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Soft Computing in Electromagnetics: Methods and Applications [Kõva köide]

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  • Formaat: Hardback, 215 pages, kõrgus x laius x paksus: 248x188x18 mm, kaal: 680 g
  • Ilmumisaeg: 14-Jan-2016
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107122481
  • ISBN-13: 9781107122482
Teised raamatud teemal:
Soft Computing in Electromagnetics: Methods and ApplicationsSuurem pilt
  • Formaat: Hardback, 215 pages, kõrgus x laius x paksus: 248x188x18 mm, kaal: 680 g
  • Ilmumisaeg: 14-Jan-2016
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107122481
  • ISBN-13: 9781107122482
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781107122482.html
Märksõnad:
Soft computing techniques are emerging as an important tool in solving design, performance and optimisation problems in electromagnetics. Soft Computing in Electromagnetics offers detailed discussion on the application of soft computing concepts in the field of metamaterial antennas, radar absorbers, transmission line characterisation and optimised radar absorbing material (RAM) and introduces implementation of soft computing tools in a relatively new area of metamaterials. The soft computing methods are used to optimise fault detection, electromagnetic propagation and path loss detection. The development of two CAD packages for design of metamaterial split ring resonators (SRR) and path-loss prediction is discussed. The concepts are explained with the help of algorithms and the corresponding software codes. Numerical examples and MATLAB codes are provided throughout the text to facilitate understanding.

Muu info

This book covers approaches to solving various complex electromagnetic problems through the novel route of soft computing.
List of Figures xiii
List of Tables xvii
Preface xix
Acknowledgments xxi
Abbreviations xxiii
Symbols xxv
1 Introduction 1(8)
1.1 Design and Optimization Scenarios
1(2)
1.1.1 Engineering applications
2(1)
1.1.2 Medical applications
2(1)
1.1.3 Finance
3(1)
1.1.4 Humanities and social sciences
3(1)
1.2 Electromagnetic Design Challenges
3(1)
1.2.1 Fabrication sensitivity
4(1)
1.2.2 Material sensitivity
4(1)
1.3 Objectives and Scope
4(1)
1.4 Organization of the Book
5(2)
1.5 Summary
7(1)
References
7(2)
2 Soft Computing Techniques 9(36)
2.1 Artificial Neural Networks
10(8)
2.1.1 Concept of ANN
10(4)
2.1.2 Back-propagation algorithm
14(3)
2.1.3 Matlab code for ANN
17(1)
2.2 Genetic Algorithm (GA)
18(8)
2.2.1 Overview
18(1)
2.2.2 Terminologies of GA
18(3)
2.2.2.1 Reproduction or selection
19(1)
2.2.2.2 Crossover
20(1)
2.2.2.3 Mutation
20(1)
2.2.3 Matlab code for GA
21(5)
2.3 Particle Swarm Optimization (PSO)
26(8)
2.3.1 Basic concept of PSO
26(5)
2.3.1.1 Binary PSO and real valued PSO (RPSO)
27(2)
2.3.1.2 Single objective PSO and multi-objective PSO
29(2)
2.3.2 Matlab code of PSO
31(3)
2.4 Bacterial Foraging Optimization
34(8)
2.4.1 Basic concept
34(1)
2.4.2 Terminologies in BFO
34(2)
2.4.2.1 Chemotaxis
35(1)
2.4.2.2 Swarming
36(1)
2.4.2.3 Reproduction
36(1)
2.4.2.4 Elimination and dispersal
36(1)
2.4.3 Algorithm of BFO
36(4)
2.4.4 Matlab code for BFO
40(2)
2.5 Summary
42(1)
References
43(2)
3 Soft Computing in Electromagnetics: A Review 45(20)
3.1 Overview
45(1)
3.2 Radar Absorbers
46(2)
3.3 Frequency Selective Surfaces
48(1)
3.4 Antenna Design and Optimization
49(4)
3.4.1 Antenna miniaturization
49(1)
3.4.2 Antenna pattern synthesis
50(2)
3.4.3 Performance enhancement
52(1)
3.5 Metamaterial Structures
53(4)
3.6 Invisibility Cloaks
57(1)
3.7 Microwave Devices
58(1)
3.8 Summary
58(1)
References
59(6)
4 Bacterial Foraging Optimization For Metamaterial Antennas 65(19)
4.1 Overview
65(2)
4.2 Challenges in Metamaterial Antenna design
67(1)
4.3 BFO for Metamaterial Antenna Design
67(14)
4.3.1 Multiband metamaterial fractal antenna
68(8)
4.3.1.1 Fractal antenna design
69(1)
4.3.1.2 Performance enhancement using BFO
70(6)
4.3.2 Mutual coupling reduction
76(9)
4.3.2.1 Design of microstrip antenna array
77(1)
4.3.2.2 Mutual coupling reduction using metamaterial
78(3)
4.4 Summary
81(1)
Reference
81(3)
5 PSO for Radar Absorbers 84(27)
5.1 Introduction
84(1)
5.2 Types of Radar Absorbers
85(1)
5.2.1 Salisbury screen
85(1)
5.2.2 Magnetic absorbers
86(1)
5.2.3 Dallenbach layer
86(1)
5.2.4 Circuit analog RAM
86(1)
5.2.5 Jaumann absorber
86(1)
5.3 Radar Absorber Design Procedure
86(1)
5.4 PSO for Design Optimization
87(8)
5.4.1 Jaumann absorber optimization
88(2)
5.4.2 Multilayer RAM optimization
90(5)
5.5 Challenges and Issues in Conventional Absorber
95(1)
5.6 Microwave Metamaterial Absorber
96(4)
5.6.1 Overview
96(1)
5.6.2 Design of microwave metamaterial absorber
97(2)
5.6.3 PSO implementation
99(1)
5.6.4 Simulation results and discussion
99(1)
5.7 Terahertz Absorber Design for Biomedical Application
100(7)
5.7.1 Overview
100(1)
5.7.2 Biomedical spectroscopy system
101(2)
5.7.3 Design of metamaterial based terahertz absorber
103(1)
5.7.4 Performance enhancement using PSO
104(2)
5.7.5 Simulation results and discussion
106(1)
5.8 Summary
107(1)
References
108(3)
6 Characterization of Planar Transmission Lines Using ANN 111(13)
6.1 Planar Transmission Line
112(1)
6.1.1 Microstrip lines
112(1)
6.1.2 Slot line transmission line
113(1)
6.2 ANN Implementation
113(2)
6.2.1 Generation of data
114(1)
6.2.2 Training of the neural network
114(1)
6.2.3 Testing
114(1)
6.3 Analysis and Design of Microstrip Transmission Line
115(4)
6.3.1 Analysis of microstrip line
115(2)
6.3.2 Design of microstrip line
117(2)
6.4 Analysis and Design of Slotline
119(4)
6.4.1 Analysis of slotline
119(2)
6.4.2 Design of slotline
121(2)
6.5 Summary
123(1)
References
123(1)
7 Fault Detection in Antenna Arrays 124(31)
7.1 Preliminaries and Overview
124(1)
7.2 Artificial Neural Network for Array Fault Detection
125(8)
7.2.1 Antenna array design
127(2)
7.2.2 ANN implementation
129(2)
7.2.3 Results
131(2)
7.3 PSO for Array Fault Detection
133(9)
7.3.1 PSO implementation
133(4)
7.3.2 Results and discussion
137(5)
7.4 BFO for Array Fault Finding
142(5)
7.4.1 BFO implementation
142(2)
7.4.2 Results and discussion
144(3)
7.5 Hybrid Technique
147(3)
7.6 Summary
150(1)
References
151(4)
8 Multi-Objective Particle Swarm Optimization for Active Terahertz Devices 155(27)
8.1 Introduction to Terahertz Technology
155(3)
8.1.1 Properties of terahertz spectrum
156(1)
8.1.2 Applications
156(1)
8.1.2.1 Space platform
156(1)
8.1.2.2 Security
157(1)
8.1.2.3 Biomedical field
157(1)
8.1.3 Challenges of terahertz technology
157(1)
8.1.3.1 Material issues
157(1)
8.1.3.2 Design issues
158(1)
8.1.3.3 Fabrication issues
158(1)
8.1.3.4 Characterization issues
158(1)
8.2 Trends in Active Terahertz Devices
158(3)
8.2.1 MEMS based tuning
159(1)
8.2.2 Photo excitation
160(1)
8.2.3 Electrical actuation
161(1)
8.2.4 Thermal actuation
161(1)
8.3 Design of Terahertz Device
161(7)
8.3.1 Design of terahertz absorber
163(3)
8.3.2 Performance enhancement analysis
166(2)
8.4 Soft Computing for Performance Enhancement
168(3)
8.4.1 MOPSO based computational engine
168(1)
8.4.2 High performance ultra-thin absorber
169(2)
8.5 Soft Computing for Active Terahertz Absorber
171(6)
8.5.1 Selection of tuning mechanism
171(2)
8.5.2 Implementation of tuning mechanism
173(2)
8.5.3 PSO for design of active absorber array
175(7)
8.5.3.1 Design procedure
176(1)
8.5.3.2 Concept of adaptive tuning
177(1)
8.6 Fabrication Sensitivity Analysis
177(1)
8.7 Summary
178(1)
References
179(3)
9 Soft Computing based CAD Packages for EM Applications 182(23)
9.1 CAD Package for Metamaterial Structures
182(13)
9.1.1 Equivalent circuit analysis of square SRR
183(4)
9.1.2 Equivalent circuit analysis of circular SRR
187(2)
9.1.3 Development of CAD package using PSO
189(1)
9.1.4 Optimization of metamaterial structures
190(3)
9.1.4.1 Square SRR
191(1)
9.1.4.2 Circular SRR
192(1)
9.1.4.3 Comparison of PSO and GA
193(1)
9.1.5 Applications of the CAD package
193(2)
9.2 Path Loss Prediction in Urban and Rural Environment
195(6)
9.2.1 Overview
195(1)
9.2.2 Propagation model and path loss prediction
196(1)
9.2.3 CAD Package using ANN
196(2)
9.2.3.1 Generation of data
197(1)
9.2.3.2 Training of the ANN
197(1)
9.2.3.3 Testing
197(1)
9.2.4 CAD model
198(1)
9.2.5 Results and discussion
199(2)
9.3 Summary
201(1)
References
201(4)
Author Index 205(8)
Subject Index 213
Balamati Choudhury has been a scientist at the Centre for Electromagnetics at the CSIR-National Aerospace Laboratories, Bangalore, since January 2008. She obtained her PhD in Microwave Engineering from Biju Patnaik University of Technology (BPUT), Rourkela, Orissa. From 2006 to 2008, she worked as a Senior Lecturer at the National Institute of Science and Technology, Orissa. Her areas of interest include soft computing techniques in electromagnetic design and optimisation, computational electromagnetics for aerospace applications and metamaterial design applications and RF and microwaves. She was the recipient of the 20132014 CSIR-NAL Young Scientist Award for her contribution in the area of computational electromagnetics for aerospace applications. She has published a number of articles in national and international journals. Rakesh Mohan Jha is currently Chief Scientist and Head at the Centre for Electromagnetics at the CSIR-National Aerospace Laboratories, Bangalore. He obtained his PhD in Engineering from the Department of Aerospace Engineering at the Indian Institute of Science, Bangalore, in 1989 in the area of computational electromagnetics for aerospace applications. Dr Jha was a SERC (UK) Visiting Post-Doctoral Research Fellow for one year at the University of Oxford's Department of Engineering Science in 1991. He worked as an Alexander von Humboldt (AvH) Fellow at the Institute for High Frequency Techniques and Electronics of the University of Karlsruhe, Germany, from 1992 to 1993, and more recently in 2007 on AvH Reinvitation. He was awarded the Sir C. V. Raman Award for Aerospace Engineering for the year 1999. He was elected a Fellow of INAE (FNAE) in 2010 for his contributions to the EM applications to aerospace engineering. He has published a number of research papers in international journals.