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 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.

Reconfigurable photonics-based millimeter wave signal aggregation for non-orthogonal multiple access.

A reconfigurable optical-to-electrical signal aggregation is proposed, for the first time, using optical signal processing and photo-mixing technology. Two optically modulated quadrature phase-shift keying (QPSK) signals are aggregated into a single 16-quadrature amplitude modulation (16-QAM) signal and, simultaneously, carried over a 28-GHz millimeter wave (MMW) carrier using an optimized heterodyne beating process through a single photodiode. To demonstrate the system reconfigurability, aggregation of two optical binary phase-shift keying signals is mapped into MMW QPSK or four-level pulse amplitude modulation signals by controlling the relative phases and amplitudes, respectively, of the input signals.