國立虎尾科技大學 |

Neuromorphic solutions for sensor fusion and continual learning systems = applications in drone navigation and radar sensing /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neuromorphic solutions for sensor fusion and continual learning systems/ by Ali Safa ... [et al.].
其他題名:	applications in drone navigation and radar sensing /
其他作者:	Safa, Ali.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xv, 214 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-031-63565-6
ISBN:	9783031635656

Neuromorphic solutions for sensor fusion and continual learning systems = applications in drone navigation and radar sensing /
Neuromorphic solutions for sensor fusion and continual learning systemsapplications in drone navigation and radar sensing /[electronic resource] :by Ali Safa ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xv, 214 p. :ill. (some col.), digital ;24 cm.

Introduction -- Bridging the accuracy gap between SNNs and DNNs via the use of pre-processing for radar applications -- Design of a drone platform for sensor fusion data acquisition -- A top-down approach to SNN-STDP networks -- Sensor-fusion SLAM with continual STDP learning -- Continually learning people detection from DVS data -- Active inference in Hebbian learning networks -- Conclusions and future work -- Appendix.

This book provides novel theoretical foundations and experimental demonstrations of Spiking Neural Networks (SNNs) in tasks such as radar gesture recognition for IoT devices and autonomous drone navigation using a fusion of retina-inspired event-based camera and radar sensing. The authors describe important new findings about the Spike-Timing-Dependent Plasticity (STDP) learning rule, which is widely believed to be one of the key learning mechanisms taking place in the brain. Readers will be enabled to create novel classes of edge AI and robotics applications, using highly energy- and area-efficient SNNs. Describes systematic design of SNN-STDP systems that significantly outperform prior works in terms of system accuracy; Presents the design of a first-of-its-kind SNN-STDP-based Simultaneous Localization and Mapping system for drones; Includes real examples of tasks such as radar-based gesture recognition and drone navigation.

ISBN: 9783031635656

Standard No.: 10.1007/978-3-031-63565-6doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Neuromorphic solutions for sensor fusion and continual learning systems = applications in drone navigation and radar sensing /
LDR:02412nam a2200325 a 4500 001 1134005
003 DE-He213
005 20240718130152.0
006 m d
007 cr nn 008maaau
008 241213s2024 sz s 0 eng d
020 $a 9783031635656 $q (electronic bk.)
020 $a 9783031635649 $q (paper)
024 7 $a 10.1007/978-3-031-63565-6 $2 doi
035 $a 978-3-031-63565-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a TJFC $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .N494 2024
245 0 0 $a Neuromorphic solutions for sensor fusion and continual learning systems $h [electronic resource] : $b applications in drone navigation and radar sensing / $c by Ali Safa ... [et al.].
260 $a Cham : $c 2024. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xv, 214 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Bridging the accuracy gap between SNNs and DNNs via the use of pre-processing for radar applications -- Design of a drone platform for sensor fusion data acquisition -- A top-down approach to SNN-STDP networks -- Sensor-fusion SLAM with continual STDP learning -- Continually learning people detection from DVS data -- Active inference in Hebbian learning networks -- Conclusions and future work -- Appendix.
520 $a This book provides novel theoretical foundations and experimental demonstrations of Spiking Neural Networks (SNNs) in tasks such as radar gesture recognition for IoT devices and autonomous drone navigation using a fusion of retina-inspired event-based camera and radar sensing. The authors describe important new findings about the Spike-Timing-Dependent Plasticity (STDP) learning rule, which is widely believed to be one of the key learning mechanisms taking place in the brain. Readers will be enabled to create novel classes of edge AI and robotics applications, using highly energy- and area-efficient SNNs. Describes systematic design of SNN-STDP systems that significantly outperform prior works in terms of system accuracy; Presents the design of a first-of-its-kind SNN-STDP-based Simultaneous Localization and Mapping system for drones; Includes real examples of tasks such as radar-based gesture recognition and drone navigation.
650 0 $a Neural networks (Computer science) $3 528588
650 0 $a Internet of things. $3 1023130
650 1 4 $a Electronics Design and Verification. $3 1387809
650 2 4 $a Embedded Systems. $3 1026431
650 2 4 $a Cyber-Physical Systems. $3 1387591
700 1 $a Safa, Ali. $3 1455314
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-63565-6
950 $a Engineering (SpringerNature-11647)