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| Presentation | 2021-10-28 15:05 Enhancement of spatio-temporal coding performance in spiking neural network and its application to hazard detection for landing of spacecrafts Hideaki Kinoshita, Shinichi Kimura (TUS), Seisuke Fukuda (JAXA)  NC2021-21 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Spiking neural networks (SNNs) are a neuromimetic computational architecture that has attracted much attention in recent years for its low power consumption in edge devices， and is expected to be applied to onboard processing of spacecrafts． SNNs have a higher coding capability in the time domain than artificial neural networks (ANNs) because each neuron can retain its own dynamic properties． In this study， we propose a method to improve the spatio-temporal coding performance by adding a gate with convolutional structure to the spiking neural unit proposed by Wozniak． We also present the results of the verification of the effectiveness and power consumption of the method， using the obstacle detection in the landing of a spacecraft as an example． | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Spiking Neural Network / LiDAR / semantic segmentation / spacecraft navigation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 223, NC2021-21, pp. 16-21, Oct. 2021. | |
| Paper # | NC2021-21 | |
| Date of Issue | 2021-10-21 (NC) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NC2021-21 |
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| Conference Information | |
| Committee | MBE NC |
| Conference Date | 2021-10-28 - 2021-10-29 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2021-10-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Enhancement of spatio-temporal coding performance in spiking neural network and its application to hazard detection for landing of spacecrafts |
| Sub Title (in English) | |
| Keyword(1) | Spiking Neural Network |
| Keyword(2) | LiDAR |
| Keyword(3) | semantic segmentation |
| Keyword(4) | spacecraft navigation |
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| 1st Author's Name | Hideaki Kinoshita |
| 1st Author's Affiliation | Tokyo University of Science (TUS) |
| 2nd Author's Name | Shinichi Kimura |
| 2nd Author's Affiliation | Tokyo University of Science (TUS) |
| 3rd Author's Name | Seisuke Fukuda |
| 3rd Author's Affiliation | Japan Aerospace Exploration Agency (JAXA) |
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| Speaker | Author-1 |
| Date Time | 2021-10-28 15:05:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2021-21 |
| Volume (vol) | vol.121 |
| Number (no) | no.223 |
| Page | pp.16-21 |
| #Pages | 6 |
| Date of Issue | 2021-10-21 (NC) |
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