End-to-End Optimization of High-Density e-Skin Design: From Spiking Taxel Readout to Texture Classification-Reference-Cited by-同舟云学术

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End-to-End Optimization of High-Density e-Skin Design: From Spiking Taxel Readout to Texture Classification

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Wang Jiaqi¹,Alea Mark Daniel¹,Van Assche Jonah¹,Gielen Georges G.E.¹

Affiliation:

1. ESAT, KU,Leuven

Funder

KU Leuven

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10136870/10136706/10137077.pdf?arnumber=10137077

Reference19 articles.

1. VibTac-12: Texture Dataset Collected by Tactile Sensors;kursun;IEEE Dataport,2020

2. Comparison of spike encoding schemes in asynchronous vision sensors: Modeling and design

3. Improving the Accuracy of Spiking Neural Networks for Radar Gesture Recognition Through Preprocessing

4. Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions

Cited by 1 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. A multimodal tactile dataset for dynamic texture classification;Data in Brief;2023-10

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