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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 36, Number 7(2) (2024)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 2943-2953
S&M3711 Research Paper of Special Issue https://doi.org/10.18494/SAM5026 Published: July 24, 2024 Temperature Optical Sensor Made of Recycled Waste Materials and Implementation of Machine Learning Method to Expand Its Measurement Range [PDF] Sergio Ivan Ramirez-Zavala, Everardo Vargas-Rodriguez, Ana Dinora Guzman-Chavez, and Oscar Manuel Salazar-Martinez (Received February 21, 2024; Accepted June 20, 2024) Keywords: tunable optical interferometer, temperature sensor, recycled waste materials, machine learning, kernel ridge regression
The efficient use of resources can contribute to the eradication of poverty and the reduction of environmental impact. Therefore, the use of recycled waste materials is desirable since it helps in the reduction of pollution. Some devices, such as interferometric optical sensors, can be implemented with this type of materials. In addition, optical sensors play an important role in the Internet of Things since they can be used to implement networks that simultaneously monitor several physical parameters. A problem that limits the measurement range of interferometric optical sensors is the 2π ambiguity. In this work, it is presented that the nominal measurement range of this type of sensor can be increased considerably by applying machine learning algorithms. In this sense, the kernel ridge regression (KRR) method is applied to the signals of a temperature sensor that is based on a tunable optical two-layer interferometer. Here, it is shown
Corresponding author: Ana Dinora Guzman-Chavez  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Sergio Ivan Ramirez-Zavala, Everardo Vargas-Rodriguez, Ana Dinora Guzman-Chavez, and Oscar Manuel Salazar-Martinez, Temperature Optical Sensor Made of Recycled Waste Materials and Implementation of Machine Learning Method to Expand Its Measurement Range, Sens. Mater., Vol. 36, No. 7, 2024, p. 2943-2953. |
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