Konkani / Bera / Paul Advances in Systems, Control and Automation

ETAEERE-2016
1. Auflage 2018
ISBN: 978-981-10-4762-6
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 442, 727 Seiten

Reihe: Lecture Notes in Electrical Engineering

This book comprises the select proceedings of the ETAEERE 2016 conference. The book aims to shed light on different systems or machines along with their complex operation, behaviors, and linear-nonlinear relationship in different environments. It covers problems of multivariable control systems and provides the necessary background for performing research in the field of control and automation. Aimed at helping readers understand the classical and modern design of different intelligent automated systems, the book presents coverage on the control of linear and nonlinear systems, intelligent systems, stochastic control, knowledge-based systems applications, fault diagnosis and tolerant control, real-time control applications, etc. The contents of this volume will prove useful to researchers and professionals alike.

Dr. Avinash Konkani is a Healthcare Technology Management (HTM) professional, researcher and an author with strong educational, research and work experience in Biomedical, Clinical, Human Factors and Systems Engineering. He is Board of Certification in Professional Ergonomics (BCPE) certified Associate Human Factors Professional (AHFP) with more than 10 years of combined work experience as an assistant professor, research scholar and a clinical engineer. He received his Ph.D in Systems Engineering from the Department of Industrial and Systems Engineering at Oakland University, Rochester, Michigan, USA. He obtained his Master's in Biomedical Ergonomic Engineering from Wright State University, Dayton, Ohio, USA and Bachelor's degree in Biomedical Engineering from Karnatak University, Dharwad, India.Prof. (Dr.) Rabindranath Bera received his B.Tech, M.Tech, and Ph.D degrees from the Institute of Radio Physics and Electronics, University of Calcutta, in 1982, 1985, and 1997, respectively. He has been working as a Professor and Dean, Head of the Department of Electronics and Communication Engineering, Sikkim Manipal University, since 2004. In 34 years of dedicated service, he has completed major projects for the MIT, All India Council for Technical Education (AICTE), Defence Research and Development Organisation (DRDO), Tata Iron and Steel Company (TISCO), Department of Science and Technology (DST), and others. He has published more than 150 journal articles and 85 conference papers. His areas of specialization include microwave/millimeter wave based broadband communication including 4G mobile, remote sensing using radar and radiometer, and advanced digital signal processing.  Dr. Samrat Paul received his Ph.D. in Energy (2012) and M.Tech in Energy Technology (2007) from Tezpur University, Assam. He is currently an Assistant Professor at the Department of Energy Engineering, North-Eastern Hill University (NEHU), Shillong. Before joining the NEHU, he served as an Assistant Professor at the Central University of Jharkhand's Centre for Energy Engineering. His research areas include the synthesis of nanomaterials for energy applications including catalytic biodiesel, biodiesel storage, etc. Dr. Paul received the Young Scientists Award from the Indian Science Congress Association (ISCA) and the Swarna Jayanti Puraskar Award from the National Academy of Sciences (NASI) (both in 2012).

Autoren/Hrsg.

Konkani, Avinash

Bera, Rabindranath

Paul, Samrat

Inhaltsverzeichnis

1;Preface;6
2;Editorial Board;8
2.1;Organizing Committee;10
2.2;Student Organizing Committee;10
3;Contents;13
4;About the Editors;20
5;1 Evaluation of Harmonics and THD in Five-Phase Inverter Constructed with High-Pass Filter by MATLAB Simulation;22
5.1;Abstract;22
5.2;1 Introduction;22
5.3;2 High-Pass Filter;23
5.4;3 Five-Phase Inverter;24
5.5;4 Simulation Results;25
5.6;5 Conclusion;27
5.7;References;28
6;2 Theoretical Analysis of the Electrical and Optical Properties of ZnS;29
6.1;Abstract;29
6.2;1 Introduction;30
6.3;2 Computational Study by CASTEP;30
6.4;3 Computational Results and Analysis;31
6.4.1;3.1 Band Structure and Electronic Density of States;31
6.4.2;3.2 Complex Dielectric Function;32
6.4.3;3.3 Absorption Spectrum;33
6.4.4;3.4 Refractive Index;34
6.4.5;3.5 Reflective Spectra;34
6.4.6;3.6 Optical Conductivity;36
6.4.7;3.7 Energy Loss Function;36
6.4.8;3.8 Vibrational Spectroscopy;37
6.5;4 Conclusion;38
6.6;References;38
7;3 Hybrid Fuzzy Recommendation System for Enhanced E-learning;40
7.1;Abstract;40
7.2;1 Introduction;40
7.3;2 Related Work;41
7.4;3 Hybrid Fuzzy Tree Matching Recommendation;43
7.4.1;3.1 Matching Algorithm;43
7.4.2;3.2 Collaborative Sequential Mapping Algorithm;44
7.4.2.1;3.2.1 Collaborative Filtering;44
7.4.2.2;3.2.2 Efficient Collaborative Filter Algorithm;44
7.4.2.3;3.2.3 Sequential Mapping;45
7.4.3;3.3 Personal Recommendation System;45
7.4.4;3.4 Cloud Database Storage;45
7.4.5;3.5 Hybrid Fuzzy-Based Matching Recommendation Algorithm;45
7.4.6;3.6 Result and Discussion;47
7.5;4 Conclusion;50
7.6;References;50
8;4 Tension Controllers for a Strip Tension Levelling Line;52
8.1;Abstract;52
8.2;1 Introduction;53
8.3;2 Description of the Strip Levelling Line;54
8.4;3 Tension Control;55
8.4.1;3.1 PI Tension Controller;56
8.4.2;3.2 Tension Controller with Ramp Generator;56
8.4.3;3.3 Stepper Tension Controller;59
8.5;4 Conclusion;62
8.6;Acknowledgements;62
8.7;References;62
9;5 Linear Synchronous Reluctance Motor—A Comprehensive Review;64
9.1;Abstract;64
9.2;1 Introduction;64
9.3;2 Introduction to Linear Motors;65
9.4;3 Introduction to Linear Synchronous Reluctance Motors;65
9.5;4 Modeling and Analysis of LSRELM;68
9.5.1;4.1 Nonlinear Two-Axis Analysis [12];68
9.5.2;4.2 Analysis of cross-magnetization effect using finite element method [13];69
9.5.3;4.3 Analysis of Cross-Saturation Effects [14–16];69
9.6;5 Design Prospects of LSRELM;70
9.6.1;5.1 Basic design [17];70
9.6.2;5.2 LSRELM with Multi-flux Barrier [18, 19];71
9.6.2.1;5.2.1 Slotless Stator;71
9.6.2.2;5.2.2 Stator with Slots;71
9.6.2.3;5.2.3 Double-Sided LSRELM [20–22];71
9.6.2.4;5.2.4 Permanent Magnet Double-Sided LRM [23–27];73
9.6.2.5;5.2.5 Four-Pole Double-Sided PMLRM;73
9.6.2.6;5.2.6 Design of Optimal Secondary Segment Shapes Using Stochastic Searching [28];75
9.6.2.7;5.2.7 LSRELM for Electromagnetic Aircraft Launch System [29, 30];75
9.6.2.8;5.2.8 Vertical-Type Double-Sided HTS LRM [31];77
9.7;6 Control Aspects of LSRELM;81
9.7.1;6.1 Parameters Identification in LSRELM [32, 33];81
9.7.2;6.2 High-Performance Position Tracking [34–39];82
9.7.3;6.3 Transverse Flux LSRELM [40–46];85
9.7.4;6.4 Lateral Force in Segment-Type Reaction Rail;86
9.8;7 Application and Future Scope;86
9.9;8 Conclusions;86
9.10;References;87
10;6 Mitigation of Circulating Current in Diode clamped MLI fed Induction Motor Drive Using Carrier Shifting PWM Techniques;90
10.1;Abstract;90
10.2;1 Introduction;91
10.3;2 Effect of High Circulating Currents;92
10.4;3 Carrier Shifting Algorithms;94
10.5;4 Analysis of Circulating Current Depends on Switching States;95
10.6;5 Circulating Current Reduction;97
10.7;6 Simulation Results;97
10.8;7 Experimental Results;100
10.9;8 Conclusions;100
10.10;References;100
11;7 Suppression of Harmonics and THD Using Three-Level Inverter with C-Type Filter at the Output of the Inverter Using Simulink/MATLAB;103
11.1;Abstract;103
11.2;1 Introduction;103
11.3;2 Traditional or Two-Level Inverter (TID);104
11.4;3 Diode-Clamped Multilevel Inverter (DCMI);105
11.5;4 Diode-Clamped Multilevel Inverter (DCMI) with C-Type Filter;106
11.6;5 Results;106
11.7;6 Conclusion;110
11.8;References;110
12;8 Computation of Actuation Voltage and Stress Made of Hafnium Oxide Materials Used in Radio Frequency Micro-electromechanical System Switch;111
12.1;Abstract;111
12.2;1 Introduction;111
12.3;2 Material;112
12.4;3 Electrostatic Actuation;112
12.5;4 Surface Contact Analysis;113
12.6;5 Stress Analysis;113
12.7;6 Flexures;113
12.8;7 Geometry and Simulation of RF MEMS Shunt Switch;114
12.9;8 Result and Discussion;114
12.10;9 Conclusion;118
12.11;References;118
13;9 Optimal Design Configuration Using HOMER;119
13.1;Abstract;119
13.2;1 Introduction;119
13.2.1;1.1 Advantages of Distributed Generation;120
13.2.2;1.2 HOMER Software;121
13.3;2 Methodology;121
13.3.1;2.1 HOMER: Simulation;121
13.3.2;2.2 HOMER: Optimization;122
13.3.3;2.3 HOMER: Sensitivity Analysis;122
13.4;3 Simulation MODEL;122
13.4.1;3.1 Load;122
13.5;4 Optimization Result;124
13.6;5 Conclusion;124
13.7;References;126
14;10 Designing of a Half-Bridge Converter for Lead-Acid Battery Charger;127
14.1;Abstract;127
14.2;1 Introduction;128
14.3;2 Methods;128
14.3.1;2.1 Proposed Block Diagram;128
14.3.2;2.2 Simulation of the Proposed Block Diagram Using Different Software Tools;129
14.4;3 Results and Discussions;130
14.4.1;3.1 Using Proteus Software Tool;130
14.4.2;3.2 Calculation of Different Parameters Using Different Software Tools;131
14.5;4 Conclusion;132
14.6;References;133
15;11 A Comparative Analysis of Determination of Design Parameters of Boost and Buck–Boost Converters Using Artificial Intelligence;134
15.1;Abstract;134
15.2;1 Introduction;134
15.3;2 Design of Converter Models;135
15.3.1;2.1 Simulation of Boost and Buck–Boost Converters;136
15.4;3 Development of the ANFIS Model;137
15.4.1;3.1 Algorithm for the Development of ANFIS Structure;138
15.5;4 Result and Analysis;139
15.6;5 Conclusion;141
15.7;References;142
16;12 Optimization of Induction Motor Using Genetic Algorithm and GUI of Optimal Induction Motor Design in MATLAB;143
16.1;Abstract;143
16.2;1 Introduction;143
16.3;2 Design Optimization of Induction Motor;144
16.4;3 Results and Discussion;145
16.5;4 Conclusion;148
16.6;References;148
17;13 High Response Photon-Counting for Phase Fraction Measurement Using Compact-RIO with FPGA;149
17.1;Abstract;149
17.2;1 Introduction;150
17.3;2 Multiphase Flow;150
17.4;3 Design Requirements;152
17.5;4 Conclusion;153
17.6;References;153
18;14 Numerical Stress Analysis of Artificial Femur Bone;154
18.1;Abstract;154
18.2;1 Introduction;155
18.3;2 Manufacturing;155
18.4;3 Modeling and Material Properties;156
18.5;4 Analysis;157
18.5.1;4.1 Analysis of the Solid Cylinder;157
18.5.2;4.2 Analysis of Human Femur Bone;163
18.6;5 Conclusion;168
18.7;References;169
19;15 Performance Metrics of Three-Phase Shunt APF Using Hybrid Control-Based Instantaneous Vector Control Theory;171
19.1;Abstract;171
19.2;1 Introduction;171
19.3;2 Shunt Active Power Filter;172
19.4;3 Instantaneous Vector Control Method;172
19.5;4 PI Controller and Hysteresis Current Controller;174
19.6;5 Results and Simulation;175
19.7;6 Table;177
19.7.1;6.1 Source Currents;177
19.7.2;6.2 Filter Currents;177
19.8;7 Conclusion;178
19.9;References;178
20;16 Energy-Efficient Illumination Control Using Image Parameters in a Machine Vision Environment for Optimum Surface Texture Identification;179
20.1;Abstract;179
20.2;1 Introduction;179
20.3;2 Experimental Setup;180
20.3.1;2.1 Image Acquisition Environment;181
20.4;3 Surface Pixel Intensities at Varying Lighting Conditions;183
20.5;4 Image Processing and Canny Edge Detection;186
20.5.1;4.1 Grayscale Image Representation;186
20.5.2;4.2 Canny Edge Criteria and Algorithm;186
20.5.3;4.3 Canny Algorithm;187
20.6;5 Canny Edge Images Under Varying Illumination Conditions;188
20.7;6 Harris Corner Points and FFT for the Image Sets;191
20.8;7 Conclusion;193
20.9;Acknowledgements;193
20.10;References;193
21;17 Accuracy Analysis of Machine Vision for Detection of Malignant Melanoma Using Pixel Intensity Matrix Parameters;194
21.1;Abstract;194
21.2;1 Introduction;194
21.3;2 Methodology;195
21.3.1;2.1 Pixel Intensity Matrix Analysis;196
21.4;3 Decision Rule;197
21.5;4 Results;198
21.6;5 Discussion;199
21.7;6 Conclusion;200
21.8;Acknowledgements;201
21.9;References;201
22;18 Investigation of Perylene as a Converter Material for Fast Neutron Detection and Spectroscopy Using GEANT4 Monte Carlo Simulations;202
22.1;Abstract;202
22.2;1 Introduction;202
22.3;2 GEANT4 Simulation Methodology;204
22.3.1;2.1 Model Description;204
22.4;3 Results and Discussion;205
22.4.1;3.1 SiC as a Neutron Detector;205
22.4.2;3.2 SiC Detector with Neutron Converter Layer;206
22.4.3;3.3 Perylene as a Converter Layer for SiC Detector;206
22.4.4;3.4 Calculation of Energy Deposition;207
22.5;4 Conclusion;208
22.6;Acknowledgements;209
22.7;References;209
23;19 Spectral Analysis and Comparison of Single-Carrier PLC Modules in Narrowband Power Line Communication System;211
23.1;Abstract;211
23.2;1 Introduction;212
23.3;2 Feature Extraction;213
23.3.1;2.1 Single-Carrier Modulation;213
23.3.2;2.2 Multicarrier Modulation;214
23.3.2.1;2.2.1 Orthogonal Frequency-Division Multiplexing;214
23.3.2.2;2.2.2 Generalized Frequency-Division Multiplexing;214
23.3.2.3;2.2.3 Filter Bank Multicarrier;215
23.4;3 Classifier;215
23.4.1;3.1 Binary Frequency-Shift Keying (BFSK);215
23.4.2;3.2 Amplitude-Shift Keying (ASK);215
23.4.3;3.3 Phase-Shift Keying (PSK);216
23.5;4 Results;216
23.5.1;4.1 Binary Frequency-Shift Keying (BFSK);216
23.5.2;4.2 Amplitude-Shift Keying (ASK);216
23.5.3;4.3 Phase-Shift Keying (PSK);216
23.6;5 Analysis;220
23.6.1;5.1 BER Analysis;220
23.7;6 Conclusion;223
23.8;References;223
24;20 Three-Level Flying Capacitor Multilevel Inverter Is Used to Suppress Harmonics at the Output of 3-Phase Inverter Drive and Study of Heat at Various Parts of 3-Phase Induction Motor;225
24.1;Abstract;225
24.2;1 Introduction;226
24.3;2 Normal or 2-Level Inverter (NID);227
24.4;3 Flying Capacitor Multilevel Inverter (FCMI);228
24.5;4 Results;229
24.6;5 Conclusion;234
24.7;References;235
25;21 Development of Measurement and Data Acquisition Setup Using LabVIEW for Sample Characterization up to Cryogenics Temperature;236
25.1;Abstract;236
25.2;1 Introduction;236
25.3;2 Measurement Techniques;237
25.3.1;2.1 Differential Measurements with Magnetic Field;237
25.3.2;2.2 I-V Measurement;238
25.3.3;2.3 Temperature Controlled I-V Measurement;238
25.4;3 Automation Setup;238
25.4.1;3.1 Differential Measurements with Magnetic Field;238
25.4.2;3.2 I-V Measurement;239
25.4.3;3.3 Temperature Controlled I-V Measurement;240
25.5;4 Results;241
25.5.1;4.1 Differential Measurements with Magnetic Field;241
25.5.2;4.2 I-V Measurement;244
25.5.3;4.3 Temperature Controlled I-V Measurement;245
25.6;5 Conclusion;246
25.7;Acknowledgements;247
25.8;References;247
26;22 Design and Analysis of a Permanent Magnet DC Motor;248
26.1;Abstract;248
26.2;1 Introduction;248
26.3;2 Mathematical Model of a PMDC Motor;249
26.3.1;2.1 Electrical Characteristics;250
26.3.2;2.2 Mechanical Characteristics;251
26.3.3;2.3 State Space Representation;251
26.3.4;2.4 Block Diagram with Transfer Function;252
26.4;3 Flow Chart;254
26.5;4 Design Calculation;255
26.5.1;4.1 Given Dimensions;255
26.5.2;4.2 Number of Slots Calculation;255
26.5.3;4.3 Type of Winding;256
26.5.4;4.4 Slot Width Calculation;256
26.5.5;4.5 Calculation of Conductor Dimensions;257
26.5.6;4.6 Commutator Design;258
26.5.6.1;4.6.1 Brush Design;258
26.6;5 Simulation Results;258
26.7;6 Conclusion;259
26.8;References;260
27;23 Design and Analysis of Performance Characteristics of Electronic Ballast Used for Fluorescent L261
27.1;Abstract;261
27.2;1 Introduction;261
27.3;2 Circuit Description and Function of Components;262
27.4;3 Testing;264
27.4.1;3.1 Evaluation of Total Luminous Flux;264
27.4.2;3.2 Reference Ballast;265
27.5;4 Conclusion and Future Scope of the Work;266
27.6;References;267
28;24 Ballistocardiogram Signal Denoising Using Independent Component Analysis;268
28.1;Abstract;268
28.2;1 Introduction;268
28.2.1;1.1 Issues and Challenges;270
28.3;2 Proposed Model;271
28.3.1;2.1 Independent Component Analysis;271
28.3.2;2.2 Spectral Coherence Computation;272
28.3.3;2.3 Computation of Partial Spectral Coherence;273
28.4;3 Results and Discussion;273
28.4.1;3.1 Dataset Description;273
28.5;4 Conclusion;275
28.6;References;276
29;25 Sleep Stage Classification Using S-Transform-Based Spectral Energy Feature;277
29.1;Abstract;277
29.2;1 Introduction;277
29.3;2 Decomposition of EEG Signal;278
29.4;3 Machine Learning Algorithms;278
29.5;4 Analysis and Classification in Time Domain;280
29.6;5 Time-Frequency Analysis;282
29.7;6 Proposed Scheme;284
29.8;7 Conclusion;288
29.9;References;288
30;26 MMSE-Based Lattice-Reduction-Aided Equalization for MIMO System in Nakagami-m Channel;289
30.1;Abstract;289
30.2;1 Introduction;289
30.3;2 System Description;290
30.4;3 Results;291
30.5;4 Conclusion;293
30.6;References;294
31;27 Fundamental Concepts of Neural Networks and Deep Learning of Different Techniques to Classify the Handwritten Digits;295
31.1;Abstract;295
31.2;1 Introduction;295
31.3;2 Neural Networks;296
31.4;3 Architecture of Neural Network;298
31.5;4 Stochastic Gradient Descent;300
31.6;5 Back Propagation Algorithm;301
31.7;6 Problem with the Quadratic Cost Function;302
31.8;7 Softmax Neurons;302
31.9;8 Use of Neural Networks in Classifying the Handwritten Digits;303
31.10;9 Conclusion;304
31.11;References;304
32;28 Error Rate Analysis of Precoded-OSTBC MIMO System Over Generalized-K Fading Channel;306
32.1;Abstract;306
32.2;1 Introduction;306
32.3;2 System and Channel Model;308
32.3.1;2.1 Precoded-OSTBC MIMO System;308
32.3.2;2.2 Generalized-K Fading;310
32.4;3 Simulation Results and Discussion;311
32.5;4 Conclusion;312
32.6;References;313
33;29 CMOS Based Sinusoidal Oscillator Using Single CCDDCCTA;315
33.1;Abstract;315
33.2;1 Introduction;315
33.3;2 CCDDCCTA Based Circuit;316
33.4;3 Sensitivity and Stability of the Oscillator;318
33.5;4 Simulations and Discussion;319
33.6;5 Conclusion;322
33.7;References;322
34;30 Investigation of Direct Torque Control-Based Synchronous Reluctance Motor Drive for Pumping;324
34.1;Abstract;324
34.2;1 Introduction;325
34.3;2 Modelling of Controller;326
34.4;3 Simulation Results;330
34.5;4 Conclusion;331
34.6;References;331
35;31 Modeling and Simulation of Synchronous Reluctance Motor for Pumping Application Using Field-Oriented Control;333
35.1;Abstract;333
35.2;1 Introduction;334
35.3;2 Modeling of Controller;335
35.4;3 Simulation Results;337
35.5;4 Conclusion;339
35.6;References;340
36;32 Advanced Variable Structure Control for Distributed Power Generation;341
36.1;Abstract;341
36.2;1 Introduction;342
36.2.1;1.1 Traditional Frequency Control Methodology of SS-HEPP;342
36.2.2;1.2 Construction of Proposed System;343
36.3;2 PI Controller and Fuzzy Logic-Based Controller;344
36.3.1;2.1 Conventional Sliding Mode Controller;344
36.3.2;2.2 Fuzzy-SMC Design;345
36.4;3 Variables, Membership Functions, and Fuzzy Rule;346
36.5;4 Proposed Control Strategy Simulation Result;347
36.6;5 Conclusions;348
36.7;References;348
37;33 Investigation of Doubly Fed Induction Generator Behavior Under Symmetrical and Asymmetrical Fault Conditions;350
37.1;Abstract;350
37.2;1 Introduction;351
37.3;2 DFIG and Network Faults Types;352
37.4;3 Dynamic Modeling of DFIG;352
37.5;4 B2B Configuration of DFIG;352
37.6;5 Numerical Simulation Results;353
37.7;6 Conclusion;355
37.8;References;355
38;34 PLC-Based Modeling and Control of Heat Exchanger;357
38.1;Abstract;357
38.2;1 Introduction;357
38.3;2 Methodology;359
38.4;3 Interfacing;361
38.5;4 Results and Conclusion;362
38.6;References;365
39;35 Digitally Controlled Hybrid Liquid Level Detection System Using Programmable Logic Controller and Microcontroller;367
39.1;Abstract;367
39.2;1 Introduction;368
39.3;2 Connection Diagram of Proposed System;369
39.3.1;2.1 Sensor to Arduino Interfacing;370
39.3.2;2.2 Arduino and PLC Interfacing;371
39.4;3 Hardware Implementations and Result;372
39.5;4 Conclusion;375
39.6;References;375
40;36 Realization of OTRA-Based Quadrature Oscillator Using Third-Order Topology;377
40.1;Abstract;377
40.2;1 Introduction;377
40.3;2 Proposed Circuit;378
40.4;3 Non-Ideality Analysis;382
40.5;4 Simulation and Experimental Results;383
40.6;5 Conclusion;387
40.7;References;388
41;37 Mathematical Models for Solving Problems of Reliability Maritime System;389
41.1;Abstract;389
41.2;1 Research Problem;390
41.3;2 Materials and Methods of Research;391
41.4;3 The Results of Research;392
41.5;4 Practical Implementation and Verification;394
41.6;5 Further Reduction of the Samples Being Formed and the Analysis of Identified Risks;394
41.7;6 Conclusion;395
41.8;References;395
42;38 Tuning PID Controller for Inverted Pendulum Using Genetic Algorithm;397
42.1;Abstract;397
42.2;1 Introduction;397
42.3;2 Mathematical Model;399
42.4;3 PID Controller;401
42.5;4 Genetic Algorithm;402
42.5.1;4.1 Characteristics of Genetic Algorithm;402
42.5.2;4.2 Objective Function Problem Formulation;403
42.6;5 Results;404
42.7;6 Conclusion;405
42.8;References;405
43;39 LQR PI Controller Design for First-Order Time-Delay Systems;407
43.1;Abstract;407
43.2;1 Introduction;408
43.3;2 Design of LQR PI Controller;409
43.4;3 Proposed Method to Selection of Weight Matrices;411
43.5;4 Simulation Results and Discussion;412
43.6;5 Conclusions;415
43.7;References;415
44;40 Determination of Protein Content of Castor Leaves Using UV-Based Sensor System;416
44.1;Abstract;416
44.2;1 Introduction;416
44.3;2 Materials and Methods;417
44.3.1;2.1 Chemical Analysis;418
44.3.2;2.2 Protein Extraction;418
44.3.3;2.3 Sensor System;419
44.3.4;2.4 Calibration Experiment;419
44.4;3 Result and Discussion;420
44.5;4 Conclusion;422
44.6;References;422
45;41 Hardware in Loop Control of Switched Capacitor Multilevel Inverter for Bus Clamping Modulation;424
45.1;Abstract;424
45.2;1 Introduction;425
45.3;2 SCMLI Topology;426
45.4;3 Modulation Methods;427
45.5;4 Result and Discussions;428
45.6;5 Conclusion;430
45.7;References;431
46;42 Estimation and Modeling of Underwater Acoustic Sensor Network;432
46.1;Abstract;432
46.2;1 Introduction;433
46.3;2 Design of UWASN;434
46.3.1;2.1 Transmitter Section;434
46.3.2;2.2 Receiver Section;434
46.4;3 Underwater Acoustic Channel Modeling;435
46.4.1;3.1 Propagation Delay;435
46.4.2;3.2 Absorption Coefficient;436
46.5;4 Underwater Acoustic Channel Estimation;436
46.5.1;4.1 SNR and SER;437
46.5.2;4.2 Optimal Frequency;437
46.5.3;4.3 Channel Estimation;438
46.5.4;4.4 A Smart Antenna Approach;439
46.6;5 Conclusion;440
46.7;References;440
47;43 A New Development Methodology for High Precision ISP;442
47.1;Abstract;442
47.2;1 Introduction;443
47.3;2 Gimbal Dynamics;443
47.4;3 Kinetic Coupling;446
47.5;4 Parameter Estimation Using Prediction Error Method;448
47.6;5 Simulation and Results;448
47.7;6 Conclusion;458
47.8;References;458
48;44 Simple FOPI Tuning Method for Real-Order Time Delay Systems;459
48.1;Abstract;459
48.2;1 Introduction;459
48.3;2 A Real-Order (Fractional) Transfer Function;460
48.4;3 FOPI Controller;461
48.4.1;3.1 Design and Tuning;461
48.4.2;3.2 Choice of M_{r};465
48.5;4 Example;466
48.6;5 Conclusion;467
48.7;References;467
49;45 Particle Swarm Optimization-Based Closed-Loop Optimal State Feedback Control for CSTR;469
49.1;Abstract;469
49.2;1 Introduction;470
49.3;2 Proposed Methodology;470
49.3.1;2.1 Formulation of Fitness Function;474
49.4;3 Comparative Analysis on Simulation Performed;474
49.5;4 Conclusion;478
49.6;References;479
50;46 Design of a Piezoresistive Microaccelerometer with High Sensitivity for Medical Diagnostic;480
50.1;Abstract;480
50.2;1 Introduction;480
50.3;2 Effect of Strain on Piezoresistor;481
50.4;3 Piezoresistive Coefficient;482
50.5;4 Piezoresistive Component;483
50.6;5 Results and Discussion;484
50.6.1;5.1 Thermal Drift of the Offset Voltage;484
50.6.2;5.2 Effect of Temperature on the Sensitivity;485
50.6.3;5.3 Effect of Doping Concentration on the Sensitivity;486
50.6.4;5.4 Sensitivity;487
50.7;6 Conclusion;488
50.8;References;488
51;47 Recognition of Human Speech Emotion Using Variants of Mel-Frequency Cepstral Coefficients;490
51.1;Abstract;490
51.2;1 Introduction;491
51.3;2 Feature Extraction Techniques;491
51.3.1;2.1 MFCC;492
51.3.2;2.2 Wavelet Analysis;493
51.3.3;2.3 MFCC in Wavelet Domain (WMFCC);493
51.3.4;2.4 Delta MFCC (DMFCC);494
51.3.5;2.5 Proposed Wavelet Delta MFCC (WDMFCC);494
51.4;3 Classification Method;494
51.5;4 Results and Discussion;495
51.6;5 Conclusion;497
51.7;References;497
52;48 Optimal and Novel Hybrid Feature Selection Framework for Effective Data Classification;498
52.1;Abstract;498
52.2;1 Introduction;498
52.3;2 Related Works;499
52.4;3 Proposed Method;500
52.4.1;3.1 Symmetrical Uncertainty;500
52.4.2;3.2 Genetic Algorithm;500
52.4.3;3.3 Proposed SU-GA Feature Selector;501
52.5;4 System Implementation and Experimental Results;502
52.5.1;4.1 Experimental Setup;503
52.5.2;4.2 Results and Discussions;504
52.5.3;4.3 Feature Selection;504
52.5.4;4.4 Classification Performance;507
52.5.5;4.5 Processing Time;507
52.6;5 Conclusion;511
52.7;References;512
53;49 Sensorless Direct Torque Control of Induction Motor Using Neural Network-Based Duty Ratio Controller;514
53.1;Abstract;514
53.2;1 Introduction;514
53.3;2 Conventional Direct Torque Control (CDTC);515
53.4;3 DTC Using Fuzzy Logic Switching Controller (FDTC);516
53.5;4 Duty Ratio Controller Using Neural Network (NNDRC);517
53.6;5 Simulation Results and Discussions;518
53.7;6 Summary/Conclusion;521
53.8;References;521
54;50 Design and Simulation of a Single-Output Multichannel Charger for Lithium-Ion Batteries;523
54.1;Abstract;523
54.2;1 Introduction;524
54.3;2 Charger System;525
54.3.1;2.1 Architecture/Hardware Configuration/Charger Overview;525
54.3.2;2.2 Buck Converter;525
54.3.2.1;2.2.1 Component Selection;526
54.3.2.2;2.2.2 Transfer Function of Buck Converter;527
54.3.3;2.3 Controller Design;527
54.3.4;2.4 DPDT Switching Circuitry;528
54.4;3 Battery Modeling;529
54.5;4 Experimental Tests;530
54.5.1;4.1 Battery Test;530
54.5.2;4.2 Charger Tests;531
54.5.3;4.3 Buck Converter;531
54.6;5 Results and Discussion;533
54.7;6 Conclusion;534
54.8;References;534
55;51 Design of Spectrum Sensing System;535
55.1;Abstract;535
55.2;1 Introduction;535
55.3;2 Methodology;536
55.3.1;2.1 Energy Detection;536
55.3.2;2.2 Match Filtering Process;537
55.4;3 Simulation Results;537
55.4.1;3.1 Energy Detection Method;537
55.4.2;3.2 Match Filtering Method;539
55.5;4 Conclusion;541
55.6;References;541
56;52 Modeling and Simulation of Switched Reluctance Motor;543
56.1;Abstract;543
56.2;1 Mathematical Model of SRM;543
56.2.1;1.1 Method-1: Look-Up Table-Based Approach;545
56.2.2;1.2 Method 2: Analytical Modeling Technique;546
56.2.3;1.3 Method 3: Inductance-Based Modeling;551
56.3;2 Comparison of the Modeling Techniques;553
56.4;3 MATLAB Simulink Model of the SRM;553
56.5;4 Conclusion;555
56.6;References;555
57;53 Fast Terminal Sliding Mode Control for High Pressure Rated Modified CSTR System;557
57.1;Abstract;557
57.2;1 Introduction;557
57.3;2 Control Strategy;558
57.3.1;2.1 First-Order SMC;558
57.3.2;2.2 Terminal Sliding Mode Control;559
57.3.3;2.3 Fast Terminal Sliding Mode Control;560
57.4;3 Numerical Example;560
57.5;4 Conclusion;565
57.6;References;566
58;54 Experimental/Simulation Study to Check the Significance of Proximity Effect;567
58.1;Abstract;567
58.2;1 Introduction;567
58.3;2 Experimental Setup;568
58.4;3 Numerical Simulation;569
58.4.1;3.1 FDTD Model;570
58.5;4 Experimental and Simulated Result;570
58.5.1;4.1 Measured Result;570
58.5.2;4.2 FDTD-Simulated Result;571
58.6;5 Discussion and Conclusion;571
58.7;References;572
59;55 Denoising of MRI Images Using Curvelet Transform;573
59.1;Abstract;573
59.2;1 Introduction;573
59.3;2 Denoising Techniques;574
59.3.1;2.1 Wavelet Transform;575
59.3.2;2.2 Curvelet Transform;575
59.4;3 Thresholding Technique;576
59.5;4 Proposed Technique;576
59.6;5 Parameter Estimations;577
59.7;6 Experimentation;577
59.8;7 Results and Discussion;577
59.9;8 Conclusion;580
59.10;References;580
60;56 Epileptic Seizure Detection from EEG Signals Using Best Feature Subsets Based on Estimation of Mutual Information for Support Vector Machines and Naïve Bayes Classifiers;582
60.1;Abstract;582
60.2;1 Introduction;583
60.2.1;1.1 EEG Data Segmentation;583
60.2.2;1.2 Discrete Wavelet Transform (DWT);584
60.3;2 Feature Selection and Ranking;584
60.3.1;2.1 Classification;584
60.4;3 Results;584
60.5;4 Conclusions;590
60.6;References;590
61;57 Intelligent Routing in MANET Using Self-Adaptive Genetic Algorithm;591
61.1;Abstract;591
61.2;1 Introduction;591
61.3;2 Proposed Solution;593
61.4;3 GA with Redefine Population on Time (RGA);593
61.5;4 GA with Continuous Perturbation (PGA);594
61.6;5 GA with Sharing Knowledge (KGA);594
61.7;6 Experimental Design;595
61.7.1;6.1 Chromosome Representation;595
61.7.2;6.2 Connectivity-Based Crossover Operator and Mutation;595
61.8;7 Conclusion;598
61.9;Acknowledgement;599
61.10;References;599
62;58 Comparison of Various Decoding Algorithms for EG-Low Density Parity Check Codes;600
62.1;Abstract;600
62.2;1 Introduction;600
62.3;2 Matrix Representation;601
62.3.1;2.1 Tanner Graph Representation;602
62.4;3 Decoding of LDPC Codes;603
62.4.1;3.1 Soft-Bit Flipping (SBF) Decoder Algorithm;603
62.4.2;3.2 Majority Logic Decoder/Detector (MLDD) Algorithm;605
62.5;4 Synthesis Results;606
62.6;5 Comparison Results;607
62.7;6 Conclusion;607
62.8;References;608
63;59 Development of a System for Quantitative Assessment of Vocal Loading;609
63.1;Abstract;609
63.2;1 Introduction;610
63.3;2 Theoretical Background;610
63.3.1;2.1 Speech Production;610
63.3.2;2.2 Source Filtering;610
63.3.3;2.3 Vocal Dose Measures;611
63.4;3 Methodology and Implementation;612
63.4.1;3.1 Speech Signal Acquisition;613
63.4.2;3.2 Pre-emphasis;613
63.4.3;3.3 Framing and Windowing;614
63.4.4;3.4 Short-Time Energy;614
63.4.5;3.5 Fundamental Frequency Estimation;614
63.5;4 Results;615
63.6;5 Conclusion;617
63.7;References;617
64;60 Solution for Multi-area Unit Commitment Problem Using PSO-Based Modified Firefly Algorithm;618
64.1;Abstract;618
64.2;1 Introduction;619
64.3;2 Problem Formulation for MAUC;620
64.3.1;2.1 Equality Constraints;621
64.3.2;2.2 Inequality Constraints;621
64.3.3;2.3 Multi-Area Economic Dispatch;622
64.3.4;2.4 Tie-Line Constraints;623
64.3.5;2.5 Modified Firefly Algorithm to Solve the Formulated MAUC Problem;624
64.4;3 Results and Discussion;626
64.5;4 Conclusion;628
64.6;References;628
65;61 Wi-Fi-Based Low-Cost Monitoring of ECG and Temperature Parameters Using Arduino and ThingSpeak;630
65.1;Abstract;630
65.2;1 Introduction;630
65.3;2 Materials and Methods;631
65.3.1;2.1 Measuring the Patient’s Body Temperature Using LM35;631
65.3.2;2.2 Measurement of Heartbeat Pulse;632
65.3.3;2.3 ECG Signal Acquisition and Filtering;633
65.3.4;2.4 Sending Data Through Wi-fi to a Server;634
65.4;3 Results and Discussion;636
65.5;4 Conclusion;638
65.6;References;639
66;62 Modelling of UPFC (Unified Power Flow Control) to Improve Stability of Power System by Real and Reactive Power Control of Transmission Line;640
66.1;Abstract;640
66.2;1 Introduction;640
66.3;2 Operating Principle of UPFC;641
66.4;3 UPFC Controller Design;642
66.5;4 Test System Modelling;644
66.6;5 Results and Discussion;644
66.7;6 Conclusion;648
66.8;References;648
67;63 Autonomous Navigation Robot Based on Real-Time Image Processing;649
67.1;Abstract;649
67.2;1 Introduction;650
67.3;2 IMAGE Acquisition and Object Recognition;650
67.4;3 Geometrical Measurements and Calculations;652
67.5;4 Methodology Used for Movement of Robot;654
67.6;5 Observations on Performance;656
67.7;6 Conclusion;660
67.8;References;661
68;64 The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance;662
68.1;Abstract;662
68.2;1 Introduction;663
68.3;2 Biclustering;663
68.4;3 Conclusion;668
68.5;References;669
69;65 Multimodal Classification of Arrhythmia and Ischemia Using QRS-ST Analysis;670
69.1;Abstract;670
69.2;1 Introduction;671
69.3;2 ECG Databases;672
69.4;3 QRS-ST Detection Methodologies;672
69.4.1;3.1 Difference Equation Operation ( {\varvec T}_{ {\rm max} } Detection);672
69.4.2;3.2 Search Interval Operation ( T_{{\rm onset}} Detection);675
69.5;4 Feature Extraction;676
69.5.1;4.1 RatioPS&PSD;676
69.5.2;4.2 Area Under a Curve (AUC) of QRS-ST;677
69.6;5 Classification Techniques;678
69.6.1;5.1 Linear Discriminant Analysis (LDA);678
69.6.2;5.2 Decision Tree;678
69.6.3;5.3 Feed-Forward Neural Networks;679
69.7;6 Discussion;681
69.8;7 Conclusion;681
69.9;References;682
70;66 Electromyogram (EMG) Signal Categorization in Parkinson’s Disease Tremor Detection by Applying MLP (Multilayer Perceptron) Technique: A Review;684
70.1;Abstract;684
70.2;1 Introduction;685
70.3;2 Related Research Works;686
70.4;3 Discussion;688
70.5;4 Conclusion;688
70.6;References;689
71;67 Performance Analysis of Gene Expression Data Using Mann–Whitney U Test;691
71.1;Abstract;691
71.2;1 Introduction;692
71.3;2 Related Work;693
71.3.1;2.1 Gene Expression Arrays;694
71.4;3 Results and Discussion;695
71.4.1;3.1 Algorithm;696
71.5;4 Conclusion;698
71.6;References;699
72;68 Comparative Analysis of Membrane Potential of Bone Cell and Its Abnormalities;700
72.1;Abstract;700
72.2;1 Introduction;700
72.3;2 Electrical Modeling of Human Cell;701
72.3.1;2.1 Electrical Modeling of Human Bone Cell (Osteoblast Cell);701
72.4;3 Abnormalities of Osteoblast Cell;702
72.4.1;3.1 Osteosarcoma;702
72.4.2;3.2 Electrical Modeling of Osteosarcoma Cell;702
72.5;4 Microelectrode;703
72.6;5 Result Analysis;703
72.7;6 Conclusion;706
72.8;References;706
73;69 Dimensionality Reduction of Facial Features to Recognize Emotion State;708
73.1;Abstract;708
73.2;1 Introduction;709
73.3;2 Motivation and Related Work;709
73.4;3 Emotion Recognition System;710
73.4.1;3.1 Feature Extraction Module;710
73.4.2;3.2 Dimensionality Reduction;711
73.5;4 Experimental Result and Performance Analysis;712
73.6;5 Conclusion;713
73.7;References;714
74;70 Electro-Optically Tunable Switching Action Enhanced by Long-Range Surface Plasmon;715
74.1;Abstract;715
74.2;1 Introduction;715
74.3;2 Proposed Scheme;716
74.4;3 Conclusion;718
74.5;References;719
75;71 Dual Six-Phase Multilevel AC Drive with Single Carrier Optimized Five-Level PWM for Star-Winding Configuration;720
75.1;Abstract;720
75.2;1 Introduction;721
75.3;2 Split-Phase Decomposition SVT;721
75.4;3 Five-Level Modulation Strategy;723
75.5;4 Numerical Simulation Results and Discussion;724
75.6;5 Conclusion;726
75.7;References;726