The subject of this book is theory, principles and methods used in radar algorithm development with a special focus on automotive radar signal processing. In the automotive industry, autonomous driving is currently a hot topic that leads to numerous applications for both safety and driving comfort. It is estimated that full autonomous driving will be realized in the next twenty to thirty years and one of the enabling technologies is radar sensing. This book presents both detection and tracking topics specifically for automotive radar processing. It provides illustrations, figures and tables for the reader to quickly grasp the concepts and start working on practical solutions. The complete and comprehensive coverage of the topic provides both professionals and newcomers with all the essential methods and tools required to successfully implement and evaluate automotive radar processing algorithms.

Ultra-Wideband Radio (UWB) earmarks a new radio access philosophy and exploits several GHz of bandwidth. It promises high data rate communication over short distances as well as innovative radar sensing and localization applications with unprecedented resolution. Fields of application may be found, among others, in industry, civil engineering, surveillance and exploration, for security and safety measures, and even for medicine. The book considers the basics and algorithms as well as hardware and application issues in the field of UWB radio technology for communications, localization and sensing based on the outcome of DFG's priority-funding program "Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications (UKoLoS)".

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

This two-volume set LNCS 13069-13070 constitutes selected papers presented at the First CAAI International Conference on Artificial Intelligence, held in Hangzhou, China, in June 2021. Due to the COVID-19 pandemic the conference was partially held online. The 105 papers were thoroughly reviewed and selected from 307 qualified submissions. The papers are organized in topical sections on applications of AI; computer vision; data mining; explainability, understandability, and verifiability of AI; machine learning; natural language processing; robotics; and other AI related topics.

This fundamental work explains in detail systems for active safety and driver assistance, considering both their structure and their function. These include the well-known standard systems such as Anti-lock braking system (ABS), Electronic Stability Control (ESC) or Adaptive Cruise Control (ACC). But it includes also new systems for protecting collisions protection, for changing the lane, or for convenient parking. The book aims at giving a complete picture focusing on the entire system. First, it describes the components which are necessary for assistance systems, such as sensors, actuators, mechatronic subsystems, and control elements. Then, it explains key features for the user-friendly design of human-machine interfaces between driver and assistance system. Finally, important characteristic features of driver assistance systems for particular vehicles are presented: Systems for commercial vehicles and motorcycles.