This book shows you how to develop a hybrid mm-wave chipless Radio Frequency Identification (RFID) system, which includes chipless 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 Artificial Intelligence (AI) into detection and chipless tag signature printable on normal plastic substrates, instead of the conventional peak/nulls in the frequency tags. You'll learn how to incorporate new AI detection techniques along with cloud computing to lower costs. You'll 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 inverse synthetic-aperture radar (iSAR) techniques. Each chapter includes practical examples of design, and a QA section to answer the possible doubts in simpler phrases. With its emphasis on mm-wave band 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.