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.
| Author/creator | Santra, Avik |
| Format | Electronic |
| Publication Info | Hoboken, New Jersey : John Wiley & Sons, Inc., [2023] |
| Description | 1 online resource |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Subjects |
| Abstract | "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. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Source of description | Description based on print version record and CIP data provided by publisher; resource not viewed. |
| Issued in other form | Print version: Santra, Avik. Methods and techniques in deep learning Hoboken, New Jersey : John Wiley & Sons, Inc., [2023] 9781119910657 |
| Genre/form | Electronic books. |
| LCCN | 2022036521 |
| ISBN | 9781119910671 (epub) |
| ISBN | 9781119910664 (adobe pdf) |
| ISBN | (hardback) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |