP430 has special feature of ultra-speed wake up from standby mode which is less than 1 µs. The MSP430 has one active mode

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Fig. 1 Basic block diagram of the proposed system

and five software-selectable low power modes [10]. It can be operated in any of the modes depending upon the designers’ requirements of power saving. In active mode, all clocks are active and thus consume more power in active mode. In low power mode 0 (LPM0), CPU and master clock (MCLK) are disabled, and ACLK and SMCLK are enabled. In low power mode 1 (LPM1), CPU and master clock (MCLK) are disabled. Additionally, DCO is disabled if they are not in use. And ACLK and SMCLK are enabled. In low power mode 2 (LPM2), CPU, MCLK, and SMCLK are in deactivated mode, whereas DCOs and ACLK are in active mode. In low power mode 3 (LPM3), CPU, MCLK, SMCLK, and DCOs are deactivated, and auxiliary clock is activated. And finally in low power mode 4 (LPM4), all power-consuming sources like CPU, MCLK, SMCLK, ACLK, DCOs, and crystal clock are disabled to optimize power saving. The temperature sensor used in this system is LM35; the major advantage of LM35 is that it is directly calibrated in centigrade. It provides an accuracy of 0.5 °C at room temperature. It offers significantly high range which varies from −55 to +150 °C. It can be operated at very low voltage which is around 4 V. Nowadays, fingerprintbased protection is famous as it offers high security. Here we are employing the fingerprint sensor module R305. The R305 module can be interfaced with microcontroller with the help of UART interface. The ultrasonic sensor employed in this system is to provide the feature of automatic braking. Whenever a vehicle encounters the obstacle (which may be stationary or moving) within the specified limits (for better understanding within the range of the designed systems), the brakes of the vehicle are automatically applied to avoid the risk of accident. Here we have employed ultrasonic sensor HCSR-04 to sense the obstacle based on the sound. The schematic of the proposed system is shown in Fig. 2.

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Fig. 2 Schematic of the proposed system

3.2 Software Development The software development is done using the popular open-source tool called Energia. Energia provides a robust framework for programming the MSP430 microcontroller with easy-to-use functional APIs and libraries. Energia is developed by Robert Wessels with the goal to bring the Arduino and Wiring framework to the Texas Instruments (TI) MSP430 Launch Pad evaluation kit. Energia currently supports several TI devices. Figure 3 shows the procedure of loading the executable file to the flash memory of MSP430G2533. Double-click Energia.exe file. Energia will start and an empty sketch window appears. Now select the Serial Port from Tools menu to view available serial ports, and then select the COM Port for Launch Pad [11]. And then select the board Launch

Fig. 3 Flowchart for loading executable file to MSP430G2533

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Pad MSP430G2533 from Tools menu, and now to open new sketch, click on File and select New or alternatively click on New button. The new sketch consists of two functions: void setup() and void loop(). Type the program and save the sketch with .INO extension. And now to compile and execute the sketch, click on File and select Upload or alternatively click on Upload button.

4 Results The implementation of security using biometric authentication of the proposed system is shown in Fig. 4. It employs fingerprint module (R305) for increasing security of the vehicle. This requires the registration (enrollment) of the vehicle owners through the print module to give them authentication to use the vehicle and to prevent the unknown users to attempt thefts. After completion of the registration, only the authenticated users (who got registered) can get access to start the vehicle. The proposed system employs alcohol sensor (MQ3) for sensing alcohol content in the vehicle, and its implementation is shown in Fig. 5. After completion of successful authentication, the system will sense the alcohol content and other harmful gases present in the atmosphere; if the sensed content of alcohol or any other harmful gases exceeds the prescribed level, the engine will be prevented from getting started and the vehicle will be stayed in OFF state. The alcohol threshold is set to 800 ml. The implementation of temperature sensing in the proposed system is shown in Fig. 6. The proposed system employs temperature sensor (LM35) for automatic control of temperature inside the vehicle. After detection of alcohol levels in the vehicle, the temperature present in the vehicle is sensed, and based on the requirement, the air conditioner will be switched ON and OFF automatically. In the implemented model, threshold value of temperature is set to 30 °C. The implementation of accident avoidance in the proposed system is shown in Fig. 7. The vehicle will be sensing the presence of obstacle in its way of travel; if an obstacle is detected, the distance at which the obstacle is present is displayed and

Fig. 4 Fingerprint authentication

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Fig. 5 Alcohol detection

Fig. 6 Automatic temperature control

Fig. 7 Collision avoidance using ultrasonic sensor

warns the driver, and if driver fails to apply the brakes at predefined distance, then ignition of the vehicle will be automatically switched OFF. The GSM, GPS, and Buzzer are employed to indicate the collision of the vehicle. The GSM and GPS are used to trace the vehicle and send the information to nearby hospitals and police stations.

5 Conclusion The proposed system is implemented using MSP430G2533. As it is implemented using MSP microcontroller, it fetches additional advantage of low power consumption than compared to any other microcontrollers. The implemented model is successful in providing security of the vehicle through fingerprint authentication, and it is also successful in automatic control of temperature which is set to 30 °C. The implemented model is also capable of detecting the presence of alcohol which is

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around 800 ml, and it is successful in avoiding accidents within range of 200 cm. However, the proposed system will not give enough information about driver fatigue. The performance of the proposed system also depends on the alcohol content present in the odor-removing fresheners that are commonly used in the vehicles.

6 Future Scope The further protection in reducing the risk of accidents can be achieved by detecting the health conditions of the driver. Primarily, this can be achieved through facial recognition and seating position of the driver by employing image processing tools. Furthermore, application of other sensors for detection of heartbeat, blood pressure, and other parameters of the human body thereby further reduces the chances of accidents.

References 1. M. Dey, M.A. Arif, M.A. Mahmud, Anti-theft protection of vehicle by GSM & GPS with fingerprint verification, in International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 916–920 (2017) 2. P.D. Das, S. Sengupta, Proposing the systems to provide protection of vehicles against theft and accident, in IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1681–1685 (2016) 3. X. Jiang, W. Wang et al., Analysis of drivers’ performance in response to potential collision with pedestrians at urban crosswalks. Intell. Transp. Syst. (IET) 11(9), 546–552 (2017) 4. Y. Yun, I.Y.H. Gu et al., Video-based detection and analysis of driver distraction and inattention, in International Conference on Signal Processing and Integrated Networks (SPIN), pp. 190–195 (2014) 5. M. Wlodarczyk, D. Kacperski et al., Evaluation of head pose estimation methods for a noncooperative biometric system, in International Conference Mixed Design of Integrated Circuits and Systems (MIXDES), pp. 394–398 (2016) 6. O. Stan, L. Miclea, A. Centea, Eye-Gaze tracking method driven by raspberry PI applicable in automotive traffic safety, in International Conference on Artificial Intelligence, Modelling and Simulation, pp. 126–130 (2014) 7. A. Gustavo, C. Peláez et al., Driver monitoring based on low-cost 3-D sensors. IEEE Trans. Intell. Transp. Syst. 5(4), 1855–1860 (2014) 8. G. León, R. Clavijo et al., Detection of visual fatigue by analyzing the blink rate, in Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–5 (2015) 9. I.J. Lee, An Accident Detection System on Highway Using Vehicle Tracking Trace (IEEE, USA, 2011 10. J. Davies, MSP430 Microcontroller Basics [Kindle Edition] 11. MSP430G2 Launch Pad Evaluation Kit User’s Guide (www.ti.com)

Performance Analysis of KNN Classifier with Various Distance Metrics Method for MRI Images Karthick Ganesan and Harikumar Rajaguru

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-Means Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 First-Order Statistical Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Segmentation-Based Fractal Texture Analysis (SFTA) Features . . . . . . . . . . . . . 4 KNN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Result and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Image classification using texture features has been increased the success rate and texture-based classification very useful in identification of abnormal and normal tissues images. In this paper, analyze first-order statistical features and Segmentation-based fractal texture analysis (SFTA) features-based classification of MRI images using K-Nearest Neighbor (KNN) classifier. The performance of KNN classifier is compared to various distance metrics like Euclidean, City block, Correlation and Cosine. The classification results show that first-order statistical features produce better classification accuracy than segmentation-based fractal texture analysis. The KNN classifier with Euclidean distance yields better classifier accuracy compare to other distance metrics. Keywords MRI images · KNN classifier · First-order statistical · SFTA

K. Ganesan (B) Jyothismathi Institute of Technology and Science, Karimnagar, Telungana, India e-mail: [email protected] H. Rajaguru Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_64

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1 Introduction In machine learning, pattern recognition is an important one for classification and identification. Due to advances in machine learning that allowed analysis of tissue information and the provision of diagnostic tools (abnormal tissues or normal tissues). In classification tasks, many approaches have been used. Although image classification technique is still a challenging one, KNN is the most common classifier used because of it is simplicity and efficiency. It has two steps both training and testing. In training step, all the training images are labeled and the extracted features are stored. In testing step, unlabeled testing images are labeled according to the trained images. The MRI system uses contrast agent to adjust the local magnetic field in the tissue being scanned. Normal and abnormal tissues react in a different way to this slight adjustment and generate different signals. These signals are transferred to the images. This paper considers feature extraction techniques for MR images and classifies images using KNN classifier with various distance methods. This is used to develop the tool for abnormal and normal classification of medical images. Figure 1 shows that steps in image classification. This work has the following stages: preprocessing, segmentation, feature extraction, classification and performance evolution. Different types of medical images have been collected from various databases. Medical images taken for analysis are 50 images (8-Glioma, 8-Meningioma, 18-Metastasis, 5-Astrocytoma and 11-Normal). At first, medical images are preprocessed by Gaussian filter and then images are segmented using K-means algorithm. After that features are extracted using first-order statistical features and segmentation-based fractal texture analysis. The KNN classifier is used to classify images based on the extracted features. Finally, accuracy, sensitivity, specificity, perfect classification and missed classification values are measured. This paper is further organized by Sect. 2 discusses K-means clustering algorithm. Section 3 describes the feature extraction technique. Section 4 describes the KNN classifier with various distance metric methods. Section 5 focuses on the performance analysis of the KNN classifier, and Sect. 6 contains results and conclusion.

2 K-Means Clustering Algorithm K-means clustering algorithm is simple and basic segmentation algorithm. K-means algorithm was proposed by MacQueen [1]. K is denoted by number of clusters and clusters are grouped by distance between the pixels. The Euclidean distance equation is used to measure the distance between intensity of the neighborhood pixels [2].

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Fig. 1 Block diagram for image classification

1. Initialize K cluster centers chosen randomly. 2. Assign each x i to its nearest cluster center ck by Euclidean Distance (d). K M(X, C) 

n 

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3. Update each cluster center ck as the mean of all x i that belongs to it. 4. Repeat steps 2–4 until the cluster centers are stable.

3 Feature Extraction The purpose of the feature extraction to extract the image to represent whole image property, since classification accuracy depends on the extracted features set. This paper uses two feature extraction techniques, which are first-order statistical features and segmentation-based fractal texture analysis.

3.1 First-Order Statistical Features First-order statistical features are calculated the relationships with neighborhood pixel. The first-order features are Mean, Standard deviation, Energy, Entropy, Skewness and Kurtosis. These features are analyzed based on the intensity value of the

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pixel and their neighborhood coordinates pixel. The histogram features are representing the statistical information of the image and used to calculate histogram of the pixels [3]. Therefore, histogram to quantitative describes first-order statistical features of the image. Here, f (x, y) represents an image. x is number of rows in the image, y is number of columns in the image and i is number of intensity level of the image. Mean: A1 (μ) 

N M  

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(6)

N M   (P(i, j) − μ)4 −3 M Nσ4 i1 j1

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A5  Kurtosis: A6 

where M and N are number of rows and columns [4].

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3.2 Segmentation-Based Fractal Texture Analysis (SFTA) Features SFTA is used to extract the features from the image. It decomposes the input image into set of binary images. Here, a Two-Threshold Binary Decomposition method is used to decompose the image. From each decomposed binary image size, mean gray level and fractal dimensions are calculated to define the segmented texture patterns [5]. The SFTA algorithm contains two steps: decomposition of binary image and feature vectors extraction. The multilevel Otsu algorithm is used to calculate T threshold values from the m the gray level distribution information in input image. The Otsu algorithm minimizes the intra-class variance and T threshold values are applied to the two segmentation algorithm [6, 7]. In this paper, decompose level is four. So that resulting binary images are seven and each image consists of three features. Therefore, total number of features for one image is equal to twenty-one. The classification is done by extracted segmented texture patterns.

4 KNN Classifier KNN or K-nearest neighbor algorithm is used for classification, pattern recognition and data mining problems. It is proposed by Cover and Hart in 1968, and it is a conventional nonparametric supervised learning classifier [8]. KNN classification consists of two steps like training period and testing period. In the training period, training feature vectors are labeled according to their feature values. In the testing period, an unlabeled test feature vectors are classified based on their known classes and its k-most similar value or it is nearest neighbor’s value. Feature values similarity measured according to the various distance metric methods. It does not need added rules because it classifies the unlabeled feature values according to their trained or labeled features value. The performance of KNN can be comparable with the state-of-the-art classification methods with simpler computation [9]. KNN classifier algorithm is described below. 1. Decide the appropriate distance metric. 2. All the trained feature values are stored in D  (x i , yi ). i  1, 2, …,n. Where x is feature values in the training data set, y is corresponding class of their features values and n is number of training data set. 3. Compute the distance between unlabeled feature values and trained feature values. 4. k-most similar value or it is nearest neighbor’s value is chosen and label the class of the test feature value. In this paper, we use four types of metrics, which are Euclidean, City block, Correlation and cosine distance [10, 11]. Depends upon these distance metric KNN classifier is find out the similarity between features value.

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5 Performance Measures Here we present the performance analysis of KNN classifier for medical images. We measure the performance of the classifier according to perfect classification, missed classification, sensitivity, specificity and accuracy [12, 13]. TP + TN (TP + TN + FP + FN) TN + FP Missed Classification (MC) (TP + TN + FP + FN) FP False Alarm = (TN + FP) PC Sensitivity  PC + FA PC Specificity  PC + MC Sensitivity + Specificity Accuracy = 2

Perfect Classification (PC) 

(8) (9) (10) (11) (12) (13)

6 Result and Conclusion This paper considers the five classes of MRI images namely Glioma, Meningioma, Metastasis, Astrocytoma and Normal images were as input. The analysis has been done over 250 images of JPG format of size 256 × 256. In preprocessing stage, Gaussian filter is used and then K-means clustering algorithm is used for segment the MRI images. Here, the number of cluster value is four. After segmentation process, the first-order statistical features and SFTA features extracted from the segmented images. KNN classifier is used to classify the MRI images. Here consider the multiclass problem and one versus all approach is used. Where a given class is considered as positive and all the other classes are considered as negative [14]. Finally, KNN classifier measured based on the confusion matrix parameters. Tables 1 and 2 show that first-order statistical and SFTA extracted features, respectively. Tables 3 and 4 result shows that performance analysis of KNN classifier using first-order statistical and SFTA feature extraction techniques, respectively. Figure 2 shows that average accuracy comparison of KNN classifier with various distance metric using first-order statistical and SFTA feature extraction techniques. From the results, first-order statistical features extraction provides better results compare to SFTA feature extraction except one cosine distance metric. The KNN classifier with Euclidean distance yields highest classification accuracy, for first-order statistical feature provides 71.1% accuracy and to SFTA features 69.23% accuracy. The goal of this paper was analyzed on KNN classifier with different distance methods and

Performance Analysis of KNN Classifier with Various Distance … Table 1 First-order statistical features extracted from MR images Features A1 A2 A3 A4

679

A5

A6

Image 1

55.00

44.25

0.30

1.77

6.32

1.50

Image 2

88.61

64.63

0.22

2.11

1.82

0.22

Image 3

57.56

43.54

0.32

1.79

3.42

0.63

Image 4

98.21

49.03

0.28

1.85

4.02

1.47

Image 5

107.28

57.55

0.28

2.16

3.71

1.34

Image 6

147.69

82.33

0.17

2.35

1.40

0.29

Image 7

25.92

46.83

0.57

1.17

6.37

1.96

Image 8

35.67

43.52

0.33

1.64

5.90

1.74

Image 9

67.69

49.74

0.21

2.18

3.69

0.73

Image 10

100.37

75.03

0.15

2.80

2.04

0.56

Table 2 SFTA features extracted from MR images Features B1 B2 B3 B4 B5 B10 B11 B12 B13 B14 B19 B20 B21

B6 B15

B7 B16

B8 B17

B9 B18

Image 1 1.28

46

1488

1.31

60

1718

1.31

88

1705

0.99 1.37 Image 2 1.22

191 97 39

241 2205 3942

1.17

29

704

1.28

47

1294

1.31

63

5092

1.38

104

7539

1.35 1.48

195 129

6734 13,569

1.24

33

4281

1.38

65

7839

Image 3 1.28

33

1373

1.37

48

2192

1.46

74

3073

1.36 1.50 Image 4 1.47

146 74 59

2178 3802 16,762

1.24

13

910

1.40

34

2322

1.47

82

16,495

1.28

139

6046

1.21

197

2960

1.58

65

32,356

1.52

92

22,600

1.34 Image 5 1.37

152 86

8860 10,185

1.46

97

16,299

1.40

189

11,754

1.37

238

9952

1.45

64

15,755

1.52

100

21,915

1.42

180

12,505

texture features. If texture feature technique is combined or extracted, more texture features the KNN classifier accuracy will be enhanced.

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Table 3 Performance analysis of KNN classifier using first-order statistical feature extraction techniques Distance PC (%) MC (%) FA (%) Sensitivity Specificity Accuracy metric (%) (%) (%) Glioma images Euclidean City block

50 38

16.67 33.43

33.33 28.57

60 57.08

75 53.2

67.5 55.14

Correlation 50 Cosine 25 Meningioma images

25 50

25 25

66.67 50

66.67 33.33

66.67 41.67

Euclidean City block

25 33.67

50 53.33

33.33 19.6

50 27.86

41.67 23.73

Correlation 25 Cosine 25 Metastasis images

21.15 17.86

53.85 57.14

31.71 30.43

54.17 58.33

42.94 44.38

Euclidean City block

9.45 9

18.33 30

79.75 54.95

88.43 54.95

84.09 87.14

Correlation 61 Cosine 72 Astrocytoma images

12.75 3.72

26.25 64.29

69.91 52.83

82.71 36.92

76.31 44.88

Euclidean City block

10 2.85

50 57.14

44.44 41.18

80 93.33

62.22 67.25

Correlation 20 Cosine 20 Normal images

37.14 60

42.86 20

31.82 50

35 25

33.41 37.5

Euclidean City block

100 100

0 0

0 0

100 100

100 100

100 100

Correlation Cosine

100 100

0 0

0 0

100 100

100 100

100 100

25 13

72.22 61

40 40

Performance Analysis of KNN Classifier with Various Distance …

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Table 4 Performance analysis of KNN classifier using SFTA feature extraction techniques Distance PC (%) MC (%) FA (%) Sensitivity Specificity Accuracy Metric (%) (%) (%) Glioma images Euclidean City block

25 25

50 50

25 25

50 50

33.33 33.33

41.67 41.67

Correlation 37.5 Cosine 37.5 Meningioma images

33.93 33.93

28.57 28.57

56.76 56.76

52.5 52.5

54.63 54.63

Euclidean City block

62.5 62.5

2.5 2.5

35 35

64.10 55.56

96.15 96.15

80.13 75.85

Correlation 50 Cosine 50 Metastasis images

10 10

40 40

45.45 45.45

83.33 83.33

64.39 64.39

Euclidean City block

10.17 10.17

6.5 6.5

92.76 92.76

89.13 89.13

90.95 90.95

Correlation 66.67 Cosine 66.67 Astrocytoma images

9.48 9.48

23.85 23.85

55.32 55.32

87.55 87.55

71.44 71.44

Euclidean City block

37.14 37.14

42.86 42.86

31.82 31.82

35 35

33.41 33.41

Correlation 0 Cosine 0 Normal images

62.5 62.5

37.5 37.5

0 0

0 0

0 0

Euclidean City block

100 100

0 0

0 0

100 100

100 100

100 100

Correlation Cosine

100 100

0 0

0 0

100 100

100 100

100 100

83.33 83.33

20 20

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Percentage of Accuracy

100 80 60

First order staƟsƟcal

40

SFTA

20 0 Euclidean

City block Correlaion Cosine KNN Classifier Distance Metric

Fig. 2 Accuracy comparison of KNN classifier with feature extraction techniques

References 1. J. MacQueen, Some methods for classification and analysis of multivariate observations, in Proceedings of 5th Symposium Mathematical Statistics Probability (1967), pp. 281–297 2. S. Ghosh, S.K. Dubey, Comparative analysis of k-means and fuzzy c-means algorithms. Int. J. Adv. Comput. Sci. Appl. 4(4) (2013) 3. A. Materka, M. Strzelecki, Texture analysis methods—a review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels (1998), pp. 1–33 4. S. Selvarajah, S.R. Kodituwakku, Analysis and comparison of texture features for content based image retrieval. Int J. Latest Trends Comput. 2(1), 108–113 (2011) 5. E.S.A. El-Dahshan, T. Hosny, A.B.M. Salem, Hybrid MRI techniques for brain image classification. Digit. Signal Proc. 20(2), 433–441 (2010) (Elsevier publication) 6. A.F. Costa, G. Humpire-Mamani, A.J.M. Traina, An efficient algorithm for fractal analysis of textures, in Conference on Graphics, Patterns and Images (SIBGRAPI), At OuroPreto, MG, Brazil (2012), pp. 1–8 7. I. El-Henawy, H.M. El Bakry, H.M. El Hadad, Cattle identification using segmentation-based fractal texture analysis and artificial neural networks. Int. J. Electron. Inf. Eng 4(2), 82–93 (2016) 8. H.B. Mitchell, P.A. Schaefer, A “soft” K-nearest neighbor voting scheme. Int. J. Intell. Syst. 16, 459–468 (2001) 9. C.G. Atkeson, A.W. Moore, S. Schaal, Locally weighted learning. Artif. Intell. Rev. 11(1–5), 11–73 (1997) 10. M.R. Peterson, T.E. Doom, M.L. Raymer, GA-facilitated KNN classifier optimization with varying similarity measures, in IEEE Congress on Evolutionary Computation, vol. 3 (2005), pp. 2514–2521 11. https://docs.tibco.com/pub/spotfire/6.5.2/doc/html/hc/hc_distance_measures_overview.htm 12. D.J. Hand, Evaluating diagnostic tests: the area under the ROC curve and the balance of errors. Stat. Med. 29, 1502–1510 (2010) 13. H. Rajaguru, K. Ganesan, V.K. Bojan, Earlier detection of cancer regions from MR image features and SVM classifiers. Int. J. Imaging Syst. Technol. 26(3), 196–208 (2016) (Wiley Periodicals, Inc.) 14. L. Nanni, S. Brahnam, S. Ghidoni, E. Menegatti, Region-based approaches and descriptors extracted from the co-occurrence matrix. Int. J. Latest Res. Sci. Technol. 3(6), 192–200 (2014)

Comparison of Low Current Mismatch CMOS Charge Pumps for Analog PLLs Using 180 nm Technology Alan Saldanha, Vijil Gupta and Vinod Kumar Joshi

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design of Charge Pumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 CP1 Charge Pump Designed by Zhang et al. [5] . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 CP2 Charge Pump Designed By Estebsari et al. [6] . . . . . . . . . . . . . . . . . . . . . . . . 2.3 CP3 Charge Pump Designed by Shiau et al. [7] . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 CP4 Charge Pump Designed by Majeed et al. [8] . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

684 685 686 687 688 689 691 691

Abstract We have reinvestigated the four different charge pumps (CPs) already reported in the literature and named them CP1, CP2, CP3 and CP4. These charge pumps are widely used in phase-locked loop (PLL) and have been compared for a number of parameters, mainly current mismatch, power consumption, voltage swing and the design complexity. We observed the current mismatch between the charging and discharging currents at control voltage of 0.9 V to be 3.88%, 2.7% and 3.55% for CP1, CP3 and CP4, respectively, while it is 6.96% for CP2 at control voltage of 1.3 V. At frequency of 50 MHz, CP4 consumes 377 µW power using 200 µA current source, CP3 consumes 1840 µW using 100 µA current source, CP1 consumes 704 µW using 80 µA current source, while CP2 consumes 756 µW power for bias voltage of 0.47 V. The voltage swing for CP1, CP2, CP3 and CP4 is obtained to be 0.2, 1.275, 0.9 and 0.3 V, respectively, at 50 MHz frequency. Keywords CP · PLL · Power consumption · Current mismatch A. Saldanha · V. Gupta · V. K. Joshi (B) Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India e-mail: [email protected] A. Saldanha e-mail: [email protected] V. Gupta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_65

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1 Introduction PLL implementing sequential logic has been popularly used in modern computer and communication systems [1]. Their popularity is due to their extended tracking range, frequency-aided acquisition and low cost. CP is a critical part of integrated circuits including delay-locked loops (DLLs) and PLLs. Power-efficient and highperformance CPs are highly demanded in many PLL systems. CPs are extensively used in such applications where we need a steady output voltage supply. They provide a wide locking range when combined with a phase frequency detector (PFD) [2]. CP is a three-position electronic switch that is controlled by the three states of the PFD. The ideal CP combined with the PFD provides infinite DC gain with passive loop filters, which results in an unbounded range for second-order PLLs [3]. The purpose of CP is to convert the logic states of the PFD into analog signals necessary for controlling the voltage-controlled oscillator (VCO). A basic idea of conventional CP has been shown in Fig. 1. Ideally, the source and sink current (Ipump) should be equal as shown in Fig. 1 [2], but the Ipump is implemented by using either current mirrors or appropriately biased transistors which creates current mismatch. Similarly, UP signal is also applied to PMOS transistor (M2) through inverter which causes timing mismatch between UP and DOWN signals. Overall, the conventional CP is affected by leakage current, current mismatch [3] between the charging and discharging currents, and timing mismatch, etc. [4, 5]. Therefore, there is a need to improve the basic CP circuits because of its high power consumption and poor current matching in real PLL systems. We have reinvestigated the four different CPs named as CP1, CP2, CP3 and CP4 [5–8] from the literature to reduce the current mismatch, leakage current and power consumption. CP1 is a high-performance NMOS-switch cascode CP, which is derived from the classic current-steering NMOS-switch CP. It uses cascode current mirrors to increase the output resistance of the current sources and reduce the cur-

Fig. 1 Schematic of charge pump [2]

Comparison of Low Current Mismatch CMOS Charge Pumps …

685

rent mismatch [5]. CP2 design utilizes an output feedback loop in order to achieve the suitable current matching and good high-frequency characteristics [6]. CP3 is the gain-boosting amplifier-based switch-in-source CMOS charge pump. It is based on the switches-in-source architecture which is improved by gain-boosting amplifiers. Two differential amplifiers are employed to reduce the effect of channel length modulation [7]. CP4 follows the current splitting technique and provides reduced reference spur and leakage current due to the equal distribution of supply current, which reduce the leakage current by 50% [8].

2 Design of Charge Pumps Four different novel and innovative CP circuits (CP1, CP2, CP3 and CP4) have been chosen from the recent literature based on different techniques and methodologies used to achieve low power, low leakage currents, higher voltage swing and reduced current mismatch, etc. The transient analysis is performed for input frequencies of 50 MHz, and the corresponding voltage swings are shown in Table 1. DC analysis has been also performed for the current mismatch of CP1, CP2, CP3 and CP4 circuits. The design of each CP has been particularly discussed in Sects. 2.1, 2.2, 2.3 and 2.4.

Table 1 Comparison of the charge pumps CP1, CP2, CP3 and CP4 Parameters CP1 CP2 CP3

CP4

Working principle

Cascode current mirror

Output feedback mechanism

Gain-boosting technique

Current splitting technique

Supply current

80 µA

–

100 µA

200 µA

Current mismatch

Maximum current matching at 117 µA

Maximum current matching at 25 µA

Maximum current matching at 100 µA

Maximum current matching at 29.5 µA

Mismatch  3.88% at 0.9 V 704 µW

Mismatch  6.96% at 1.3 V 756 µW

Mismatch  2.7% at 0.9 V 1840 µW

Mismatch  3.55% at 0.9 V 377 µW

Input control signals needed

UP, DOWN, UP, DOWN

UP, DOWN

UP, DOWN

UP, DOWN, UP, DOWN

Complexity

20 transistors

17 transistors

25 transistors 26 transistors including 10 transistors for A1 and A2 0.9 V 0.3 V

Power consumption (at 50 MHz)

Voltage swing (at 0.2 V 50 MHz)

1.275 V

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2.1 CP1 Charge Pump Designed by Zhang et al. [5] Figure 2 represents the schematic of NMOS-switch cascode charge pump made of 20 transistors, true (UP, DOWN) and complementary signals (UP, DOWN). The cascode current mirrors are used for the charging (Iup) and discharging (Idown) currents, which offer a higher output resistance than simple current mirrors, resulting in lower current mismatch as compared to simple current mirrors. The charging current is provided by the p-channel cascode current mirror (Q5, Q6, Q7, Q8 and Q9), and the discharging current is provided by the n-channel cascode current mirror (Q11, Q17, Q18, Q19 and Q20). Q3, Q4 and Q12 act as a current source to improve the slow node problem in charge pump [9]. When both UP and DOWN signals go low, both Q13 and Q15 will be turned off and all current mirrored from Icp (80 µA) will be steered into Q12 and Q16, which does not change the output control (V ctrl) voltage. When UP  ‘1’ and DOWN  ‘0’, Q13 and Q16 turn on, the supply current is mirrored from transistors Q10 and Q11 and supplied to the pmos cascode current mirrors to provide the Iup current, which increases the V ctrl voltage (by charging the capacitor C1) (Fig. 3a). When UP  ‘0’ and DOWN  ‘1’, Q12 and Q15 turns on, the supply current Icp is mirrored by Q1, Q2, Q14 and Q15, which is sent to the nmos cascode current mirrors to provide the Idown current, which reduces the V ctrl voltage (by discharging the

Fig. 2 Schematic of NMOS-switch cascode charge pump CP1 [5]

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Fig. 3 a Transient analysis and b DC analysis of current matching in NMOS cascode charge pump CP1

capacitor C1) (Fig. 3a). The current matching of Iup and Idown current of CP1 has been shown in Fig. 3b, which is affected by channel-length-modulation effect.

2.2 CP2 Charge Pump Designed By Estebsari et al. [6] CP2 charge pump has a single-ended configuration and work on the principle of negative feedback mechanisms, which reduces the channel length modulation in current mirrors (Fig. 4). When the UP signal is ‘1’ and DOWN signal is ‘0’, Q9 will be turned on and the current mirrored through Q13 and Q14 starts charging the capacitor (C1) through Iup current, and subsequently, output node (V ctrl) of charge pump increases (Fig. 5a).

Fig. 4 Schematic of CP2 charge pump using feedback output [6]

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Fig. 5 a Transient analysis and b DC analysis of current matching in CP2 charge pump

When DOWN signal is ‘1’ and UP signal is ‘0’, Q8 will be turned on and the current mirrored through Q1, Q2, Q3 and Q17 starts discharging the capacitor C1 through Idown current, thereby decreasing the V ctrl of charge pump (Fig. 5a). A differential amplifier made of Q6, Q7, Q8 and Q9 is at the center of the CP, in which Q6 and Q7 transistors are biased at 0.47 V. When Vctrl is increased, feedback transistors Q4 and Q5 stay in the active region, and VGSQ4 will be decreased. Therefore, with the increase in on-resistance (Ron), Iup is reduced (Fig. 5b). In addition to this, increasing V ctrl causes transistors Q15 and Q16 to stay in the active region and VGSQ15 increases. Hence, this decreases the Ron of these transistors (Q15 and Q16), which increases the Idown current (Fig. 5b).

2.3 CP3 Charge Pump Designed by Shiau et al. [7] CP3 charge pump uses two differential voltage amplifiers, A1 and A2, which are used to reduce the sensitivity of the charging (Iup) and discharging (Idown) currents to V ctrl as shown in Fig. 6. Iup and Idown currents are less affected by V ctrl even at the presence of the channel-length-modulation effect. The gain boost obtained using A1 and A2 increases the output resistance of the Q2 and Q5 [10]. In A1 and A2, the size of PMOS and NMOS transistors is chosen appropriately to match their output resistance. A1 and A2 are also designed for the same tail currents as that of the supply current of charge pump (Icp  100 µA). The role of Q12 and Q13 transistors are to make the branch currents much similar to Q10 and Q11 by maintaining the same drain–source voltage across the transistors. When UP signal is low, Q1 turns on and the Iup current increases the output (V ctrl) node by charging the capacitor C1 (Fig. 7a). Since the bias conditions for Q3 and Q1 are very similar, the Iup current becomes the same as the branch current flowing through Q3. When DOWN signal is high, Q6 turns on and the Idown current decreases the output (V ctrl) node by discharging the capacitor C1 (Fig. 7a). The compensation in the variation of V ctrl is done by Q2 and Q5 transistors whose gate voltage is controlled by A1 and A2 to keep the same current flowing through them. When UP signal is low and DOWN signal is high, Q1 and Q6 turn on and the voltages

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Fig. 6 Schematic of charge pump CP3 with gain-boosting amplifiers [7]

Fig. 7 a Transient analysis and b DC analysis of current matching in switch-in-source charge pump CP3

at each input node of the differential amplifiers are kept at the same potential due to the negative feedback mechanism. The current matching of Iup and Idown current of CP3 is shown in Fig. 7b.

2.4 CP4 Charge Pump Designed by Majeed et al. [8] Figure 8 represents the schematic of CP4 charge pump working on the principle of current splitting technique. When the UP signal is high (DOWN signal is low), PMOS

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Fig. 8 Schematic of current splitting charge pump CP4 [8]

Fig. 9 a Transient response and b DC analysis of current matching of the current splitting charge pump CP4

transistors P10, P11, P12, P16, P17, P18 and NMOS transistors N1, N2, N3, N4, N5, N6, N8 transistors are turned on, which mirror the supply current (Icp) to charge the loop filter capacitor (C1), subsequently increasing the control voltage (V ctrl) (Fig. 9a). When the DOWN signal is high (UP signal is low), PMOS transistors P7, P8, P9, P13, P14, P15 and NMOS transistors N1, N2, N3, N4, N5, N6, N7 are turned on, and the loop filter capacitor C1 will be discharged through N4, N5 and N6, subsequently decreasing the control voltage V ctrl (Fig. 9a). When both UP and DOWN signals are high (UP signal is low), the transistors P1, P2, P3 provide additional path to mirror the supply current. Similarly, when DOWN

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signal is low and UP is high, P4, P5, P6 transistors provide additional path current mirroring for the supply current. The current matching of Iup and Idown currents of CP4 is shown in Fig. 9b. UP and DOWN signals are complementary to UP and DOWN signals, respectively.

3 Results and Conclusion All the designs (CP1, CP2, CP3 and CP4 charge pumps) have been simulated in Cadence Virtuoso Spectre Simulator using 180 nm CMOS technology with suitable transistor sizing, and the results have been compared for various parameters like current mismatch, power consumptions, voltage swing and complexity as shown in Table 1. From the above results, the following observations can be inferred. • Nearly perfect matching between the charging and discharging currents has been obtained for CP4 due to the use of current splitting technique (Fig. 9b). CP3 also shows good current matching using gain-boosting amplifiers (Fig. 7b). For CP2, the current graph of charging and discharging currents shows higher mismatch than that of CP3 and CP4 (Fig. 5b). CP1 shows current matching only in the middle portion of the curve (near 0.9–1.1 V) while the extreme ends represent higher current mismatch due to the channel-length-modulation effect (Fig. 3b). • CP3 consumes the highest power due to the use of two differential amplifiers A1 and A2, while CP4 consumes least power due to the use of current splitting technique which reduces the leakage current by 50%. • CP2 and CP3 have higher voltage swing compared to CP1 and CP4, so they are less affected by noise due to current mismatch, which makes them suitable for a wide range of frequency operation. • CP2 uses a very low capacitance value of 40 fF as loop filter compared to other CPs which makes it the smallest in area among all the CPs. The other CPs use a capacitor of 1 pF. • In the design, we must refrain from using complementary inputs, due to the delay between the true and complementary signals, causing timing mismatch. Hence, CP1, CP3 and CP4 need to be used with additional circuitry to avoid timing mismatch. CP2 is preferred as no complementary inputs are needed.

References 1. F. Gardner, Charge pump phase-lock loops. IEEE Trans. Commun. 28(11), 1849–1858 (1980) 2. B. Razavi, Design of Analog CMOS Integrated Circuits (McGraw-Hill, New York, NY, 2001) 3. W. Rhee, Design of high-performance CMOS charge pumps in phase-locked loops, in Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS 1999, Orlando, FL, USA, pp. 554–557 (1999) 4. J. Maneatis, Low-jitter process-independent DLL and PLL based on self-biased techniques. IEEE J. Solid-State Circuits 31(11), 1723–1732 (1996)

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Optimized Node Swapping for Efficient Energy Usage in Heterogeneous Network Satyanarayan K. Padaganur and Jayashree D. Mallapur

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Simulation and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Network lifetime has become a new challenge for wireless heterogeneous network because of battery base nodes or limited power nodes. To increase the network lifetime, we have to plan the new approach with an alternate solution of distribution of the load on multiple nodes in order to maintain guaranteed services. In our proposed work, we have planned to distribute the load with optimized node swapping scheme, based on energy level and distance. The node swapping will be carried out only with the optimized register neighbor nodes with their energy level and distance between them. The resulting analysis shows that the proposed work enhances the overall lifetime of the network and reduces the packet loss, which leads to an increase in the efficiency with minimum node swapping latency. Keywords Node Swapping · Energy Efficient Network Lifetime and Optimization

S. K. Padaganur (B) Department of Electronics & Communication Engineering, B.L.D.E.A’s Dr. P.G. Halakatti College of Engineering and Technology, Vijayapur, Karnataka, India e-mail: [email protected] J. D. Mallapur Department of Electronics and Communication, Basaveshwar Engineering College, Bagalkot, Karnataka, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_66

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1 Introduction Future heterogeneous networks are based GSM/UMTS system, which merge IPbased services into the communication system for reducing packet latency. The demand for more data and Internet access has been increasing tremendously in recent years. This can be overcome by using LTE heterogeneous network. The data rate of LTE forward link is more than reverse link. The multiple access method used for downlink is different that of uplink. For downlink, it uses OFDMA and SC-FDMA for uplink transmission. The request for more speed data rates from user is rising drastically, and standardized mobile networks will undergo different issues, while handling with data of network. These boundaries are correlated with the existing bandwidth and capability of the system. However, heterogeneous systems are combination of two or more different technologies, which enhance the data rates, efficiency and to reduce traffic problems. Figure 1 illustrates the structure of heterogeneous network. Heterogeneous LTE network supports many applications in real-time environment. Therefore, such application consumes more power in the network. Hence, to execute time_critical_application, each node should possess more energy. The power supplied to each mobile node is very limited; therefore to enhance the power of each mobile node, the mobile node has to be swapped with other register neighbor mobile nodes. The remainder of the paper consists of sections such as Sect. 2 representing the related papers, where we study how the previous scheme discusses on the efficient

Fig. 1 Heterogeneous Network

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schemes. Section 3 will explain the information of proposed work. Section 4 presents simulation platform, network configurations, performance metrics, and comparative results. Section 5 discusses the conclusion of the proposed work.

2 Related Work Many different types of methods have been planned for enhancing lifetime of network [1]. In this paper, the method of power saving is been done by differential criteria using data mules or mobile sinks [2, 3]. In this chapter, paper explained how the power is saved using data mules approach. In data mules, the powerful mobile node visits each node of network, collects the information about mobile node, and physically takes data to sink. The purpose is to rotate the mobile node one by one to minimize the differential power consumption [4]. This paper presents how mobility of the node is controlled by means of low-power method in embedded network. This also explains the data mule approach for the mobility of the node [5]. This paper described how the power saving can be done using data reduction approach. In this approach, the mobile node reduces the amount of power in the data transmission and/or generation [6]. This also explains minimizing the power in network to improve network lifetime using the data mule approach [5]. In this chapter, it explains the data reduction method in the network. The mobile nodes consume less power during data transmit and generation [7]. Power saving is done using mobile relay approach. In the relay approach, some mobile nodes are having more power and memory to process the data [8]. This paper represents how the power is controlled in cognitive networks. It also explains how the external factors like user behavior, network load, and quality of channel are affecting the system performance. In [9], the authors are considering the factors like modulation and power control for accessing the network to reduce consumption in power for achieving efficient energy. In [10], author expressed how the channel is allocated to the requested user. To allocate the channel for users, the spectrum map concept is used. In [11], authors are explained about the resource allocation using distributed methods. This method depends on queue balance, which is converted into number of hops in the cognitive network. In [12], author presented opportunistic spectrum allocation in LTE heterogeneous network. It also explains how the algorithm controls the power for bandwidth sharing. In [13], author presented about trade-off between power efficiency, network capacity, and backhaul capacity. In [14], author done survey on D2D communication in LTE network. It also explains how the spectrum is allocated in heterogeneous network.

3 Proposed Work In this paper, the optimization of node swapping is proposed. In the present system, swapping of node is not implemented, so consumption of power is more at the critical

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Fig. 2 Optimized Node Swapping Scheme Diagram

node. Critical node is the one which is identified prior to the sink node. In the simple swapping of the node method, it only considers for power remaining in the node. But in our proposed work, we consider the optimization of node swapping which is done. Optimization is done with respect to power constraint as well as distance or location of the neighbor node for selection of critical node. The major issue in heterogeneous network is the power consumption for the data transmission and data generation. This will reduce the life span of system. Hence to improve the overall network lifetime, we propose the Optimized Node Swapping method, which is shown in Fig. 2. In this proposed scheme, whenever application arrives, the network will check its source and sink node to transmit the incomingdata. At the same time, it also identifies the critical node. Critical node is the node, which is prior to the sink node. Since critical node consumes more power because of most of the data communication takes place through it. At regular instant of time, our scheme monitors the power at the critical node. If its sufficient power exists at critical node, then it continues with data transmission. If power at critical node is not sufficient, then it applies optimized node swapping approach. In the optimized node swapping method, it is not only swapping critical node with high-power neighbor register node but also it considers the distance with that of neighbor register node. This will lead to enhance the overall network lifetime as well as to reduce the latency for node swapping. Figure 3 shows the simulation flowchart and architecture.

4 Simulation and Results Simulations: The proposed optimized node swapping performance is implemented and tested by simulation using ns2 network simulator. From Table 1, it is clear that different simulation parameters are considered for the proposed work scenario. It has been observed that, by continuous monitoring of the critical node, whenever power is reduced, then we execute our proposed optimized

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Fig. 3 Simulation flowchart and architecture Table 1 Simulation Parameter for Evaluation

Parameter

Value

Space of simulation

300 × 300

Number of nodes Wireless model

10 Two-way model

Antenna Model of energy

Omnidirection Energy model

Channel type

Wireless

Simulation time Link type

160 Duplex-link

node swapping method. The proposed work enhances the overall lifetime of the network. Figure 4 represents the generation of heterogeneous network. After executing the optimized node swapping method for different simulation time, the following parameters of the network are analyzed.

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Fig. 4 Proposed Work Simulation

Fig. 5 Network Lifetime Analysis

• • • •

Network Life time Analysis

Network Lifetime Analysis. Data Packet Analysis. Energy Consumption Analysis. Node Swapping Latency.

Figure 5 describes the improvement in the network lifetime. In the proposed work, the optimized node swapping is executed which enhances the overall lifetime of the network. Figure 6 represents the data packet analysis, in which the present system where node swapping not exists; therefore, packet loss is more. In the proposed system,

Optimized Node Swapping for Efficient Energy Usage … Fig. 6 Data Packet Analysis

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Data Packet Analysis

Fig. 7 Energy Consumption Analysis

because of the optimized node swapping, it reduces the packet loss and maximizes the throughput of the system. From Fig. 7, it indicates that energy utilized for the proposed system is a smaller amount related to the present system. This is because we deploy optimized node swapping in the proposed system. Figure 8 describes the analysis of node swapping latency. Even though it degrades the performance of the network, it is very negligible. From the graph, it is clear that node swapping time is less than 10%, which is very less.

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Fig. 8 Node Swapping Latency

5 Conclusion Future heterogeneous systems are the solution to recent demand of great speed access for Internet and its related applications. Hence, they consume more power to fulfill their demands of end user. Therefore in this work, we propose new scheme called Optimized Node Swapping Scheme, which will improve the total lifespan of the system. This scheme is implemented on critical node basedon their power-level and distance. The results show that the proposed work improves the overall lifetime of the system, reduces the packet loss of the system, and increases the efficiency with minimum Node Swapping Latency.

References 1. S. Jain, R. Shah, W. Brunette, S. Roy, Exploiting mobility for energy efficient data collection in wireless sensor networks. MONET 11(3), 327–339 (2006) 2. C.-C. Ooi, C. Schindelhauer, Minimal energy path planning for wireless robots, in ROBOCOMM, p. 2 (2007) 3. J. Luo, J.-P. Hubaux, Joint mobility and routing for lifetime elongation in wireless sensor networks, in INFOCOM, pp. 1735–1746 (2005) 4. A. Kansal, D.D. Jea, D. Estrin, M.B. Srivastava, Controllably mobile infrastructure for low energy embedded networks. IEEE Trans. Mob. Comput. 5(8), 958–973 (2006) 5. H. Xu, L. Huang, Y. Zhang, H. Huang, S. Jiang, G. Liu, Energy-efficient cooperative data aggregation for wireless sensor networks. J. Parallel Distrib. Comput. 70(9), 953–961 (2010) 6. S. Jain, R. Shah, W. Brunette, G. Borriello, S. Roy, Exploiting mobility for energy efficient data collection in wireless sensornetworks. Mobile Netw. Appl. 11(3), 327–339 (2006) 7. C. Tang, P.K. McKinley, Energy optimization underinformed mobility. IEEE Trans. Parallel Distrib. Syst. 17(9), 947–962 (2006) 8. W. Ren, Q. Zhao, A. Swami, Power control in cognitive radio networks: How to cross a multilane highway. IEEE J. Sel. Areas Commun. 27(7), 1283–1296 (2009)

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9. V. Osa, C. Herranz, J.F. Monserrat, X. Gelabert, Implementing opportunistic spectrum access in LTE-advanced. EURASIP J. Wireless Commun. Netw. 2012, 99 (2012) 10. C.-E. Weng, J.-M. Zhang, H.-L. Hung, An efficient power control scheme and joint adaptive modulation for wireless sensor networks. Comput. Electr. Eng. 40, 641–650 (2014) 11. Q. Song, Z. Ning, Y. Huang, L. Guo, X. Lu, Joint power control and spectrum access in cognitive radio networks. J. Netw. Comput. Appl. 41, 379–388 (2014) 12. F. Baccelli, N. Bambos, N. Gast, Distributed delay-power control algorithms for bandwidth sharing in wireless networks. IEEE/ACM Trans. Netw. 19(5), 1458–1471 (2011) 13. D.W.K. Ng, E.S. Lo, R. Schober, Energy-efficient resource allocation in multi-cell OFDMA Systems with limited Backhaul capacity. IEEE Trans. Wireless Commun. 11(10) (2012) 14. J. Liu, N. Kato, J. Ma, N. Kadowaki, Device-to-Device Communication in LTE-Advanced Networks: A Survey (IEEE, USA, 2013), pp. 1553-877X (c)

Content-Based Video Shot Boundary Detection Using Multiple Haar Transform Features D. Asha and Y. Madhavee Latha

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discrete Haar Transform (DHT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed Video Shot Boundary Detection Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Multiple Feature Vector Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Feature Extraction Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Construction of Continuous Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Procedure-Based Shot Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Experiment Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Dataset and Evaluation Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In the entire content-based video technologies, video shot boundary detection (VSBD) is a crucial task. VSBD is used to segment the video into shots. In this paper, we propose a VSBD technique which identifies the abrupt (cut) gradual transitions in video sequence. In our technique, we first extract the multiple feature vectors from video sequence using discrete Haar transform (DHT) for N  4. The similarity between successive video frames is measured using extracted features, and a continuous signal is computed. To identify shot transition, procedure-based shot detection algorithm is applied on continuous signal. The proposed technique is evaluated on TRECVID test collection videos. The experimental results show that the proposed technique performs better than the existing method in identifying cuts and gradual transitions. Keywords Video shot boundary detection (VSBD) Discrete Haar transform (DHT) · Cut transition (CT) · Gradual transition (GT) D. Asha (B) JNTUH, Hyderabad, Telangana, India e-mail: [email protected] Y. Madhavee Latha Malla Reddy Engineering College for Women, Hyderabad, Telangana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_67

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1 Introduction By the progression in multimedia technology, digital information has an explosive growth. This radical enhancement in the accessibility of digital videos has derived the emergent demand of innovative technology and requirement for automatic tools that can competently perform video retrieval, browsing, indexing, and managing video sequence. In all the video technologies, analyzing video structure is necessary. The major two levels in content-based video structure are shots and frames. Shot identification is considered to be a suitable phase for video indexing and contentbased video retrieval (CBVR). A shot is an array of frames captured by an uninterrupted camera operation. The transition among the shots called as shot boundaries can be further classified as: cut transition (CT) and gradual transition (GT). Cut transition is an abrupt change from one shot to the next shot. The gradual transition is a slow change in video information, and it lasts for several frames. GT occurs because of the editing process. There are various different types of editing effects such as fade out, fade in, dissolve. The procedure that automatically segments a video sequence into shots is called as video shot boundary detection (VSBD) [1, 2]. The VSBD method is accomplished in three steps: feature extraction, building a continuous signal (measures the similarity between successive video frames), and the shot boundary identification (identifies the type of transition). In the paper, we put forward a VSBD technique. In our technique, the feature extraction is the initial step where DHT kernels are used to extract multiple features like color, texture, edge, and motion vector. The correlations between the extracted feature vectors of the successive video frames are evolved, and a continuous signal is constructed. The continuous signal is finally used for VSBD algorithm to identify shot transitions. The outline of our paper is structured as follows: Sect. 2 gives the literature review of the existing VSBD techniques. In Sect. 3, the basics of DHT are discussed. In Sect. 4, we explain our proposed VSBD technique. In Sect. 5, our technique’s experimental results and discussions are presented. Lastly, the conclusion of our paper is illustrated in Sect. 6.

2 Literature Review A variety of automatic VSBD techniques has been implemented and proposed in the past few decades. The early work on VSBD was focused on abrupt (cut) transition than on gradual transition. An extensive range of features have been adopted in the literature initially from the pixel-based matching, grayscale, and color histogrambased detection, edge feature based, combined spatial features, motion vector-based approaches. Various automatic VSBD techniques have been studied in [1–5]. In [4], Asha et al. used simplest color histogram scene change detection where color his-

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tograms were computed on successive video frames. The drawback of this technique is that there is a possibility of two different frames having the same histogram. The visual content is different extremely, and it may lead to miss in transition detection. In [3], Lakshmipriya et al. discussed block-based histogram difference where each video frame is divided into a number of blocks, and histograms are computed between the respective blocks of successive video frames. The demerit of this technique is sensitive to large camera motion. In [2], Lu et al. proposed a scheme, i.e., SVD-based VSBD where single value decomposition (SVD) is computed on video frames, and pattern matching algorithm was applied on a segment of video. Literature review papers on VSBD recommend that the most important challenges to the current VSBD techniques are identification of gradual transition (GT) and exclusion of disorders caused by varying lighting or quick object/camera motion. These disorders are time and again falsely identified as video shot boundaries. As a result, it is an exigent task to develop a technique that is not only insensible to various disorders, but also sensible enough to identify video shot changes. In addition, we practically observed that a technique with a single feature vector is not sufficient to identify diverse types of video shot boundaries in the existence of varying lighting or quick object/camera motion. Therefore, there is a need for a video shot detection technique and feature vector description that decreases the influence of the abovementioned problems. The feature vector extraction method with multiple features [6] like color, texture, edge, and motion vectors can design better VSBD. However, the techniques must be uncomplicated so that it must use an efficient tool for feature extraction.

3 Discrete Haar Transform (DHT) Discrete Haar transform (DHT) is vastly used in various digital image processing applications due to its simplicity, energy compaction, robustness, and flexibility in performance. DHT is used in digital image/video applications because it offers fast computation of discrete Haar coefficients requires less memory storage space. Representation of frames in transform domain rather than in spatial domain has two benefits. The first advantage is it splits the low- and high-energy contents of video frames; it helps in decreasing the feature vector size. Second, video information in transform domain generally is free from illumination (lighting) and rotational variations of spatial domain information. The merits of transform domain create an inevitable option for feature vector extraction [7]. Let f (x, y) be an image of size M × M whose forward DHT X(u, v) can be expressed as X (u, v) 

M−1  M−1  x0 y0

f (x, y)h b (x, y, u, v)

(1)

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For x, y, u, v  0, 1, …., M − 1 and hb (x, y, u, v) is forward kernels or basis. For Haar transform, Haar basis functions hb (z) is defined under closed interval z ε[0,1] for b  0, 1, 2, … N − 1, where N  2n . Here integer b is defined as b  2i + j − 1, where 0 ≤ i ≤ n − 1, j  0 or 1 for i  0, and 1 ≤ j ≤ 2i for i  0. Then Haar basis or kernel functions is ⎫ ⎧ i/2 i i⎪ ⎪ ( j − 1)/2 ≤ z < ( j − 0.5)/2 2 ⎪ ⎪ ⎬ 1 ⎨ i/2 i i (2) h b (z)  h i j (z)  √ −2 ( j − 0.5)/2 ≤ z < j/2 ⎪ N⎪ ⎪ ⎪ ⎭ ⎩ 0 otherwise, z ∈ [0, 1] For N  4, the Haar transform matrix [8] is shown in Eq. (3), and for simplicity, we approximated it by considering the normalized Haar transform matrix shown below. ⎤ ⎡ ⎤ ⎡ 1 1 11 1 1 1 1 ⎥ ⎢ ⎥ ⎢ −1 −1 ⎥ ⎢ 1 1 −1 −1 ⎥ 21 1 ⎢ ⎥ ⎢ ⎥ ⎢ (3) H4  √ ⎢ √ ⎥ ⎥≈⎢ √ ⎢ ⎢ ⎥ 4⎣ 2 − 2 0 0 ⎦ ⎣ 1 −1 0 0 ⎥ ⎦ √ √ 0 0 1 −1 2− 2 0 0

4 Proposed Video Shot Boundary Detection Technique The proposed VSBD Technique is discussed in this section. Video is read into the system, and DHT kernels are projected on each successive video frames, and multiple features are extracted. The correlation between consecutive feature vectors is calculated, and a continuous signal is constructed to measure the similarity and dissimilarity between the successive video frames. Procedure-based video shot detection algorithm is applied to identify shot transition. The proposed VSBD technique block diagram is shown in Fig. 1a.

4.1 Multiple Feature Vector Extraction The feature vector extraction stage is very important in the identification of video shot boundaries. Therefore, we have to choose efficient features from the video sequence, which are robust to false transitions such as varying lighting or quick object/camera motion. We propose a technique that extracts features like color, texture, edge, and motion vectors by applying DHT basis functions in video sequence. The DHT kernel for N  4 is shown in Fig. 1b. The basis functions of DHT are represented in vectors as W  {w1 , w2 , …, w16 } shown from left to the right of the

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Fig. 1 a Proposed video shot boundary detection technique block diagram. b Discrete Haar transform (DHT) kernel

DHT Kernel specified in Fig. 1b. These basis vectors or kernels of the DHT are used for extracting features. The inspiration behind selecting the kernel is that normally in image transforms, we have low frequencies communicating significant image features like intensity and high frequencies communicating the edges of the image. Accordingly, we used the kernel w1 for low frequency and from w2 to w16 for the high-frequency demonstration. The DHT values obtained by applying WH1 are used for color feature vector extraction, and w1 , w5 , w6 are used for edge and texture feature extraction. Let the kernels used for feature extraction be represented as W 1  w1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], W 2  w5  [1, 1, 1, 1, 1, 1, 1, 1, −1, −1, −1, −1, 0, 0, 0, 0], W 3  w6  [1, 1, −1, −1, 1, 1, −1, −1, −1, 1, 1, 1, 0, 0, 0, 0], and W 4  H 4 . The edge feature vector is calculated by projecting w5 , w6 kernels, and the texture feature vector is calculated by projecting w1 , w5 , w6 kernels. The motion feature vector is computed by first projecting DHT matrix on successive video frames and then motion vector (ME) is estimated using the sum of absolute difference (SAD) method [4]. The motion strength is extracted by subtracting motion vector (ME) from projected W 4 successive frames, and the correlation between successive motion strength frames is calculated.

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4.2 Feature Extraction Procedure Let X m  {xm 1 , xm 2 , xm 3 , xm 4 }, m  1,2,…. no of blocks, X m represent the projected values of blocks by calculating the inner product of mth block K (K m ) and W j , j  1, 2, 3, 4, respectively. X m are obtained as follows:   X m  x1m  K m , W1 , x2m  K m , W2 , x3m  K m , W3 , x4m  K m , W4 (4) p where K m , W1  i1 K mi ∗ W ji . At this point, p is the number of pixels in each block K m , K mi is the ith value of K m, and W ji is the ith value of W j basis vector. The color feature vector (C m ), edge feature vector (E m ), texture feature vector (T m ), and motion feature vector (M m ) of the consequent block are obtained as: Cm  x1m  K m , W1     2 2 Em  x2m + x3m   Tm   K 2 − Z 2  m

where Z  x1m W1 + x2m W2 + x3m W3 

(5) (6) (7)

3

i1 K m , Wi Wi

  Mm  x4m − M E 

(8)

4.3 Construction of Continuous Signals After obtaining multiple features, the subsequent step in the VSBD is to construct continuous signals for each feature vector, which is prepared by finding the correlation between successive frames in video frames. The correlation values between successive video frames are computed between the blocks of f and f + 1 frames. Consequently, for each feature vector, color (C), texture (T), edge (E), motion (M), and the corresponding continuous signals are computed. We compute the mean of all individual signals, and the continuous signal is generated whose values are in the range of [0, 1]. These combined values are given to procedure-based video shot detection algorithm for identifying shots.

4.4 Procedure-Based Shot Detection Algorithm With the combined continuous signal curve, we detect shot transitions by following the procedure-based algorithm [1]. This algorithm is based on some set of rules which identifies the cuts and gradual transitions. In a video sequence, when the successive

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frames are alike, the continuous value will be high and when the successive frames are divergent, continuous value is less. The mean of continuous signal is considered as a threshold. Based on continuous correlation values, it is simple to identify the existence of abrupt (cut) transition in the video. The abrupt transition (cut) is a sudden change from one video frame to another; the continuous correlation value will be less between the successive frames.

5 Experiment Results and Discussion 5.1 Dataset and Evaluation Criterion The performance of our proposed VSBD technique is tested on TRECVID 2001 data released on “open-video project,” listed in Table 1. We implemented our proposed technique in MATLAB 7.6 on the Windows Xp Professional with CPU of 2.10 GHz. To facilitate evaluation with the other VSBD method, the proposed technique was tested on the TRECVID 2001 test database [9, 10] containing 11 videos; these videos have on average 75 s duration with both cuts (CT) and gradual (GT) transitions. The comprehensive evaluation criteria used to estimate the performance of the VSBD technique are precision (Pr ) and recall rate (Rc ). The precision and recall rate are evaluated by using Eqs. (9) and (10). Superior the value, enhanced is the performance. Pr 

Correct no.of transition ∗ 100; Total no. of transition

Correct no. of transition ∗ 100 (9) Actual no. of transition 2 ∗ Pr ∗ Rc F1 Measure  (10) (Pr + Rc ) Rc 

The explanation of the test video database and performance estimation is given in Tables 1 and 2. The overall transition efficiency of our proposed VSBD technique is compared with a VSBD method using single value decomposition (SVD) [2]; we observe that our proposed technique performs better than VSBD using SVD in identifying the shot transition. Comparison values are shown in Table 3. The average Pr , Rc , F 1 values for the cut transition are 95.14, 92.20, 93.26 and for the gradual transition are 90.19, 90.23, 90.20, respectively. The overall transition Pr , Rc , F 1 measure values for our proposed technique is 93.14, 91.56, and 92.24, respectively. There are few false transitions identified because of fast camera motion or object motion, zooming of an object, and few transitions are missed because of closed similarity between shots. The average implementation time of proposed VSBD technique is 581 s.

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Table 1 Video database used and their respective descriptions Sources Name of the Number of Shot transitions video frames CT GT TRECVID 2001 test data [9], (NASA 25th Anniversary, NASA, documentary, water airline, wildlife)

Anni001

763

1

9

10

Anni007 1327 Anni008 2775 UGS04_001 2349

4 0 4

6 7 9

10 7 13

BOR09_003 3561 Short movie “Piper 2018” Videos downloaded from Google. video [10] (documentary, wildlife, etc.)

Total

5

10

15

Piper

4647

54

4

58

VD1

1151

3

6

9

VD2 VD3 VD4 VD5

1026 1351 401 751

10 13 2 7

6 0 2 0

16 13 4 7

83.33

98.10

100.00 90.00 100.00 100.00 100.00 95.14

BOR09_003

Piper

VD1 VD2 VD3 VD4 VD5 Average

75.00 90.90 92.80 100.00 100.00 92.20

100.00

83.30

F1

85.71 90.45 96.27 100.00 100.00 93.26

99.04

83.31

100.00 88.89 – 88.89

85.70 85.70 – 100.00 – 90.19

100.00

90.00

90.00 85.70 85.70 88.90

85.71 85.70 – 100.00 – 90.23

100.00

90.90

90.00 83.33 87.50 88.90

Rc

Pr

Rc

100.00 100.00 – 80.00

Pr

100.00 80.00 – 100.00

Gradual transition

Cut transition

Anni001 Anni007 Anni008 UGS04_001

Video database

Table 2 Performance estimation of proposed VSBD technique

F1

85.70 85.70 – 100.00 – 90.20

100.00

90.45

90.00 84.50 86.59 88.90

92.85 87.85 100.00 100.00 100.00 93.13

99.05

86.67

95.00 82.85 85.70 94.45

Pr

80.36 88.30 92.80 100.00 100.00 91.56

100.00

87.10

95.00 91.67 87.50 84.45

Rc

Overall transition

86.15 88.07 96.27 100.00 100.00 92.24

99.52

86.88

95.00 87.03 86.59 89.17

F1

Content-Based Video Shot Boundary Detection … 711

95.14

Proposed VSBD

92.20

87.96 93.26

90.66 90.19

84.29

Pr

94.03

GT (Average) F1

Pr

Rc

CT (Average)

VSBD using SVD [2]

Methods

90.23

81.18

Rc

Table 3 A comparison of the proposed technique with the VSBD using SVD [2]

90.20

81.93

F1

93.13

92.34

Pr

91.56

84.57

Rc

Overall (Average)

92.24

86.29

F1

712 D. Asha and Y. Madhavee Latha

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6 Conclusions A new content-based VSBD technique has been proposed. Simple DHT kernels are used to extract multiple features from video frames. Successive video frames feature vectors are compared, and a continuous signal is computed. Procedure-based shot detection algorithm has been designed such that all the shot transitions, i.e., both abrupt and gradual transitions, are identified. The performance of the proposed technique is tested on TRECVID 2001 test data and general videos, and the robustness of the technique is evaluated. The experimental results show that our technique performs better than VSBD using the SVD method in identifying cuts and gradual transitions.

References 1. G. Lakshmi Priya, S. Domnic, Walsh-Hadamard transform Kernel-based feature vector for shot boundary detection. IEEE Trans. Image Process. 23(12), 5187–5197 (2014) 2. Z.-M. Lu, S. Yong, Fast video shot boundary detection based on SVD and pattern matching. IEEE Trans. Image Process. 22(12), 5136–5145 (2013) 3. G.L. Priya, S. Domnic, Video cut detection using block based histogram differences in RGB color space, in International Conference on Signal and Image Processing, pp. 29–33 (2010) 4. D. Asha, Y. Madhavee Latha, V.S.K. Reddy, Content based video retrieval system using multiple features. Int. J. Pure Appl. Math. (IJPAM) 118(14), 287–294 (2018) 5. J. Lankinen, J.K. Kamarainen, Video shot boundary detection using visual bag-of-words, in Proceedings of International Conference Computing Vision Theory Application (VISAPP), pp. 788–791 (2013) 6. Lian, Automatic video temporal segmentation based on multiple features. Soft. Comput. 15(3), 469–482 (2011) 7. S.D. Thepade, N. Yadav, Novel efficient content based video retrieval method using CosineHaar hybrid wavelet transform with energy compaction, in International Conference Computing Communication Control and Automation, pp 615–619 (2015) 8. D.F. Walnut, The discrete Haar transform, in An Introduction to Wavelet Analysis. Applied and Numerical Harmonic Analysis (Birkhäuser, Boston, MA.S., 2004) 9. TREC Video Retrieval Test Collection [online] (2001), Available on website: http://trevid.nist. gov/ and www.open-video.org 10. Google video.URL http://video.google.com/

An Analysis of IPv6 Protocol Implementation for Secure Data Transfer in Cloud Computing Anitha Patibandla, G. S. Naveen Kumar and Anusha Meneni

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Typical Concerns with Hybrid Cloud Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Issues in the Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Importance of IPv6 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Secure Data Transmissions Between Clouds Via IPv6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Information surveillance and approach subordination were superior among the majority ungovernable determined research endeavors in cloud cipher, in perspective of clients deploying their exquisite data to cloud suppliers. Cloud compile maneuvers the client’s records to flexible data focuses that were remotely symphonized, on whichever client has no restraint. It might be significant to apply patterns to establish the impingement in enforcing IPv6 locations on the cloud that might necessitate diversities to routers, terminals, defense strategies, and thus forth. IPAM software tools shall facilitate in scaling IP data to business rationale preferences for instance sectors, preferences, or framework via illustration map and facsimiles of a probable novel groundwork. This article investigates the stumbling blocks and interpretations for realizing an efficient cloud computing environment employing IPv6. This paper delves into cloud computing in an intense elevation that is universally applied to take advantage of IPv6 quickly and effectively, with minimal interruption. Keywords Ipv6 · Cloud computing · Security threats · Data transfers A. Patibandla (B) · G. S. Naveen Kumar · A. Meneni Department of ECE, Malla Reddy College of Engineering and Technology, Dhulapally, Telangana, India e-mail: [email protected] G. S. Naveen Kumar e-mail: [email protected] A. Meneni e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_68

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1 Introduction The multifarious strategies that utilize existing cryptography frameworks to counterpart the redemption and admittance management issues sense the awful effects of staggering computational overhead on the data title holder and moreover the cloud advantage supplier for key arrangement and association. This novel piece of the cloud postures diverse new security threats which should be evidently procured and managed. Cloud computing has been emphasized as a cutting-edge working of Striving Resource Organizations. Reliable data transport and correspondence speculate an essential part in cloud computing. Cloud gauge is a prerequisite for processor assets which are attainable on appeal and access by modus operandi of a structure [1]. The term cloud computing is fluttered by the cloud panorama that is by and large utilized to address in flowcharts and graphs. It is one of the contemporary most torrid examination domains in consideration of its capacity to minimize expenditure associated with computing in the meantime elaborating the adaptability associated with computing business. Cloud computing has escalated as the frontline technology in the contempt age and is being contemplated as the advanced technological impact in the years ahead. Over the years, it has escalated from an application to a highly adaptable technology of IT business. Cloud computing is exhaustively authorized as a stipulation of virtualization, useful computing and it conjointly tackles 3 variations of Models named: SaaS, PaaS, and IaaS as shown in Fig. 1 [2, 3]. Endurance of customer data in the cloud reimbursing infinitesimal respect to its preferences has a lot of exciting defense concerns which ought to be generally explored for making it a solid response for the issue of keeping away from close proximity capacity of data [4]. All these different points of interest offered by the cloud can savor the experience of exercising duties presented via a private cloud by compensating a couple of charges. However, a similar object might be valued with open clouds at any rate or no price tag. In any case, exploiting open cloud service likewise goes through an extra risk in regard to the security of data set away at open cloud.

2 Typical Concerns with Hybrid Cloud Security Cloud computation can be accomplished with a diversity of applicability and categorization facsimiles, with a significant discrepancy among them in the way application and data security are intended. Preferences on employing the cloud and which associations to embrace as recurrently as possible and whether IT administration is indoctrinated that the cloud will recommend competent security conventions and controls are a highly difficult task as indicated in Table 1 [5]. With the sense of commitment concerning reliable and obscure secure data, ensuring persistent application’s ease of use, and meeting the corporate and industrial consistence headings with inclusions

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Fig. 1 Hybrid cloud model Table 1 Identifying the organization’s security requisites helps to fix upon which cloud model would meet your needs Security requisite Private cloud Product cloud Bluelock virtual information Motion enciphered data

Not applicable

✓

✓

Enciphered data at rest

✓

X

Optional

ICSA-Compliance firewall

✓

✓

✓

Protected remote way in

✓

✓

✓

Backing frequency

24 h

Not applicable

24 h

Obligatory background test

X

X

✓

of quality and supporting advancement in the business, the present IT pioneers must progress with their cloud activities while supervising cloud security.

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3 Issues in the Cloud Cloud defense is translating into primary differentiae and pinpoint connecting cloud provider. Utilizing mere prime safety channels, conventions, cloud defense might augmentedly be secured to the height that IT workplaces terminate employing the possessions in scrupulous gear and programming [6]. A way-out hindrance for poignant IT structures in a cloud is the absence of reliance on a cloud granter. The cloud source, in an analogous mode desires to implement stringent defense measures, which requires confidence in the consumers. To keep posted the normal reliance among patron and cloud contributor, a magnificent trust establishment should be associated. Cloud computing can represent precise effects to diverse individuals. The assurance and security issues will apparently contradict between a purchaser employing a moderately spread and open cloud stroll utilizing collections of trade functions on cloud stages, and it delivers a counterfeit assemblage of points of relevance and liability.

4 The Importance of IPv6 Protocol IPv6 has the potential for up-surging addressing capabilities, internetworking, QOS for data communication, mobile computing, and for voice over IP. IPv6 has been designed to operate synchronously with IPv4 while providing better internetworking potential. IPv6 has more authentication and privacy, improved support for extensions hence providing greater flexibility as shown in Fig. 2.

5 Secure Data Transmissions Between Clouds Via IPv6 Considering we have to drive protected data between clouds A and B by employing digital signatures and information enciphering. Let us assume that attacks on the data being transmitted from cloud A might occur. There is a necessity to promise security to the data being transmitted over the cloud. IPv6 protocol has a host of features making it more secure compared to the existing version of IPv4. The following features of IPv6 make it worth utilizing as shown in Fig. 3 [7]. Data Integrity: Information reliability is uncomplicatedly defined into a framework amid a unique catalog. Integrity of data in an uncontrolled structure was kept up by techniques for database goals and trades, which is recurrently encased up by a database organization framework (DBMS). The data should not be gone astray or altered by illegitimate clients [8]. Data Confidentiality: Data confidentiality is essential for clients to pile up their concealed or anonymous information in a cloud. Ratification and admittance control architectures should be exercised to assure data secrecy. Data privacy is the measure

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Fig. 2 IPv6 header format

Fig. 3 IPv4 versus IPv6 authentication [12]

of individuals’ or groups’ data retention as shown in Fig. 4. ORAM expertise visits only some transcripts of information disguising a legitimate transitory by points of

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Fig. 4 Data transfer

customers. ORAM has been largely employed as a segment of software security in the cloud as a plausible technology [7]. Homomorphic Encrypting: Encrypting is typically employed to guarantee the secrecy of information. Cryptographic computation named Diffie–Hellman is anticipated for protected connection, which is significantly not in any mode approximating the key distribution organization ingredient [9]. For additional considerable adaptability and redesigned security, a combined system with unambiguous encryption reckons. Service Abusing: Service exploitation suggests that intruders may mistreat the cloud overhaul and attain superfluous information or else strike the safety of diverse clients. Client data might be corrupted by different clients [10, 11]. Deflecting Attacks: The cloud computing supports gigantic gauge of public assets over the Internet. Cloud infrastructures must be robust for dismissing Denial of Service (DoS) violations. The established model should deliver characteristics of familiarity, reasonably fabricating place set in zones and vitalizing the services. Cloud enactments necessitate that the client transfers their data into cloud fundamentally in the perception of reliance [6].

6 Conclusion The losses incurred by placing highly sensitive data in the cloud can be counterfeited by deploying IPv6. IPv6 has a stronger impact on creating a secure environment for data transfers in a cloud. Concrete defense architecture for secure data records by employing IPv6 framework has been proposed. In our anticipated stratagem, we have essentially strived to sustain the reliability of the data. Data perseverance in cloud computing, a sector flooding with defiance and of vital vastness, is at a halt in its beginning now, and numerous research controversies are yet to be seen. An amiable invocation in this model is if we can accrue an itinerary of accomplishment to terminate both unwrap confirmable status and frontier suitability assertion of live data.

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References 1. L. Badger, T. Grance, R. Patt-Corner, J. Voas, Cloud computing synopsis and recommendations (draft), NIST special publication 800-146, Recommendations of the National Institute of Standards and Technology, Technical Report (2011) 2. P. Mell, T. Grance, The NIST definition of cloud computing, National Institute of Standards and Technology, vol. 53, no. 6, article 50 (2009) 3. V. Goncalves, P. Ballon, Adding value to the network: Mobile operators’ experiments with Software-as-a-Service and Platform-as-a-Service models. Telematics Inform. 28(2011), 12–21 (2011) 4. D. Greenwood, A. Khajeh-Hosseini, J. Smith, I. Sommerville, The cloud adoption toolkit: addressing the challenges of cloud adoption in enterprise. Cloud Computing Co-laboratory, School of Computer Science, University of St Andrews, UK (2011) 5. Cloud Security Alliance, Security guidance for critical areas of focus in cloud computing (2009), https://cloudsecurityalliance.org/csaguide.pdf. Retrieved 24 May 2012 6. V. Gampala, Data security in cloud computing with elliptic curve cryptography. Int. J. Soft Comput. Eng. (IJSCE) 2, 138–141 (2012) 7. B.P. Rimal, E. Choi, I. Lumb, A taxonomy and survey of cloud computing systems, in International Conference on Networked Computing and Advanced Information Management (2009) 8. D. Agrawal, S. Das, A.E. Abbadi, Data management in the cloud: challenges and opportunities. Synth. Lect. Data Manage. 4(6), 1–138 (2012) 9. I. Foster, Y. Zhao, I. Raicu, S. Lu, Cloud computing and grid computing 360-degree compared, in Grid Computing Environments Workshop (GCE’08) (2008). https://doi.org/10.1109/gce. 2008.4738445 10. A.K. Hosseini, I. Sommerville, I. Sriram, Research challenges for enterprise cloud computing. Unpublished, http://arxiv.org/abs/1001.3257 (2010) 11. I. Crawford, Marketing Research and Information Systems. Food and Agriculture organization of the UN (1997), http://www.fao.org/docrep/W3241E/W3241E00.htm. Retrieved 26 May 2012 12. D.G. Chandra, M. Kathing, D.P. Kumar, A comparative study on IPV4 and IPV6, in 2013 International Conference on Communication Systems and Network Topologies, 6–8 April 2013

Anomaly Detection in Crowd Using Optical Flow and Textural Feature Pranali Ingole and Vibha Vyas

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Background Subtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Optical Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Crowd Motion Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

724 725 726 727 727 728 728 729 729 731 731

Abstract This paper aims at solving real-world surveillance problems using computer vision and motion estimation techniques. It focuses on detecting abnormal crowd behaviour and locating it in dynamic crowd condition. In this paper, a combined approach is the proposed using the crowd motion analysis and texture-based analysis. Lucas–Kanade optical flow method is used for the estimation of motion in the scene. Also, texture-based feature and entropy give the statistical measure of randomness which is used for localization of crowd. The University of Minnesota (UMN) database has been used for testing. Keywords Motion estimation · Optical flow · Crowd motion energy (CME) Crowd motion intensity (CMI) · Threshold · Entropy · Kinetic energy (KE)

P. Ingole (B) · V. Vyas (COEP) College of Engineering, Pune, India e-mail: [email protected] V. Vyas e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_69

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1 Introduction Nowadays, public security has been focused since it is a very important issue. Due to the rise in population and urbanization, human activities are becoming more frequent. Social activities like mass gatherings are very popular and recurrent these days; such large events may lead to stampede. Human stampedes also occur in episodes of panic. Most of the crowd disasters could have been prevented by simple management strategies. Table 1 shows some examples of stampede. For better security video, surveillance is used either manually or automatically. Video surveillance is a hot research topic. Traditional image and video processing have a drawback that it was not able to find all the valuable clues at the first sight, while in intelligent video surveillance system, the relationship between the variations of pixel can be seen in a frame sequence. Due to this, the development and evolution of an event can be estimated. Traditional methods can be less efficient due to false positives and false negatives; also, it can cause prolonged visual fatigue, decreased attention. Crowd varies in different circumstances [1]. It is challenging to detect the anomaly in high density, heavy occlusion conditions and in panic situations due to complicated behaviour of people. Recently, researches are focusing on such circumstances. This paper focuses on the abnormal behaviour of crowd detection and localization of the anomaly in the scene using motion estimation techniques. Optical flow has been used for calculating the motion vectors; further, entropy has been used for localization of the crowded area. The existing work related to the domain of abnormal crowd behaviour detection, crowd motion and energy estimation is discussed in Sect. 2. Methodology and flow of the system are explained in details is Sect. 3. The experimental results are discussed in Sect. 4. Conclusion and future scope are discussed in Sect. 5.

Table 1 Examples of stampedes and crushes occurred worldwide S. No. Year Location Deaths 1 2

2017 2017

Mumbai Turin, Italy

3

2015

4

2014

Annual Hajj in Saudi Arabia Shanghai, China

5

2011

Sabarimala, in Kerala, India

6

2006

Pasig City, Philippines

22 1 2177

Injuries 35 1500+ 934

36

47

106

100

73

400

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2 Existing Work For detection of the anomaly, the study of characteristics of emergency behaviour of crowd is important. Sharbini and Bade [2] discussed these in details. In paper [3], Junior et al. discussed various models using computer vision techniques to calculate the number of people in crowded scenes. These techniques include pixel-level analysis, texture-level analysis and object-level analysis. Rabiee et al. [4] presented a novel crowd dataset containing about 45,000 videos which are annotated by fine-grained abnormal behaviour. In image processing, background subtraction is an important preprocessing step. Zivkovic [5] developed an adaptive algorithm using Gaussian mixture probability density function for background subtraction. The algorithm is based on pixel-level approach and automatically selects the required number of components per pixel and hence completely adapts to observed scene. Optical flow techniques are most commonly used by researchers for motion and direction tracking in any video. Cao et al. [6] estimated the crowd kinetic energy and motion direction using optical flow. The paper also suggests using machine learning methods to build up more intelligent systems which can work in more complicated circumstances. Graphics processing unit (GPU) can be used for high computation power. A novel method based on kinetic energy and potential energy is proposed by Xiong et al. [7] to detect typical abnormal activities: running and pedestrian gathering. Kajo et al. [8] summarized optical flow methods in purpose of crowd velocity and direction estimation. Additionally, some of the crowd analytic systems are listed using these methods, and strengths and limitations of each method are discussed. Wang et al. [9] proposed a method for the real time which is based on adjacent flow location estimation. This method solves the problem of characteristic points losing in traditional foreground computing methods. For crowd modelling and processing in real time surveillance, crowd energy is defined by Zhong et al. in [10]. In this paper, a new approach is presented using wavelet analysis of the energy curve and crowded environment is focused to deal with crowd abnormalities. Liu et al. [11] proposed a method which is adaptive to changing light conditions. This method suggests setting a dynamic threshold to adapt related to unstable optical flow, instead of conventional static threshold. To improve energy model, the intensity of crowd is considered, which is the addition of all velocity vectors. Halbe et al. [12] proposed an algorithm which combines energy model and threshold. Displacement vectors are estimated using optical flow method, and CME is computed which is modified to CMI. CMI peaks indicate the abnormal activity and are detected applying threshold. It suggests adaptive threshold method which can improve the accuracy further. Wang et al. [13] proposed innovative texture-based analysis method for crowd dynamics and for distinguishing crowd behaviours. There is so much work done on this topic to detect abnormal crowd behaviour. Many researchers have used optical flow techniques to estimate the motion. In this paper, also optical flow is being used for motion estimation, crowd motion intensity

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is calculated further by applying threshold, the frames in which more anomaly is present are detected, and texture feature is used for the anomaly localization. The methodology is explained in details in the next section.

3 Methodology The aim of this paper is to detect the anomaly in a video sequence at the moment it happens and detect the location. Figure 1 shows the outline of steps involved in detection of abnormal crowd behaviour and localization.

Fig. 1 Outline of steps involved in methodology

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3.1 Background Subtraction Background subtraction (BS) is a method in which the foreground of any image is extracted for processing. It is also known as foreground extraction. In image processing and computer vision, BS is used to find the region of interest, for example, humans, cars and text. It is done by frame differencing in which a frame B, which is taken as background image, and F(t) which is frame obtained at time ‘t’ are compared, and image subtraction is done. Mathematically, it is represented as shown in (1). I [F G(t)]  I [F(t)] − I [B]

(1)

where I [F G(t)] is intensity of pixel in foreground image, I [F(t)] is intensity of pixel in frame at time ‘t,’ and I [B] is intensity of pixel in background image.

3.2 Optical Flow Optical flow is defined as the pattern of motion of the objects in an image between the consecutive frames. It can be caused due to the motion of objects or the camera. It can be expressed as a displacement vector in 2D vector field which shows movement of pixels from one frame to another. There are some assumptions on which optical flow works: 1. Between consecutive frames, the pixel intensities remain the same. 2. Neighbouring pixels show a similar motion. Consider P(x, y, t) as a pixel in the first frame; it travels by (dx, dy) distance in the next frame which is taken after time dt. So following the assumptions, it can be said that those pixels have not changed and intensity is also the same; thus, (2) is obtained. P(x, y, t)  I (x + dx, y + dy, t + dt)

(2)

Now, take Taylor series approximation of RHS and remove common terms. Dividing by dt, following Eq. (3) is obtained: f x u + f y v + ft  0 where fx 

∂f ∂f dx dy , fy  , u , v ∂x ∂y dt dt

(3)

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Above is optical flow equation in which f x and f y are image gradients and dt is time gradient. Here, (u, v) is unknown, and several methods are given to solve this equation for (u, v). The method used in this paper is Lucas–Kanade method. In this method,  a 3 × 3 square is taken about the pixel, all nine points have same motion, and f x , f y , f t for such pixels is calculated. Now, nine equations are to be solved with two unknown variables; this makes it overdetermined. Least square fit method gives better solution. Equation (4) is as follows: ⎡ ⎤−1 ⎡ − f x2i f xi f yi f xi f ti   ⎢ i ⎥ ⎢ i i u ⎢ ⎥ ⎢  ⎣ 2 ⎦ ⎣ v − f yi f ti f xi f yi f yi i

⎤ ⎥ ⎥ ⎦

(4)

i

i

3.3 Crowd Motion Energy Optical crowd energy is described as crowdedness of the scene which is calculated by using kinetic energy [10]. The object in motion has some kinetic energy which is in proportion to the displacement vector in image [14]. This energy can be calculated by using (5). ECn 

m

w j v 2j

(5)

j1

where ECn is the crowd energy of nth frame, w j is weight, and v j is displacement vector of motion feature [10]. Further, this is modified to (CMI) by multiplying the crowd energy by αi which is the ratio of foreground to background [12]. The equation for the same is given in (6). CMIi  αi

n

v 2j

(6)

j1

3.4 Segmentation The frame which has the high value of crowd motion intensity is segmented into 16 sub-images for localization of the crowd. Segmentation is required since the whole frame cannot be processed for the same value of threshold as it contains some empty area or crowded area.

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3.5 Entropy Crowd behaviour is often considered as a group of individuals performing some similar activities over a period of time. For detecting such behaviour, this paper uses the crowd texture as a feature which is similar to the entropy of an image [13]. Entropy of any image is the level of randomness. Lower entropy indicates directional crowd movement, and high entropy indicates no people or crowded area with movement in all direction.

4 Experimental Results The results for a video from the publicly available standard UMN database are shown below using the proposed methodology. The database contains set of videos of people walking and running and has resolution 1280 × 720. Figure 2 shows the results for the preprocessing in which background subtraction is done. Figure 3 shows the initial and final stages in the optical flow vector calculation. In (a), the feature points are shown, and in (b), the path followed by these features during the motion is tracked. Figure 4 shows the optical flow for the 270th frame which happens to be

Fig. 2 a Previous frame. b Next frame. c Background subtracted image

Fig. 3 a Optical flow features in initial frame. b Optical flow vectors tracking the motion till last frame

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Fig. 4 Optical flow in frame 270 which shows more anomalies

Fig. 5 Segmented 270th frame for crowd localization

the frame in which large anomaly is present; i.e., people started running. Figure 5 shows the segmentation of the frame in 16 sub-images which are further processed to calculate entropy for the anomaly localization. The frame is segmented in 16 sub-images, and entropy of each sub-image is calculated. Table 2 shows the entropy values of the segmented frame. Table 3 shows the density which is divided into three sub-categories dense, sparse and no people in the corresponding sub-image.

Anomaly Detection in Crowd Using Optical Flow … Table 2 Entropy values of the segmented frame

Table 3 Density of the crowd

1 2 3 4

731

1

2

3

4

4.0390 3.9745 4.0186 4.2099

6.0879 7.0063 6.7625 6.5870

6.1455 6.8794 6.5966 6.5267

4.4593 4.1555 4.4555 3.8950

1

2

3

4

1

No people

Dense

Dense

No people

2

No people

Dense

Dense

No people

3

No people

Dense

Dense

No people

4

No people

Dense

Dense

No people

5 Conclusion In this paper, the preprocessing is done using the background subtraction by using different methods. Then, the optical flow is calculated using the Lucas–Kanade method which gives the displacement vectors which are utilized to find the crowd motion intensity. CMI values are further utilized to find the frame which contains the anomaly in large scale. To localize the anomaly, entropy is calculated and threshold is applied to find dense area. The threshold is considered on experimental basis. Further, the results can be improved for different illumination conditions.

References 1. H.Y. Swathi, G. Shivakumar, H.S. Mohana, Crowd behavior analysis: a survey, in International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT) (IEEE, 2017), pp. 169–178 2. H.B. Sharbini, A. Bade, Analysis of crowd behaviour theories in panic situation, in International Conference on Information and Multimedia Technology, 2009. ICIMT’09 (IEEE, 2009), pp. 371–375 3. J.C.S.J. Junior, S.R. Musse, C.R. Jung, Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27(5), 66–77 (2010) 4. H. Rabiee, J. Haddadnia, H. Mousavi, M. Kalantarzadeh, M. Nabi, V. Murino, Novel dataset for fine-grained abnormal behavior understanding in crowd, in 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2016 (IEEE, 2016), pp. 95–101 5. Z. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction, in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 2 (IEEE, 2004), pp. 28–31 6. T. Cao, X. Wu, J. Guo, S. Yu, Y. Xu, Abnormal crowd motion analysis, in IEEE International Conference on Robotics and Biomimetics (ROBIO), 2009 (IEEE, 2009), pp. 1709–1714 7. G. Xiong, X. Wu, Y.L. Chen, Y. Ou, Abnormal crowd behavior detection based on the energy model, in IEEE International Conference on Information and Automation (ICIA), 2011 (IEEE, 2011), pp. 495–500

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8. I. Kajo, A.S. Malik, N. Kamel, Motion estimation of crowd flow using optical flow techniques: a review, in 9th International Conference on Signal Processing and Communication Systems (ICSPCS), 2015 (IEEE, 2015), pp. 1–9 9. G. Wang, H. Fu, Y. Liu, Real time abnormal crowd behavior detection based on adjacent flow location estimation, in 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), 2016 (IEEE, 2016), pp. 476–479 10. Z. Zhong, W. Ye, S. Wang, M. Yang, Y. Xu, Crowd energy and feature analysis, in IEEE International Conference on Integration Technology, 2007. ICIT’07 (IEEE, 2007), pp. 144–150 11. Y. Liu, X. Li, L. Jia, Abnormal crowd behavior detection based on optical flow and dynamic threshold, in 11th World Congress on Intelligent Control and Automation (WCICA), 2014 (IEEE, 2014), pp. 2902–2906 12. M. Halbe, V. Vyas, Y.M. Vaidya, Abnormal crowd behavior detection based on combined approach of energy model and threshold, in International Conference on Pattern Recognition and Machine Intelligence (Springer, Cham, 2017), pp. 187–195 13. J. Wang, Z. Xu, Y. Cao, Y. Xu, Wavelet-based texture model for crowd dynamic analysis, in 23rd International Conference on Automation and Computing (ICAC), 2017 (IEEE, 2017), pp. 1–5 14. Z. Zhong, M. Yang, S. Wang, W. Ye, Y. Xu, Energy methods for crowd surveillance, in International Conference on Information Acquisition, 2007. ICIA’07 (IEEE, 2007), pp. 504–510

Automatic Tonic (Shruti) Identification System for Indian Classical Music Mahesh Y. Pawar and Shrinivas Mahajan

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Pitch Frequency Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Dominant Frequency Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Features Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

734 734 735 736 737 739 739 740 741 741

Abstract In Indian classical music, identification of basic pitch is a crucial task to explore information of full raga. Here recorded dataset of both instrumental and vocal raga excerpts is used. Pitch frequency is calculated using subharmonic-to-harmonic ratio. Pitch estimation using SHR is tested for nine different instruments, and it shows 95.06% pitch detection accuracy which is better compared to other methods. Each frame of the raga is processed to increase efficiency in the tonic identification. Pitch histogram is used to extract various features of the raga. The various machine learning algorithms are tested, and the best algorithm is used to find set of rules for the evaluation of Tonic. The very famous J48 decision-tree algorithm shows highest test accuracy of 92.86% for tonic identification. This test model is further used to build an iterative system for tonic identification with the highest confidence. The proposed system is tested for two datasets. Tonic identification accuracy for the first dataset is 90.5%, and it is 93.05% for the second dataset. Keywords Tonic identification · Indian art music · Raga · Swara Raga identification · Musical information retrieval Subharmonic-to-harmonic ratio M. Y. Pawar (B) · S. Mahajan College of Engineering, Pune 411005, India e-mail: [email protected] S. Mahajan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_70

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1 Introduction In Indian classical music (ICM), the raga structure composed of a main vocal excerpt and the drone instrument, which is played in the background [1]. Raga composed of swaras (i.e., notes). ICM includes both Hindustani (North Indian) and Carnatic (South Indian) music. In ICM, drone instrument is Tambora, Veena, Sitar, or like another string instrument. The singer syncs with the voice of drone instrument to form a beautiful raga melody [2]. Tonic is the basic pitch used for raga’s rendition. Tonic is recognized and full pitch range is explored. Tonic is also called as Shruti in ICM. It is essentially Sa(shadaja) swara. All other notes in the phrase of the raga are constantly related to this tonic pitch. Therefore, automatic tonic identification will become an important prerequisite for automatic raga recognition. From the correct Tonic the octave where that Tonic frequency resides can be obtained. Spectrogram of Kalyani raga of duration 20 s is shown in Fig. 1. This shows two different harmonic series out of which one is leading voice of a singer and another is sound of drone instrument which is played in the background. Voice of a singer is present near 3500 Hz while drone instrument sound is present near 1000 Hz.

2 Related Work Autocorrelation method for pitch estimation is suggested to obtain fundamental score of music [2]. First effort for fully automated alapana analysis is done by Ranjani et al. [3]. The cepstrum-based approach in lower octaves is suggested for tonic identifica-

Fig. 1 Spectrogram of Kalyani raga showing sound of drone instrument and singer’s voice separately

Automatic Tonic (Shruti) Identification System for Indian …

735

tion. It was shown that tonic pitch values for a male are in the range 110–180 Hz, for female 150–250 Hz, and for instruments 120–170 Hz [4]. Use of group delay function for identifying Tonic in Carnatic music is used by Ashwin Bellur and Hema A. Murthy. Tallest peak and template-matching methods are used for group delay histogram. Tallest peak method shows 83.85% accuracy, and template-matching method shows 90.70% accuracy in the identification of the tonic pitch value [5]. Various features such as harmonics, fundamental frequency, signal energy, and zero crossing rate are used in the analysis of an Indian classical music [6]. Authors in [7] proposed the two methods which are evaluated for ICM. Results for male singers show the accuracy of 93.83% compared to those sung by female singers which are 90.3%. Pillai and Mahajan [8] proposed a method to identify Melakartha ragas from audio recordings of Carnatic classical music. Modified autocorrelation method in acoustic model detects pitch frequency of musical note being played. Bellur et al. [9] described several techniques for detecting tonic pitch value in Indian classical music. Processing of pitch histograms using group delay functions and its ability to amplify certain traits of Indian music in the pitch histogram is discussed. Three different strategies to detect Tonic, namely the concert method, the template matching, and segmented histogram methods are proposed. These results show a great progress, but there is still scope for improvement. (1) Finding the best set of rules using machine learning technique can increase tonic identification accuracy (2) Extracting best features and selecting relevant minimum attributes for machine learning is a challenging task. (3) Tonic should be identified as quickly as possible using less audio data. Gender information can be used as a prominent feature in a situation where tonic identification system shows lower confidence. (4) Exceptional cases can be handled by manual annotation with less work if we have 3–5 best candidates, out of which one will be an actual Tonic.

3 Methodology Spectrogram of raga maya Mayamalavagowla avaroha is shown in Fig. 2. The blue line shows pitch frequencies present in the raga. It ranges from 100 to 350 Hz. Last note or swara has a frequency of 138 Hz, which is a frequency of the Tonic or Shruti. The yellow line is energy contour, which is in the range 50–100 dB. All ragas are recorded or obtained from IITs, Gulati et al. [7] and some other for research work. A total of 195 full-length ragas of ICM are trimmed to 3 min. Labeling is done by finding the rank of the Tonic in the selected top candidates which are from peaks of the pitch histogram. These ragas are sung by India’s renowned classical singers. It includes both the Hindustani and Carnatic ragas. As machine learning is used inside raga, therefore length of raga should be as large as possible.

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3.1 Pitch Frequency Calculation To remove noise from an audio, fast Fourier transform is used. This is done with trial and error method by observing the frequency distribution in frequency spectrum and selecting lower and higher frequencies. A bandpass filter is designed with these lower and higher frequencies. Low frequency noise includes humming noise while higher noise is above 12,000 Hz. The proposed system for tonic identification is shown in Fig. 3. Pitch frequency is calculated using the subharmonic-to-harmonic (SHR) ratio [10]. SHR is an amplitude ratio between subharmonics and harmonics. When the ratio is small, perceived pitch remains the same. As the ratio increases above a certain threshold, the subharmonics become clearly visible on the spectrum, and the perceived pitch becomes one octave lower than the original pitch. frames → windowing → A( f )

Fig. 2 Spectrogram of raga maya Mayamalavagowla avaroha of 20 s duration with pitch contour (shown in blue color) and energy contour (shown in yellow color)

Fig. 3 Proposed system for tonic identification

Automatic Tonic (Shruti) Identification System for Indian …

737

A(f ): It denotes the amplitude spectrum function. Pitch search range is [f min , f max ]. If f 0 is fundamental frequency, then sum of harmonic amplitude (SH) is, SH 

N 

A(n f 0)

(1)

n1

N  number of harmonics present in the spectrum, N  floor( f max / f min ) f min and f max are minimum and maximum frequencies present in an amplitude spectrum function, respectively. f max value is 1250 Hz [11]. If only consider the harmonic frequency, that is, at one half of fundamental frequency, the sum of subharmonic amplitude (SS) is defined as: SS 

N 

A((n − 0.5) f 0)

(2)

n1

Frame length: 40 ms, Frequency bound: As research interest is to find the vocal frequency, frequency bound is applied to discard all unwanted frequencies. Generally, range of male pitch frequency is from 110 to 230 Hz and range of female pitch frequency is from 150 to 300 Hz. So, frequency bound [110 Hz, 300 Hz] is used here. SHR  SS/SH

(3)

Based on the analysis in [12] SHR, value 0.2 is selected but other values in the range [0.2, 0.4] not make any distinguishable difference. For the proposed method, 0.4 SHR threshold value is used. If the estimated SHR is greater than the threshold, then subharmonic is preferred and it is regarded as pitch frequency candidate, otherwise harmonic is favored. Henceforth pitch frequency candidates are referred as F0-candidates. Table 1 shows the comparison of pitch detection accuracy calculated for five different algorithms, viz SHR, SDF, AMDF, FFT, and CQT. SHR shows better performance (average accuracy of 95.06%) compared to other methods. The dataset includes notes played in the second, third, and fourth octaves. Detected note frequencies are compared with the standard frequencies of notes. Percentage accuracy is calculated from correctly detected notes.

3.2 Dominant Frequency Selection Dominating frequency is calculated by finding peaks in the pitch histogram. Based on the energy threshold or some other thresholds such as percentage of low-energy content in a raga and only prominent frames are extracted which is followed by constructing pitch histogram. But the main problem in above methods is first peak or prominent peak in pitch histogram may or may not represent Tonic at that frequency.

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It may be present at other peaks also. Sometimes, the sound of drone instrument or vocal part of raga has relatively small amplitude which gives less energy in that particular frame. Figure 4 shows pitch histogram for raga Mayamalavagowla of length 120 s with 25 bins. If there is a peak at zero, then it is created due to those frames whose pitch frequency is not found or that frames where pitch frequency is out of bound [100 Hz, 300 Hz]. P1, P2, P3, P4, and P5 are top five peaks of the pitch histogram. These are the top five candidates where the probability of the presence of Tonic is higher. These top candidates are also referred as top peaks, F0-candidates or Tonic candidates. Actual Tonic and relationship between those top candidates are unknown. Use of machine learning techniques is the best possible solution for this. Table 1 Pitch detection accuracy for five algorithms tested for nine different musical instruments Instrument SHR (%) SDF (%) AMDF (%) FFT (%) CQT (%) Grand Piano Harmonium Violin Accordion Harmonica Dulcimer Trumpet

97.22 94.44 97.22 97.22 94.44 100 100

100 100 97.22 88.89 38.89 100 100

80.55 61.11 86.11 50.00 52.78 83.33 41.67

97.22 33.33 8.33 80.55 38.89 41.67 38.89

25 5.56 13.89 30.56 5.56 50.00 36.11

Marimba Xylophone

91.67 83.33

94.44 97.22

47.22 33.33

36.11 52.78

50 13.89

Average

95.05

90.74

59.57

47.53

25.62

SHR subharmonic-to-harmonic ratio, SDF square difference function, AMDF average magnitude difference function, FFT fast Fourier transform, CQT constant-Q-transform

Fig. 4 Pitch histogram

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739

3.3 Features Extraction The best set of features are suggested by Gulati et al. [7] and Salamon et al. [13]. Some features are like the energy of tonic candidates, amplitude ratios of the tonic candidates, and pitch intervals of the tonic candidates obtained from pitch histogram. In addition to these features, some statistical features based on the spectrum of an audio are also extracted. Frequency ratio features are calculated by subtracting frequencies of second, third, fourth, and fifth F0-candidates from the first F0-candidate which is the most prominent peak in the pitch histogram. Resultant frequency difference in Hertz is converted to cents scale. C  1200 ∗ log2 (F(1)/F(i))

(4)

where F0(1) is the pitch frequency of top candidate in the pitch histogram, F0(i) is the pitch frequency of the remaining most dominating candidates in the pitch histogram for i  2, 3, 4, 5, and C is the Cents value One complete octave covers 1200 cents. The negative cents value represents the presence of pitch frequency to the higher octave or at higher note as compared to the tonic frequency. Amplitude ratio features are calculated as shown below: A  A(1)/A(i)

(5)

where A0(1) is an amplitude of the top candidate in the pitch histogram and A0(i) is the amplitude of the remaining dominating candidates for i  2, 3, 4, 5.

3.4 Machine Learning Proper annotation is the biggest challenge before the use of machine learning. From all five candidates, Tonic is required to be labeled properly. The only solution to this problem is asking a singer to sing a raga and tune drone instrument in the background with all possible frequencies and take matched frequency as a tonic frequency. Attributes analysis show stop 2 F0-candidates contribute 86.66% as a Tonic. For machine learning, open-source Weka 3.6.13 data mining software which was developed by University of Waikato, New Zealand, is used. Attribute relationship file created in MATLAB 2017a is uploaded to Weka software for machine learning. By MATLAB–Weka interface, test model generated in Weka is implemented in MATLAB for validation. Table 2 shows various machine learning algorithms tested for raga dataset. The highest testing accuracy of 92.86% is obtained from decision tree J48-C 0.25M2, multilayer perceptron, and lazy-IBK (KNN) classifier. In a proposed method, very famous J48-C 0.25M2 algorithm of decision-tree classifier is

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Table 2 Various machine learning algorithms tested for correct tonic selection Classifier Accuracy (in percentage) Decision tree J48-C 0.25–M2 BayesNet

92.86 78.57

Multilayer perceptron

92.86

RBF network Simple logistic

78.57 78.57

Lazy-IBK (KNN)

92.86

Regression classifier

85.71

Decision stump

85.71

Random forest –I 100-K 0-S 1

85.71

used to find the best possible set of rules to find Tonic. The J48 pruned tree has 18 numbers of leaves and size of the tree is 35. Time taken to build model is 0.1 s.

4 Results The proposed system of tonic identification is tested on two different datasets. The first dataset is of randomly selected 42 ragas of 7 different raga classes. Out of 42, Tonic is identified correctly for 38 ragas. As shown in Table 3, for all 42 ragas average percentage error of 1.14% is obtained for tonic identification. Second dataset is 72 Melakartha ragas. Ragas of these two datasets are unique and different from train and test data. As shown in Table 4, average tonic identification accuracy for 72 Melakartha ragas is 93.05%.

Table 3 Tonic identification error percentage for seven raga classes (dataset—1) Raga Quantity Average % error in tonic identification Kalyani 6 0.98 Harikambhoji

6

1.74

Kharaharapriya

6

1.10

Mayamalavagowla

6

1.05

Panthuvarali Shankarabharana Shanmukhapriya

6 6 6

0.79 1.46 0.85

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Table 4 Tonic identification accuracy for 72 Melakartha ragas (dataset—2) Melakartha ragas Correctly identified Percentage accuracy Suddha Madhyama (36)

33

91.67

Prati Madhyama (36)

34

94.44

Average percentage

93.05

5 Conclusion The main objective of this work is to study and implement best possible methodology to find Tonic automatically. SHR shows better results in pitch frequency estimation for music notes. Tonic can be used to find musical phrase of raga and as a prominent feature for further music analysis such as Motif and Intonation analysis. Test accuracy for tonic identification in machine learning is 92.86%. Gender information of an artist and use of full-length raga data lead to increase tonic identification accuracy. The critical point of the machine learning is the dominance of the male artist in raga database. This method can be used to replace conventional Shruti-Box in Indian classical music by an application software. Future work includes the use of full-length and biased raga database for machine learning.

References 1. https://en.wikipedia.org/wiki/Hindustani_classical_music 2. S.R. Mahendra, H.A. Patil, N.K. Shukla, Pitch estimation of notes in Indian Classical Music, in 2009 Annual IEEE India Conference, Gujarat (2009), pp. 1–4 3. H.G. Ranjani, S. Arthi, T.V. Sreenivas: Carnatic music analysis: Shadja, swara identification and raga verification in alapana using stochastic models, in 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY (2011), pp. 29–32 4. A. Bellur, H.A. Murthy, A cepstrum based approach for identifying tonic pitch in Indian classical music, in 2013 National Conference on Communications (NCC), New Delhi, India (2013), pp. 1–5 5. A. Bellur, H.A. Murthy, A novel application of group delay function for identifying tonic in Carnatic music, in 21st European Signal Processing Conference (EUSIPCO 2013), Marrakech (2013), pp. 1–5 6. S. Ghisingh, S. Sharma, V.K. Mittal, Acoustic analysis of Indian classical music using signal processing methods, in TENCON 2017–2017 IEEE Region 10 Conference, Penang (2017), pp. 1556–1561 7. S. Gulati, A. Bellur, J. Salamon, H.G. Ranjani, V. Ishwar, H.A. Murthy, X. Serra, Automatic tonic identification in Indian art music: approaches and evaluation. J. New Music Res. 43(1), 53–71 (2014) 8. R.T. Pillai, S.P. Mahajan, Automatic Carnatic raga identification using octave mapping and note quantization, in ICCSP, Chennai (2017) pp. 0645–0649 9. A. Bellur, V. Ishwar, X. Serra, H. A. Murthy, A knowledge-based signal processing approach to tonic identification in Indian classical music, in Proceedings of 2nd CompMusic Workshop, Istanbul, Turkey (2012)

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10. X. Sun, A pitch determination algorithm based on sub-harmonic to harmonic ratio, in 6th International Conference on Spoken Language Processing, Beijing, China (2000) 11. D.J. Hermes, Measurement of pitch by subhannonic summation. J. Acoust. Soc. Am. 83, 257–264 (1988) 12. X. Sun, Y. Xu, Perceived pitch of synthesized voice with alternate cycles. J. Voice 16(4), 443–459 (2002) 13. J. Salamon, E. Gomez, D.P.W. Ellis, G. Richard, Melody extraction from polyphonic music signals: approaches, applications and challenges. IEEE Sig. Process. Mag. 31(2), 118–134 (2014)

Single-Plane Scene Classification Using Deep Convolution Features Nikhil Damodaran, V. Sowmya, D. Govind and K. P. Soman

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Places CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

744 745 745 746 747 748 752 752

Abstract Scene classification is considered as one of the challenging tasks of computer vision. Due to the availability of powerful graphics processing unit and millions of images, deep learning techniques such as convolutional neural networks (CNNs) have become popular in the image classification. This paper proposes the use of a pre-trained CNN model known as Places CNN, which was trained on scene-centric images. In this work, the pre-trained CNN is used as a feature extractor. The features are then used as input data for support vector machines (SVMs). The effect of grayscale images on the performance of pre-trained CNN-based scene classification system is analyzed by means of classification accuracy and equal error rate. The dataset used for this purpose is Oliva Torralba (OT) scene dataset, which consists of eight classes. The classification experiments are conducted using the feature vector from the ‘fc7’ layer of the CNN model for RGB, RGB2Gray, and SVD decolorized images. The classification experiment was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature N. Damodaran (B) · V. Sowmya · D. Govind · K. P. Soman Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] V. Sowmya e-mail: [email protected] D. Govind e-mail: [email protected] K. P. Soman e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_71

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vector. The results obtained from classification experiments show that RGB2Gray and SVD decolorized images were able to give results similar to that of RGB images. The grayscale images were able to retain the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of scene images. Keywords SVM · CNN · Transfer Learning · RGB2Gray · SVD

1 Introduction Pattern recognition is one of the categories of machine learning, where the focus is on finding patterns and regularities in data. Pattern recognition is divided into supervised and unsupervised learning. Supervised learning involves learning or inferring a function from a properly labeled training dataset. The image classification tasks mostly come under the head of supervised learning of pattern recognition [1]. The function that any classifier tries to learn from is basically of the form y = f (x), where x represents the feature vector and y is the corresponding predicted score. The classifier tries to predict y for a given x. Such tasks when performed on scene-centric data are known as scene classification problem [2]. The scene classification problem is considered as one of the challenging tasks of computer vision [3]. Even though there are many approaches to solve this problem, it is far from perfect, due to variations in spatial position, illumination, and scale of scene-centric images [4, 5]. Humans are extremely efficient at categorizing natural scenes, despite the fact that different classes of natural scenes often share similar image statistics. One important property of the human brain is its hierarchical organization in layers of increasing processing complexity. This has inspired an architecture called convolutional neural networks or CNNs [6, 7]. With the availability of large databases, CNN was able to obtain good performance on image classification tasks. Convolutional neural networks are similar to neural networks which comprise of neurons that have weights and biases. But, the uniqueness of CNN is that the inputs are taken as images, which helps in encoding properties into the architecture and makes the forward function more efficient. This also helps in reducing the number of parameters in the network. In almost all architectures of CNN, a downsampling operation takes place after convolution. A common method of downsampling in CNN is the maxpooling operation in which the maximum value of the neighboring features is taken. A deep convolution layer is nothing but alternate stacking of convolutional and pooling layers [8]. A better way to approximate the internal representations of CNN is by performing classification on a scene-centric data [9]. This paper proposes an experiment, which makes use of a pre-trained CNN model known as Places CNN [10]. Since the OT dataset [11] consists of only 2688 images, it is very difficult to get a desirable performance from a new CNN model. So, here we go for a transfer learning approach,

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where we use the pre-trained CNN model as feature extractor for both train and test sets and use those features as a dataset for the support vector machines [12]. The features extracted from the CNN are mainly used as global or part descriptors and are able to outperform classic image features in both transfer and non-transfer image classifications. The features obtained from the fully connected layer of the CNN are preferred. The reason behind this preference is due to the strong generalization and semantics–descriptive ability. Since the features obtained from pre-trained CNN are a high dimensional vector, we can reduce this dimension by means of a dimensionality reduction technique known as principal component analysis or PCA. In PCA, the features of the dataset undergo a linear transformation which in turn moves it from the original space to a new space formed by the principal components. PCA actually looks for properties that show as much variation across classes as possible to generate the principal component space. This feature of PCA allows the classifier to categorize each sample of the dataset more efficiently, and also due to reduction in dimensionality of feature vector, the computation cost and time for training and testing are reduced significantly. The RGB dataset was converted into two kinds of grayscale images, where the number of planes is reduced to one. The pixel values in grayscale image range from 0 representing black color to 255 representing white color. The numbers in between 0 and 255 represent different shades of black or white. The RGB2Gray image is obtained by the weighted average of the pixel values of all the three color planes with 30, 59, and 11% for red, green, and blue planes. Higher weightage is given to green plane since green is the most sensitive color to human eyes. In the second type of grayscale image [13], chrominance information from the color images is combined with the luminance information. The eigenvectors and eigenvalues from singular value decomposition are used for the reconstruction of chrominance information in the CIEL*a*b* color space. In this paper, this version of the dataset is referred to SVD dataset for ease of use. The paper analyzes the effect of two types of single-plane images known as RGB2Gray and SVD decolorized on the same classification task performed on RGB images. In addition to this experiment, dimensionality reduction is also employed on the features of all image types and classification task is performed on the same. The organization of the paper is as follows: The methodology of the experiments is explained in Sect. 2. Section 3 gives a brief description of the dataset used in the experiment, and Sect. 4 deals with the results obtained from the experiment. The inferences obtained from the results are explained in Sect. 5, conclusion.

2 Methodology 2.1 Places CNN The pre-trained model, Places CNN, was trained with 2,448,873 randomly selected images from 205 categories of the Places-205 dataset, with minimum 5000 and

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maximum 15,000 images per category. The architecture is based on the AlexNet [14], with exception of the output layer. It consists of eight layers, out of which five are convolutional layers and the remaining three are the fully connected layers. The output layer consists of 205 neurons for the 205 categories of the Places-205 dataset. Places CNN model was trained on an NVIDIA Tesla K40 GPU and took about 6 days and 300,000 iterations of training [10].

2.2 Experimental Procedure Feature Extraction The train/test dataset of RGB, RGB2Gray, or SVD decolorization images is fed into the pre-trained CNN model. The output layer from the pretrained model is ignored, and the weights of the remaining layers were frozen. This, in turn, causes the CNN to follow only forward propagation. The image features from the fc7 layer were extracted and used as a training data for the classifier. The dimension of the feature for each image from the fc7 layer is 4096 × 1. So, for the training, a feature matrix of size N × 4096 is obtained, where N is the number of samples. Labels are numerically encoded as 0, 1, 2, etc. This results in a label vector of dimension N × 1. The same operation is done for test set giving a feature matrix of dimension n × 4096 (where n is the number of test samples) and a label vector of dimension n × 1. Experiments In this paper, three experimental methods are proposed. In the first method, after extracting the fc7 features of the training set, it is used for training the classifier. The parameters of the classifier were found by grid search algorithm. The prediction of class labels is made by the trained model on the feature vectors from the test set. The flow chart for the first experiment is shown in Fig. 1. The second experiment follows the same set of experiments from the first method, using test data as training data and training data as test data. In the third experiment,

Fig. 1 Workflow: Experiments I and II

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Fig. 2 Workflow: Experiment III

a dimensionality reduction technique known as PCA is applied to the feature vectors. The flowchart for the third experiment is shown in Fig. 2. The 4096 × 1 feature of each image is reduced to a K number of eigencomponents. The size of training feature matrix is reduced from N × 4096 to N × K, obtaining savings in both space and time. Rest of the experiment is same as that of the first one. The optimum number of eigenvectors K is selected by first varying the value of K from 10 to 500 eigenvector or principal components. Then, the model which gives minimum possible equal error rate is chosen, and the corresponding number K is set as the optimum K value. The experiments mentioned above were conducted on RGB, RGB2Gray, and SVD decolorized images separately.

3 Dataset The classification experiments were conducted on Oliva Torralba (OT) [6] scene dataset, which consists of eight classes. The Oliva Torralba (OT) scene data consists of 2688 color images of size 256 × 256. Initially, the dataset was split into a training set consisting of 1888 images and testing set of 800 images, respectively. Table 1 shows the number of training and testing images per class. Train/test split is same for both RGB2Gray and SVD decolorized grayscale images. Since the pre-trained model was already trained with 2,448,873 images, we went for training the model with the minimum number of feature vectors. For this purpose, in the second experiment, the train/test split was taken in reverse order. So, the number of training images is 800 with 100 images from each class and the remaining 1888 images form our testing image set. This train/test is referred to train/test split-II for ease of use.

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Table 1 Train/test split of OT scene dataset Class Train Open country Coast Forest Highway Inside city Street Mountain Tall building

Test

310 260 228 160 208 192 274 256

100 100 100 100 100 100 100 100

Table 2 Overall accuracy and EER for first and second experiment Test/train split-I Test/train split-II Data RGB (%) RGB2Gray SVD (%) RGB (%) RGB2Gray (%) (%) Overall accuracy EER

SVD (%)

93.37

93.50

93.25

92.10

92.90

92.10

3.39

3.42

3.53

3.08

3.67

3.82

4 Results Table 2 shows the accuracy and equal error rates (EERs) obtained for RGB, RGB2Gray, and SVD decolorized image datasets for the first and second experiment. The equal error rate is the value when false acceptance rate and false rejection rate are equal. It indicates that the proportion of false acceptances is equal to the proportion of false rejections. The lower the equal error rate value, the higher the accuracy of the system. The classification accuracy of RGB2Gray, SVD decolorized, and RGB image was found to be almost same with RGB2Gray giving a higher accuracy than RGB by 0.13% for the first experiment (split-I). Classification on SVD decolorized images also gave a comparable performance to RGB image classification. The EER of the model is also low for the classification in RGB, RGB2Gray, and SVD decolorized images. As a result, the fast acceptance rate and the fast rejection rate of the model are found to be minimum and optimal. This shows that RGB2Gray and SVD decolorized images were able to preserve the necessary shape and texture information from the original RGB images and were also sufficient to describe the classes of OT eight scene images. The second section of Table 2 shows the results for the second experiment (split-II). Even with the minimal number of training image features, the SVM was able to achieve accuracy scores closer to the previous experiment (train/test split-I). This shows that deep scene features obtained from the higher levels of pre-trained CNN have proven to be effective generic features. In the second experiment, the classification accuracy of RGB, SVD decolorized images,

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Fig. 3 Variation of accuracy and EER based on number of principal components (Experiment III)

Accuracy vs Number of Components

EER vs Number of Components

and RGB2Gray images is almost same. The ERR of the model is also low for the classification systems. The third experiment for train/test split-II was implemented with dimensionality reduction on deep convolution features. The required number of principal components was selected by first training and testing the model for features with the number of components ranging from 10 to 500. From these sets of models, the model with minimum EER is selected as the optimum model. Figure 3 shows the graphs obtained for the accuracy and EER for the model trained on feature vector with dimensions varying from 10 to 500. All these experiments were conducted on all color spaces, i.e., RGB, RGB2Gray, and SVD decolorized images. From Fig. 3, we can see that as the number of components increases, the accuracy graph goes in a decreasing manner, whereas the EER value goes on increasing with increase in number of principal components. Table 3 shows the accuracy and equal error rates obtained for RGB, RGB2Gray, and SVD decolorized image datasets (train/test split-II) for the third experiment using PCA. As mentioned before, the value of K or the number of principal components for a model is selected by one which gives minimum possible equal error rate. So, from

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Table 3 Overall accuracy and EER for train/test split-II with PCA Data RGB RGB2Gray Overall accuracy No. of principal components EER

92.57% 200

91.73% 120

91.47% 140

3.64%

4.11%

4.18%

Table 4 Precision for train/test split-II Class Without PCA RGB RGB2Gray SVD Open country Coast Forest Highway Inside city Street Mountain Tall building Average/total

0.86 0.88 0.92 0.94 0.93 0.96 0.94 0.98 0.92

SVD decolorized

0.89 0.86 0.94 0.94 0.95 0.95 0.95 0.98 0.93

0.84 0.87 0.97 0.92 0.94 0.96 0.94 0.97 0.92

With PCA RGB

RGB2Gray SVD

0.84 0.87 0.89 0.96 0.96 0.94 0.94 0.97 0.92

0.85 0.85 0.93 0.95 0.96 0.93 0.92 0.98 0.92

0.85 0.87 0.94 0.89 0.94 0.93 0.94 0.97 0.91

the above results by the minimum equal error rate condition, the model trained on RGB2Gray and SVD decolorized images requires only 120 and 140 principal components, respectively, whereas for the model trained on RGB image dataset, the number of principal components required to achieve the optimum performance is higher. The class-wise precision, recall, and f1-score for RGB, RGB2Gray, and SVD decolorized image datasets in the experiment without dimensionality reduction for test/train split-II were calculated. The precision, recall, and f1-score were calculated using the following equations. Precision = Recall = F1-score =

tp (t p + f p)

tp (t p + f n)

2 ∗ Precision ∗ Recall (Precision + Recall)

(1)

(2)

(3)

where t p, f p, and f n refer to the true positive, false positive, and false negative, respectively.

Single-Plane Scene Classification Using Deep Convolution Features Table 5 Recall for train/test split-II Class Without PCA RGB RGB2Gray SVD Open country Coast Forest Highway Inside city Street Mountain Tall building Average/total

0.81 0.92 0.95 0.96 0.97 0.95 0.90 0.98 0.92

0.80 0.93 0.97 0.95 0.95 0.96 0.95 0.98 0.93

0.80 0.92 0.95 0.97 0.95 0.96 0.91 0.98 0.92

Table 6 F1-score for train/test split-II Class Without PCA RGB RGB2Gray SVD Open country Coast Forest Highway Inside city Street Mountain Tall building Average/total

0.83 0.89 0.93 0.95 0.95 0.96 0.92 0.98 0.92

0.85 0.89 0.96 0.94 0.95 0.95 0.94 0.98 0.93

0.82 0.89 0.96 0.95 0.94 0.96 0.92 0.97 0.92

751

With PCA RGB

RGB2Gray SVD

0.83 0.91 0.95 0.96 0.90 0.97 0.88 0.97 0.92

0.70 0.88 0.96 0.97 0.93 0.96 0.93 0.98 0.92

0.82 0.90 0.95 0.96 0.91 0.95 0.91 0.97 0.91

With PCA RGB

RGB2Gray SVD

0.83 0.89 0.92 0.96 0.93 0.96 0.91 0.97 0.92

0.82 0.87 0.95 0.96 0.94 0.94 0.93 0.98 0.93

0.83 0.89 0.94 0.92 0.93 0.94 0.93 0.97 0.91

Tables 4, 5, and 6 give details about class-wise classification on RGB, RGB2Gray, and SVD decolorized images for experiments with and without dimensionality reduction technique. For all color space classes such as tall building, inside cities scored very high in precision, recall, and f1-score metrics. The results are almost similar in both experiments. These results were consistent even with PCA on the minimal number of 800 images. This is due to highly rich scene-specific features that we derived from the network. In the third experiment, tall building, inside cities scored very high in precision, recall, and f1-score metrics. The open country class was the class with the lowest score among all the classes. Upon closer inspection of some of the predicted labels, it was found that some of the open country class images were mis-classified as highways, mountains, coast, and forest. From the misclassified samples, it was found that the class open country itself is an amalgamation

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of forests, lakes, mountains, streets and since mountains, streets, forests, and coasts are also classes of OT scene dataset, the images get easily mis-classified into any of the above-mentioned classes and vice versa.

5 Conclusion From the results obtained, we can conclude that RGB2Gray and SVD decolorized images were able to preserve the necessary shape and texture information from the original RGB images and were also sufficient to describe the classes of images. So, grayscale images are also found to be reliable for scene image classification. Even with the minimal number of training image features and with reduced dimension of the features, the SVM classifier was able to achieve similar accuracy, recall, and precision scores on all image types. This is due to the fact that the deep scene features obtained from the higher levels of pre-trained CNN are effective scenegeneric features.

References 1. X. Liu, Supervised classification and unsupervised classification (2005) 2. R. Sachin, V. Sowmya, D. Govind, K. Soman, Dependency of various color and intensity planes on CNN based image classification, in International Symposium on Signal Processing and Intelligent Recognition Systems (Springer, Cham, 2017), pp. 167–177 3. M. Szummer, R.W. Picard, Indoor-outdoor image classification, in Proceedings of International Workshop on Content-Based Access of Image and Video Database (IEEE, 1998), pp. 42–51 4. B.S.R. Dutt, P. Agrawal, S. Nayak, Scene classification in images (2009) 5. J. Zou, W. Li, C. Chen, D. Qian, Scene classification using local and global features with collaborative representation fusion. Inf. Sci. 348, 209–226 (2016) 6. Y. Bengio, Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009). https://doi.org/10.1561/2200000006 7. Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989) 8. M.D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in European conference on computer vision (Springer, Cham, 2014), pp. 818–833 9. B. Zhou, A. Khosla, A Lapedriza, A. Oliva, A. Torralba, Object detectors emerge in deep scene CNNs. ArXiv e-prints (2014) 10. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, A. Oliva, Learning deep features for scene recognition using places database, in Neural Information Processing Systems, NIPS (Curran Associates, Inc., 2014), pp. 487–495 11. A. Oliva, A. Torralba, Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001) 12. M.A. Hearst, S.T. Dumais, E. Osuna, J. Platt, B. Scholkopf, Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998) 13. V. Sowmya, D. Govind, K.P. Soman, Significance of incorporating chrominance information for effective color-to-grayscale image conversion. Signal Image Video Process. 11(1), 129–136 (2017) 14. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2012)

A Novel Methodology for Multiplication of Three n-Bit Binary Numbers Anirban Mukherjee, Niladri Hore and Vinay Kumar

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Proposed Methodology to Multiply Three n-Bit Binary Numbers . . . . . . . . . . . . . . . . . . 3 Case Study for Multiplication of Three 4-Bit Binary Numbers . . . . . . . . . . . . . . . . . . . . . 4 Simulation Results for Multiplier of Three 4-Bit Binary Numbers . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

754 754 757 757 759 759

Abstract Multipliers play an important role in any processor or computing machine. The performance of arithmetic circuits is evaluated from the performance of multipliers of those circuits. The conventional multiplier circuits process only two binary data at a time for multiplication. This paper proposes a methodology to multiply three binary numbers at once. This methodology reduces the complexity of the circuit for multiplying three binary numbers as compared to the existing methods of multiplying. When implemented in a circuit, this has turned out to increase the performance of the multiplier based on proposed methodology in terms of power, gates and delay as compared to existing multipliers. Based on simulation results for design of circuit for multiplying three 4-bit binary numbers, the proposed methodology shows a reduction of 37.56, 62.90 and 41.35% in propagation delay, power consumption and gate counts, respectively, as compared to existing techniques. Keywords Multipliers · Microcontrollers · Digital signal processors Propagation delay · Power consumption A. Mukherjee · N. Hore Department of Electronics and Communication Engineering, Jalpaiguri Government Engineering College, Jalpaiguri 735102, West Bengal, India e-mail: [email protected] N. Hore e-mail: [email protected] V. Kumar (B) Department of Electronics and Communication Engineering, National Institute of Technology Meghalaya, Shillong 793003, Meghalaya, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_72

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1 Introduction Arithmetic operations especially those involving multiplication consume lot of processing time in a digital processor [1, 2]. The multiplier circuits suffer from another disadvantage since they require more hardware as compared to circuits for addition or subtraction [3, 4]. Multiplications constitute approximately 9% of the applications in arithmetic units [5]. In many real-time signal or image processing applications, arithmetic operations with high throughput are a basic necessity in order to achieve the desired performance [6, 7]. Hence, it is important to perform large number of mathematical calculations in a very less time [8, 12]. It is evident that in performing multiplication, a processor spends a considerable amount of time; to increase the overall speed of the computer, an improvement in speed of math coprocessor for performing multiplication is required [13–15]. The demand of high-speed processing has been increasing with time as a result of expanding computer and signal processing applications. Addand-shift algorithm provides the most common method of multiplying [8]. The main parameter that determines the performance of the parallel multipliers is the number of partial products needed to be added. Modified booth algorithm aims to reduce the number of partial products to be added [1, 4, 9]. In order to achieve improvements in speed of processing, Wallace tree algorithm may be used which helps to reduce the number of sequential adding stages [10]. Further by combining both Wallace tree technique and modified booth algorithm, the advantage of both algorithms can be seen in one multiplier [11, 12]. However as parallelism has been increasing, the amount of shifts between the partial products and intermediate sums generated will increase which may cause irregularity of structure resulting in increase in silicon area. This also leads to increased power consumption due to increase in interconnect resulting from complex routing and also reduced speed as a whole. On the contrary, serial–parallel multipliers compromise speed to achieve better performance in terms of area and power consumption. The choice of a serial or parallel multiplier is actually dependent upon the nature of the application and usage. In Vedic mathematics approach even before the beginning of actual operations, the partial products arising of the multiplication are calculated [13, 14]. These partial products are added according to Vedic algorithm to obtain the final result, which turns out to have high-speed calculation [15].

2 Proposed Methodology to Multiply Three n-Bit Binary Numbers The proposed methodology allows multiplying three n-bit numbers (where n can take any value). This method obtains the final result at a faster speed because we do not have to wait to generate the product for the first two numbers to multiply it with the third number. The three numbers can be multiplied simultaneously, and the result can be shown at a time. This has a significant edge over the regular multipliers in terms

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of speed because the result of the multiplication of first two numbers is not required to be stored in memory of the processor while the third number is being fetched. There are also advantages in terms of power consumption and area because less number of gates is required for implementation of the multiplier based on proposed methodology. For multiplication of three n-bit numbers, there will be n columns and three rows to represent the numbers. Assign the columns with numbers from 0 to (n − 1) and the rows as R1 , R2 and R3 . The multiplication will be done according to the bits contained in the columns of the three corresponding rows. The distribution of input bits for three n-bit binary numbers is shown in Table 1. We define a sequence that represents the column in the form R1 -R2 -R3 for multiplication. For example, “2-3-0” represents the bits to be multiplied are from column 2 of R1 , column 3 of R2 and column 0 of R3 . The steps for multiplication of three n-bit numbers are as follows: 1. Start with 0-0-0. It means multiply the bits which are under column 0 of each of the three rows. Write the product in the lowest significant bit. 2. Take the bit in R1 whose column is 1 more than the previous one. The columns of R2 and R3 remain the same. Multiply the three bits under these columns, and write the product in the next higher bit of the result. 3. Repeat step 2 until we reach the column (n − 1) in R1 . Remember the columns of R2 and R3 do not change in this process. 4. Increase the column of R2 by 1 while column of R1 is taken as 0 again. 5. Now move on to the next line of the product. Add the columns of R2 and R3 . Put that number of zeroes (sum) at the lower significant bits of the result. 6. Take the bit in R2 under the incremented column (done in step 4). Repeat step 2 to step 4 until you reach the column (n − 1) in R2 . 7. Increase the column of R3 by 1 while the columns of R1 and R2 are taken as 0 again. Then multiply the bits under these columns from each row. 8. Repeat step 5. 9. Repeat step 2 to step 8 until the column of R3 becomes (n − 1). 10. Add the partial products to get the final result. We also present an algorithm for proposed methodology in Table 2 for automation of this methodology which will be helpful when the number of input bits is quite large. Table 1 Multiplication sequence table Column n − 1 n−2 … → 1st An−1 An−2 … number 2nd Bn−1 Bn−2 … number 3rd Cn−1 Cn−2 … number Result

…

2

1

0

Rows ↓

…

A2

A1

A0

R1

…

B2

B1

B0

R2

…

C2

C1

C0

R3

756 Table 2 Algorithm for multiplication of three n-bit binary numbers

A. Mukherjee et al. 1: 3:

START A[n-1:0],B[n-1:0],C[n-1:0]=inputs

4:

P=product

5: 6:

for x from 0 to n-1 for y from 0 to n-1

8: 9:

for z from 0 to n-1 u=c[x]

10:

v=b[y]

11:

w=a[z]

12:

temp=u+v+w

13:

if temp contain n times “1”

14:

put “1” in list

15: 16:

else: put “0” in list

17: 18: 19: 20: 22:

end if end for end for end for //Arranging partial product bits into grid

23:

PP=partial product grid

25: 26:

r=n*3 varpos=0

27:

varpos1=0

28: 29: 30: 31:

element=0 for k from 0 to r if k!=0 && k%3=0 varpos1=varpos1+1

32: 33:

else: varpos1=varpos1

34: 35: 36:

for i from 0 to n varpos=varpos1 PP[k][varpos]=list[element]

37: 38: 39:

element=element+1 i=i+1 varpos=varpos+1

40:

If grid element in PP=NULL

41:

put that grid element as 0

42:

Sum=Sum all grid elements of PP column wise

43: 44:

P=Sum END

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The algorithm has O(n3 ) worst-case time complexity, where n is the length of the binary representation of the largest number.

3 Case Study for Multiplication of Three 4-Bit Binary Numbers We use the proposed methodology to multiply three 4-bit binary numbers. The arrangement of the sequence of input bits in the column (Clmn) is shown in Table 3. Note that [0] represents the binary number 0 and not any kind of sequence. Simply a 0 should be put when a [0] is encountered. Three 4-bit binary numbers, 1001, 1110 and 1111, are taken for multiplication, and final result is shown in Table 4.

4 Simulation Results for Multiplier of Three 4-Bit Binary Numbers The circuit for multiplier based on proposed methodology and existing multipliers has been designed and simulated in 90-nm technology at 1.2 V power supply with the help of Cadence Virtuoso tools.

Table 3 Multiplication of three 4-bit numbers Clmn9 Clmn8 Clmn7 Clmn6 Clmn5

3-3-0

3-3-1

Clmn3

Clmn2

Clmn1

Clmn0

3-1-0

3-0-0 2-1-0

2-0-0 1-1-0

1-0-0 0-1-0

0-0-0 [0]

3-2-0

2-2-0

1-2-0

0-2-0

[0]

[0]

2-3-0

1-3-0

0-3-0

[0]

[0]

[0]

3-0-1

2-0-1

1-0-1

0-0-1

[0]

3-1-1

2-1-1

1-1-1

0-1-1

[0]

[0]

3-2-1

2-2-1

1-2-1

0-2-1

[0]

[0]

[0]

2-3-1

1-3-1

0-3-1

0

[0]

[0]

[0]

3-0-2

2-0-2

1-0-2

0-0-2

[0]

[0]

3-1-2

2-1-2

1-1-2

0-1-2

[0]

[0]

[0]

3-2-2

2-2-2

1-2-2

0-2-2

[0]

[0]

[0]

[0]

2-3-2

1-3-2

0-3-2

[0]

[0]

[0]

[0]

[0]

3-0-3

2-0-3

1-0-3

0-0-3

[0]

[0]

[0]

3-1-3

2-1-3

1-1-3

0-1-3

[0]

[0]

[0]

[0]

3-2-3

2-2-3

1-2-3

0-2-3

[0]

[0]

[0]

[0]

[0]

2-3-3

1-3-3

0-3-3

[0]

[0]

[0]

[0]

[0]

[0]

3-3-2

3-3-3

Clmn4

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Table 4 Example for multiplication of three 4-bit numbers 10 9 8 7 6 5 4 3 2

1

1

1

1 1

1

1 0

1 0 1

1 0 0 0

1 0 1 0 0 0 0 0 1 1

1 0 1 0 0 0 0 0 1 0 0 1 0 1

1 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0

1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0

0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0

1

0

0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column ← R1 R2 R3 1st line of PP 2nd line of PP 3rd line of PP 4th line of PP 5th line of PP 6th line of PP 7th line of PP 8th line of PP 9th line of PP 10th line of PP 11th line of PP 12th line of PP 13th line of PP 14th line of PP 15th line of PP 16th line of PP Final Result

Table 5 Comparison of simulation results for different multipliers Parameters Proposed Array Wallace tree Vedic multiplier multiplier [7, multiplier multiplier [6, [Present] 8] [10] 13–15]

Booth multiplier [1, 4]

Propagation delay (ns)

0.251

0.358

0.259

0.253

0.402

Power consumption (µw)

194.8

293.4

258.1

413.6

347.9

Gate counts

828

946

932

1412

1188

The results obtained for the proposed multiplier are compared with the different existing multipliers for the same data length. The delay time and power of these multipliers are compared in Table 5. The gate counts of the corresponding multipliers have been also shown in Table 5 for comparison.

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5 Conclusion The proposed multiplier shows a significant reduction in delay as compared to other multipliers discussed in this paper. The gate counts of proposed multiplier have also reduced in comparison to other multipliers for multiplying three 4-bit binary numbers. The power consumption for the circuit of proposed multiplier for 4-bit inputs is significantly lower than the regular circuits taken into consideration. The simulation results prove that the proposed methodology can reduce the propagation delay, gate counts and power consumption when compared with other multipliers that are used in arithmetic circuits. This methodology can be used to multiply higher bit input patterns to obtain less propagation delay and power consumption for arithmetic circuits.

References 1. S. Kuang, J. Wang, C. Guo, Modified booth multipliers with a regular partial product array. IEEE Trans. Circ. Syst. II Express Briefs 56(5), 404–408 (2009) 2. F. Lamberti et al., Reducing the computation time in (short bit-width) twos complement multipliers. IEEE Trans. Comput. 60(2), 148–156 (2011) 3. N. Petra et al., Design of fixed-width multipliers with linear compensation function. IEEE Trans. Circ. Syst. I Regul. Pap. 58(5), 947–960 (2011) 4. K. RaniTsoumanis et al., An optimized modified booth recoder for efficient design of the add-multiply operator. IEEE Trans. Circ. Syst. I Regul. Pap. 61(4), 1133–1143 (2014) 5. A. Cilardo et al., High speed speculative multipliers based on speculative carry-save tree. IEEE Trans. Circ. Syst. I Regul. Pap. 61(12), 3426–3435 (2014) 6. T. Rakshith, R. Saligram, Design of high speed low power multiplier using reversible logic: a vedic mathematical approach, in Proceedings of IEEE International Conference on Circuits, Power and Computing Technologies (2013), pp. 775–781 7. R. Nalina, S. Ashwini, M. Kurian, Implementation of unsigned multiplier using area-delaypower efficient adder. Int. J. Res. Appl. Sci. Eng. Technol. 3(7), 429–432 (2015) 8. K. Dharshini, S. Kumar, S. Saravanan, Design and implementation of efficient multiplier architectures. Int. J. Res. Eng. Appl. Technol. 4(3), 28–38 (2015) 9. S. Deshmukh, D. Bhombe, Performance comparison of different multipliers using Booth algorithm. Int. J. Eng. Res. Technol. 3(2), 1957–1961 (2014) 10. B. Singh, R. Kumar, Design & implementation 8-bit Wallace tree multiplier. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 5(4), 2307–2312 (2016) 11. P. Avinash, S. Deepika, Implementation of VLSI architecture for signed-unsigned high speed booth multiplier. Int. J. VLSI Syst. Des. Commun. Syst. 2(1), 8–17 (2014) 12. E. Jaya, K. Rao, Power, area and delay comparision of different multipliers. Int. J. Sci. Eng. Technol. Res. 5(6), 2093–2100 (2016) 13. S. Hudda, M. Kalpana, S. Mohan, Novel high speed vedic mathematics multiplier using compressors, in Proceedings of IEEE International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (2013), pp. 465–469 14. M. Ali, A. Sahani, Study, implementation and comparison of different multipliers based on Array, KCM and Vedic Mathematics using EDA tools. Int. J. Sci. Res. Publ. 3(6), 1–8 (2013) 15. S. Garg, V. Sachdeva, Comparative analysis of 8 × 8 Bit Vedic and Booth Multiplier, in Proceedings of IEEE International Conference on Advances in Computing, Communications and Informatics, (2014) pp. 2607–2610

Speed-Breaker Early Warning System Using 77 GHz Long-Range Automotive Radar Umarani Deevela, Swapna Raghunath and Srinivasa Rao Katuri

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Raw Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Pre-processing Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Road safety measures are one of the most important requirements of present times. An early warning system to avoid accidents due to speed-breakers has been proposed in this paper. The novelty of this method is detecting a speedbreaker and alerting the driver in advance. For this purpose, long-range automotive FMCW radar (LRR) with transmitting frequency of 77 GHz has been used. This paper mainly deals with detection of a speed-breaker using fast Fourier transform (FFT) as a pre-processing technique. The main steps involved in a speed-breaker detection are receiving backscattered signals from radar module, filtering the data, applying a pre-processing technique, and classifying the output of pre-processing using support vector machine (SVM) classifier and K-nearest neighbors (KNN). Keywords Speed-breaker · Automotive radar · FFT · Classification Feature extraction · SVM · KNN

U. Deevela (B) · S. Raghunath G. Narayanamma Institute of Technology and Science, Shaikpet, Hyderabad, India e-mail: [email protected] S. Raghunath e-mail: [email protected] S. R. Katuri Ineda Systems Pvt. Ltd, Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_73

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1 Introduction An automotive radar is a sensing device which is used to locate the targets within the vicinity of radar. This radar comes in with the three frequency bands, namely shortrange radar (22–24 GHz), medium-range radar (76–77 GHz), and long-range radar (77–81 GHz) [1]. A long-range automotive radar has been used to detect objects within the range of 250 m. The type of radar used here is frequency-modulated continuous-wave (FMCW) radar which transmits continuously varying frequencies with known rate and receives backscattered signals from the targets. A short note on speed-breaker early warning system is given in [2]. In this methodology, speedbreaker detection has been done using an accelerometer. This method needs two android phones—one for data collection and other for fetching the user input. Time synchronization had to be maintained which is not always possible. A review of speed-breaker detection using Gaussian filtering is mentioned in [3]. This approach is based on the processing of an image. Here every image must be resized for speedbreaker detection, and image is sensitive to weather conditions. Automatic pothole and speed-breaker detection using the android system is mentioned in [4], where the database for each road must be maintained. Hence, there would be a problem when it is required to travel a new route.

2 Methodology Figure 1 depicts 77 GHz voltage control oscillator (VCO) which generates RF signal to the transmitter, and some part of the transmitted signal is given to local oscillator which is the input to a mixer. The backscattered signals from the targets are received by a receiver and inputted into the mixer. The mixer generates in-phase and quadrature-phase components called as chirp. This signal is digitized and processed by DSP algorithms, and processed data is converted back to analog using digital-to-analog converter (DAC) [4]. The proposed method uses FMCW radar. Figure 2 illustrates the building blocks of the proposed technique, where raw data collected from radar is processed by fast Fourier transform (FFT) and features have been extracted from it. These set of features are useful in classifying the targets.

2.1 Raw Data Set In the proposed method, a raw data set is collected by considering the backscattered energy from the targets. The FMCW module used in this experiment has single transmitting antenna and four receiving antennas. Each frame of data contains 128 chirps, and each chirp has 512 samples for each receiving channel. This raw data is a time series data. The data is collected by mounting radar module at the front of a

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77GHz VCO

Transmitter

Receiver DAC

Digitizer

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Fig. 1 Block diagram of automotive FMCW radar

Sensor Data

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Feature Extraction

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Fig. 3 Raw data using FMCW radar

car such that it faces toward the road surface, so that no other targets can come into radar view. In the proposed technique, the max range of the radar is fixed to 20 m. The raw data is shown in Fig. 3.

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2.2 Pre-processing Technique Speed-breaker early warning system uses fast Fourier transform (FFT) as a preprocessing technique. Exploratory data analysis is performed on the raw data collected. The raw data shown in Fig. 3 using MATLAB R2009a shows the existence of the peak in presence of speed-breaker. Continuous intervals of time series have been converted to frequency intervals using FFT as shown in Fig. 4. A window is a function which has certain value inside the interval and zero value outside the interval. Blackman window has been used in this approach. Windowing is to remove background artifacts. Blackman window is given by Eq. 1. W (n)  0.42 − 0.5 ∗ cos(2pi ∗ n/M) + 0.08 ∗ cos(4pi ∗ n/M)

(1)

Here M is segment length of the window. For the windowed data, FFT has been applied as given by Eq. 2. Y (k)  FFT|X (n) ∗ W (n)|

(2)

Here X (n) is raw data, and w (n) is the window function. Figure 5 represents the magnitude of FFT of a plain road surface, where x-axis indicates the number of data samples that are being considered and y-axis indicates amplitude corresponding to each sample in the data.

Fig. 4 FFT signal chain 10

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Fig. 5 FFT plot of a road before speed-breaker detection

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Fig. 6 FFT of a road surface in the presence of speed-breaker

The FFT of road surface in the presence of speed-breaker is shown in Fig. 6. There is a clear significant difference between Figs. 5 and 6. In Fig. 6, the peak is rising to the maximum value at 120th bin which indicates that a speed-breaker is detected within the range of 8 m. The detection range of speed-breaker can be calculated using Eq. (3). This is an evidence that there is a sudden rise in the peak when a radar is detecting the speed-breaker. Range  Peak ∗ Range Resolution

(3)

where range resolution is given by Eq. (4). Range Resolution 

ADC sampling frequency ∗ 0.001 ∗ Velocity of light (m/s) 2 ∗ Slope Constant ∗ FFT length (4)

2.3 Feature Extraction Features are extracted from the spectrum obtained using Eq. 2. Some standard statistical features are used in machine learning to classify the targets. The list of features is shown in Table 1. Feature extraction can also be done using principal component analysis (PCA), which is a dimension reduction tool. PCA returns several features, namely mean, eigenvalues, eigenvectors, and covariance matrix [5]. Only the significant components would be returned by PCA [6]. The patterns in data can be easily identified by PCA by finding the maximum correlation between the variables.

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Symbol

Mean

µ

Variance

σ2

Root mean square

X RMS

Kurtosis

α4

Shape factor

SF

Impulse factor

IF

Formula N i1

Xi

N N

i1 (X i −μ)

 1 N 1 N

2

N N 

X i2 i1 N 4 i1 (X i −μ) 4 X RMS

1 N

X RMS N i1 |X i |

1 N

X peak N n1 |X n |

2.4 Classification Classification of pre-processed data is done using classification learner toolbox in MATLAB R2009a, where the two classes such as normal plain road and speedbreaker road pre-processed data are fed to classifier along with its respective labels which basically define a name of the class. For primary validation of data prediction, K-nearest neighbor (KNN) classifiers have been used due to simplicity of it [7], and then support vector machine (SVM) classifier is used with the intuition that it would mispredict lesser number of data points. The test cases for SVM will be lesser than KNN and provides better prediction.

3 Results Confusion matrix of SVM and KNN is shown in Tables 2 and 3, respectively. Here linear SVM is used as the classifier with 1000 samples for prediction, and classification is done based on two sets of features. One is standard and statistical features, and other is PCA-based features. Among these two sets of features, statistical features provide better prediction percentages. Percentage of correctly predicted samples from the training samples gives prediction accuracies. The scatter plot between statistical features of training data is shown in Fig. 7, which clearly indicates the separation between two classes.

Speed-Breaker Early Warning System Using 77 GHz Long … Table 2 Confusion matrix and classification accuracy using SVM Feature-set 1 Feature-set 2 True Speed-breaker Plain road Speed-breaker class/predicted class Speed-breaker 1000 0 880 Plain road Speed-breaker prediction accuracy

1 100%

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Table 3 Confusion matrix and classification accuracy using KNN Feature-set 1 Feature-set 2 True Speed-breaker Plain road Speed-breaker class/predicted class

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Fig. 7 Scatter plot of speed-breaker versus plain road

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4 Conclusions Speed-breaker detection became very challenging in avoiding the accidents. Here it is shown that speed-breaker can be detected by analyzing the backscattered energies from radar of speed-breaker as well as the normal road surface. Whenever there is an existence of speed-breaker in the radar range, the corresponding data samples are tending to exceed the mean. Among the two sets of features, Statistical features are giving better prediction percentages. Among the classifiers, SVM is giving good results. Statistical features with SVM predict the speed-breaker more accurately and efficiently than KNN. Acknowledgements I wish to express my sincere gratitude to Ineda Systems Pvt. Ltd. (www. inedasystems.com) Hyderabad, for providing me an opportunity to complete this project. Ineda Systems is one of the leading autonomous driving technologies and develops several algorithms related to radar, and it manufactures and distributes low-power System-on-a-Chip (SoC).

References 1. V.V. Viikari, T. Varupula, M. Kantanen, Road-condition recognition using 24-GHz automotive radar. IEEE Trans. Intell. Transp. Syst. 10(4), 639–648 (2009) 2. M. Jain, A.P. Singh, Speed-breaker early warning system, Noida, India 3. W. Deva Priya, C. Nelson Kennedy Babu, T. Srihari, Real time speed bump detection using Gaussian filtering and connected component approach. Circ. Syst. 7(10), 2168–2175 (2016) 4. D.K.A. Jeffrey, Signal processing for automotive radar, in IEEE School of Electrical and Electronics Engineering (2005), pp. 7803–8882 5. A.K. Mishra, B. Mulgrew, Radar signal classification using PCA based features, in IEEE Acoustics, Speech and Signal Processing, Toulouse, France (2006) 6. M.S. Park, J.H. Na, J.Y. Choi, PCA-based feature extraction using class information, in IEEE Systems, Man, and Cybernetics, Waikoloa, USA (2005) 7. S. Kent, N.G. Kasapoglu, M. Kartal, Radar target classification based on support vector machines and high-resolution range profiles, in IEEE Radar Conference, Rome, Italy, 2008

Face Recognition using Invariant Feature Vectors and Ensemble of Classifiers A. Vinay, Abhijay Gupta, Harsh Garg, Aprameya Bharadwaj, Arvind Srinivas K. N. Balasubramanya Murthy and S. Natarajan

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Method Proposed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Facial Feature Detection and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Feature Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Classification by Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In this paper, we have proposed an efficient and computationally inexpensive approach toward two mainstreams of image recognition, i.e, face recognition and person identification. Our proposed model is invariant to pose, expression, scale, A. Vinay · A. Gupta (B) · H. Garg · A. Bharadwaj · A. Srinivas K. N. Balasubramanya Murthy · S. Natarajan PES University, Bangalore, India e-mail: [email protected] A. Vinay e-mail: [email protected] H. Garg e-mail: [email protected] A. Bharadwaj e-mail: [email protected] A. Srinivas e-mail: [email protected] K. N. Balasubramanya Murthy e-mail: [email protected] S. Natarajan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_74

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illumination, and translation with the application of different techniques and implementation of their algorithms. For the purpose of making the model illumination invariant, we have preprocessed the image with linear transformation. Features are extracted from preprocessed facial images using a Modified-Local Difference Binary descriptor which incorporates gradient knowledge from a nonlinear scale space. The extracted facial features are then quantized to a vector. Subsequently, an extreme gradient boosting algorithm is used, resulting in fast and high-performance classification. The proposed method has been experimented on three benchmark datasets like GRIMACE, FACES95, FACES96 producing significant results in terms of speed, accuracy, and efficiency. We extended our proposed method and tested it on Raspberry Pi 3 to conclude that it is fast on limited processor and memory settings. This pipeline has resulted in a faster and efficient face recognition approach with decrease in error rate around +8 to +10%. Keywords Face recognition · Person identification · Cyber-security Surveillance system

1 Introduction Image detection and recognition has always been an interesting topic in the computer vision community. Every year, a large number of computer vision enthusiasts work in the domain of face recognition (FR) resulting in improved methods and techniques. Various algorithms are implemented on different hardware platforms increasing their usage in numerous applications like surveillance systems at theaters, airports, for the identification of unwanted and mischievous people, in curbing crime by security agencies in government organizations by recognizing criminals based on their different facial characteristics. These techniques have also become an important aspect for cyber-security to restrict unauthorized access to data, programs in laptops, computers from attack or damage. In the current scenario, facial authentication techniques have been in widespread use along with traditional techniques like passwords and patterns. This paper proposes a faster and an efficient approach for face recognition and its performance on Raspberry Pi 3. FR has always been a challenging and an interesting job especially in case where facial images show variance in pose, expression, scale, illumination, translation, and occlusions. Decades of work have been done in this field to make the task faster, efficient, and invariant to the above-mentioned constraints.

2 Related Work Significant amount of work has been done in the domain of face recognition and its applications mainly involving handcrafted detectors and descriptors like SIFT [1],

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SURF [2], ORB [3], FAST [4], BRIEF [5] in combination with classifiers like SVM [6], LDA [7], or ensemble of classifiers. Few others have used deep learning methods like convolutional neural networks as well. This section describes the techniques frequently used and their applications in the field of image processing. KAZE features have been used in offline signature verification. In [8], KAZE features are detected in both the strokes and background space to introduce the various correlations between strokes and the stroke itself. It is demonstrated as a novel multiscale 2D algorithm in nonlinear space for detection and description of keypoints from the signature. KAZE features along with bag of visual words model in this paper have proved that this method is better than popular SURF and achieves better performance. Similar works for KAZE features are demonstrated in the classification of echocardiogram videos in [9]. Li et al. [9] show the comparison of classification in which when SIFT features being used gives an accuracy of 72% but when KAZE features are integrated with the same model gives an overall performance around 80%. KAZE has an increased computational cost disadvantage compared to SURF. So, it got evolved to A-KAZE and showed excellent speeded execution in contrast with traditional algorithms like SIFT, SURF, ORB. A-KAZE found its use in free-form object recognition in [10]. In this module, A-KAZE is used for feature description of the free-form object. The description of the different viewpoints is stored in histograms. These histograms are fed as GCS input in which the GCS does the nearest neighbor association resulting in classified outputs. Works in the field of robotics [11] also find use of A-KAZE where its features are used for global description technique. Caramazana et al. [11] use this descriptor for the evaluation of efficiency and robustness adding gradient information in a nonlinear space, in addition to achieve rotation and scale-invariant description. Extreme gradient boosting finds its use in numerous applications like storage sales prediction, automatic music instrument detection [12], prediction of emotional intensity of tweets [13]. It is also exploited in image classification tasks [14], facial landmark detection [15], driver drowsiness detection [16]. Also, this technique can be used in medical fields for automatic detection of diabetic retinopathy [17]. In [14], extreme gradient boosting is used in combination with CNN for feature extraction from input and as a recognizer acting at the top of the model for producing more accurate results. Penev and Boumbarov [15] use an ensemble of three different cascading linear regressors and their results using gradient boosting tree for predictions of landmarks in facial images. Applications like driver drowsiness detection mentioned in [16] used gradient boosting classifier for the classification of driver drowsiness and suggested that this method is potential and applicable for real application. The method proposed in [16] achieved an accuracy of 87.56% making it suitable for the purpose.

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3 Method Proposed Facial images are passed to the model in a sequential manner as shown in Fig. 1.

3.1 Image Enhancement The image sent to the model is first preprocessed and enhanced because in some cases face is not visible due to variance in illumination. It becomes an important step for preserving the relevant information by eliminating noise from the image. Image preprocessing begins by setting the parameters for brightness and contrast control, i.e., α and β, respectively. Then, the following linear transformation is applied to the input image, f (i, j),

Fig. 1 Proposed approach

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(1)

The resulting image, g(i, j), is brighter and enhanced so that it can be further sent deep into the model for processing.

3.2 Facial Feature Detection and Description The preprocessed image is first converted to grayscale picture before further processing. The first step toward face recognition is the detection of facial features. For the detection of facial features at multiple scale levels, the determinant of the Hessian is computed for each image [18]. Let L a be a filtered image in the nonlinear scale space, then the determinant of the Hessian is computed by 2 (L ax x L ayy − L ax y L ax y ) L aHessian = σi,norm

(2)

Here, second-order derivatives are computed using concatenated Scharr filters with step size σi,norm . Such filters are used for approximating invariance to rotation better than others. The algorithm verifies the detector response at each evolution level i and checks if it is higher than threshold defined previously. Such methodology is adapted to remove non-maxima responses. Subsequently for each potential maxima obtained from the previous step, the response is checked for its maxima in range [i + 1, i − 1] w.r.t. other keypoints. At the end, the two-dimensional position of keypoint is calculated by fitting a two-dimensional quadratic function of the determinant of Hessian response present in neighborhood. For the purpose of feature description, Modified-Local Difference Binary (MLDB) is used which characterizes the keypoints detected from the feature detector. In nonlinear scale space, M-LDB exploits gradient and intensity information. It uses binary tests between the mean of areas for additional robustness in contrast to BRIEF which uses single pixels. Also, this method uses the mean of horizontal and vertical derivatives in the areas to be used for comparison. Invariance to rotation can be gained by computing the main orientation of the interest point resulting in the rotation of the grid of LDB accordingly. M-LDB exploits the use derivatives which are computed in the detection of features in previous step resulting in reduction of number of computations to construct the descriptor. Location of interest points detected using this method can be seen in Fig. 2. The descriptor computed is further sent to the subsequent steps of the model for vector quantization and classification.

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Fig. 2 Interest points detected using accelerated-KAZE

3.3 Feature Quantization The descriptors calculated in the previous section describe the features of the facial image. A concise representation of these features is required. Most popular feature quantization methods include SPM [19] and ScSPM [20]. As proposed in [21], we used locally constrained linear coding to quantize our extracted descriptors. This is achieved by using a locality constraint in place of sparsity constraint. This method speeds up the encoding process by forming a better local coordinate system. Each vector consists of 128 real numbers which represent the complete facial profile of a person. This vector is then used for classification as explained in the subsequent section.

3.4 Classification by Boosting After the features are quantized into a vector, it is passed to an end-to-end tree boosting algorithm called XGBoost [22] for the classification of facial images. Boosting combines a set of relatively weak learners to form a complex predictor which tends to have a low error rate as they learn from the mistakes of the previous learner. The previous learner’s weights are also accounted for and at each iteration they are updated with respect to the residual weights. The ensemble tree model is given by, L(φ) =

 i

l( yˆi , yi ) +



ω( f k )

(3)

k

where ω penalizes the complexity of regression tree functions, l is a convex loss function which is differentiable and calculates the difference between the prediction

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yˆi and the target yi . The above equation contains functions as parameters which are difficult to optimize using traditional methods which use Euclidean space. Contrary to this, the proposed method is trained in an additive manner. We greedily add f t that mostly improves our model. Generally, the objective function is optimized by second-order approximation. The scoring function to measure the quality of tree structure q is given by,  2 t 1  ( i∈I j gi )  +γT L (q) = − 2 j=1 i∈I j h i + λ (t)

(4)

where I j = {i|q(xi ) = j} is the instance set of leaf j. For evaluating the decision trees, the score computed from this function is like the impurity score which can be used for wider range of objective functions. Multiple decision trees are constructed with a specific number of terminal nodes in the decision tree, six in our case. This allows intercommunication of node values within tree resulting in better feature understanding. Gradient descent is used to minimize the error. Chen and Guestrin [22] designed a sparsity aware algorithm providing robust and inexpensive computation.

4 Experiments 4.1 Datasets In order to test the performance and efficiency of our model, we use FACES95, FACES96, and GRIMACE datasets, respectively. Sample images from the dataset showing variations in pose, illumination, expression, and scale are shown in Fig. 3. FACES95 contains 1440 images of 72 individuals, 20 images each. The dataset was constructed by taking snapshots of 72 individual with a delay of 0.5 s between successive frames in the sequence. Head movement variations were introduced between images of the same individual. Similar to the above methodology, FACES96 was constructed for 3040 images consisting of 126 individuals. GRIMACE consists of 360 images of 18 individuals. The images taken are variant to scale, lighting, and position of face in the image. In addition, the subject made grimaces after moving his/her head which gets extreme toward the end of the sequence.

4.2 Results To test the performance of our model as depicted in Fig. 1, we randomly sampled 75% of each dataset as training and the remaining as testing dataset. The training is run for 300 epochs. The trained model achieves an accuracy of 92.37% on FACES95,

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Fig. 3 Datasets reflecting variance in pose, expression, scale, translation, and expression

88.46% on FACES96, and 91.96% on GRIMACE dataset. Accuracies, precision, recall, and error rate of the results on the testing data have been tabulated in Table 1. This concludes that the proposed approach performs well on facial images showing significant head movements, expression, position, and scale. Images are made illumination invariant by performing a simple linear transformation as described earlier. The classification result changes drastically with increasing number of epochs of the extreme gradient boosting method. This has been speculated in Table 2. The

Table 1 Accuracy of proposed model on different datasets Database Accuracy (%) Precision Recall FACES95 FACES96 GRIMACE

92.37 88.46 91.96

0.924 0.885 0.919

0.931 0.889 0.924

Error rate 0.1133 0.1384 0.1184

Face Recognition using Invariant Feature Vectors … Table 2 Variation of accuracy of our model with epoch size Epochs FACES95 (%) FACES96 (%) 10 30 50 100

78.85 84.28 85.14 88.23

71.5 78.33 80.01 82.83

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GRIMACE (%) 85.85 86.65 88.44 90.45

Table 3 Efficiency of the proposed approach on a single image and the complete GRIMACE dataset Time (single image) (ms) Time (full dataset) Feature description Extreme gradient boosting

20 0.00171

9.373 s 0.154 ms

classification method used does not vary much when the maximum depth of the boosted tree’s is fluctuated given the model is being trained for a longer time. We further extended our experiments to embedded systems such as Raspberry Pi 3 which shows promising result in terms of both efficiency and accuracy. Time to compute descriptors for a facial image and classify them are tabulated in Table 3. This table shows the efficiency of the proposed approach for a single image and for a set of images. We used the Grimace dataset to test our model on a Raspberry Pi. From our observation, we can conclude that the proposed model is efficient in timeand computation-scarce scenarios.

4.3 Conclusion In this paper, we propose a person identification model which is useful for hardwareconstrained devices. With a recognition error rate of less than 9%, this robust method can be implemented on surveillance cameras at crowded areas such as airports, railway stations, and malls. A direct comparison cannot be done with other state-of-the-art models available in the literature. Some of the major reasons for not such a direct comparison are mentioned below in the context. Testing of methods on different hardware and implementation of the same results in the difference in efficiency of algorithms. The preprocessing methods involved and variance in datasets also lead to the change in results. However, [23] carries about comparative study between different face recognition methods which are made to work under unconstrained scenarios. Such a comparison was made on three datasets, namely FERET, LFW, UCHFaceHRI. These datasets pose variations in illuminations, inaccurate eye’s annotations, occlusions, scale, pose, resolution, focus, etc.

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There are still many cases which can be considered for efficient face recognition so that it can be used in more real-time scenarios. However, the analysis of preprocessing algorithms should be done to make the model more efficient in times of occlusions and significant head rotations.

References 1. D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004) 2. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008) 3. E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient alternative to SIFT or SURF, in 2011 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2011), pp. 2564– 2571 4. E. Mair, G.D. Hager, D. Burschka, M. Suppa, G. Hirzinger. Adaptive and generic corner detection based on the accelerated segment test, in European Conference on Computer Vision (Springer, Berlin, 2010), pp. 183–196 5. M. Calonder, V. Lepetit, C. Strecha, P. Fua, Brief: binary robust independent elementary features, in Computer Vision ECCV 2010 (2010), pp. 778–792 6. M.A. Hearst, S.T. Dumais, E. Osuna, J. Platt, B. Scholkopf, Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998) 7. E.I. Altman, G. Marco, F. Varetto, Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). J. Bank. Finan. 18(3), 505– 529 (1994) 8. M. Okawa, Offline signature verification based on bag-of-visual words model using KAZE features and weighting schemes, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016), pp. 184–190 9. W. Li, Y. Qian, M. Loomes, X. Gao, The application of KAZE features to the classification echocardiogram videos, in Multimodal Retrieval in the Medical Domain (Springer International Publishing, 2015), pp. 61–72 10. K.L. Flores-Rodrguez, F. Trujillo-Romero. Free form object recognition module using AKAZE and GCS 11. L. Caramazana, R. Arroyo, L.M. Bergasa, Visual odometry correction based on loop closure detection, in Open Conference on Future Trends in Robotics (RoboCity16) (2016), pp. 97–104 12. O. Slizovskaia, E. Gómez Gutiérrez, G. Haro Ortega, Automatic musical instrument recognition in audiovisual recordings by combining image and audio classification strategies, in Proceedings SMC 2016. 13th Sound and Music Computing Conference; 2016 Aug 31; Hamburg, Germany, ed. by R. Großmann, G. Hajdu. Hamburg (Germany): ZM4, Hochschule fr Musik und Theater Hamburg; 2016. pp. 442–4477. Zentrum fr Mikrotonale Musik und Multimediale Komposition (ZM4), Hochschule fr Musik und Theater Hamburg, 2016 13. S. Madisetty, M.S. Desarkar, An ensemble based method for predicting emotion intensity of tweets, in International Conference on Mining Intelligence and Knowledge Exploration (Springer, Cham, 2017), pp. 359-370 14. X. Ren, H. Guo, S. Li, S. Wang, J. Li, A novel image classification method with CNN-XGBoost model, in International Workshop on Digital Watermarking (Springer, Cham, 2017), pp. 378– 390 15. M. Penev, O. Boumbarov, Facial landmark detection using ensemble of cascaded regressions. Int. J. 128 (2015) 16. X.-P. Huynh, S.-M. Park, Y.-G. Kim, Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine, in Asian Conference on Computer Vision (Springer, Cham, 2016), pp. 134–145

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17. K. Xu, D. Feng, H. Mi, Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22(12), 2054 (2017) 18. P.F. Alcantarilla, T. Solutions, Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2011) 19. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (IEEE, 2006), pp. 2169–2178 20. J. Yang, K. Yu, Y. Gong, T. Huang, Linear spatial pyramid matching using sparse coding for image classification, in IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 (IEEE, 2009), pp. 1794–1801 21. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, Locality-constrained linear coding for image classification, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2010), pp. 3360–3367 22. T. Chen, C. Guestrin, Xgboost: a scalable treeboosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2016), pp. 785–794 23. G. Hermosilla, J. Ruiz-del-Solar, R. Verschae, M. Correa, A comparative study of thermal face recognition methods in unconstrained environments. Pattern Recogn. 45(7), 2445–2459 (2012)

Pattern and Frequency Reconfigurable MSA for Wireless Applications Deeplaxmi V. Niture, Chandrakant S. Patond and S. P. Mahajan

Contents 1 2 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antenna Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antenna Design and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Square Ring Patch Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Effect of Switch Position (L4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Effect of Feed-Line Width (FW) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Effect of L3 and W6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Frequency Reconfigurability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Pattern Reconfigurability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract This paper presents a new pattern and frequency reconfigurable (PFR) microstrip patch antenna (MSA). The presented PFR antenna has a microstrip patch with nearly square slot on top layer and partial ground plane with two vertical gaps on bottom layer. A microstrip feed line is used to feed the antenna. To change the operating frequency and radiation pattern of the antenna, the surface current distribution is altered by connecting or disconnecting the vertical gaps on ground plane using ideal switches (small metal strips). Depending on the ON/OFF states of switches, antenna resonates at 5.36 GHz or 3.4 GHz, and it can have 180° switchable pattern at 3.4 GHz. The proposed PFR antenna is designed, simulated, and analyzed using computer-aided design package (CADFEKO). The simulated and measured reflection coefficient graphs show good agreement. D. V. Niture (B) · C. S. Patond · S. P. Mahajan Electronics & Telecommunication Department, College of Engineering Pune (COEP), Pune, India e-mail: [email protected] C. S. Patond e-mail: [email protected] S. P. Mahajan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_75

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Keywords Reconfigurable antenna · Frequency reconfigurability Pattern reconfigurability · Square ring slot

1 Introduction The next-generation wireless communication systems require compact, multifunctional, smart antenna that will adapt to the changing system requirements and communication environment. Reconfigurable antenna (RA) is a promising solution for it. RA is an antenna which is capable of changing its fundamental properties such as frequency, radiation pattern, and polarization independently or simultaneously. RAs having different diversity in single antenna structure are called as compound RAs. Several fixed performance antennas in wireless communication systems can be replaced with a single compound RA. Thus, the use of single compound RA in wireless system simplifies the system integration by making them compact and less costly and also provides performance flexibility and improved functionality. Among compound RA, frequency and pattern diversity on a single antenna is used to provide improved spectral efficiencies. For frequency reconfiguration RF switches, impedance loading or tunable materials are used to change the antenna dimensions physically or electrically. Pattern reconfiguration can be achieved by using parasitic elements and Yagi-based structures, changing effective antenna’s structure using electric switches, or using multiple feeding or switched feed network or tunable materials [1]. Combining above-mentioned techniques, several designs for PFR antenna were proposed and implemented. Major work has been done with ideal switches [2, 3] and active switches [4, 5]. Due to promising benefits of PFR antenna, a novel and simple PFR square ring antenna is designed and proposed in this paper. This antenna achieves both pattern and frequency reconfigurability by using only two switches. The antenna operates at two frequencies, viz. 3.4 and 5.36 GHz, and it switches the pattern by 180° while operating at 3.4 GHz, making it very useful where the power and interference are critical. The remaining part of the paper is sequenced as follows. Sect. 2 explains the geometry of the proposed PFR-MSA. Various attributes that affect the characteristics of designed antenna are discussed in Sect. 3. In Sect. 4, the results obtained for the proposed antenna, i.e., reflection coefficient graphs and radiation pattern, are discussed. Finally, Sect. 5 comprises of conclusion and future scope.

2 Antenna Geometry The design of the proposed PFR-MSA is described in this section. The front and back views of proposed antenna are shown in Fig. 1. A FR4 substrate of 45 mm (Lw) ×

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Fig. 1 Geometry of PFR-MSA a top layer and b bottom layer Table 1 Antenna dimensions Parameter Dimension (mm) L1 W1 L2 W2 FL FW

18.40 13.90 9.70 6.90 9.20 1.20

Parameter

Dimension (mm)

L3 W3 L4  L5 W4  W5 L6  L7 W6

8.80 13.20 6.35 1.50 1.40 16.30

40 mm (Lg) × 1.6 mm (h) is used to construct the antenna. This FR4 substrate has dielectric constant (εr) of 4.4 and loss tangent of 0.02. The PFR-MSA consists of a square ring patch fed with a microstrip line on upper layer and a defected ground structure with two vertical gaps on bottom layer. To achieve pattern and frequency diversity, two switches SW1 and SW2 are placed in the vertical gaps of ground plane. Here, metallic strips of 1.4 mm × 1.5 mm are used as switches to validate the concept. Other optimized dimensions of antenna are mentioned in Table 1.

3 Antenna Design and Analysis 3.1 Square Ring Patch Design A square ring patch antenna is formed when a square slot is etched in the center of the square patch. Because of ring structure, the current will have a longer path along the circumference of square ring resulting in resonant frequency that is proportional

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Fig. 2 Reflection coefficient (dB) curve for different locations of the switch

to average circumference of square ring, i.e., 4(L-W), where L and W are length and width of the square ring respectively. The size of square ring is reduced to nearly about half of the original dimension, and the bandwidth is also improved (5–15%). The length, the width, and other design parameters of square ring patch antenna are calculated as mentioned in our earlier paper [6]. The antenna is designed and simulated in simulation software CADFEKO. The optimized antenna design parameters are obtained through series of simulations. It is observed that the switch position (L4), feed-line width (FW), L3, and W6 have significant effect on reflection coefficient and resonant frequency of antenna.

3.2 Effect of Switch Position (L4) Figure 2 shows the effect of switch position (from the ground edge) on the resonant frequency. If switch is moved towards the edge, the 3.4 GHz band (Case 2 and Case 3) remains unaffected, but the 5.36 band (Case 1) shifts downwards. Changing the switch position with respect to the ground edge will not change the current path length significantly in Case 2 and Case 3 as at a time only one switch will be ON in these cases. But in Case 1 both the switches will be ON simultaneously, so the path length will change significantly when we change the switch position. The reconfigurable antenna shows good impedance matching at desired resonant frequency with switch position L4  6.34 mm from the ground edge.

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Fig. 3 Reflection coefficient (dB) curve for different values of feed-line width

3.3 Effect of Feed-Line Width (FW) The effect of variation of the feed-line width (FW) on the resonant frequency is shown in Fig. 3. The impedance of the feed line depends on width of the feed line. When feed width changes, impedance also varies. There is an increasing impedance mismatch with increasing feed width, and the reflection coefficient is higher. Also, the feed line is connected to the SMA connector which is having diameter of 0.8 mm, so the width of feed line must be greater than the diameter of the SMA connector. So to achieve good S11 at desired frequency band, dimension of FW is selected as 1.2 mm.

3.4 Effect of L3 and W6 Figures 4 and 5 show the variation of reflection coefficient with change in dimensions of L3 and W6, respectively. From Figs. 4 and 5, we observe, as dimensions of L3 and W6 are increased, the higher resonant frequency shifts upwards and lower resonant frequency shifts downwards. And when dimension of L3 or W6 is decreased, the higher resonant frequency shifts downwards and lower resonant frequency shifts upwards. The values of L3  8.79 mm and W6  16.29 mm are selected so that antenna resonates at desired frequency bands. Finally, the optimized dimensions of the presented antenna for desired pattern and frequency reconfigurability are listed in Table 1.

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Fig. 4 Reflection coefficient (dB) curve for different dimensions of L3

Fig. 5 Reflection coefficient (dB) curve for different dimensions of W6

4 Results and Discussion The designed PFR-MSA is fabricated and tested using ROHDE-SCHWARZ vector network analyzer (VNA).Top and bottom layers of fabricated antenna are shown in Fig. 6. The proposed antenna switches between 3.4 GHz and 5.36 GHz by changing the states of the switches SW1 and SW2. Also, the pattern is switched by making one of the switches ON. Table 2 shows the different cases of antenna reconfigurability.

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Fig. 6 Fabricated PFR-MSA a top view and b bottom view Table 2 Different cases with their switch status and observations Cases Case 1 Case 2 Switch status

Case 3

Both SW1 and SW2 are ON 5.36

SW1 is ON, and SW2 is OFF 3.404

SW1 is OFF, and SW2 is ON 3.396

BW (%)

5.46

10.64

10.71

Gain (dB)

3.14

5.24

5.25

HPBW

41.34

70

70

fr (GHz)

4.1 Frequency Reconfigurability When both SW1 and SW2 are ON, i.e., Case 1, antenna does not have any vertical gap in the ground plane and hence it resonates at 5.36 GHz. The simulated and measured results of reflection coefficient for Case 1 are shown in Fig. 7. Simulated band is from 5.2 to 5.5 GHz where measured band is from 5.0 to 5.6 GHz. When either of switches SW1 or SW2 is ON and other is OFF, i.e., Case 2 or Case 3, the proposed reconfigurable MSA gets a vertical gap on the right side or left side of ground plane, and because of that, the current gets a longer path and resonant frequency shifts to downside, i.e., 3.4 GHz. The simulated and measured results of reflection coefficient for Case 2 and Case 3 are shown in Figs. 8 and 9, respectively. In Case 2, the simulated and measured reflection coefficient bandwidth is nearly same, i.e., 3.26–3.63 GHz. In Case 3, measured center frequency is shifted upward with more bandwidth compared to the simulated frequency and bandwidth. There is a deviation in the simulated and measured results because the switch gap width and switch positions are not accurately fabricated. Still measured results satisfy the criteria for WiMAX (3.4–3.6 GHz) and WLAN (5.15–5.35 GHz).

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Fig. 7 Reflection coefficient (dB) curve for Case 1

Fig. 8 Reflection coefficient (dB) curve for Case 2

4.2 Pattern Reconfigurability The antenna radiation pattern is determined by current distribution. Hence, in the proposed RA, switches SW1 and SW2 are used in the ground plane to change the current distribution and to direct the antenna radiation in the desired direction. To show how the antenna functions for different cases, the surface current distribution was studied. Figure 10 shows the surface current distribution in all cases. In Case 1, current distributes symmetrically about y-axis on upper patch and ground plane which leads to omnidirectional pattern. The simulated radiation patterns in different

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Fig. 9 Reflection coefficient (dB) curve for Case 3

Fig. 10 Surface current distributions for a Case 1 at 5.4 GHz, b Case 2, and c Case 3 at 3.4 GHz

plane for Case 1 are shown in Fig. 11. The antenna has nearly same gain of 3.14 dBi in all directions. In Case 2, when switch SW1 is ON, the surface current distributions on square ring and ground plane are no longer symmetrical about y-axis. The surface current on right portion of square ring and the surface current on ground plane are in opposite direction; their phase relationship results in null radiation on right-hand side, and radiation pattern will be left-directed. So, we can say that in Case 2, the right-side portion of ground plane acts as a reflector. Similarly, in Case 3, the left portion of the ground plane acts as reflector and radiation is in the right direction. Figure 12 shows radiation patterns in Case 2 and Case 3. It can be seen that pattern is switched by an angle of 180◦ and has a peak gain of 5.24 dB. In Case 2 and Case 3, the antenna geometry is antisymmetric and so the antenna resonates at 3.4 GHz for both cases.

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Fig. 11 Simulated radiation pattern a XY plane and b XZ plane at 5.36 GHz for Case 1

Fig. 12 Simulated radiation patterns a XY plane and b XZ plane at 3.4 GHz for Case 2 and Case 3

5 Conclusion A new pattern and frequency reconfigurable MSA has been proposed. The antenna operates in three different modes depending on states of two switches. It generates left-directed and right-directed directional radiation patterns at 3.4 GHz and nearly omnidirectional radiation pattern at 5.36 GHz. Simulated and measured results of return loss of the antenna are in good agreement. Measured results indicate the suitability of antenna for the WiMAX (3.5 GHz) and WLAN (5 GHz) applications. The antenna is relatively simple and compact, so it can be easily integrated with other

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microwave components in portable wireless devices. Future work will concentrate on the implementation of the PIN diodes on the proposed antenna.

References 1. Alam, M.S., A. Abbosh, Planar pattern reconfigurable antenna with eight switchable beams for WiMax and WLAN applications. IET Microwaves, Antennas Propag. 10(10), 1030–1035 (2016) 2. G.H. Huff, et al.: A novel radiation pattern and frequency reconfigurable single turn square spiral microstrip antenna. IEEE Microwave Wirel. Compon. Lett. 13, 257–59 (2003) 3. Li, W., L. Bao, Y. Li, A novel frequency and radiation pattern reconfigurable antenna for portable device applications. Appl. Comput. Electro Magn. Soc. J. 30(12) (2015) 4. H.A. Majid et al., Frequency and pattern reconfigurable slot antenna. IEEE Trans. Antennas Propag. 62(10), 5339–5343 (2014) 5. P.K. Li, et al. Frequency-and pattern-reconfigurable antenna for multi standard wireless applications. IEEE Antennas Wirel. Propag. Lett. 14, 333–336 (2015) 6. D.V. Niture, P.A. Govind, S.P. Mahajan, Frequency and polarisation reconfigurable square ring antenna for wireless application. Region 10 Conference (TENCON), IEEE (2016)

Feature Fusion and Classification of EEG/EOG Signals Ayushi Mishra, Vikrant Bhateja, Aparna Gupta, Apoorva Mishra and Suresh Chandra Satapathy

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed EEG/EOG Fusion and Classification Approach . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Canonical Correlation Analysis (CCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Linear Discriminant Analysis (LDA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Electroencephalogram (EEG) refers to the brain waves, whereas electrooculogram (EOG) represents the eyeblinking signals. Both the signals possess complexities and various artifacts when they are recorded. In order to use these signals in biometric and clinical applications, preprocessing needs to be done. Stationary wavelet transform (SWT) with the combination of independent component analysis (SWT + ICA) is used to perform EEG signal preprocessing, and empirical mode decomposition (EMD) is used to process EOG data. After the signals are alleviated of the noise, feature extraction is done. Time delineation in case of EOG and autoregressive modeling (AR) technique in case of EEG data is applied for feature extraction. Fusion of extracted features is performed using canonical correlation analysis (CCA) so that the number of features is minimized. Classification is performed A. Mishra · V. Bhateja (B) · A. Gupta · A. Mishra Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow 226028, Uttar Pradesh, India e-mail: [email protected] A. Mishra e-mail: [email protected] A. Gupta e-mail: [email protected] A. Mishra e-mail: [email protected] S. C. Satapathy School of Computer Engineering, KIIT (Deemed to Be University), Bhubaneswar, Odisha, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_76

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to classify the features into sets or classes in order to perform dimensionality reduction. Linear discriminant analysis (LDA) is used to form suitable sets and evaluates the performance of the classifier. EOG signal is present as an artifact in EEG; but in this paper, their combination is considered so as to gain more distinct features. The main aim is to develop a multimodal system which possesses high classification and recognition accuracy so that biometric authentication can be performed. Keywords AR · CCA · EEG · EOG · EMD · LDA · SWT-ICA

1 Introduction EEG signals represent the electrical activity of the brain which can be recorded by placing several electrodes. In this case, the features extracted from the Fp1 electrode are considered; i.e., single channel database is considered. Their frequency ranges from 0.01 to 100 Hz and varies from a few µV–100µ. Due to their complex nature, they are mostly contaminated by various artifacts such as ocular, muscular, cardiac, glossokinetic, and environmental [1–3]. EOG signals represent the electrical activity of the eyeball and eyelid motions recorded by measuring the potential difference between the cornea and the fundus of the eye [4]. They possess a frequency range from 0.5 to 15 Hz [4]. More often the EOG signals are present as an artifact in EEG data. However, in this paper EOG is regarded as the source of features in order to boost the recognition and classification accuracy [4]. Preprocessing of the signals is performed to suppress the various artifacts that present in the signal. From the processed signal, suitable features are extracted and this stage is known as feature extraction. Various techniques have been developed for the preprocessing of signals. For EEG signal preprocessing, a combinational approach of SWT-ICA [5–7] is used as discussed in Mishra et al. [5]. First, the noisy EEG [8–11] data is decomposed using stationary wavelet transform (SWT). Once the decomposition is performed, fast ICA approach is applied to denoise the noisy signal. The EOG features are extracted using empirical mode decomposition (EMD). After the preprocessing of both the signals, feature extraction is performed using AR modeling for the EEG data and time delineation of eyeblinks for EOG data as discussed in Gupta et al. [12]. Feature fusion and classification are a vital task so that the redundant features are eliminated. Various techniques have been proposed to perform fusion and classification of signals. Naidu et al. [13] used simple averaging method for fusion purpose, but the major drawback was that it was unable to maintain the clarity of image. Sahu et al. [14] used the method of calculating principal components and then using them for fusion. This method proved to be incompetent as it lacked the stable group of basis vectors and suffered spectral degradation. Zheng et al. [15] used CCA to estimate the multi-channel EEG data. It was successful to achieve higher signal-tonoise ratio (SNR) values by exploring the coordinate system. Blankertz et al. [16] used Fisher discriminant analysis for classifying the fused set of features, but this approach suffered drawback of less number of training data sets leading to increased

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errors in results. Subasi et al. [17] used support vector machines (SVMs) for the classification of data sets which turned inefficient because it experienced optimization problems and data could not be separated linearly. Murugappan et al. [18] used linear discriminant analysis (LDA) for separating the fused features into various classes. This approach appeared simpler to implement and in turn produced faster outputs. The classified features help in effective analysis of the multi-level human authentication. The multi-level here signifies the combined usage of EEG and EOG signals. The remaining part of this paper is organized as follows. Section 2 describes the proposed EEG/EOG feature fusion and classification approach. The methodologies used at each stage are explained in detail in the sub-Sects. 2.1 and 2.2, respectively. The experiments performed and the achieved results for the fused and classified features from EEG/EOG signals are discussed in Sect. 3. Finally, Sect. 4 summarizes the concluded work.

2 Proposed EEG/EOG Fusion and Classification Approach Based on the proposed fusion and classification approach, first of all the EEG signals are preprocessed using the SWT-ICA combinational approach [5] in order to eliminate the noise present in the signal. From the preprocessed EEG signals, the eyeblinks (EOG) are extracted and detected using the EMD technique [19]. After the preprocessing of the signals, suitable features are extracted. This is accomplished using time delineation of eyeblinks in case of EOG, whereas AR modeling in case of EEGs, respectively [12]. When the two feature sets of EEG and EOG are constructed, they are fused using an approach named as CCA [15]. After the fusion stage, the classification is performed and data is processed for dimensionality reduction. This is done using LDA [18]. The complete methodology has been pictured as per the block diagram in Fig. 1. Various sub-modules of above block diagram are explained in the following section.

EEG Data + EOG Artifacts

EEG Data

Pre-Processing using EMD

Pre-Processing using SWTICA

Feature Extraction Time Delineation

Feature Extraction using AR Modeling

Fig. 1 Block diagram of EEG/EOG fusion and classification

Fusion using CCA

Classi-fication using LDA

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2.1 Canonical Correlation Analysis (CCA) CCA is a fusion approach and is used to analyze correlation among two data sets. The coefficients so obtained are referred to as the correlation coefficients and describe the existence of linear relationship amid two variables. These coefficients can be investigated by calculating the covariance matrices. When the value of correlation coefficient is zero, the variables are said to be uncorrelated, and there exists no linear relationship between them [15]. CCA is effective in solving problems like dimensionality reduction, and understanding dependency structures. [15]. Various steps to define the CCA algorithm [16] are listed as under in Table 1.

2.2 Linear Discriminant Analysis (LDA) LDA is a commonly known technique for data classification and dimensionality reduction [18]. It is based on the idea of searching for a linear combination of variables that is best suited to separate two targets/classes. LDA helps to model the dissimilarities between the two respective classes. Before performing the classification task, it is important to assume that the components or features to classified are normally distributed. Various steps to define the LDA algorithm [16] are listed as under in Table 2. In our work, three parameters have been considered for test-

Table 1 Algorithm of CCA BEGIN Step 1: Calculate mean of the signals X and Y . Denote the respective means as M X and MY Step 2: Deduct mean from the signal. This is shown in Eqs. (1) and (2) X  X − M X (1) Y  Y − MY (2) Step 3: Calculate the covariance matrices as shown in Eqs. (3) and (4) C X  X · X T (3) CY  Y · Y T (4) Step 4: Calculate eigenvalues and eigenvector from covariance matrices Step 5: Regard the diagonal components of the eigenvector as the principal components PC Step 6: Multiply principal components with the training data sets and insert zeroes, respectively. This is illustrated in Eqs. (5) and (6) X  X + PC  0 (5) Y  Y + PC  0 (6) Step 7: Compute correlation and summate (fuse) the two sets X and Y as shown in Eq. (7) Z  X + Y (7) END

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Table 2 Algorithm of LDA BEGIN Step 1: Compute the scattering matrices (in-between and within the classes) Step 2: Compute the eigenvector and corresponding eigenvalues from the scattering matrices Step 3: Sort the eigenvalues in the descending order and choose the highest-valued eigenvector so as to construct the dimensional matrix Step 4: Use this dimensional eigenvector matrix to transform samples onto new subspaces END

ing the classifier. They are classification accuracy (%), sensitivity and specificity, respectively [20].

3 Results and Discussions The EEG recordings during visual relaxation and EOG recordings during eyeblinks were downloaded from PhysioBank ATM [21, 22]. EEG/EOG is mostly recorded by placing several electrodes like Cp1 and Fp1. But in this case, only a single electrode placement is considered, i.e., the Fp1 electrode. The recordings of Fp1 electrode consist of 15 subjects with duration of 10 s each and 160 samples per frame. AWGN noise considering different SNRs (5, 10, 15, 20 dBs) is added to the original signal taken from database. The fidelity assessment parameter known as SNR is calculated, and it comes out to be −7.35 dB for the noisy signal. Preprocessing is then performed by using SWT-ICA approach in case of EEG, whereas EMD in case of EOG. The SNR recorded after signal reconstruction comes out to be 25.20 dB. After the preprocessed signals are obtained, suitable features are extracted. This is achieved using AR modeling approach in case of EEG, whereas time delineation in case of EOG. A set of 20 features is attained from EEG signal feature extraction and a set of 7 features from EOG. These form a feature array of 86 × 1 and 13 × 1, respectively. After the two feature sets are obtained, fusion is carried so as to eliminate redundant features. CCA technique is implemented to fuse the EEG/EOG feature sets. The resultant feature array obtained has dimensions of 1 × 86 which shows the elimination of unwanted features. Fused feature set is then processed to perform classification. LDA is used to form suitable classes, and performance of the classifier is evaluated. The classification ability of a feature set can be measured or analyzed from the classification accuracy. The higher value of accuracy can be attained by achieving larger values of true positives, i.e., more number of matched sets. The classifier should possess low sensitivity so that it does not give variable results. The classified parameters from LDA are provided in Table 3.

798 Table 3 Various results obtained from classification

A. Mishra et al. Performance parameters

LDA classifier

Training set

15

Testing set

14

True positives (TP)

12

True negatives (TN)

9

False positives (FP)

1

False negatives (FN)

0

Accuracy (%)

95.454

Sensitivity

1

Specificity

0.90

4 Conclusion In this paper, a methodology is proposed for fusion and classification of extracted EEG/EOG features. Fusion is done to reduce the redundant features and attain the objective of human biometric recognition with high recognition accuracy. Further, the fused signals are classified into different classes using LDA classifier. Classified features can be used to develop a multimodal system which can be used for biometrical human authentication. In future, the classification task can be checked and compared using other classifiers. The approach effectively concludes that the higher accuracy of the classifier depicts the classification performed by LDA classifier is error-free, and no useful information is lost. This is helpful in performing the task of human authentication accurately. For future application, it is required to increase the number of features so that high recognition accuracy can be attained. Declaration The authors’ would like to clarify herein that: No human subjects are directly involved in the study.

References 1. D.W. Klass, The continuing challenge of artifacts in the EEG. Am. J. EEG Technol. 35(4), 239–269 (1995) 2. W.O. Tatum, B.A. Dworetzky, D.L. Schomer, Artifact and recording concepts in EEG. J. Clin. Neurophysiol. 28(3), 252–263 (2011) 3. A. Lay-Ekuakille, P. Vergallo, G. Griffo, S. Urooj, V. Bhateja, F. Conversano, S. Casciaro, A. Trabacca, multidimensional analysis of EEG features using advanced spectral estimates for diagnosis accuracy, in Proceedings of Medical Measurements and Applications (IEEE, 2013), pp. 237–240 4. S.N. Abbas, M.A. Zahhad, Eye blinking EOG signals as biometrics, in Biometric Security and Privacy in Signal Processing (Springer, Cham, 2016), pp. 121–140

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5. A. Mishra, V. Bhateja, A. Gupta, A. Mishra, Noise removal in EEG signal using SWT-ICA combinational approach, in 2nd International Conference on Smart Computing and Informatics (SCI-2018) 6. M.A. Zahhad, S.M. Ahmed, S.N. Abbas, A new multi-level approach to EEG based human authentication using eye blinking. J. Pattern Recogn. Lett. 82, 216–225 (2015) 7. V. Bhateja, R. Verma, R. Mehrotra, S. Urooj, A non-linear approach to ECG signal processing using morphological filters. Int. J. Meas. Technol. Instrum. Eng. 3(3), 46–59 (2013) 8. A. Lay-Ekuakille, P. Vergallo, G. Griffo, F. Conversano, S. Casciaro, S. Urooj, V. Bhateja, A. Trabacca, Entropy index in quantitative EEG measurement for diagnosis accuracy. IEEE Trans. Instrum. Meas. 63(6), 1440–1450 (2014) 9. R. Verma, R. Mehrotra, V. Bhateja, An improved algorithm for noise suppression and baseline correction of ECG signals, in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) (Springer, Berlin, 2013), pp. 733–739 10. R. Verma, R. Mehrotra, V. Bhateja, An integration of improved median and morphological filtering techniques for electrocardiogram signal processing, in 3rd International Advance Computing Conference (IACC) (IEEE, 2013), pp. 1223–1228 11. D. Anand, V. Bhateja, A. Srivastava, D.K. Tiwari, An approach for the preprocessing of EMG signals using canonical correlation analysis, in Smart Computing and Informatics (Springer, Singapore, 2018), pp. 201–208 12. A. Gupta, V. Bhateja, A. Mishra, A. Mishra,: Auto regressive modeling based feature extraction of EEG/EOG signals, in International Conference Information and Communication Technology for Intelligent Systems (2018) 13. V.P.S. Naidu, J.R. Raol, Pixel-level image fusion using wavelets and principal component analysis. J. Defence Sci. J. 58(3), 338–352 (2008) 14. D.K. Sahu, M.P. Parsai, Different image fusion techniques—a critical review. Int. J. Mod. Eng. Res. 2(5), 4298–4301 (2012) 15. Y. Zheng, X. Wan, C. Ling, An estimation method for multi-channel EEG data based on Canonical correlation analysis. J. Electron. 24(3), 569–572 (2015) 16. B. Blankertz, G. Dornhege, C. Schafer, R. Krepki, J. Kohlmorgen, K.R. Muller, V. Kunzmann, F. Losch, G. Curio, Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 127–131 (2003) 17. A. Subasi, M.I. Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machines. J. Expert Syst. Appl. 37(12), 8659–8666 (2010) 18. M. Murugappan, R. Ramachandran, V. Sazali, Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 3(4), 390–396 (2010) 19. D.K. Tiwari, V. Bhateja, D. Anand, A. Srivastava, Z. Omar, Combination of EEMD and morphological filtering for baseline wander correction in EMG signals, in Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics & Telecommunications, vol. 434 (Springer, Singapore, 2018), pp. 365–373 20. A. Gautam, V. Bhateja, A. Tiwari, S.C. Satapathy, An improved mammogram classification approach using back propagation neural network, in Data Engineering and Intelligent Computing, vol. 542 (Springer, Singapore, 2018), pp. 369–376 21. A. Lay-Ekuakille, G. Griffo, F. Conversano, S. Casciaro, A. Massaro, V. Bhateja, F. Spano, EEG signal processing and acquisition for detecting abnormalities via bio-implantable devices, in International Symposium on Medical Measurements and Applications (MeMeA), (IEEE, 2016), pp. 1–5 22. PhysioBank ATM, https://physionet.org/cgi-bin/atm/ATM

High-Efficiency Video Coding De-blocking Filter: Through Content-Split Block Search Algorithm Perla Anitha, P. Sudhakara Reddy and M. N. Giri Prasad

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 H.26x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Study About HEVC De-blocking Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Coding Tree Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Coding Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Algorithm for De-blocking Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Implementation Process of Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Structure of Content and Block Layer Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

802 802 803 803 804 805 806 806 806 808 809

Abstract The video coding standards came into being by ISO/IEC, including MPEG-1, MPEG-2, and MPEG-4. The above standards came into being with ITU including H.261, H.262, H.263, and H.264/AVC. The H.264/AVC video coding standard is developed by the JVT of MPEG, and ITU has found many successful applications in video streaming. As follows, high-efficiency video coding standard is the amplification concept of H.264/AVC. In this work, HEVC de-blocking filter is designed with content-split block search algorithm. The HEVC DB is designed to boost perceptional quality and coding efficiency. In this paper, anticipating the threads and complexity of de-blocking filter with hinge on the coding unit and transform unit depth is the split information. The objective of this paper is to provide decoding time of de-blocking filter according to different threads to demand for HEVC development in video and communication broadcasting. This paper provides an approach to the de-blocking filter with less decoding time with parallelization transform unit and P. Anitha (B) · M. N. Giri Prasad JNTUA, Ananthapuram, Andhra Pradesh, India e-mail: [email protected] M. N. Giri Prasad e-mail: [email protected] P. Sudhakara Reddy SKIT, Srikalahasti, Andhra Pradesh, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_77

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analyzes with different quantization parameters (QP) such as 22, 27, 32, and 37 with various test sequences, and also improves the performance of BD-rate, BD-PSNR, and T compared with the existing work. Keywords H.26x · Recommendations · Compression · Filter · Looping

1 Introduction Video has become an important data representation in modern data and video communications, multimedia applications, and transformation systems [1]. Video communication and channels with different characteristics had been developed and explored as the base for video data transmission [2, 3]. To employ these, it is necessary to design and implement the video codec in video communication equipment [4]. In this paper, the standards of video codec were studied and translated into evaluation of successful encoding and decoding schemes, to maximize the video quality with HEVC standard.

1.1 H.26x Nowadays, technologies of networking and video processing have to bring tremendous approaches resembling the range of good quality [5]. Video applications achieve lucrative success in multimedia technologies and bio-medical markets [6]. An early digital method for video compression was released by Consultative Committee for International Telegraph and Telephone (CCITT), currently the International Telecommunications Union (ITU) in 1984, and also the form of the standard recommendation H.120 [7, 8]. During 1990, improved coding standard was released in standard H.261 [9]. As with H.120, a major emphasis included videophone and videoconference within the audiovisual services [4]. However, the standard made allowances for P x 64kbit/s services, where P can be specified anywhere from 1 to 30 [10]. The H.261 standard having colloquial video resolutions is used with common intermediate format (CIF) with its resolution 352 × 288 pixels or quarter common intermediate format (QCIF) with resolution 176 × 144 pixels [11, 12]. H.261 further improved the coding methods by introducing a hybrid video coding method that is still a key basis for modern coding standards [4]. Hybrid video coding combines into two methods. They are: (1) Frame to Frame motion is estimated and compensated for using data from previously encoded frames (2) Spatial domain data is de-coupled and transformed into the frequency domain which can be quantized [3]. The following are the sections in this paper: Sect. 2 shows study about HEVC de-blocking filter, Sect. 3 shows the proposed algorithm for de-blocking filter, Sect. 4 shows experimental results, and Sect. 5 shows the conclusion.

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2 Study About HEVC De-blocking Filter 2.1 Coding Tree Unit A slice can be divided into CTUs in the form of integer multiples, as in the H.265. It can be termed as micro block in H.264 [13]. Within a slice, the processing of CTU used raster scan method among the sizes of 64 × 64, 32 × 32, 16 × 16, and 8 × 8 as shown in Fig. 1. In the HEVC standard, the video frame is divided into non-overlapping coding tree units (CTU) based on the quad-tree structure. Figure 1a. shows coding tree unit, and CTU is divided into (PU) prediction unit or (TU) transform unit. Figure 2 shows the coding depth analysis; each CTU could be recursively split into four equally partitioned sub-CUs and each sub-CU is additionally split until reaching the smallest coding unit size [12, 13]. The coding tree unit is distributed into 8 × 8 coding units from the 64 × 64 CTUs. CU can be represented as a square block as shown in Fig. 1a. For example, 16 CUs can be formed as a CTU has split into non-identical sizes and positions as shown in Fig. 1b shows the corresponding coding tree skeleton of the CTU [14]. HEVC is a way to readjust various applications such as encoding as well as decoding pipeline constriants in the memory for the hardware design with limberness of the CTU [6, 12]. CTU partitioning is possible from 8 × 8 to 64 × 64 execrated filtereation mealed to non-esssential smoothing of the picture information [6]. Although lack of filtering may leave blocking artifacts that would reduced the subjective quality [14, 15].

Fig. 1 a Partition of the coding tree unit and b partition of the CU in terms of PU

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Fig. 2 Depth analysis of coding tree unit

2.2 Coding Unit The CTU contains multiples of the CU to adapt the heterogeneous local characteristics. A coding tree can be denoted as a quad tree and is used to partition the CTU into multiples of the CU [2, 4]. (1) Partitioning recursively from CTU, let CTU size be 2 N × 2 N where N is one of the values of 32, 16, or 8. The HEVC standard is established with fast and optimized encoders and decoders are very essential to make sure that HEVC is rapidly distributed in the market. The de-blocking filter and sample adaptive offset filter are the two in-loop filters to improve perceptual quality and coding efficiency [7, 13, 16]. Further with motion pictures in the reduction of the interpretation modules based on the prediction performance. Table 1 shows the decoding time of the de-blocking filter and sample adaptive filter of different QP (22, 27, 32, 37) with variable classification of test sequences. HEVC de-blocking filter is as same as H.264 de-blocking filter; however, the designing complexity is much lower, and HEVC DF has reduced the evaluation of repentance bounded by adjacent blocks so that it can be performed through parallelism of the de-blocking filter [9, 13]. In the process of the video compression and quantization reaction in the quality of degradation and blocking artefacts with blockbased approach [2]. The parallelization technique has followed two approaches: One is task level parallelism (TLP) and the other one is data level parallelism (DLP) [5]. In general, when the data transfer quantity between the tasks is not much, there the TLP is used. Where as DLP is using the data transfering is as much as DLP branches, then all the data assigns each data item to theard or cored [2, 3]. In the parallelization process of DLP, efficiency determines to divide the workload and assign the core partition. Because every core complies process of video decoding for the nominate area of the picture. Considering there is no upward data communication between the threads. Furthermore, high scalability is much in DLP than TLP and also has a lot of flexibility to increase or decrease the number of cores [7]. In such case, scheduling overhead can become assertive time-consuming part. The maximum size of the HEVC CTU is 64 × 64; whereas in H.264, its size of the macroblock is 4 × 4 [15].

High-Efficiency Video Coding De-blocking Filter … Table 1 Decoding time of the de-blocking filter and sample adaptive offset filter

Sequence

QP

805 Decoding time (S) DBF

SAO

Total

BQTerrace 22

6.93

2.31

87.01

27 32 37 Cactus 22 27 32 37 Kimono 22 27 32 37 Park Scene 22 27 32 37

5.79 5.04 4.48 9.18 7.61 7.05 6.37 4.39 3.33 2.99 2.70 6.52 3.81 3.45 5.90

1.96 1.17 0.73 2.40 1.69 1.16 0.74 1.07 0.78 0.45 0.23 1.54 0.90 0.63 0.37

69.71 60.99 54.93 87.22 63.96 55.61 49.53 42.90 31.66 27.85 25.41 69.32 34.42 29.56 25.97

3 Proposed Algorithm for De-blocking Filter The proposed content-split block search algorithm improves the visual quality of coded video frame and reduces the blocking artifacts of video frame passed through multistage of H.265. The proposed algorithm uses (GOP) group of pictures, to facilitate the I frame, P frame, and B frame through repeatedly generated motion vectors that have faster content moving speed in the video frame. Multiple rate distortion optimization (MRDO) process needs to transverse each coding tree unit (CTU) in order to find out the block structure (CU) which leads best rate distortion performance. The MDO process in the HM 15.0 tool resolves regardfully starting from the large CU size, i.e., depth 0. In the encoding settings, various predictions are examined and finest candidates in the MD sense are chosen. The square block can be divided into four sub-blocks; the MDO is resourcefully applied to these blocks and so on. The structure of block united with the predictions which give the smallest MD cost is chosen tolerable to enlarge the performance of the MD. In addition, the blocks of the encoding will mostly have a lower or commensurate depth compared with tremendous quality reference encoding and the amount of block having greater depth than in the reference encoding is proportionately small. Conceptually, a low-quality reference could be recycled in addition to the encoding process of MDO, where the smallest blocks would be handled mounting the content tree to larger blocks.

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3.1 Implementation Process of Proposed Work Content and block layer structure (CBLS) consists of several corresponding layers in different block levels of the H.265 video. These block levels represent the quality of video frame, varying bit depths for video frame quality, and video coding standards. The layers of proposed search method in CBLS are (1) content layer (2) block later. The first level of CBLS, block layer (BL), represents the video frame blocks with high bit depths and lowest quality level to be video encoded. The second level of CBLS, content layer (CL), represents the video frame blocks with low bit depths and highest quality level using video coding information from BL temporal and spatial resolutions, to multiplex in a single video content bit stream.

3.2 Structure of Content and Block Layer Search The proposed search method is structured into two layers, content and block layers, which are interlaced together with inter- and intra-layer predictions. Coding efficiency of the Block Layer (BL) is achieving with coding information from the Content Layer (CL); each BL is used as a reference for each CL and is predicted by inter-layer prediction which allows texture and motion information for the frame prediction. Based on the proposed structure, content and block layers encode the frames in same spatial resolutions with different sampling rate levels. The encoding information of content and block layers is processed, multiplexed, and interlaced in a single bit stream. This processed information is used for inter- and intra-layers prediction. Figure 3 represents the proposed structure with content and block layers, and requires multiplexing and prediction elements to reduce the computational complexity. In spatial resolution, the content block is down sampled to the predicted layer size and up sampled to increase the reference layer operation, using 8-tap filter and 4-tap filter for Luma and Chroma picture components, respectively. Therefore, comparing HEVC encoder with the proposed encoder, it encodes the content separately through sampling. When decoding operation is performed, the reconstructed frame is copied from the decoded frame buffers (DFB) of the content and block to the predicted unit and is available until inter- and intra-layer elements are available.

4 Experimental Results Table 2 shows parameter of the work, encoding condition, quantization parameter values, resolution, and Open MP version. To assess the performance of the proposed de-blocking filter with the parallelization technique implemented with HM 15.0 ref-

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Fig. 3 Proposed content and block layer search Table 2 Contents of the proposed work Hochan Ryu et al. Reference software Encoding conditions

Proposed

QP

HM 12.0 Low delay, random access, all intra 22, 27, 32, 37

HM 15.0 Low delay, random access, all-intra 22, 27, 32, 37

Resolution

(1920 × 1080)

(2460 × 1600), (1920 × 1080), (832 × 480), (416 × 240)

Open MP version

2.0

2.0

erence software, HM 12.0 uses open MP2.0 for the parallelization and uses standard HEVC sequences for the evaluation of the proposed de-blocking filter with Linux Destro. SPEED − UP(SU)  DT R /DT P

(1)

MTS  SUprop −SURef /SURef

(2)

The HM 12.0 values are the speedup factors when the same number of CTUs is allocated to each core of the de-blocking filtering, the values are shown in the column [7] shows the speed up factor values, HM 15.0 also showing the time values, and proposed values are showing in the column named as proposed. The proposed values are improved compared with the existing methods as shown in Table 3. Table 4

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Table 3 Decoding time of the de-blocking filter in seconds QP Sequence HM 12.0 [7]

HM 15.0

Proposed

22

BQTerrace

11.65

6.68

10.24

6.65

22 22 22 27

Cactus Kimono ParkScene BQTerrace

9.18 3.71 4.39 8.97

5.12 2.07 2.57 5.35

8.91 2.56 3.96 7.56

4.95 2.05 2.58 5.16

27 27 27 32

Cactus Kimono ParkScene BQTerrace

7.61 3.33 3.81 7.76

4.29 1.90 2.32 4.60

6.58 3.12 3.80 7.12

4.19 1.82 2.09 4.33

32 32 32 37

Cactus Kimono ParkScene BQTerrace

7.05 2.99 3.45 6.87

3.96 1.74 2.07 4.15

6.91 2.51 3.15 6.12

3.91 1.59 1.72 3.81

37 37 37

Cactus Kimono ParkScene

6.37 2.70 3.03

3.59 1.51 1.78

6.14 2.59 3.00

3.33 1.48 1.68

Table 4 Comparative results with proposed work and ETCUSD [17] Sequence Proposed ETCUSD [17] People street Cactus BQTerrace Basketball pass

BD-rate

BD-PSNR

T

BD-rate

BD-PSNR

T

0.85

−0.04

−19.02

0.9

−0.05

−50

0.79 0.36

−0.03 −0.01

−35.74 −31.91

1.3 0.6

−0.05 −0.04

−45 −39

0.62

−0.03

−35.12

0.6

−0.04

−45

shows the overall performance of the algorithm incorporating BSIP and ETCUSD compared to the HM encoder that is tested with online training.

5 Conclusion HEVC is a current standard which improves the quality of subjective, objective analysis, and also attainment of the de-blocking filter in parallel and multiple processors. This paper described the technical analysis of the CTU, CU, TU, and PU concept along with desiring of the de-blocking filter. In the view of this paper, proposed deblocking filter through the parallelization method of the HEVC with the content-split block search algorithm, improves average time saving with HM 15.0 decoder than

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HM 12.0 with different video sequences. This paper is concluded with improving decoding time (s) of the HEVC de-blocking filter according to the number of threads (2) and also compared the BD-PSNR, BD-rate, and T values with the existing work.

References 1. M.T. Pourazad, C. doutre, M. Azimi, P. Naisopoulos, HEVC: the gold standarrd for video compression how does HEVC 2. G. Sullivan et al., High Efficiency Video Coding (HEVC) (Springer, Berlin, 2014) 3. L. Shen, Z. Zhang, Z. Liu, Effective CU size decision for HEVC intra coding. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 23(10), 4232–4241 (2014) 4. S.-Y. Lee, K. Aggarwal, A system design and scheduling strategy for parallel image processing. IEEE Trans. Pattern Anal. Mach. Intell. 12(2), 193–204 (1990) 5. S. Saponara, K. Denolf, G. Lafruit, C. Blanh, J. Bormans, Performance and complexity coevaluation of the advanced video coding standard for cost-Effective multimedia communication. EURAPIS J. Appl. Sig. Process. 220–235 (2004) 6. N. Kamaci, Y. Altunbasak, Performance comparison of the emerging H.264 video coding standard with the existing standards, in ICME’03. Proceedings. 2003, vol. 1, pp. I,345–8, 6–9 July 2003 7. CE12 Subset: Parallel deblocking filter, ITU–T/ISO/IEC joint. JCTVC E181 March 2011 8. P. Anitha, P. Sudhakara Reddy, M.N. Giri Prasad, A formulation approach to with Hybrid Wavelet Transform for High Efficiency Video Coding has to be presented in 1st International Conference on Intelligent Communication and Computational Techniques ICCT-2017. https:// doi.org/10.1109/intelcct.2017.8324024 9. G.J. Sullivan, J. Ohm, W.J. Han, T. Wiegand, Overview of the High Efficiency Video Coding (HEVC) standard. IEEE Trans. Circ. Syst. Video Technol. 22(12), 1649–1668 (2012) 10. M.H. Pinson, S. Wolf, G. Cermak, HDTV subjective quality of H.264 vs. MPEG-2 with and without packet loss. IEEE Trans. Broadcast. 56(1), 86–91 (2010) 11. J.-R. Ohm, G.J. Suvillan, H. Schwarz, T.K. Tan, T. Wiegand, Comparison of the coding efficiency of video coding standards—including High Efficiency Video Coding (HEVC). IEEE Trans. Circ. Syst. Video Technol. 22(12), 1609–1684 (2012) 12. I.E. Richardson, H.264 and MPEG-4 Video Compression: Video Coding for Next-Generation Multimedia (Wiley, Chichester, 2004) 13. T. Wiegand, G. Sullivan, G. Bjontegard, A. Luthura, Overview of the H.264/AVC video coding standard. IEEE Trans Circ. Syst. Video Technol. 13(7), 560–576 (2003) 14. H. Jo, D. Si, B. Jeon, Hybrid parallelization for HEVC decoder, in International Congress on Image Signal Processing, 16–18 Dec 2013, pp. 170–175 15. N. Kamaci, Y. Altunbasak, Performance comparison of the emerging H.264 video coding standard with the existing standards. Paper presented at the Multimedia and Expo, 2003. ICME’03 (2003) 16. I.E. Richardson, H.264 and MPEG-4 Video Compression: Video Coding for Next Generation Multimedia (Wiley, Chichester, 2004) 17. L. Shen, Z. Zhang, Z. Liu, Effective CU size decision for HEVC intracoding. IEEE Trans. Image Process. 23(10), 4232–4241 (2014)

Key Frame Extraction Using Content Relative Thresholding Technique for Video Retrieval K. Mallikharjuna Lingam and V. S. K. Reddy

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Outcome of the Parallel Researches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Consumer Video Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Dataset Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

812 812 814 814 814 815 816 817 819 819

Abstract With the continuous growth in the space of information sharing over the Internet, the sharing of video information is also growing. The video information is highly appreciable for the online distance education, security, and distance healthcare, interactive communication over the Internet and also in multimedia news systems. The information captured in the video format demands summarization for effective processing and efficient storage. In order to summarize any video information, the best possible strategy is extracting key frames from videos. A number of research attempts are made in order to establish the most efficient key frame extraction framework. Nonetheless, most of the parallel research outcomes are affected by either high or low key frame extractions. Thus, the demand from the modern research is to build an optimal framework to extract key frames from motion videos. The major challenges are identified in this work and addressed in the finest way possible. This work demonstrates the framework for few different cases such as object in motion, camera in motion or both in case of highly colour contrast video sequences. The results of this framework demonstrate lowest time complexity and higher level of information preservation compared to the parallel research outcomes. K. Mallikharjuna Lingam (B) · V. S. K. Reddy Malla Reddy College of Engineering & Technology, Hyderabad, India e-mail: [email protected] V. S. K. Reddy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3_78

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Keywords Key frame extraction · Automated framework Data replication control · Reduced time complexity · Video stabilization

1 Introduction Any key frame extracted from a video can represent a lot of information. As the key frames provides a suitable metadata for indexing, browsing and retrievals. The review work by Aigrain et al. [1] presents a significant proof of concepts demonstrating the benefits of key frame extractions for various video processing and information extraction methods. The notable work by Zhang et al. [2] also proves that the key frame extraction and key frame-based indexing, searching and retrieving can be faster for any video data sets. The most recent outcome from the parallel research for extracting the key frames is based on the perceived motion energy by Liu et al. [3]. The work by Liu et al. [4] and the work of Liu et al. [3] were analysed, and an attempt to combine these two approaches was made by Gargi et al. [5]. Henceforth, it is natural to understand that none of the single framework provides the optimal solution to the key frame extraction problem. Thus, this work proposes a framework for video key frame extraction. The result of the work is furnished such that the outcomes from the parallel researches are been compared and analysed in Sect. 2. The statistics behind the motivation of the research is furnished in terms of consumer video statistics in Sect. 3. The proposed framework for key frame extraction is elaborated in Sect. 4, and the algorithm deployed for the key frame extraction on the framework is demonstrated in Sect. 5. The analysis of the data set is carried out and presented in Sect. 6. The proposed method is compared with the existing framework based on architecture, time complexity, network load and data replication control analysis, and the findings are furnished in Sect. 7. The results obtained from this method are elaborated and analysed in Sect. 8. The conclusion of the work is presented in Sect. 9.

2 Outcome of the Parallel Researches The main objective of identifying key frames is to provide checkpoints for classification and segmentation of video events into frames in a timely manner. Most of these algorithms are having certain limitations in finding relevant objects because of predefining the heuristic rules in relevance to the application. Another limitation is identification of motion patterns which are more meaningful. As a result, these algorithms are resourceful only when the experiments are chosen carefully. Thus, based on the detailed survey on the recent research outcomes, this work formulates a guideline comprising of dos and don’ts while building a key frame extraction framework or algorithm or model or process (Table 1).

Key Frame Extraction Using Content Relative Thresholding … Table 1 Parallel research outcome review conclusion Method name and Characteristics Advantages author Clustering method Analysis on short Faster processing Zhuangt et al. [6] boundary video

813

Shortcomings of the method • Less key frame selection for single-shot activity • More key frame selection for multiple-shot activity

Entropy method Mentzelopoulos et al. [7]

Best method for unpredictable data set

Local feature selection • External effects such as lighting condition affect the performance

Histogram method Rasheed et al. [8]

Similarity measure between key frames

High-level segmentations

• Cannot consider the local similarities

Motion analysis Method Wolf et al. [9]

Optical flow-based analysis

Faster mid-range key frame selection

• Highly depends on the static frame references

Triangle-based method Liu et al. [10]

Determination of the motion characteristics

Reduces the motion effects on the video

• Cannot detect the colour-based information change

3-D augmentation method Chao et al. [11]

Processing short and Combines the video fast motion video data data into multidimensional model Best method for Faster processing due continuously growing to probabilistic video sequence by analysis adopting the temporary key frame

Optimal key frame selection method Sze et al. [12]

Context-based method Best method for Chang et al. [13] repetitive information contents

• Highly time complex

• Highly time complex

Generates a multilevel • Information loss due abstract of the to less key frame information selection

Motion-based extraction method Luo et al. [14]

Adopts the advantages Reduces the from digital capture spatio-temporal devices effects

• High-quality video information expected

Robust principal component analysis method Dang et al. [15]

Adopts the decomposition method for sparse component analysis

• Assumptions are not always reflecting better results

Analyses the frames for consumer videos with less contents or rapid content shift

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The proposed framework can overcome the shortcomings identified, and the detailed analysis is provided in the further sections.

3 Consumer Video Statistics The understanding of the video data segment is important to plan and evaluate any algorithm for video data mining and key frame extraction. Thus, this work presents a survey of the available video data and presents a statistical analysis of the consumer video data information (Table 2). Thus, the knowledge is the design guidelines for building the framework and the algorithm, demonstrated in the next phase.

4 Proposed Framework The proposed framework is an automated framework for extraction of the key frames. The novelty of the framework is to extract the reduced number of frames with least time complexity. Also, this framework provides a built-in video capturing and video stabilization component. The available correlation agent is capable of adjusting threshold during comparison and can learn based on the video types. The components of the proposed framework are elaborated here (Fig. 1).

5 Proposed Algorithm The proposed algorithm is the driver algorithm for the proposed framework and elaborates the tasks of each agent into the framework. The proposed algorithm is furnished here. Step 1. Read frames from a Web URL. Step 2. Collect silent points from each frame.

Table 2 Consumer video data analysis Type of the video Content

Average length

Camera motion

Animated

20% with people 80% without people

5 min

95% steady

Indoor

30% without people 70% with people

15 min

60% steady

Outdoor

50% without people 50% with people

20 min

15% steady

Key Frame Extraction Using Content Relative Thresholding …

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Fig. 1 The proposed framework

Step 3. Step 4. Step 5. Step 6. Step 7.

Select correspondences between points. Estimate the transform from noisy correspondences. Transform approximation and smoothing. Present the final video. Image Metadata extraction a. b. c. d. e. f. g. h. i.

Extract frames per second. Extract scan system. Extract actual aspect ratio. Extract augmented aspect ratio. Extract BPP. Extract compression ratio. Extract channel. Calculate the global threshold of the video. Extract the frames from the video.

Step 8. Compare the key frame threshold with the global threshold. Step 9. If the frame threshold is greater than the global threshold, then accept the frame as key frame. Step 10. Else reject the frame. Step 11. Convert the images into the two-dimensional images. Step 12. Calculate the correlations between all frames, and calculate the replication factor. Step 13. Present the final key frames.

6 Dataset Analysis This framework and the algorithm are rigorously tested on the following data (Table 3). The data set is comprised of three different categories with multiple videos with different lengths. The videos are with 24 FPS.

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Table 3 Data set description Category name Number of videos in the category Animated Indoor Outdoor

85 94 122

Average duration (min)

Capture frames per second

2.90 6.46 3.19

24 24 24

The results of the algorithm and the framework execution on the mentioned data set are furnished in the further section of this work.

7 Comparative Analysis A. Time Complexity Analysis The time complexity for the present framework is analysed here and compared with the existing methods (Table 4). Hence, it is natural to understand that the proposed algorithm is faster than the majority of the methods. B. Network Load Analysis The simulation of the framework is carried out on stand-alone system without network components. Nonetheless, the number of key frames extracted from the video is transmitted over the network and generates the network load. The generated network load will provide the analysis of the network loads or activities (Table 5). The analysis demonstrates the higher load in case of the outdoor videos. Nevertheless, for the animated and indoor videos, the network load is reduced significantly. C. Data Replication Analysis As stated by the algorithm in this work, the replication control is also been taken care during the extraction of key frame process. This is counted as one of the major

Table 4 Time comparison analysis Model name Average time complexity analysis (ms) Animated video

Indoor video

Outdoor video

Clustering method

995

1698

703

Entropy method

893

1221

644

1258

1713

733

894

1519

695

Histogram method Proposed threshold-based method

Key Frame Extraction Using Content Relative Thresholding …

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Table 5 Network load analysis Model name Average data replication (%) (ms) Animated video

Indoor video

Outdoor video

Clustering method

1.9

1.3

0.7

Entropy method

2.1

1.4

0.9

Histogram method

1.0

1.1

0.4

Proposed threshold-based method

0.1

0

0.1

Table 6 Data replication analysis [10] Model name Average data replication (%) (ms) Animated video

Indoor video

Outdoor video

Clustering method

1.9

1.3

0.7

Entropy method

2.1

1.4

0.9

Histogram method

1.0

1.1

0.4

Proposed threshold-based method

0.1

0

0.1

outcomes of the work. Henceforth, the comparative analysis of the data or key frame replication control is furnished here (Table 6). Hence, it is natural to understand the proposed framework with the underlying algorithm generates the optimal number of key frames.

8 Results and Discussion In this section of the work, the results captured from the framework are analysed. The results demonstrate significant improvement over the traditional methods and demonstrate low time complexity. In order to understand the component-based outcomes from the framework, this work elaborates the results into multiple phases. Firstly, the frame separation process outcomes are showcased (Table 7). Secondly, the threshold analysis results are pictured (Table 8). This proposed framework is capable of analysing the local and the global thresholds as per frame threshold and for the complete video. Furthermore, the analysis of the number of key frame selection process is analysed (Table 9). Finally, the analysis of the time complexity results is furnished (Table 10). Thus, under the light of the comparative analysis and the results obtained from the framework, this work presents the conclusion in the next section.

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Table 7 Frame separation process Video data Category Duration item (min)

Camera motion

Object motion Total frames

Sample—1

Animated

1.56

No

Yes

2802

Sample—2

Animated

4.23

No

Yes

7904

Sample—3

Animated

4.25

No

Yes

7940

Sample—4

Outdoor

3.19

Yes

Yes

4784

Sample—5

Indoor

8.41

No

No

12,973

Sample—6

Indoor

4.50

No

Yes

7221

Mean

Std. dev.

Table 8 Mean, std. dev and threshold analysis Video data Category Size item

Threshold

Sample—1

Animated

156.00

12,919.75

37,757.91

89,436.92

Sample—2

Animated

423.00

14,383.59

48,845.77

106,380.11

Sample—3

Animated

425.00

9418.27

31,048.01

68,721.09

Sample—4

Outdoor

319.00

10,054.95

22,034.04

62,253.83

Sample—5

Indoor

841.00

11,014.45

26,121.14

70,178.93

Sample—6

Indoor

450.00

10,465.47

25,766.24

67,628.11

Table 9 Key frame selection Video data item Category

Total frames

Total extracted key frames

Key frame %

Sample—1

Animated

2802

81

2.8908

Sample—2

Animated

7904

186

2.3532

Sample—3

Animated

7940

84

1.0579

Sample—4

Outdoor

4784

66

1.3796

Sample—5

Indoor

12,973

126

0.9712

Sample—6

Indoor

7221

84

1.1633

Table 10 Time complexity analysis c Category

Total key frames

Sample—1

Animated

2802

Accepted key frames 81

CPU time (ms)

Sample—2

Animated

7904

186

1093.59

Sample—3

Animated

7940

84

1201.84

Sample—4

Outdoor

4784

66

695.69

Sample—5

Indoor

12,973

126

1972.08

Sample—6

Indoor

7221

84

1067.14

386.84

Key Frame Extraction Using Content Relative Thresholding …

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9 Conclusion The information extraction from the video data or the video summarization is the most important process. This work evaluates the popular parallel outcomes from the recent researches and establishes the improvements in the proposed framework. This work demonstrates the accuracy of the key frame extraction and reduces the time complexity. The reductions in the network load and replication measure of the key frames are also one of the notable outcomes of this work. The proposal from the framework of including the extraction library is included as recommender system into the work and demonstrates the significant improvements for key frame extraction irrespective of the video length rather on the content type. This is made possible due to another thought of inclusion of metadata extraction process and incorporating that information during the frame separation process. With the significantly satisfactory results, this work lays the foundations for further analysis of information extraction from the key frame and supports the video indexing and information retrieval processes for making the information representation better for the science.

References 1. P. Aigrain, H. Zhang, D. Petkovic, Content-based representation and retrieval of visual media: a state-of-the-art review. Multimedia Tools Appl. 3, 179–202 (1996) 2. H.J. Zhang, J.Y.A. Wang, Y. Altunbasak, Content-based video retrieval and compression: a united solution, in Proceeding of IEEE International Conference on Image Processing, vol. 1, pp. 13–16 (1997) 3. T. Liu, H.-J. Zhang, Q. Feihu, A novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans. Circuits Syst. Video Technol. 13(10) (2003) 4. G. Liu, J. Zhao, Key frame extraction from MPEG video stream, in Second Symposium International Computer Science and Computational Technology (ISCSCT’09), Huangshan, P. R. China, pp. 007–011, 26–28 Dec 2009 5. U. Gargi, R. Kasturi, S.H. Strayer, Performance characterization of video-shot-change detection methods. IEEE Trans. Circuits Syst. Video Technol. 10(1) (2000) 6. Y. Zhuangt, Y. Rui, T.S. Huang, S. Mehrotra, Adaptive key frame extraction using unsupervised clustering, in Proceeding of IEEE International Conference on Image Processing (1998) 7. M. Mentzelopoulos, A. Psarrou, KeyFrame extraction algorithm using entropy difference, in Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR, New York, NY, USA, 15–16 Oct 2004 8. Z. Rasheed, M. Shah, Detection and representation of scenes videos. IEEE Trans. Multimedia 7(6) (2005) 9. W. Wolf, Key frame selection by motion analysis, in Proceeding of IEEE International Conference on Acoustics, Speech Signal Processing, vol. 2, pp. 1228–1231 (1996) 10. T. Liu, H.-J. Zhang, F. Qi, A novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans. Circuits Syst. Video Technol. 13(10) (2003) 11. G.-C. Chao, Y.-P. Tsai, S.-K. Jeng, Augmented 3-D keyframe extraction for surveillance videos. IEEE Trans. Circuits Syst. Video Technol. 20(11) (2010) 12. K.-W Sze, K.-M. Lam, G. Qiu, A new key frame representation for video segment retrieval. IEEE Trans. Circuits Syst. Video Technol. 15(9) (2005) 13. H.S. Chang, S. Sull, S.U. Lee, Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circuits Syst. Video Technol. 9(8) (1999)

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14. J. Luo, C. Papin, K. Costello, Towards extracting semantically meaningful key frames from personal video clips: from humans to computers. IEEE Trans. Circuits Syst. Video Technol. 19(2) (2009) 15. C. Dang, H. Radha, RPCA-KFE: key frame extraction for video using robust principal component analysis. IEEE Trans. Image Process. 24(11) (2015)

Author Index

A Aishwarya Badgujar, 233 Ajay Jose, 507 Akshay Raul, 101 Alan Saldanha, 683 Anil Kumar Reddy, 497 Anirban Mukherjee, 753 Anirudh Siripragada, 549 Anitha Patibandla, 715 Anjaiah, A., 389 Anjali, K. S., 527 Ankit Kumar Patel, 635 Anusha Meneni, 715 Aparna Gupta, 793 Apoorva Mishra, 793 Arpan Patel, 291 Arvind Srinivas, 769 Asha, D., 703 Ashish Sharma, 201 Ashish Ubrani, 345 Avani Sakhapara, 233 Ayush Goyal, 83 Ayushi Mishra, 793 B Balasubramanya Murthy, K. N., 769 Baskar Kasi, 153 Bharadwaj, Aprameya, 769 Bhave, Aishwarya, 135 Biswajit Nayak, 91 Biswas, Tanmay, 53 Boghosian, Alexandra L., 1 Boswell, Steven M., 1

C Chandrakant S. Patond, 781 Chandrasekar, V., 281 Chandra Sekharaiah, K., 187 Chandra Sekhar Reddy, P., 273 Chandrasekhar Reddy, P., 457 Chandrashekar, D. K., 253 Chauhan, Surjeet Singh, 243 D Damodaran, Nikhil, 743 Daniel, Gera Victor, 27 Deeba, K., 409 Deepika Saxena, 53 Deeplaxmi V. Niture, 781 Devottam Gaurav, 83 Dilendra Hiran, 175 Dipti Pawade, 233 Divya Adepu, 233 Divya Shah, 541 Diwakar Yagyasen, 325 F Faruk A. S. Kazi, 345 G Ganpat Singh Chauhan, 63 Garg, Harsh, 769 Garikipati, Vani, 35 Geddati China Babu, 261 Giri Prasad, M. N., 801 Godfrey Winster, S., 145 Govardhan, A., 389

© Springer Nature Singapore Pte Ltd. 2019 J. Wang et al. (eds.), Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing 900, https://doi.org/10.1007/978-981-13-3600-3

821

822 Govind, D., 743 Gupta, Abhijay, 769 H Hareesha, K. S., 119 Harikumar Rajaguru, 673 Harish Thodupunoori, 507 Hemalatha Eedi, 399 J Jaisankar, N., 419 Janghel, Rekh Ram, 135 Jayalakshmi Annamalai, 429 Jayashree D. Mallapur, 693 Jay Kant Pratap Singh Yadav, 83 Jignesh Sisodia, 643 Jyotiprava Dash, 603 Jyoti Rana, 175 K Kailas Devadkar, 101 Kamakshi Prasad, V., 15 Kapil Gupta, 53 Karthick Ganesan, 673 Karthik, S., 379 Kaur, Jatinder, 243 Kiran Kumar Reddy, P., 273 Kishore, D., 625 Kolekar, Sucheta V., 299 Komal Jaiswal, 635 Kora, Padmavathi, 477, 519 Koteswara Rao, N. V., 561 Kshitij Yerande, 101 Kumud Kumar Bhardwaj, 449 Kunthavai, A., 201 L Lakshmikanthan, C., 429 Lakshmi, Muddana A., 27 Lakshmi, R., 223 M Madan Mohan, K., 187 Madhavee Latha, Y., 703 Mahajan, S. P., 781 Mahesh Y. Pawar, 733 Malathi, B., 187 Mallikarjuna Prasad, A., 561 Mallikharjuna Lingam, K., 811 Manohara Pai, M. M., 299 Manuj Darbari, 325 Manukonda Sumathi Rani, 261 Maria Jones, G., 145 Meenakshi, K., 477, 519

Author Index Meenal Narkhede, 593 Melvita Andrade, 233 Mohankumar, N., 549, 613 Mohd Abdul Rasheed, 399 Mukta Goyal, 165 N Nadia Siddiqui, 655 Naga Malleswara Rao, N., 35 Nair, Binoy B., 507 Natarajan, S., 769 Naveen Kumar, G. S., 715 Neeraj Seth, 345 Nehal Patel, 291 Nidhi Arora, 175 Nidhya, R., 379 Niladri Hore, 753 Nilamani Bhoi, 603 P Padmalaya Nayak, 359 Pai, Radhika M., 299 Pargunarajan, K., 223 Pathak, Neeraj, 369 Patikineti Sreenivasulu, 439 Perla Anitha, 801 Poornima Panduranga Kundapur, 119 Prabha, G., 527 Pranali Ingole, 723 Prasant Kumar Pattnaik, 91 Prashant Gaurav, 73 Prem Raheja, 643 Priti Sehgal, 571 Purva Jhaveri, 643 R Raghunatha Sarma, R., 497 Rajagopal Smitha, 119 Rajalakshmi Krishnamurthi, 165 Rajeev Kumar, 369 Rajeev Singh, 635 Rajeshwar Goud, J., 561 Raju, C., 467 Ramesh Karandikar, 487 Ram Suchit Yadav, 635 Rashmika Patole, 583, 593 Ravi, G., 111 Ravi Kumar, 497 Ravi Kumar, S., 187 Ravi Shankar Reddy, C., 665 Reddy, V. S. K., 811 Richa Gupta, 571 Rohit Kumar Kaliyar, 83 Rupali Patil, 487

Author Index S Saai Nithil, R., 201 Saiful Islam, 655 Sandeep Banerjee, 541 Sanjay Kumar Dubey, 73 Sanjay Kumar Padhi, 91 Sanjeeva Reddy, P., 665 Santhoshi, N., 187 Santhosh Kumar, S. V. N., 145 Sarangam Kodati, 111 Saranya Rubini, S., 201 Saravanaguru, RA. K., 409 Saryar, Shivam, 299 Sathiyamurthy, K., 211 Satyanarayan K. Padaganur, 693 Sendhil Kumar, K. S., 419 Seshathiri Dhanasekaran, 153 Shadab Siddiqui, 325 Shanthi, S., 359 Sharma, Mayank, 135 Shekhar Yadav, 635 Shenoy, Gurudat, 497 Shiva Prasad, R., 549 Shrinivas Mahajan, 733 Shwetha Kalyanaraman, 101 Singh, Pavitdeep, 243 Siva Kumar Reddy, V., 665 Smilarubavathy, G., 379 Sneha Mane, 345 Soman, K. P., 743 Sowmya, V., 743 Sravan Kumar, G., 313 Sreenivasulu Reddy, T., 467 Sridhar, M., 273 Srikantaiah, K. C., 253 Sri Krishna, A., 313 Srinivasa Rao, Ch., 625 Srinivasa Rao, D., 27 Srinivasa Rao Katuri, 761 Srinivasa Rao, S., 457 Srinivas Kumar, S., 625 Sudhakara Reddy, P., 801

823 Sujatha Dandu, 359 Suresh Babu, N., 613 Suresh Chandra Satapathy, 793 Surmeet Kaur Jhajj, 643 Sushil Kumar, 53 Swapna Raghunath, 761 Swapna Rani, T., 449 Swaraja, K., 477, 521, 523, 525 Sweta Singh, 635 Swetha, P., 457 Syeda Shira Moin, 655 T Tapaswini Singh, 53 Tejas Bhangale, 583 U Umarani Deevela, 761 V Vaitheeswaran, S. M., 223 Vara Prasad Rao, P., 273 Varun Alur, 541 Vazralu, M., 389 Venugopal, K. R., 253 Vibha Vyas, 723 Vijayalata, Y., 15 Vijayaprabakaran, K., 211 Vijil Gupta, 683 Vikas, Sai, 497 Vikrant Bhateja, 793 Vinay, A., 769 Vinay Kumar, 753 Vinay Simha Reddy, T., 665 Vinod Kumar Joshi, 683 Viswa Bharathy, A. M., 281 Vivekanandam, R., 111 Y Yadala Sucharitha, 15 Yogesh Kumar Meena, 63