國立虎尾科技大學 |

Language: English

Back

Methods and techniques in deep learning : = advancements in mmwave radar solutions /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Methods and techniques in deep learning :/ Avik Santra ... [et al.]
Reminder of title:	advancements in mmwave radar solutions /
remainder title:	Advancements in mmwave radar solutions
other author:	Santra, Avik.
Published:	Hoboken, NJ :John Wiley & Sons, Inc., : c2023.,
Description:	xxiv, 312 p. :ill. ; : 24 cm.;
Subject:	Millimeter wave radar - Data processing. -
ISBN:	9781119910657 (hbk.) :

Methods and techniques in deep learning : = advancements in mmwave radar solutions /
Methods and techniques in deep learning :advancements in mmwave radar solutions /Advancements in mmwave radar solutionsAvik Santra ... [et al.] - Hoboken, NJ :John Wiley & Sons, Inc.,c2023. - xxiv, 312 p. :ill. ;24 cm.

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"--

ISBN: 9781119910657 (hbk.) :NT4752 Subjects--Topical Terms:

1423941
Millimeter wave radar
--Data processing.

LC Class. No.: TK6592.M55

Dewey Class. No.: 621.3848

Methods and techniques in deep learning : = advancements in mmwave radar solutions /
LDR:01597cam a2200241 a 4500 001 1111374
003 OCoLC
005 20230127113639.0
008 231218s2023 njua b 001 0 eng d
020 $a 9781119910657 (hbk.) : $c NT4752
020 $z 9781119910664 (adobe pdf)
020 $z 9781119910671 (epub)
035 $a (OCoLC)1346360047
035 $a 1346360047
040 $a DLC $b eng $e aacr2 $c DLC $d OCLCF $d IEEEE
042 $a pcc
050 0 0 $a TK6592.M55
082 0 0 $a 621.3848
245 0 0 $a Methods and techniques in deep learning : $b advancements in mmwave radar solutions / $c Avik Santra ... [et al.]
246 3 0 $a Advancements in mmwave radar solutions
260 # $a Hoboken, NJ : $b John Wiley & Sons, Inc., $c c2023.
300 $a xxiv, 312 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
520 # $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"-- $c Provided by publisher.
650 # 0 $a Millimeter wave radar $x Data processing. $3 1423941
650 # 0 $a Radar targets $x Identification $x Data processing. $3 1423942
650 # 0 $a Radar receiving apparatus $x Data processing. $3 1423943
650 # 0 $a Deep learning (Machine learning) $3 1381171
700 1 # $a Santra, Avik. $3 1423940