Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding.
Ahmad Elleathy, Faris Alhumaidan, Mohammed Alqahtani, Ahmed S Almaiman, Amr M Ragheb, Ahmed B Ibrahim, Jameel Ali, Maged A Esmail, Saleh A Alshebeili
Author Information
Ahmad Elleathy: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia.
Faris Alhumaidan: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia.
Mohammed Alqahtani: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia.
Ahmed S Almaiman: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia. ORCID
Amr M Ragheb: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia. ORCID
Ahmed B Ibrahim: KACST-TIC in Radio Frequency and Photonics (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia. ORCID
Jameel Ali: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia. ORCID
Maged A Esmail: Smart Systems Engineering Laboratory, Communications and Networks Engineering Department, Faculty of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia. ORCID
Saleh A Alshebeili: Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia. ORCID
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.
3-17-09-001-0012/National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia.
