00000nam a22000008i 4500 UP-8028155945064369140 Buklod 20260327125702.0 a r |||| u| ta 260327s2023 nju r b|||| u|eng d 2022036520 9781119910657 (hardback) 9781119910664 (adobe pdf) 22755690 UL-0053 NCB 17,990.00 Rommel R. Real (Requesting faculty) BSDS-CSM DLC eng rda DLC eng TK6592.M55 S26 2023 TK 6592 M55 S26 2023 Santra, Avik author. Methods and techniques in deep learning advancements in mmwave radar solutions Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey, Infineon Technologies, Munich, Germany. Hoboken, New Jersey John Wiley & Sons, Inc. [2023] pages cm text txt rdacontent unmediated rdamedia volume nc rdacarrier Includes bibliographical references and index. "The advent of deep learning has transformed many fields and resulted in state-of-art solutions in computer vision, natural language processing and speech processing, etc. However, the application of deep learning algorithms to radars is still by and large at its nascent stage. A radar system consists of two parts: first, the radar hardware, including the RF transceiver, waveform generator, receiver unit, antenna and system packaging. State-of-art SiGe and CMOS are candidate technologies for mm-wave short-range radars and offer flexibility for integration and smaller form-factor. Second part is the sensing aspect, which relies on signal processing or deep learning algorithms that parses the radar return echo into meaningful target information facilitating a desired application"-- Provided by publisher. Deep learning (Machine learning) Millimeter wave radar Data processing. Radar receiving apparatus Data processing. Radar targets Identification Data processing. Deep Learning DS175 College of Science and Mathematics, UP Mindanao. Online version Santra, Avik. Methods and techniques in deep learning Hoboken, New Jersey : John Wiley & Sons, Inc., [2023] 9781119910664 (DLC) 2022036521. FO UPMIN UPMIN-MAIN TK 6592 M55 S26 2023 Book