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On the Use of Spiking Neural Networks for Ultralow-Power Radar Gesture Recognition

  • Published:2021 Issue: Volume: Page:1-4
  • ISSN:1531-1309
  • Container-title:IEEE Microwave and Wireless Components Letters
  • language:
  • Short-container-title:IEEE Microw. Wireless Compon. Lett.

Author:

Safa AliORCID,Bourdoux AndreORCID,Ocket IljaORCID,Catthoor Francky,Gielen Georges G. E.ORCID

Funder

Onderzoeksprogramma Artificiële Intelligentie AI Vlaanderen Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An ultra low power spiking neural encoder of microwave signals;Solid-State Electronics;2024-06

2. Learned Spike Encoding of the Channel Response for Low-Power Environment Sensing;2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops);2024-03-11

3. Benchmarking of computer vision methods for energy-efficient high-accuracy olive fly detection on edge devices;Multimedia Tools and Applications;2024-03-07

4. RT-SCNNs: real-time spiking convolutional neural networks for a novel hand gesture recognition using time-domain mm-wave radar data;International Journal of Microwave and Wireless Technologies;2024-01-09

5. A Hardware and Software Co-Design for Energy-Efficient Neural Network Accelerator With Multiplication-Less Folded-Accumulative PE for Radar-Based Hand Gesture Recognition;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024
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