Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
Author:
Elleathy Ahmad1, Alhumaidan Faris1, Alqahtani Mohammed1, Almaiman Ahmed S.1ORCID, Ragheb Amr M.12ORCID, Ibrahim Ahmed B.2ORCID, Ali Jameel12ORCID, Esmail Maged A.3ORCID, Alshebeili Saleh A.12ORCID
Affiliation:
1. Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia 2. KACST-TIC in Radio Frequency and Photonics (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia 3. Smart Systems Engineering Laboratory, Communications and Networks Engineering Department, Faculty of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
Abstract
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.
Funder
National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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Cited by
2 articles.
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