Bindhu / Chen / Tavares International Conference on Communication, Computing and Electronics Systems

Proceedings of ICCCES 2019
1. Auflage 2020
ISBN: 978-981-15-2612-1
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 637, 742 Seiten

Reihe: Lecture Notes in Electrical Engineering

This book includes high impact papers presented at the International Conference on Communication, Computing and Electronics Systems 2019, held at the PPG Institute of Technology, Coimbatore, India, on 15-16 November, 2019. Discussing recent trends in cloud computing, mobile computing, and advancements of electronics systems, the book covers topics such as automation, VLSI, embedded systems, integrated device technology, satellite communication, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, Internet of Things, precision models, bioinformatics, and healthcare informatics.

Dr. V. Bindhu is a Professor at the Department of ECE, PPG Institute of Technology, Coimbatore, India. She completed her Ph.D. in Information and Communication Engineering at Anna University, M.E. at Maharaja Engineering College, and B.E. at Government College of Technology, Coimbatore. Joy Chen received his B.Sc. degree in Electronics Engineering from the National Taiwan Technical University, Taipei, Taiwan, his M.Sc. degree in Electrical Engineering from Dayeh University, Changhua, Taiwan, in 1985 and 1995, respectively, and his Ph.D. degree in Electrical Engineering from the National Defense University, Taoyuan, Taiwan, in 2001. He is currently a Professor at the Department of Communication Engineering, Dayeh University at Changhua, Taiwan. Prior to joining Dayeh University, he worked as a technical manager at the Control Data Company (Taiwan). He has published about 40 international journal papers and acted as guest editor for several international journals. His research interests include AI, IoT development, wireless communications, spread spectrum technical, OFDM systems, and wireless sensor networks. Dr. João Manuel R. S. Tavares graduated in Mechanical Engineering from the University of Porto - Portugal (1992). He holds an M.Sc. in Electrical and Computer Engineering, with a focus on industrial informatics, from the University of Porto (1995); and a Ph.D. in Electrical and Computer Engineering, from the University of Porto (2001). From 1995 to 2000, he was a researcher at the Institute of Biomedical Engineering (INEB). He is the co-author of more than 350 scienti?c papers published in national and international journals and conferences, co-editor of 18 international books, and guest editor of several special issues of international journals. In addition, he is editor-in-chief and associate editor of various journals and also a reviewer for several international scienti?c journals. He has also supervised and co-supervised several M.Sc. and Ph.D. books and been involved in numerous research projects, both as a researcher and as a scienti?c coordinator. Additionally, he is co-author of 3 international patents and 2 national patents. His main research areas include computational vision, medical imaging, computational mechanics, scienti?c visualization, human-computer interaction, and new product development. He has been the co-chairman of various international conferences and numerous mini-symposia, workshops, and thematic sessions. In addition, he has been a member of scienti?c and organizing committees of several national and international conferences.

Autoren/Hrsg.

Bindhu, V.

Chen, Joy

Tavares, João Manuel R. S.

Inhaltsverzeichnis

1;Preface;7
2;Acknowledgements;8
3;About the Conference;9
4;Contents;10
5;About the Editors;17
6; Enhancing the Performance of Software-Defined Wireless Mesh Network;19
6.1;1 Introduction;19
6.2;2 Literature Survey;20
6.3;3 Proposed System;21
6.3.1;3.1 LEACH Algorithm;22
6.3.2;3.2 Mathematical Equation of SD-WMN;23
6.3.3;3.3 System Architecture;24
6.3.4;3.4 Performance Evaluation;26
6.4;4 Implementation;26
6.5;5 Results and Analysis;28
6.5.1;5.1 Node Creation in Net Animator;28
6.5.2;5.2 Data Transmission Between the Nodes in Net Animator;28
6.5.3;5.3 Graphical Approach;29
6.6;6 Conclusion and Future Enhancement;29
6.7;References;30
7; Performance Comparison of Machine Learning-Based Classification of Skin Diseases from Skin Lesion Images;33
7.1;1 Introduction;33
7.2;2 Related Work;34
7.3;3 Overview of Convolutional Neural Network;35
7.3.1;3.1 Convolutional Layer;35
7.3.2;3.2 Pooling Layer;36
7.3.3;3.3 Fully Connected Layer;36
7.4;4 Proposed Methodology;36
7.4.1;4.1 Dataset;36
7.4.2;4.2 Proposed Methodology;37
7.5;5 Experimental Results;40
7.6;6 Conclusion;43
7.7;References;43
8; FastICA Algorithm Applied to Scattered Electromagnetic Signals;44
8.1;1 Introduction;44
8.2;2 Theory;45
8.3;3 Results and Discussion;47
8.4;4 Conclusion;50
8.5;References;50
9; Deep Convolution Neural Network Model for Indian Sign Language Classification;52
9.1;1 Introduction;52
9.2;2 Indian Sign Language: Number Datasets;54
9.3;3 Methodologies;54
9.4;4 Result Analysis;56
9.5;5 Conclusion;59
9.6;References;60
10; Opinion Mining of Bengali Review Written with English Character Using Machine Learning Approaches;62
10.1;1 Introduction;63
10.2;2 Literature Survey;64
10.3;3 Methodology;64
10.4;4 Result and Discussions;66
10.5;5 Conclusion;73
10.6;References;74
11; Big Data Feature Selection to Achieve Anonymization;75
11.1;1 Introduction;75
11.1.1;1.1 Our Contributions;77
11.2;2 Background and Key Issues;77
11.2.1;2.1 Data Preprocessing Dealing with Big Data Scalability Problem;77
11.2.2;2.2 Anonymization Algorithm—K-Anonymity;78
11.3;3 Big Data: MapReduce;78
11.3.1;3.1 Preprocessing Algorithm—A Correlation-Based Filter Approach;79
11.3.2;3.2 Algorithm and Analysis;79
11.4;4 Empirical Study;80
11.4.1;4.1 FCBF and k-Anonymity in MapReduce Framework;81
11.5;5 Conclusion;82
11.6;References;82
12; Interoperability in Smart Living Network—A Survey;84
12.1;1 Introduction;84
12.2;2 Related Works;85
12.3;3 Home Framework Designing;86
12.3.1;3.1 Middleware or Gateway;87
12.3.2;3.2 Communication Protocols;87
12.3.3;3.3 Home API;89
12.3.4;3.4 Service Projects;89
12.4;4 Proposed Interoperability in Living Network System;90
12.5;5 Research Consideration;92
12.6;6 Conclusion;92
12.7;References;93
13; Sentiment Analysis of Bengali Reviews for Data and Knowledge Engineering: A Bengali Language Processing Approach;95
13.1;1 Introduction;96
13.2;2 Methodology;97
13.2.1;2.1 Objective of the Research;98
13.2.2;2.2 Designing System;98
13.2.3;2.3 Data Procession, Parts of Speech Tagging and Identify Negative Words (Phrase);99
13.2.4;2.4 Classifier Description;99
13.2.5;2.5 Implementation of the Work;101
13.3;3 Result and Discussions;101
13.4;4 Conclusion;104
13.5;References;105
14; Imbalanced Dataset Analysis with Neural Network Model;106
14.1;1 Introduction;106
14.2;2 Related Works;108
14.3;3 Methodology;109
14.4;4 SCRUM Database;110
14.5;5 Results and Discussion;111
14.6;6 Conclusion;114
14.7;References;116
15; Review of Parallel Processing Methods for Big Image Data Applications;118
15.1;1 Introduction;118
15.2;2 Literature Review;120
15.2.1;2.1 Review of Parallel Processing Methods—Many Applications;120
15.2.2;2.2 Review of Parallel Processing Methods—Image Processing Applications;122
15.3;3 Inference from the Review;123
15.4;4 Solutions;123
15.5;5 Conclusion and Future Work;127
15.6;References;127
16; Exploring the Potential of Virtual Reality in Fire Training Research Using A’WOT Hybrid Method;130
16.1;1 Introduction;130
16.2;2 Fire Training in a VR;131
16.3;3 Research Methodology;131
16.3.1;3.1 SWOT Analysis;131
16.3.2;3.2 The AHP Method;132
16.3.3;3.3 A’WOT (SWOT-AHP) Methodology;132
16.4;4 Results and Discussion;134
16.5;5 Conclusion;137
16.6;Appendix;137
16.7;References;139
17; Two-Way Sequence Modeling for Context-Aware Recommender Systems with Multiple Interactive Bidirectional Gated Recurrent Unit;141
17.1;1 Introduction;141
17.2;2 Related Work;143
17.3;3 Proposed Two-Way Sequence Modeling Approach;145
17.3.1;3.1 Problem Definition;145
17.3.2;3.2 The MiBiGRU Model;145
17.3.3;3.3 Back Ground;146
17.4;4 Conclusion and Future Work;148
17.5;References;148
18; Stage Audio Classifier Using Artificial Neural Network;150
18.1;1 Introduction;150
18.2;2 Paper Preparation;151
18.2.1;2.1 Description of Speech Classification;151
18.3;3 Proposed Model;152
18.3.1;3.1 Scope of Neural Networks;152
18.3.2;3.2 Feature Selection;153
18.4;4 Results and Discussion;155
18.4.1;4.1 Database;155
18.4.2;4.2 Training Set;155
18.4.3;4.3 Evaluation;156
18.5;5 Conclusion;157
18.6;References;157
19; Predicting Short-Term Electricity Demand Through Artificial Neural Network;159
19.1;1 Introduction;160
19.2;2 Development;160
19.2.1;2.1 The Training Set;160
19.2.2;2.2 Validation Set;160
19.2.3;2.3 Test Set;160
19.2.4;2.4 Selection of the Neural Network Architecture;161
19.2.5;2.5 Evaluation Criteria;162
19.2.6;2.6 Implementation of the Prediction Model with Neural Networks;162
19.2.7;2.7 Calculation of the Error of the Proposed Model;163
19.3;3 Results;164
19.4;4 Conclusions;166
19.5;References;166
20; Detection of Tomatoes Using Artificial Intelligence Implementing Haar Cascade Technique;168
20.1;1 Introduction;169
20.2;2 Flow Chart;169
20.3;3 Procedure;170
20.3.1;3.1 Collection of Data Set;171
20.3.2;3.2 Collection of Images;171
20.3.3;3.3 Image Classification;171
20.3.4;3.4 Haar Cascade;173
20.3.5;3.5 Haar Features;173
20.3.6;3.6 Algorithm: AdaBoost Classifier;174
20.3.7;3.7 Integral Images;174
20.3.8;3.8 Generating Model Using Haar Classifier;175
20.3.9;3.9 Using PyCharm to Demonstrate Classifier;176
20.3.10;3.10 Important Packages in Our Programme;176
20.3.11;3.11 Important Function Calls in Our Programme;177
20.4;4 Results;179
20.5;5 Conclusion;180
20.6;6 Future Scope;180
21; Passive Safety System for Two- and Four-Wheeled Vehicles;181
21.1;1 Introduction;181
21.2;2 Literature Survey;182
21.3;3 Technical Studies;183
21.3.1;3.1 Arduino Nano Microcontroller;183
21.3.2;3.2 RF Communicator;184
21.3.3;3.3 LM35 Temperature Sensor;184
21.3.4;3.4 Ultrasonic Sensor;185
21.3.5;3.5 Radio Frequency Identification (RFID);185
21.4;4 Construction;186
21.4.1;4.1 Two-Wheeler Unit;186
21.4.2;4.2 Four-Wheeler Unit;187
21.5;5 Results and Discussion;188
21.6;6 Conclusion and Future Scope;189
21.7;References;191
22; Measuring the Financial Performance of MSMEs Through Artificial Neural Networks;193
22.1;1 Introduction;194
22.2;2 Methodology;194
22.2.1;2.1 Sample Data;194
22.2.2;2.2 Methods;195
22.3;3 Results and Analysis;196
22.3.1;3.1 Econometric Application;196
22.3.2;3.2 The ANN Model;197
22.4;4 Conclusions;199
22.5;References;200
23; Automation of Admission Enquiry Process Through Chatbot—A Feedback-Enabled Learning System;201
23.1;1 Introduction;201
23.2;2 Related Work;202
23.3;3 Proposed Methodology;203
23.3.1;3.1 Preprocessing;204
23.3.2;3.2 TF-IDF Approach;204
23.3.3;3.3 Identifying the Search Text;205
23.3.4;3.4 Generating Response and Analyzing the Feedback;205
23.4;4 Experimental Setup;205
23.5;5 Experimental Results and Analysis;207
23.6;6 Conclusions;208
23.7;References;209
24; Hardware-Assisted QR Code Generation Using Fault-Tolerant TRNG;210
24.1;1 Introduction;210
24.1.1;1.1 Reversible versus Fault-Tolerant Reversible Gates;211
24.2;2 Proposed Design;213
24.3;3 Results and Analysis;215
24.4;4 Conclusion;218
24.5;References;218
25; Classification of Digitized Documents Applying Neural Networks;220
25.1;1 Introduction;221
25.2;2 Method;221
25.2.1;2.1 Sample Data;221
25.2.2;2.2 Network Architecture Design;222
25.2.3;2.3 Learning from the Network;223
25.3;3 Results;224
25.3.1;3.1 Sample 1;224
25.3.2;3.2 Sample 2;224
25.4;4 Conclusions;226
25.5;References;226
26; Plant Leaf Diseases Recognition Using Convolutional Neural Network and Transfer Learning;228
26.1;1 Introduction;228
26.2;2 Related Work;229
26.3;3 Feature Extraction;230
26.3.1;3.1 Image Pre-processing and Labeling;230
26.3.2;3.2 Dataset;231
26.3.3;3.3 Augmentation Process;231
26.4;4 Convolutional Neural Network Training;232
26.4.1;4.1 Transfer Learning;233
26.4.2;4.2 Performed Tests;233
26.5;5 Experimental Results and Discussions;233
26.6;6 Conclusion;235
26.7;References;235
27; A Shape-Based Character Segmentation Using Artificial Neural Network for Mizo Script;237
27.1;1 Introduction;237
27.2;2 Related Work;239
27.3;3 Proposed Work;240
27.3.1;3.1 Find the Area of Isolated Shape (Blobs);240
27.3.2;3.2 Find the Required Amount of Morphological Value;241
27.3.3;3.3 Train ANN;241
27.3.4;3.4 Load Model;241
27.3.5;3.5 Line Segmentation;242
27.3.6;3.6 Character Segmentation;242
27.4;4 Result Analysis;243
27.5;5 Conclusion;244
27.6;References;244
28; Feasibility Study for a Mini-Hydropower Plant in Dreznica, Bosnia, and Herzegovina;246
28.1;1 Introduction;246
28.2;2 Technical and Energy Analysis;247
28.3;3 Hydrologic Study;248
28.4;4 Economic Analysis;251
28.5;5 Environment Analysis;253
28.5.1;5.1 Inventory of the Environment;253
28.5.2;5.2 Project Analysis;253
28.5.3;5.3 Identification and Evaluation of Environmental Influences;254
28.5.4;5.4 Program for the Protection of the Environment;254
28.5.5;5.5 Positive Effects;255
28.6;6 Conclusions;255
28.7;References;255
29; Feature Selection Using Neighborhood Component Analysis with Support Vector Machine for Classification of Breast Mammograms;257
29.1;1 Introduction;257
29.2;2 Methodology;258
29.3;3 Results;261
29.4;4 Conclusion;262
29.5;References;264
30; Performance Analysis of Implicit Pulsed and Low-Glitch Power-Efficient Double-Edge-Triggered Flip-Flops Using C-Elements;265
30.1;1 Introduction;265
30.2;2 Circuit Description;267
30.3;3 Modified LG-C Flip-Flop;268
30.3.1;3.1 Modified Implicit DET Flip-Flop;269
30.4;4 Results and Discussion;270
30.4.1;4.1 Temperature Variation;272
30.4.2;4.2 Supply Variation;273
30.4.3;4.3 Process Variation;274
30.5;5 Conclusion;276
30.6;References;278
31; Cost-Effective Waste Collection System Based on the Internet of Wasted Things (IoWT);280
31.1;1 Introduction;281
31.2;2 Related Work;282
31.3;3 Proposed Work;283
31.3.1;3.1 System Design and Implementation;284
31.3.2;3.2 Shortest Path Implementation;285
31.4;4 Prototype Testing and Result;285
31.5;5 Conclusions and Further Work;288
31.6;References;289
32; Leveraging Artificial Intelligence for Effective Recruitment and Selection Processes;290
32.1;1 Introduction;290
32.2;2 Related Work;292
32.3;3 Proposed Conceptual Model;293
32.4;4 Conclusion;294
32.5;References;296
33; Trust Computing Model Based on Meta-Heuristic Approach for Collaborative Cloud Environment;297
33.1;1 Introduction;298
33.2;2 Motivation;298
33.3;3 Related Work on Cloud Monitoring and Trustworthy Cloud Service [5, 9, 12];298
33.4;4 Trust-Based Pricing Includes Two Main Problems that Need to Be Addressed;302
33.5;5 Analysis and Discussions;303
33.6;6 Conclusion and Feature Scope;304
33.7;References;304
34; Integration of Generation Y Academician Attributions with Transformational Leadership Style: Association Rules Technique on Minimizing Turnover Intention;305
34.1;1 Introduction;306
34.2;2 Literature Review;307
34.2.1;2.1 Related Work;307
34.3;3 Methodology;309
34.3.1;3.1 Data Sets;309
34.3.2;3.2 Association Rules Algorithms;311
34.4;4 Results and Discussion;311
34.5;5 Conclusion;313
34.6;References;314
35; An Improved Self-tuning Control Mechanism for BLDC Motor Using Grey Wolf Optimization Algorithm;316
35.1;1 Introduction;316
35.2;2 Design of BLDC Motor;317
35.2.1;2.1 Modelling of BLDC Motor;317
35.2.2;2.2 Modelling of Encoder;318
35.3;3 Design of Grey Wolf Algorithm;319
35.4;4 Results and Discussion;321
35.5;5 Conclusion and Future Scope;323
35.6;References;323
36; A Wearable Wrist-Based Pulse Oximetry for Monitoring Cardiac Activities—A Pilot Study;325
36.1;1 Introduction;325
36.1.1;1.1 Background of PPG;326
36.1.2;1.2 Related Work;327
36.2;2 Methodology;327
36.2.1;2.1 Data Collection;327
36.2.2;2.2 Hardware Development;329
36.3;3 Results;330
36.4;4 Conclusion;331
36.5;References;333
37; Data Sciences and Teaching Methods—Learning;334
37.1;1 Introduction;335
37.2;2 Description of Methods;335
37.2.1;2.1 Artificial Neural Networks (ANN);335
37.2.2;2.2 Artificial Vision (CV);336
37.2.3;2.3 Natural Language Processing (NLP);337
37.3;3 Results;338
37.4;4 Conclusions;340
37.5;References;341
38; Data Security in Cloud Computing Using Three-Factor Authentication;342
38.1;1 Introduction;343
38.2;2 Related Work;343
38.3;3 Proposed Work;346
38.3.1;3.1 Elliptical Curve Cryptosystem;346
38.3.2;3.2 Fuzzy Extractor;346
38.3.3;3.3 Three-Factor Authentication;346
38.4;4 Result;350
38.5;5 Conclusion;352
38.6;6 Future Scope;352
38.7;References;353
39; An Effective Machine Learning-Based File Malware Detection—A Survey;354
39.1;1 Introduction;354
39.2;2 Literature Review;356
39.3;3 Discussion;357
39.4;4 Conclusion;358
39.5;References;358
40; Data Mining and Neural Networks to Determine the Financial Market Prediction;360
40.1;1 Introduction;361
40.2;2 Method;362
40.3;3 Results;363
40.4;4 Conclusions;365
40.5;References;366
41; Research on the Cloud Archiving Process and Its Technical Framework of Government Website Pages;368
41.1;1 Introduction;368
41.2;2 Connotation of Government Web site Pages’ Cloud Archive;369
41.2.1;2.1 Conceptual Definition;369
41.2.2;2.2 The Basic Characteristics;369
41.3;3 Government Web site Pages Cloud Archiving Processes;370
41.3.1;3.1 Collection Strategy for Cloud Archiving of Government Web Pages;371
41.3.2;3.2 Data Management for Cloud Archiving of Government Web Pages;373
41.3.3;3.3 Cloud Storage Architecture for Archiving Government Web Pages;374
41.3.4;3.4 Government Web Page “Cloud” Utilization Services and Safety;375
41.4;4 Technical Framework for Cloud Archiving of Government Web Pages;376
41.5;5 Conclusion;378
41.6;References;378
42; Determination of Contents Based on Learning Styles Through Artificial Intelligence;380
42.1;1 Introduction;381
42.2;2 Method;381
42.2.1;2.1 Search, Analysis and Classification of Information About the Content Generation Process for the Courses;382
42.2.2;2.2 Analysis and Coding of the Software Component for Content Generation According to Learning Styles;382
42.2.3;2.3 Validation and Acceptance Tests;382
42.3;3 Experimental Design;383
42.3.1;3.1 Analysis and Determination of Functional Requirements, Roles and Actors;383
42.3.2;3.2 Design of the System Knowledge Base Model;383
42.4;4 Results;385
42.4.1;4.1 Knowledge Base;385
42.5;5 Conclusions;386
42.6;References;386
43; Evaluation Computing of Cultural Tourism Resources Potential Based on SVM Intelligent Data Analysis and IoT;388
43.1;1 Introduction;388
43.2;2 The Proposed Methodology;391
43.2.1;2.1 Resources Potential Model;391
43.2.2;2.2 SVM Data Analytic Framework;392
43.2.3;2.3 IoT Framework for the Measurement;393
43.2.4;2.4 Evaluation Computing of Cultural Tourism Resources Potential;395
43.3;3 Conclusion;396
43.4;References;396
44; Data Mining and Social Network Analysis on Twitter;399
44.1;1 Introduction;400
44.2;2 Data;400
44.3;3 Method;400
44.4;4 Results;401
44.5;5 Conclusions;404
44.6;References;405
45; Numerical Modeling and Simulation of High-Efficiency Thin Cu(In,Ga)Se Photovoltaic by WxAMPS;407
45.1;1 Introduction;408
45.2;2 Simulation and Modeling;409
45.2.1;2.1 Simulations Details;409
45.2.2;2.2 Reference Cell;410
45.3;3 Results and Discussions;413
45.3.1;3.1 I–V Characteristics of Single Junction CIGS Solar Cell at 300 K;414
45.4;4 Conclusion;419
45.5;References;419
46; Design of a Two-Stage Folded Cascode Amplifier Using SCL 180nm CMOS Technology;420
46.1;1 Introduction;420
46.2;2 Architecture of the Two-Stage Amplifier Circuit;421
46.2.1;2.1 Proposed Circuit of Folded Cascode Amplifier;421
46.2.2;2.2 Methodology;421
46.3;3 Folded Cascode Amplifier Design;421
46.3.1;3.1 Design Procedure;423
46.4;4 Simulation results;425
46.5;5 Conclusion;427
46.6;References;427
47; Electromagnetic Simulation of Optical Devices;428
47.1;1 Introduction;428
47.2;2 Related Work;429
47.3;3 Proposed Work;429
47.4;4 Result Analysis;430
47.5;5 Discussion;430
47.6;References;433
48; Review on Radio Frequency Micro Electro Mechanical Systems (RF-MEMS) Switch;434
48.1;1 Introduction;434
48.2;2 Classification of RF Switches;435
48.3;3 Classification of MEMS-Based RF Switches;436
48.3.1;3.1 Configuration;437
48.3.2;3.2 Actuation Mechanism;438
48.3.3;3.3 Contact Type;439
48.3.4;3.4 Structure;440
48.4;4 Design Considerations of RF-MEMS Switch;441
48.4.1;4.1 Effect of Beam;441
48.4.2;4.2 Effect of Dielectric;442
48.4.3;4.3 Gap Between Membrane and Dielectric;443
48.5;5 MEMS Capacitance and RF Parameter;443
48.6;6 Effect of Perforation on the Beam;445
48.7;7 Material Selection;445
48.8;8 Conclusion;446
48.9;References;447
49; Design of Generalized Rational Sampling Rate Converter Using Multiple Constant Multiplication;451
49.1;1 Introduction;451
49.2;2 Overall System Model;453
49.3;3 MCM-Based Design;454
49.4;4 Results and Analysis;457
49.5;5 Conclusion;459
49.6;References;461
50; Comparison of Decoupled and Coupled PWM Techniques for Open-End Induction Motor Drives;463
50.1;1 Introduction;463
50.2;2 Dual-Based Inverter Configuration;464
50.3;3 PWM Methods Designed for Dual-Inverter (DI) Configuration;464
50.3.1;3.1 Decoupled PWM Techniques;465
50.3.2;3.2 Coupled PWM Methods;466
50.4;4 Summary;469
50.5;References;469
51; Computer Tools for Energy Systems;471
51.1;1 Introduction;471
51.2;2 Computer Tools for Energy System;472
51.2.1;2.1 AEOLIUS;473
51.2.2;2.2 BALMOREL;473
51.2.3;2.3 BCHP Screening Tool;473
51.2.4;2.4 COMPOSE;474
51.2.5;2.5 E4cast;474
51.2.6;2.6 EMCAS;474
51.2.7;2.7 EMINENT;475
51.2.8;2.8 EMPS;475
51.2.9;2.9 Energy PLAN;475
51.2.10;2.10 Energy PRO;476
51.2.11;2.11 ENPEP-BALANCE;476
51.2.12;2.12 GTMax;477
51.2.13;2.13 H2RES;477
51.2.14;2.14 HOMER;477
51.2.15;2.15 HYDROGEMS;478
51.3;3 Conclusion;478
51.4;References;478
52; Three-Interacting Tank Controlled with Decentralized PI Controller Tuned Using Grey Wolf Optimization;481
52.1;1 Introduction;482
52.2;2 Cylindrical Three-Tank Interacting System;483
52.3;3 Interaction Analysis;487
52.4;4 Decentralized Controller Design Tuning Using Grey Wolf Optimizer;488
52.4.1;4.1 Grey Wolf Optimizer;488
52.4.2;4.2 Online Optimization of the Decentralized Controller Using Grey Wolf Optimizer;491
52.5;5 Conclusion;495
52.6;References;495
53; An IEC 61131-3-Based PLC Timers Module Implemented on FPGA Platform;497
53.1;1 Introduction;498
53.2;2 Materials and Methods;498
53.2.1;2.1 Materials;498
53.2.2;2.2 Methods;499
53.3;3 Theory;501
53.3.1;3.1 Results of Literature Survey;501
53.4;4 Results and Discussions;502
53.4.1;4.1 Timer ON Delay;502
53.4.2;4.2 Timer OFF Delay;503
53.4.3;4.3 Retentive Timer ON Delay;503
53.4.4;4.4 Retentive Timer OFF Delay;504
53.4.5;4.5 Pulse Timer;504
53.4.6;4.6 Oscillator Timer;505
53.4.7;4.7 Real-Time Applications of Timers;506
53.4.8;4.8 Comparative Result Analysis of Proposed FPGA-Based PLC Design with Existing PLC;508
53.5;5 Conclusion;510
53.6;References;511
54; Impact of Temperature on Circuit Metrics of Various Full Adders;513
54.1;1 Introduction;513
54.2;2 Distinct Full Adder Structures;514
54.2.1;2.1 Conventional CMOS Logic Adder;514
54.2.2;2.2 Complementary Pass-Transistor Logic Adder;515
54.2.3;2.3 Transmission Gate Logic Adder;516
54.3;3 Experimental Set-up;516
54.4;4 Results and Discussion;516
54.5;5 Conclusion;519
54.6;References;520
55; Novel Approach for Power Analysis in Microcontrollers;521
55.1;1 Introduction;521
55.2;2 Related Work;522
55.3;3 Methods of Fault Attacks;522
55.3.1;3.1 Fault Attack—Non-invasive;522
55.3.2;3.2 Fault Attack—Semi-invasive;523
55.3.3;3.3 Invasive Fault Attacks;523
55.4;4 Power Analysis;523
55.4.1;4.1 Fault Injection Attack;523
55.4.2;4.2 Clock Glitch?ng Attack;524
55.5;5 Proposed System;524
55.6;6 Experimental Results;525
55.6.1;6.1 Power Analysis for Addition Operation;525
55.7;7 Conclusion;526
55.8;References;527
56; Evolving Reversible Fault-Tolerant Adder Architectures and Their Power Estimation;528
56.1;1 Introduction;528
56.2;2 Literature Survey;529
56.2.1;2.1 Reversible Gates;529
56.2.2;2.2 Proposed Adder Architectures;530
56.2.3;2.3 Ripple Carry Adder;531
56.2.4;2.4 Carry Skip Adder;531
56.2.5;2.5 Carry Look-Ahead Adder;531
56.2.6;2.6 Power Estimation and Hardware Complexity;531
56.3;3 Conclusion;533
56.4;References;534
57; A Wide-Band, Low-Power Grounded Active Inductor with High Q Factor for RF Applications;536
57.1;1 Introduction;536
57.2;2 Background;537
57.3;3 Proposed AI Circuit;538
57.4;4 Simulation Results and Discussions;539
57.5;5 Conclusion;542
57.6;References;542
58; Design and FPGA Realization of Digital Lightweight Numerically Controlled Quadrature Wave Oscillator;544
58.1;1 Introduction;544
58.2;2 Literature Survey;545
58.3;3 NCO Architecture;546
58.4;4 Building a Numerically Controlled Quadrature Wave Oscillator (NCQO);546
58.5;5 Components of NCQO;547
58.6;6 Simulation Results;549
58.7;7 Results and Analysis;550
58.8;8 Conclusion;551
58.9;References;552
59; Efficient Multimedia Data Transmission Model for Future Generation Wireless Network;553
59.1;1 Introduction;554
59.2;2 Efficient Multimedia Data Transmission Model for Future Generation High-Speed Wireless Network with Proposed Compression Technique;556
59.3;3 Experimental Outcome and Discussion;560
59.3.1;3.1 Bit Error Rate Performance Evaluation Considering Varied SNR;560
59.3.2;3.2 Symbol Error Rate Performance Evaluation Considering Varied SNR;561
59.3.3;3.3 Throughput (Sum Rate) Performance Evaluation Considering Varied SNR;561
59.3.4;3.4 Compression Ratio Performance Evaluation;562
59.4;4 Conclusion;562
59.5;References;563
60; Smart Fleet Monitoring System in Indian Armed Forces Using Internet of Things (IoT);566
60.1;1 Introduction;566
60.2;2 Literature Review;567
60.3;3 IoT Architecture;568
60.4;4 Related Work;568
60.5;5 Working of Fleet Monitoring;569
60.5.1;5.1 GPS Tracker Module;570
60.5.2;5.2 Load Management Module;570
60.5.3;5.3 Fuel Monitoring Module;571
60.6;6 Results and Applications;571
60.6.1;6.1 Implementation;571
60.6.2;6.2 Results;571
60.7;7 Conclusion;573
60.8;8 Future Work;573
60.9;References;573
61; Greenhouse Monitoring System Based on Internet of Things;574
61.1;1 Introduction;574
61.2;2 Related Works;575
61.3;3 System Architecture;576
61.3.1;3.1 DHT11 Sensor;576
61.3.2;3.2 Soil Moisture Sensor;577
61.3.3;3.3 Arduino Uno;577
61.3.4;3.4 Ultrasonic Module HC-SR04;577
61.3.5;3.5 LDR Sensor;577
61.3.6;3.6 Pressure Sensor;578
61.3.7;3.7 PIR Sensor;578
61.3.8;3.8 Motor Module;578
61.4;4 Results and Discussion;578
61.5;5 Conclusion;583
61.6;References;584
62; Building Personal Marionette (Ritchie) Using Internet of Things for Smarter Living in Homes;585
62.1;1 Introduction;585
62.2;2 Background and Key Issues;586
62.2.1;2.1 Marionette System;586
62.2.2;2.2 System Architecture;587
62.3;3 Home Automation;590
62.3.1;3.1 Structural Design of the System;591
62.3.2;3.2 Predictable Results and Analysis;591
62.4;4 Conclusion and Future Work;593
62.5;References;593
63; The Internet of Things (IoT) Routing Security—A Study;595
63.1;1 Introduction;595
63.2;2 Related Works;596
63.2.1;2.1 Authentication and Integrity;597
63.2.2;2.2 Access Control for Ensuring Security;597
63.2.3;2.3 Privacy and Confidentiality;597
63.3;3 IoT Routing Attacks;598
63.3.1;3.1 Grouping of Attacks;598
63.4;4 Proposed Work Discussing the Attacks and Techniques Which Addresses the IoT Attacks;601
63.5;5 Future Research Direction;601
63.6;6 Conclusion;601
63.7;References;603
64; Efficient Hybrid Method for Intrinsic Security Over Wireless Sensor Network;605
64.1;1 Introduction;606
64.2;2 Literature Review;607
64.3;3 Proposed Methodology;608
64.3.1;3.1 Network Module;608
64.3.2;3.2 Wireless Tap Channel in Network Setting;610
64.4;4 Simulation Results;611
64.4.1;4.1 Packet Delivery Ratio (PDR);612
64.4.2;4.2 End-to-End Delay;612
64.4.3;4.3 Throughput;612
64.5;5 Conclusion;615
64.6;References;616
65; Cloud-Based Healthcare Portal in Virtual Private Cloud;617
65.1;1 Introduction;617
65.2;2 Requirements;618
65.2.1;2.1 Confidentiality;618
65.2.2;2.2 Integrity;619
65.2.3;2.3 Collision Resistance;619
65.2.4;2.4 Anonymity;619
65.3;3 Architecture Design;619
65.3.1;3.1 Healthcare System (HCS) in the Public Cloud Service;619
65.3.2;3.2 Proposed Architecture;620
65.4;4 Establish Flexibility of Access in VPC;621
65.4.1;4.1 Create the Public and Private Subnet for VPC IP Address 10.0.0.0/16;622
65.4.2;4.2 Network Address Translation (NAT);622
65.5;5 Evaluation Framework;624
65.6;6 Conclusion and Proposed Work;624
65.7;References;624
66; Interference Aware Cluster Formation in Cognitive Radio Sensor Networks;626
66.1;1 Introduction;626
66.2;2 Related Work;627
66.3;3 Proposed Protocol;629
66.4;4 Channel Allotment Scheme;630
66.5;5 Clustering Process;632
66.6;6 Performance Evaluation;633
66.7;7 Conclusion;634
66.8;References;635
67; Efficient Utilization of Resources of Virtual Machines Through Monitoring the Cloud Data Center;636
67.1;1 Introduction;636
67.2;2 Related Work;637
67.3;3 Proposed System;638
67.4;4 Experimental Results;641
67.5;5 Conclusion;642
67.6;References;644
68; A Study of Energy Management Techniques for Smart City Applications on Educational Campus;645
68.1;1 Introduction;645
68.2;2 Data;646
68.3;3 Results and Discussion;647
68.3.1;3.1 Data Monitoring and Supervision;647
68.3.2;3.2 Data Storage;648
68.3.3;3.3 Server;648
68.3.4;3.4 Information System;650
68.4;4 Conclusions;654
68.5;References;655
69; Low-Noise Amplifier for Wireless Local Area Network Applications;656
69.1;1 Introduction;656
69.2;2 Wireless Local Area Network;658
69.3;3 Low-Noise Amplifier Topologies Used for WLAN Applications;658
69.4;4 Conclusion and Future Scope;665
69.5;References;668
70; Indoor Mobile Robot Path Planning Using QR Code;670
70.1;1 Introduction;670
70.2;2 Literature Survey;671
70.3;3 Implementation;672
70.3.1;3.1 Architecture of Proposed System;672
70.3.2;3.2 Flow Diagram of the System;672
70.3.3;3.3 Proposed Path Planning Algorithm;675
70.4;4 Results;676
70.5;5 Conclusion;679
70.6;References;681
71; A Novel Privacy Preservation Scheme for Internet of Things Using Blockchain Strategy;683
71.1;1 Introduction;684
71.1.1;1.1 IoT and Attacks;684
71.2;2 Related Work;685
71.2.1;2.1 Review Stage 1: Security Issues in Various Layers of IoT;685
71.2.2;2.2 Review Stage 2: Security Solutions for IoT;687
71.2.3;2.3 Overview of Blockchain Technology;688
71.2.4;2.4 Problem Statement;688
71.3;3 Proposed Work;689
71.3.1;3.1 Security Framework for IoT;689
71.3.2;3.2 Implementation;689
71.4;4 Result Analysis;690
71.5;5 Conclusion and Future Work;690
71.6;References;692
72; Logically Locked I2C Protocol for Improved Security;694
72.1;1 Introduction;694
72.1.1;1.1 Problems with I2C;695
72.2;2 Literature Survey;695
72.2.1;2.1 Logic Locking;696
72.3;3 Securing I2C Protocol;697
72.4;4 Conclusion;701
72.5;References;702
73; Broadband Circularly Polarized Microstrip Patch Antenna with Fractal Defected Ground Structure;704
73.1;1 Introduction;704
73.2;2 Proposed Antenna Design;705
73.3;3 Parametric Analysis;705
73.3.1;3.1 Varying the Parameters;706
73.4;4 Results and Discussion;707
73.5;5 Conclusion;710
73.6;References;710
74; A Novel Technique for Vehicle Theft Detection System Using MQTT on IoT;712
74.1;1 Introduction;712
74.2;2 Literature Review;713
74.3;3 System Design;714
74.4;4 System Implementation;715
74.5;5 Conclusion;719
74.6;6 Future Work;719
74.7;References;720
75; Secure Wireless Internet of Things Communication Using Virtual Private Networks;721
75.1;1 Introduction;721
75.2;2 USP of IoT—Challenges for Security;722
75.3;3 Approaches and Their Feasibility;722
75.3.1;3.1 Hardware-Implemented Security;722
75.3.2;3.2 Device Authentication;723
75.3.3;3.3 Lightweight Encryption;723
75.3.4;3.4 Securing the Network;723
75.4;4 Tunneling;724
75.4.1;4.1 Point-to-Point Tunneling Protocol;724
75.4.2;4.2 Layer 2 Tunneling Protocol;724
75.5;5 Campus Building Network;725
75.6;6 Major IoT Cybersecurity Concerns;727
75.6.1;6.1 Network Attacks;727
75.6.2;6.2 Other Attacks;727
75.7;7 Future Work;727
75.8;8 Conclusion;728
75.9;References;728
76; A Contingent Exploration on Big Data Tools;729
76.1;1 Introduction;729
76.2;2 Characteristic Features of Big Data;731
76.3;3 Beyond 5 V’s, 3 P’s;731
76.4;4 Levels of Big Data Tools;732
76.5;5 Exploration of Big Data Tools;733
76.6;6 Key Technologies;735
76.7;7 Big Data Applications;735
76.8;8 A Comparative Analysis of Big Data Tools;735
76.9;9 Conclusion;738
76.10;10 Limitation;738
76.11;11 Future Scope;738
76.12;References;739
77;Author Index;740