03272nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001800153072001700171072002300188072001600211100007200227245021900299250001800518264006900536300006400605336002600669337002600695338003600721347002400757505042900781520094501210650003102155650003102186650004402217650004102261650002202302650002802324700007802352700007802430700008002508710003402588773002602622776003602648776003602684776003602720856004602756978-3-031-63565-6DE-He21320251030112636.0cr nn 008mamaa240718s2024    sz |    s    |||| 0|eng d  a97830316356567 a10.1007/978-3-031-63565-62doi 4aTK7867-7867.5 7aTJFC2bicssc 7aTEC0080102bisacsh 7aTJFC2thema1 aSafa, Ali.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aNeuromorphic Solutions for Sensor Fusion and Continual Learning Systemsh[electronic resource] :bApplications in Drone Navigation and Radar Sensing /cby Ali Safa, Lars Keuninckx, Georges Gielen, Francky Catthoor.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXV, 214 p. 89 illus., 88 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- 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.  aThis 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. 0aElectronic circuit design. 0aEmbedded computer systems. 0aCooperating objects (Computer systems).14aElectronics Design and Verification.24aEmbedded Systems.24aCyber-Physical Systems.1 aKeuninckx, Lars.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aGielen, Georges.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aCatthoor, Francky.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303163564908iPrinted edition:z978303163566308iPrinted edition:z978303163567040uhttps://doi.org/10.1007/978-3-031-63565-6