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Special Issue "Advanced Optical Sensors Based on Machine Learning"

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Dear Colleagues,

Optical sensors have attracted broad scholarly interest due to their immunity to electromagnetic interference, high sensitivity, multiplexing, and remote sensing capabilities. Various optical structures, such as integrated waveguides, optical fibers and optical microcavities, have been developed for sensing applications over the past decades. Although conventional optical sensing platforms have displayed impressive performances, most sensing information relies on manual analysis, which is time-consuming and prone to human error. As a result, there are significant limitations in sensing accuracy, sensing range, and real-time detection. With the dramatic increase in the availability of computational resources and the rapid development of machine learning, new sensor design paradigms and signal processing methods have become available for advanced optical sensing technology. For example, deep learning algorithms can be used to automatically identify key features in sensing information and quickly identify changes in optical signals, thus further improving detection accuracy and response speed. We believe that optical sensors, taken in combination with machine learning, open up a new opportunity for next-generation intelligent optical sensors in the terms of hardware design and signal readout.

This Special Issue aims to attract original contributions. These should focus on a wide array of topics, related to both experiments on and the theory of advanced optical sensors and relying on machine learning.

Dr. Jinhui Chen
Prof. Dr. Daquan Yang
Guest Editors

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Keywords

machine learning
intelligent sensor design
computational sensing
hyperspectral imaging and sensing
inverse design optics
wearable sensors
intelligent spectroscopy

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Research

Open AccessArticle

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

and

Sensors 2023, 23(11), 5015; https://doi.org/10.3390/s23115015 - 24 May 2023

Viewed by 386

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. Full article

(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)

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Open AccessArticle

Deep Learning-Based Speech Enhancement of an Extrinsic Fabry–Perot Interferometric Fiber Acoustic Sensor System

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Sensors 2023, 23(7), 3574; https://doi.org/10.3390/s23073574 - 29 Mar 2023

Cited by 1 | Viewed by 541

Abstract

To achieve high-quality voice communication technology without noise interference in flammable, explosive and strong electromagnetic environments, the speech enhancement technology of a fiber-optic external Fabry–Perot interferometric (EFPI) acoustic sensor based on deep learning is studied in this paper. The combination of a complex-valued convolutional neural network and a long short-term memory (CV-CNN-LSTM) model is proposed for speech enhancement in the EFPI acoustic sensing system. Moreover, the 3 × 3 coupler algorithm is used to demodulate voice signals. Then, the short-time Fourier transform (STFT) spectrogram features of voice signals are divided into a training set and a test set. The training set is input into the established CV-CNN-LSTM model for model training, and the test set is input into the trained model for testing. The experimental findings reveal that the proposed CV-CNN-LSTM model demonstrates exceptional speech enhancement performance, boasting an average Perceptual Evaluation of Speech Quality (PESQ) score of 3.148. In comparison to the CV-CNN and CV-LSTM models, this innovative model achieves a remarkable PESQ score improvement of 9.7% and 11.4%, respectively. Furthermore, the average Short-Time Objective Intelligibility (STOI) score witnesses significant enhancements of 4.04 and 2.83 when contrasted with the CV-CNN and CV-LSTM models, respectively. Full article

(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)

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