Springer Semi-physical Verification Technology for Dynamic Performance of Internet of Things System

This book combines semi-physical simulation technology with an Internet of Things (IOT) application system based on novel mathematical methods such as the Fisher matrix, artificial neural networks, thermodynamic analysis, support vector machines, and image processing algorithms. The dynamic testing and semi-physical verification of the theory and application were conducted for typical IOT systems such as RFID systems, Internet of Vehicles systems, and two-dimensional barcode recognition systems. The findings presented are of great scientific significance and have wide application potential for solving bottlenecks in the development of RFID technology and IOT engineering. The book is a valuable resource for postgraduate students in fields such as computer science and technology, control science and engineering, and information science. Moreover, it is a useful reference resource for researchers in IOT and RFID-related industries, logistics practitioners, and system integrators. From the Back Cover This book combines semi-physical simulation technology with an Internet of Things (IOT) application system based on novel mathematical methods such as the Fisher matrix, artificial neural networks, thermodynamic analysis, support vector machines, and image processing algorithms. The dynamic testing and semi-physical verification of the theory and application were conducted for typical IOT systems such as RFID systems, Internet of Vehicles systems, and two-dimensional barcode recognition systems. The findings presented are of great scientific significance and have wide application potential for solving bottlenecks in the development of RFID technology and IOT engineering. The book is a valuable resource for postgraduate students in fields such as computer science and technology, control science and engineering, and information science. Moreover, it is a useful reference resource for researchers in IOT and RFID-related industries, logistics practitioners, and system integrators. About the Author Xiaolei Yu obtained his doctorate degree in Electrical and Electronic Engineering as a joint student of the University of Melbourne and Nanjing University of Aeronautics and Astronautics in 2011. He is now an Assistant Professor at Nanjing University of Aeronautics and Astronautics as well as the Chief Executive Engineer of China National RFID Product Quality Supervision and Testing Center (Jiangsu). His research interests include intelligent sensor networks, signal detection technology, adaptive algorithms, and image processing algorithms. He holds 15 patents and has published more than 40 academic papers, including 20 SCI papers in leading academic journals and international conferences.