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E-grāmata: Chipless RFID Systems Using Advanced Artificial Intelligence

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  • Formāts: 240 pages
  • Izdošanas datums: 31-Jan-2023
  • Izdevniecība: Artech House Publishers
  • ISBN-13: 9781630819491
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Chipless RFID Systems Using Advanced Artificial IntelligencePalielināt
  • Formāts: 240 pages
  • Izdošanas datums: 31-Jan-2023
  • Izdevniecība: Artech House Publishers
  • ISBN-13: 9781630819491
Citas grāmatas par šo tēmu:

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This book shows you how to develop a hybrid mm-wave chipless Radio Frequency Identification (RFID) system, which includes chip-less tag, reader hardware, and detection algorithm that use image processing and machine learning (ML) techniques. It provides the background and information you need to apply the concepts of AI into detection and chip-less tag signature printable on normal plastic substrates, instead of the conventional peak/nulls in the frequency tags. Youll learn how to incorporate new AI detection techniques along with cloud computing to lower costs. Youll also be shown a cost-effective means of image construction, which can lower detection errors. The book focuses on side-looking-aperture-radar (SLAR) with a combination of deep learning to provide a much safer means of chipless detection than the current iSAR technique. Each chapter includes practical examples of design. With its emphasis on mm-waveband and the practical side of design and engineering of the chipless tags, reader and detection algorithms, this is an excellent resource for industry engineers, design engineers and university researchers.
Preface xi
1 Introduction
1(18)
1.1 Overview Model of RFID
2(1)
1.2 Different Types of RFID
3(1)
1.3 Different Types of Chipless RFID
4(4)
1.4 Market Aspects for Chipless RFID
8(1)
1.5 RFID Frequency Spectrum
9(2)
1.6 Challenges in Implementing Chipless RFID in the mm-Wave Spectrum
11(4)
1.7 Book Outline
15(4)
References
16(3)
2 Chipless Tag Design
19(48)
2.1 Introduction
19(3)
2.2 Chipless RFID Tags
22(15)
2.2.1 Time-Domain Tags
22(2)
2.2.2 Frequency-Domain Tags
24(1)
2.2.3 Image-Based Tags
25(3)
2.2.4 Letter-Based Tags
28(5)
2.2.5 Screen Printing for the Chipless Tags
33(1)
2.2.6 Screen Printing Experimental Observations
34(3)
2.3 Letter-Based Tag Design
37(6)
2.3.1 Effect of Substrate on Backscattered Signal
37(2)
2.3.2 Encoding Capacity Considerations
39(1)
2.3.3 Tag Design Based on the Peyote Alphabet
40(2)
2.3.4 Peyote Tag Frequency Response
42(1)
2.4 Backscattering Theory and RCS Calculations
43(6)
2.5 Tag Performance Simulations
49(7)
2.5.1 Tag Design Improvement
51(1)
2.5.2 Discussion of the Results
52(4)
2.6 Tag Response Measurements
56(3)
2.7 Conclusions
59(1)
2.8 Tag Design Questions and Answers
60(7)
References
61(6)
3 Chipless Reader Design
67(46)
3.1 Introduction
67(3)
3.2 Chipless RFID Readers
70(2)
3.2.1 Frequency-Based Readers
70(1)
3.2.2 Image-Based Readers
71(1)
3.3 A 60-GHz System Block Diagram
72(5)
3.3.1 Maximum Reader Power and Link Budget Calculations
73(2)
3.3.2 Maximum Reading Distance Calculations
75(2)
3.4 60-GHz TX/RX Boards
77(1)
3.5 Designing and Integration: RF, IF, Controller, and Peripheral Circuits
78(22)
3.5.1 60-GHz Transmitter/Receiver
80(7)
3.5.2 Local Voltage-Controlled Oscillator
87(2)
3.5.3 Gain/Phase Comparator
89(3)
3.5.4 Digital Control Board
92(5)
3.5.5 Peripheral Circuits
97(3)
3.6 Reader Characterization
100(4)
3.6.1 Scanning Time and Frequency Resolution Calculations
100(2)
3.6.2 RCS Calibrations
102(2)
3.7 Conclusions
104(1)
3.8 Chipless Reader Questions and Answers
105(8)
References
109(4)
4 Tag Decoding
113(50)
4.1 Introduction
113(2)
4.2 Machine Learning and Pattern Recognition
115(11)
4.2.1 Tag Decoding Using Feedforward Networks and Backpropagation
116(1)
4.2.2 Feedforward Concept
117(3)
4.2.3 Support Vector Machines
120(1)
4.2.4 KNN as a Lazy Learner
120(3)
4.2.5 Decision Trees Ensembles
123(1)
4.2.6 Deep Learning Methods and Frameworks
124(2)
4.2.7 Machine Learning in Chipless RFID and the Gaps
126(1)
4.3 Data Collection Methodology
126(5)
4.3.1 Data Collection in the Simulations
128(2)
4.3.2 Data Collection in the Experiments
130(1)
4.4 Using Feedforward Networks
131(4)
4.4.1 Feedforward Network Results
132(3)
4.5 Using Pattern Recognition Methods
135(7)
4.5.1 Pattern Recognizer Results
137(5)
4.6 Using CW-SLAR Imaging
142(11)
4.6.1 One-Port VNA
147(3)
4.6.2 Two-Port Reader
150(1)
4.6.3 Computational Costs
150(1)
4.6.4 Tag Imaging and Experimental Results
151(2)
4.7 A Reliable Tag Decoder Architecture
153(2)
4.8 Conclusions
155(1)
4.9 Chipless Tag Decoding Questions and Answers
156(7)
References
157(6)
5 Cloud-Based Deep Learning
163(24)
5.1 Introduction
163(1)
5.2 Cloud Computing Considerations
164(5)
5.2.1 Cloud Computing Challenges
167(2)
5.3 Cloud Computing Hardware Architecture
169(4)
5.3.1 IaaS Model
170(1)
5.3.2 SaaS Model
171(2)
5.4 Deep Learner in Action
173(5)
5.4.1 2-D Image Representation of 1-D Frequency Data
173(1)
5.4.2 Data Augmentation
174(2)
5.4.3 Deep Learner Structure
176(1)
5.4.4 Deep Learning Results
177(1)
5.5 A Reliable Reader Based on Cloud Deep Learning
178(4)
5.6 Conclusions
182(1)
5.7 Cloud-Based Deep Learning Questions and Answers
183(4)
References
184(3)
6 Conclusions
187(6)
6.1 Conclusions
187(3)
6.2 Fulfilling Research Goals
190(1)
6.3 Recommendations for Future Work
191(2)
References
191(2)
Appendix A Code Listing 193(12)
Appendix B PCB Layout 205(2)
List of Acronyms 207(6)
About the Authors 213(2)
Index 215
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