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Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding

This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection syste...

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Autores principales: Elleathy, Ahmad, Alhumaidan, Faris, Alqahtani, Mohammed, Almaiman, Ahmed S., Ragheb, Amr M., Ibrahim, Ahmed B., Ali, Jameel, Esmail, Maged A., Alshebeili, Saleh A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255305/
https://www.ncbi.nlm.nih.gov/pubmed/37299742
http://dx.doi.org/10.3390/s23115015
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author Elleathy, Ahmad
Alhumaidan, Faris
Alqahtani, Mohammed
Almaiman, Ahmed S.
Ragheb, Amr M.
Ibrahim, Ahmed B.
Ali, Jameel
Esmail, Maged A.
Alshebeili, Saleh A.
author_facet Elleathy, Ahmad
Alhumaidan, Faris
Alqahtani, Mohammed
Almaiman, Ahmed S.
Ragheb, Amr M.
Ibrahim, Ahmed B.
Ali, Jameel
Esmail, Maged A.
Alshebeili, Saleh A.
author_sort Elleathy, Ahmad
collection PubMed
description This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college’s gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder’s existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB.
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spelling pubmed-102553052023-06-10 Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding Elleathy, Ahmad Alhumaidan, Faris Alqahtani, Mohammed Almaiman, Ahmed S. Ragheb, Amr M. Ibrahim, Ahmed B. Ali, Jameel Esmail, Maged A. Alshebeili, Saleh A. Sensors (Basel) Article This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college’s gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder’s existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB. MDPI 2023-05-24 /pmc/articles/PMC10255305/ /pubmed/37299742 http://dx.doi.org/10.3390/s23115015 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Elleathy, Ahmad
Alhumaidan, Faris
Alqahtani, Mohammed
Almaiman, Ahmed S.
Ragheb, Amr M.
Ibrahim, Ahmed B.
Ali, Jameel
Esmail, Maged A.
Alshebeili, Saleh A.
Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
title Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
title_full Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
title_fullStr Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
title_full_unstemmed Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
title_short Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
title_sort strain fbg-based sensor for detecting fence intruders using machine learning and adaptive thresholding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255305/
https://www.ncbi.nlm.nih.gov/pubmed/37299742
http://dx.doi.org/10.3390/s23115015
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