End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints

This paper introduces a low-cost indoor localization system using sound spectrum of light fingerprint. An Artificial Intelligence (AI), algorithm will be implemented in a low-cost Micro-Control Unit (MCU), to perform the localization function. The unique light fingerprints with complex and tiny diff...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chung-Wen Hung, Hiroyuki Kobayashi, Jun-Rong Wu, Chau-Chung Song
Formato:	article
Lenguaje:	EN
Publicado:	Atlantis Press 2021
Materias:	Light fingerprint machine learning indoor localization Technology T
Acceso en línea:	https://doaj.org/article/313a68b5a81049ad9dfa86284aa55ef9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:313a68b5a81049ad9dfa86284aa55ef9
record_format	dspace
spelling	oai:doaj.org-article:313a68b5a81049ad9dfa86284aa55ef92021-11-17T08:59:13ZEnd-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints10.2991/jrnal.k.210922.0071259613882352-6386https://doaj.org/article/313a68b5a81049ad9dfa86284aa55ef92021-10-01T00:00:00Zhttps://www.atlantis-press.com/article/125961388/viewhttps://doaj.org/toc/2352-6386This paper introduces a low-cost indoor localization system using sound spectrum of light fingerprint. An Artificial Intelligence (AI), algorithm will be implemented in a low-cost Micro-Control Unit (MCU), to perform the localization function. The unique light fingerprints with complex and tiny differences are caused by the different characteristics of the discrete components used in lighting devices. Only sound spectrum of light fingerprint is adopted for the identification of the lighting device to reduce the memory size requirement for implementation in a low-cost MCU. So, the grid search is used to optimize the hyperparameters for the smallest AI model. The system architecture and algorithm development are discussed in this paper, and the experimental results will be present to show the performance of the proposed system.Chung-Wen HungHiroyuki KobayashiJun-Rong WuChau-Chung SongAtlantis PressarticleLight fingerprintmachine learningindoorlocalizationTechnologyTENJournal of Robotics, Networking and Artificial Life (JRNAL), Vol 8, Iss 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Light fingerprint machine learning indoor localization Technology T
spellingShingle	Light fingerprint machine learning indoor localization Technology T Chung-Wen Hung Hiroyuki Kobayashi Jun-Rong Wu Chau-Chung Song End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
description	This paper introduces a low-cost indoor localization system using sound spectrum of light fingerprint. An Artificial Intelligence (AI), algorithm will be implemented in a low-cost Micro-Control Unit (MCU), to perform the localization function. The unique light fingerprints with complex and tiny differences are caused by the different characteristics of the discrete components used in lighting devices. Only sound spectrum of light fingerprint is adopted for the identification of the lighting device to reduce the memory size requirement for implementation in a low-cost MCU. So, the grid search is used to optimize the hyperparameters for the smallest AI model. The system architecture and algorithm development are discussed in this paper, and the experimental results will be present to show the performance of the proposed system.
format	article
author	Chung-Wen Hung Hiroyuki Kobayashi Jun-Rong Wu Chau-Chung Song
author_facet	Chung-Wen Hung Hiroyuki Kobayashi Jun-Rong Wu Chau-Chung Song
author_sort	Chung-Wen Hung
title	End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
title_short	End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
title_full	End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
title_fullStr	End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
title_full_unstemmed	End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
title_sort	end-to-end deep learning by mcu implementation: indoor localization by sound spectrum of light fingerprints
publisher	Atlantis Press
publishDate	2021
url	https://doaj.org/article/313a68b5a81049ad9dfa86284aa55ef9
work_keys_str_mv	AT chungwenhung endtoenddeeplearningbymcuimplementationindoorlocalizationbysoundspectrumoflightfingerprints AT hiroyukikobayashi endtoenddeeplearningbymcuimplementationindoorlocalizationbysoundspectrumoflightfingerprints AT junrongwu endtoenddeeplearningbymcuimplementationindoorlocalizationbysoundspectrumoflightfingerprints AT chauchungsong endtoenddeeplearningbymcuimplementationindoorlocalizationbysoundspectrumoflightfingerprints
_version_	1718425662456332288

End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints

Ejemplares similares