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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 34, Number 8(1) (2022)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 3047-3061
S&M3022 Research Paper of Special Issue https://doi.org/10.18494/SAM3912 Published: August 2, 2022 Indoor Fingerprinting Positioning System Using Deep Learning with Data Augmentation [PDF] Luomeng Liu, Qianyue Zhao, Shoma Miki, Jumpei Tokunaga, and Hiroyuki Ebara (Received April 1, 2022; Accepted May 27, 2022) Keywords: indoor positioning, fingerprinting, residual network, data augmentation, deep learning
We propose an indoor positioning system based on deep learning and fingerprinting. On the mobile side, we designed an Android application with received signal strength information (RSSI) signal reading, database storage, and real-time online positioning module functions. In addition, we placed a trained neural network model on the built server to achieve real-time positioning using the developed Android application. The deep learning framework of this paper uses a residual network (ResNet) and a data augmentation technique called mean and uniform random numbers in the preparation of the dataset. By using this data augmentation method, we significantly reduced the collection time of the dataset and increased the test accuracy of the neural network from 20.4% before the augmentation to 97.5% after the augmentation.
Corresponding author: Hiroyuki Ebara  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Luomeng Liu, Qianyue Zhao, Shoma Miki, Jumpei Tokunaga, and Hiroyuki Ebara, Indoor Fingerprinting Positioning System Using Deep Learning with Data Augmentation, Sens. Mater., Vol. 34, No. 8, 2022, p. 3047-3061. |
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