End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints
- https://doi.org/10.2991/jrnal.k.210922.007How to use a DOI?
- Light fingerprint; machine learning; indoor; localization
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.
- © 2021 The Authors. Published by Atlantis Press International B.V.
- 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/).
Cite this article
TY - JOUR AU - Chung-Wen Hung AU - Hiroyuki Kobayashi AU - Jun-Rong Wu AU - Chau-Chung Song PY - 2021 DA - 2021/10/09 TI - End-to-End Deep Learning by MCU Implementation: Indoor Localization by Sound Spectrum of Light Fingerprints JO - Journal of Robotics, Networking and Artificial Life SP - 186 EP - 192 VL - 8 IS - 3 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.210922.007 DO - https://doi.org/10.2991/jrnal.k.210922.007 ID - Hung2021 ER -