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Produktbild: Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems
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Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems Applications in Drone Navigation and Radar Sensing

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Beschreibung

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...

Beschreibung

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

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

18.07.2024

Abbildungen

XV, 214 p. 89 illus., 88 illus. in color.

Verlag Springer

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

18.07.2024

Abbildungen

XV, 214 p. 89 illus., 88 illus. in color.

Verlag Springer
Seitenzahl

214

Maße (L/B/H)

24,1/16/1,9 cm

Gewicht

466 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63564-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems

  • 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.