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Bio-Inspired Technologies for the Hardware of Adaptive Systems: Real-World Implementations and Applications Book

Bio-Inspired Technologies for the Hardware of Adaptive Systems: Real-World Implementations and Applications
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  • Bio-Inspired Technologies for the Hardware of Adaptive Systems: Real-World Implementations and Applications
  • Written by author Mircea Gh Negoita
  • Published by Springer-Verlag New York, LLC, March 2009
  • Evolvable Hardware (EHW) has emerged as a sub-domain of artificial evolution represented by a design methodology (consortium of methods) involving the application of Evolutionary Algorithms (EA) to the synthesis of digital and analogue electronic circuits
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1 Bio-Inspired Computational Intelligence for the Hardware of Adaptive Systems 1

1.1 Techniques of Computational Intelligence 1

1.2 Features and Classifications of Hybrid Intelligent Systems 9

1.3 Emergent Intelligent Technologies and the Adaptive Hardware Systems: AIS-A Technology for the Adaptive Systems 13

2 Advanced Hardware Implementation of the Computational Intelligence and Intelligent Technologies 27

2.1 Evolvable Hardware: An Overview 28

2.1.1 EHW Classification, Practical Engineering Remarks 30

2.1.2 EHW Technological Support (FPGA, FPAA, FPTA, FPMA, PsoC) 37

2.1.2.1 Introduction to Programmable Integrated Circuits 38

2.1.2.2 FPGA Families and Advanced Type of FPGA 40

2.1.2.3 Field Programmable Analog Arrays(FPAA) 44

2.1.2.4 Field Programmable Transistor Arrays (FPTA) Produced by NASA JPL 46

2.1.2.5 Practical Remarks on the Technological Support of EHW 48

2.1.3 EC Based Methods in EHW Implementation: EHW Architectures 50

2.1.4 An Application of GA for the Design of EHW Architectures 63

2.1.5 Global Remarks on Current Methods in EHW Technology and Its Prospectus 70

2.2 Hardware Implementation of the Artificial Immune Systems 73

2.3 Hardware Implementation of DNA Computing 75

2.4 Elements of Intercommunications Inside the AHS/EHW International Community (Conferences; Books; Journals; Elite Departments) 76

3 Bio-Inspired Analogue and Digital Circuits and Their Applications 83

3.1 Introduction 83

3.2 Genetic Algorithms for Analogue Circuits Design 84

3.2.1 GA as Tools to Design Analogue Circuits 84

3.2.2 Overview of the Genetic Algorithm 86

3.2.3 Representation 89

3.2.4 Analogue Applications with FPTA Cells 91

3.2.5Design Optimization of a CMOS Amplifier 92

3.2.5.1 Formulation of the Optimization Problem 92

3.2.5.2 Evaluation Engine 94

3.2.5.3 Optimization Engine 94

3.2.5.4 Design Optimization of a CMOS Amplifier 95

3.2.6 Evolving Software Models of Analogue Circuits 97

3.3 Evolutionary Design of Digital Circuits 99

3.3.1 Combinational Logic Circuits Evolutionary Design 99

3.3.2 Conventional Design Techniques for Arithmetic Adders and Multipliers 102

3.3.2.1 One Bit Full Adders 102

3.3.2.2 Parallel Processing Adder 105

3.3.2.3 CMOS Gates Full Adder 106

3.3.2.4 The Mirror Adder 107

3.3.2.5 Full Adder with CMOS Transmission Gates 107

3.3.2.6 Serial Processing Adder 109

3.3.2.7 Conventional Binary Multipliers 109

3.3.3 Arithmetic Circuits Designed with Evolutionary Algorithms 112

3.3.3.1 Full Adders Design 112

3.3.3.2 Gate-Level Evolutionary Design 114

3.3.3.3 Binary Multipliers Designed with Evolutionary Algorithms 116

3.3.4 Concluding Remarks on Digital Circuits Evolutionary Design 118

3.4 Reconfigurable Analogue Circuits in Mobile Communications Systems 119

3.4.1 Multi-standard Terminals for Mobile Telecommunications 119

3.4.2 Reconfigurable Multi-Standard Analogue Baseband Front-End Circuits in Mobile Communications Systems 122

3.4.3 Reconfigurable RF Receiver Architectures 125

3.4.3.1 Superheterodyne Receiver 125

3.4.3.2 Direct - Conversion Architectures 126

3.4.3.3 Low IF Architecture 127

3.4.3.4 Software Defined Radio 127

3.4.3.5 Digital - IF Receiver 129

3.4.4 Fully Reconfigurable Analogue Filters Design 129

3.4.5 Reconfigurable Filter Stage for a Combined Zero-IF/Low-IF Radio Architecture 131

3.4.5.1 Flexible Zero-IF/Low-IF Radio Architecture 131

3.4.5.2 Transconductor-Based Reconfigurable and Programmable Analogue Array 133

3.4.5.3 Modular Gm-C State-Variable "Leapfrog" Filters 135

3.4.5.4 Simulation Results 139

3.4.5.5 Conclusions 140

3.4.6 Variable Gain Amplifiers 141

3.4.7 Genetic Algorithms for Reconfigurable Analogue IF Filters Design 146

3.5 Biomedical Engineering Applications 148

3.5.1 Electrical Stimulation and Neural Prosthesis 148

3.5.2 Cochlear Prosthesis via Telemetric Link 150

3.5.3 Reconfigurable Circuits in Implantable Auditory Prosthesis 151

3.5.4 AGCs in Auditory Prosthesis 153

3.5.5 Binary Controlled Variable Gain Amplifiers 156

3.5.5.1 Introduction 156

3.5.5.2 Digitally Controlled Gain Amplifier with Current Mirrors 156

3.5.5.3 Current Division Network 160

3.5.5.4 Programmable Amplifiers with CDN 162

3.5.5.5 Digitally Controlled Current Attenuator 165

3.6 Concluding Remarks 168

References 169


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