Kirjojen hintavertailu – 12 903 724 kirjaa ja 27 kauppaa

Kirjailija

Lars Keuninckx

Kirjat ja teokset yhdessä paikassa: 2 kirjaa, julkaisuja vuosilta 2024–2025, suosituimpiin kuuluu Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems. Vertaile teosten hintoja ja tarkista saatavuus suomalaisista kirjakaupoista.

2 kirjaa

Kirjojen julkaisuvuodet: 2024–2025.

Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems

Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems

Ali Safa; Lars Keuninckx; Georges Gielen; Francky Catthoor

Springer International Publishing AG
2025
nidottu
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
Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems

Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems

Ali Safa; Lars Keuninckx; Georges Gielen; Francky Catthoor

Springer International Publishing AG
2024
sidottu
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