Methods and techniques in deep learning: advancements in mmWave radar solutions
"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 c...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Piscataway, NJ
IEEE Press
[2023]
Hoboken, New Jersey Wiley |
| Schlagworte: | |
| Zusammenfassung: | "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"-- |
| Beschreibung: | xxiv, 312 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119910657 111991065X |
Internformat
MARC
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| 245 | 1 | 0 | |a Methods and techniques in deep learning |b advancements in mmWave radar solutions |c Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey (Infineon Technologies, Munich, Germany) |
| 264 | 1 | |a Piscataway, NJ |b IEEE Press |c [2023] | |
| 264 | 1 | |a Hoboken, New Jersey |b Wiley | |
| 300 | |a xxiv, 312 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | 3 | |a "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"-- | |
| 653 | 0 | |a Millimeter wave radar / Data processing | |
| 653 | 0 | |a Radar targets / Identification / Data processing | |
| 653 | 0 | |a Radar receiving apparatus / Data processing | |
| 653 | 0 | |a Deep learning (Machine learning) | |
| 700 | 1 | |a Hazra, Souvik |e Verfasser |4 aut | |
| 700 | 1 | |a Servadei, Lorenzo |e Verfasser |4 aut | |
| 700 | 1 | |a Stadelmayer, Thomas |e Verfasser |4 aut | |
| 700 | 1 | |a Stephan, Michael |e Verfasser |4 aut | |
| 700 | 1 | |a Dubey, Anand |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 9781119910664 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 9781119910671 |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033949214 | ||
Datensatz im Suchindex
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| author | Santra, Avik Hazra, Souvik Servadei, Lorenzo Stadelmayer, Thomas Stephan, Michael Dubey, Anand |
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| id | DE-604.BV048573186 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:02:27Z |
| indexdate | 2024-07-10T09:41:52Z |
| institution | BVB |
| isbn | 9781119910657 111991065X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033949214 |
| oclc_num | 1352883062 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xxiv, 312 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | IEEE Press Wiley |
| record_format | marc |
| spelling | Santra, Avik Verfasser (DE-588)1259098281 aut 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) Piscataway, NJ IEEE Press [2023] Hoboken, New Jersey Wiley xxiv, 312 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier "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"-- Millimeter wave radar / Data processing Radar targets / Identification / Data processing Radar receiving apparatus / Data processing Deep learning (Machine learning) Hazra, Souvik Verfasser aut Servadei, Lorenzo Verfasser aut Stadelmayer, Thomas Verfasser aut Stephan, Michael Verfasser aut Dubey, Anand Verfasser aut Erscheint auch als Online-Ausgabe, PDF 9781119910664 Erscheint auch als Online-Ausgabe, EPUB 9781119910671 |
| spellingShingle | Santra, Avik Hazra, Souvik Servadei, Lorenzo Stadelmayer, Thomas Stephan, Michael Dubey, Anand Methods and techniques in deep learning advancements in mmWave radar solutions |
| title | Methods and techniques in deep learning advancements in mmWave radar solutions |
| title_auth | Methods and techniques in deep learning advancements in mmWave radar solutions |
| title_exact_search | Methods and techniques in deep learning advancements in mmWave radar solutions |
| title_exact_search_txtP | Methods and techniques in deep learning advancements in mmWave radar solutions |
| title_full | 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) |
| title_fullStr | 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) |
| title_full_unstemmed | 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) |
| title_short | Methods and techniques in deep learning |
| title_sort | methods and techniques in deep learning advancements in mmwave radar solutions |
| title_sub | advancements in mmWave radar solutions |
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