RFID系统物理防碰撞:理论与实践(英文版)

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作者：俞晓磊，赵志敏，章学周

出版社：科学出版社

本类榜单：工业技术

分类：工业技术 > 电子通信 > 一般性问题

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图文详情

ISBN：9787030674081
装帧：一般胶版纸
册数：暂无
重量：暂无
开本：B5
页数：276
出版时间：2021-06-01
条形码：9787030674081 ; 978-7-03-067408-1

本书特色

创新性强，创新性提出提出物理防碰撞理论、方法和硬件平台验证.

内容简介

本书以物理学基本原理（包括电磁学、光学、热力学等）为基础，以工程数学方法进行检测控制算法设计为核心，创新性地将半物理验证与检测技术应用于射频识别（RFID）系统动态性能测试的科学实践中，提出物理防碰撞新理论、新方法以及基于光电传感技术构建半物理硬件平台的相关试验验证方法，将为物联网系统在智慧物流、车联网、食品溯源与防伪等民生领域的应用提供重要的理论和技术支撑。本书共有六章，分别论述了RFID系统防碰撞基本概念和软件防碰撞、物理防碰撞等技术研究进展、RFID系统物理防碰撞的实验验证系统搭建、RFID系统物理防碰撞的物理学基础、图像学基础、优化算法与RFID系统物理防碰撞、深度学习与RFID系统物理防碰撞。

Contents
1 Overview of RFID system anti-collision technology 1
1.1 RFID collision problem and research progress of anti-collision 1
1.2 RFID system anti-collision technology 10
1.2.1 Information channel and channel capacity 11
1.2.2 Software anti-collision 18
1.2.3 Physical anti-collision 25
1.3 Physical anti-collision 29
1.3.1 Definition of physical collision avoidance 29
1.3.2 The main features of physical collision avoidance 29
1.3.3 The structure of the physical anti-collision system 29
References 30
2 RFID system physical anti-collision experimental verification 34
2.1 RFID tag group performance measurement 35
2.1.1 Tag group sensitivity 35
2.1.2 Measurement system designed 36
2.1.3 Single tag sensitivity measurement 40
2.1.4 Tag group sensitivity measurement 45
2.2 Image feature matching experiment of RFID system physical anti-collision 51
2.2.1 Image feature matching based on SIFT algorithm 52
2.2.2 Image feature matching based on SURF algorithm 56
2.2.3 Image feature matching based on ORB algorithm 58
2.2.4 Algorithm comparison results and analysis 61
2.2.5 Image feature matching 62
2.3 Conclusion 66
References 66
3 Physical theory of RFID system physical anti-collision 68
3.1 Thermodynamic analysis of physical anti-collision — Research on the effect of temperature on tag performance 68
3.1.1 Fundamental principles 70
3.1.2 Design of the experimentation system 73
3.1.3 Experimental method and result analysis 77
3.2 Electromagnetism analysis of physical anti-collision—Research on the effect of humidity and salt fog on tag performance 82
3.2.1 Effect of humidity on the moving identifying performance of UHF RFID System 82
3.2.2 Effect of salt mist on the moving identifying performance of UHF RFID System 93
3.3 Kinetic analysis of physical anti-collision — Fisher informationmatrix 104
3.3.1 Channel model 106
3.3.2 Computer simulation and analysis 109
3.3.3 Antenna selection techniques 111
3.4 Conclusion 118
References 118
4 Image theory of RFID system physical anti-collision 123
4.1 Tag distribution based on image matching 123
4.1.1 Algorithm design 124
4.1.2 Experiment 126
4.2 Image processing in multi-tag movement 131
4.2.1 Image deblur theory 131
4.2.2 Estimation of image blurred angle based on Gabor filter 134
4.2.3 Estimation of image blurred length based on generalized regression neural network (GRNN) 136
4.2.4 De-motion blur analysis and analysis 141
4.3 RFID tag positioning method 144
4.3.1 Image matching overview 144
4.3.2 Image threshold segmentation technology 146
4.3.3 Edge detection algorithm 147
4.4 3D space RFID tag location 152
4.5 A novel reverse design method of tag antenna based on image analysis 157
4.5.1 Introduction 157
4.5.2 Design of UHF RFID dipole tag antenna 159
4.5.3 UHF RFID tag bend dipole antenna analysis 163
4.6 Conclusion 173
References 174
5 Optimization algorithm and RFID system physical anti-collision 176
5.1 Physical anti-collision based on Particle Swarm Optimization (PSO) 177
5.1.1 Design and application of detection system 177
5.1.2 Establishment of 3D tag network based on template matching and edge detection 180
5.1.3 Prediction of tag distribution based on PSO neural network 186
5.2 Physical anti-collision based on Support Vector Machine (SVM) 191
5.2.1 RFID detection system 191
5.2.2 Position location of tags 197
5.2.3 Predict model of RFID tags’ distribution based on SVM 199
5.3 Physical anti-collision based on Wavelet 203
5.3.1 Design of image analysis system 203
5.3.2 3D coordinate measurement of RFID tag 206
5.3.3 RFID multi-tag wavelet neural network 214
5.4 Conclusion 219
References 220
6 Deep learning and RFID system physical anti-collision 223
6.1 RFID multi-tag 3D measurement system 224
6.1.1 System architecture 224
6.1.2 Image process module 227
6.2 The theory RFID multi-tag image denoising by FDnCNN 230
6.2.1 The proposed FDnCNN network architecture 230
6.2.2 Noise level map 232
6.2.3 Loss function 234
6.2.4 Experimental result and real-time analysis 234
6.3 Image motion blur removal 240
6.4 Multi-level wavelet-CNN for image restoration in pre-processing sub-system 244
6.4.1 Image restoration based on denoising prior 244
6.4.2 Half Quadratic Splitting (HQS) 245
6.4.3 Method from multi-level WPT to MWCNN 246
6.4.4 Network architecture 247
6.5 Multi-tag 3D position measurement based on image match 251
6.5.1 The image matching method 251
6.5.2 RFID tag 2D position measurement 251
6.5.3 RFID tag vertical position measurement 253
6.6 Nonlinear modeling method based on DBN 255
6.6.1 Restricted Boltzmann Machine 255
6.6.2 DBN 256
6.6.3 RFID tag group model based on DBN 257 <>

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节选

1 Overview of RFID system anti-collision technology 　　This book is based on the basic principles of physics (including electromagnetics， optics， thermodynamics， etc.)， with engineering mathematical methods as the core of detection and control algorithm design. Furthermore， this book innovatively applies semi-physical verification and detection technology to the dynamic performance testing of radio frequency identification (RFID) systems. This book proposes a series of new theories and methods for physical collision prevention， as well as related test verification methods for building semi-physical hardware platforms based on photoelectric sensing technology， which will provide important theories and technical support for the applications of internet of things (IoT) systems in smart logistics， car networking， food traceability， anti-counterfeiting， and other livelihood fields. The main contributions of this book are as follows: 　　(1) It introduces a new theory of physical anti-collision for improving the integral performance of RFID multi-tag system. 　　(2) It studies the principles and optimization algorithms of innovative multi-tag network topologies， which enhance the sensing capability of the RFID system. 　　(3) It provides valuable guidance for RFID system physical anti-collision based on deep learning. 　　(4) It combines physical and electrical expertise from an interdisciplinary standpoint towards the analysis and design of dynamic performance testing systems. 　　1.1 RFID collision problem and research progress of anti-collision 　　Internet of things (IoT) as a new technology has developed rapidly in recent years， which is an important part of a new generation of information systems. The emergence of IoT is another information technology revolution after computers， the Internet， and mobile communications [1]. As one of the core technologies in the field of perception of IoT， radio frequency identification (RFID) technology is a non-contact automatic identification technology that emerged and developed rapidly in the 1990s. It uses radio waves by induction or electromagnetic wave radiation. The non-contact two-way communication achieves the purpose of automatically identifying target objects， obtaining relevant data， and data exchange. Internationally， RFID systems have been widely used in logistics supply chains， transportation， electronic tickets， production and assembly， anti-counterfeiting and anti-theft， smart agriculture， environmental protection， military defense， and many other fields [2-5]. 　　Major developed countries and regions in the world have successively carried out research on RFID and IoT technology. The National Development and Reform Commission of China included RFID in the first batch of national key industry adjustment and revitalization plans in the electronic information field. In recent years， China has successively carried out the research and development and industrialization of RFID-related technologies， and has initially established a certain foundation in RFID and IoT technology research and development， standard development， industry cultivation， and industry applications. At present， although the annual scale of China’s RFID technology market exceeds RMB 10 billion， it ranks third in the world， after the United Kingdom and the United States. However， compared with existing foreign technologies， China’s RFID technology foundation is relatively weak compared with developed countries. There is still a gap， especially in the research of chips， tags， and sensor networks compared with developed countries. After years of development， the accuracy and performance stability of low-frequency and high-frequency RFID-related products in China have reached the international level， in UHF and microwave frequency bands， many studies are still in the preliminary explorations and laboratory research stages. Moreover， there is a big gap between indicators such as the sensitivity and reliability of the performance and foreign products. The immaturity of these technologies limits the pace of RFID development， and has become a hot spot and difficulty for breakthroughs in the research of RFID technology in the frequency bands above UHF. With the continuous rapid development of China’s economy and the continuous upgrading of the industrial structure， in the large-scale intelligent and information high-tech industry， there are several key technological problems of RFID systems above the UHF frequency band that urgently need to be solved in the field of RFID application promotion. The obvious asymmetry phenomenon of advanced technology and weak basic research has severely restricted the development of RFID and IoT engineering disciplines. 　　Since the data transmission of the RFID system is a weak signal wireless communication， the integrity and accuracy of the data will be interfered by the external environment. The actual communication environment (multipath interf

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