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

Authors
Chung-Wen Hung1, *, Hiroyuki Kobayashi2, Jun-Rong Wu1, Chau-Chung Song1, 3
1Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Taiwan
2Department of System Design Engineering, Osaka Institute of Technology, Osaka, Japan
3Department of Aeronautical Engineering, National Formosa University, Yunlin, Taiwan
*Corresponding author. Email: [email protected]
Corresponding Author
Chung-Wen Hung
Received 13 November 2020, Accepted 9 July 2021, Available Online 9 October 2021.
DOI
https://doi.org/10.2991/jrnal.k.210922.007
Keywords
Light fingerprint; machine learning; indoor; localization
Abstract
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.
Copyright
© 2021 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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