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Advanced Optical Sensors Based on Machine Learning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 1 September 2024 | Viewed by 8105

Special Issue Editors


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Guest Editor
Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen 361005, China
Interests: optical sensors; microcavity photonics; optoelectronics; machine learning-inspired photonics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: photonic crystal sensors; microcavity photonics; micro-nano optical precision measurement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

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Keywords

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

Published Papers (9 papers)

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Research

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21 pages, 37955 KiB  
Article
FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion
by Kaixuan Yang, Wei Xiang, Zhenshuai Chen and Yunpeng Liu
Sensors 2024, 24(8), 2466; https://doi.org/10.3390/s24082466 - 11 Apr 2024
Viewed by 298
Abstract
The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion [...] Read more.
The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion neural network model to solve this complex problem that few people have touched. Specifically, we designed an attention module combining a traditional fusion knowledge prior with channel attention to extract modal-specific features efficiently. We used a shared attention layer to perform the early fusion of modal-shared features. Adopting parallel dilated convolution layers further reduces the network’s parameter count. Our network is trained recursively, featuring minimal model parameters, and requires only a few training batches to achieve excellent fusion results. This significantly reduces the consumption of time, space, and computational resources during model training. We compared our method with nine SOTA methods on three public datasets, demonstrating our method’s efficient training feature and good fusion results. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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11 pages, 2586 KiB  
Article
Soft Polymer Optical Fiber Sensors for Intelligent Recognition of Elastomer Deformations and Wearable Applications
by Nicheng Wang, Yuan Yao, Pengao Wu, Lei Zhao and Jinhui Chen
Sensors 2024, 24(7), 2253; https://doi.org/10.3390/s24072253 - 01 Apr 2024
Viewed by 648
Abstract
In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, [...] Read more.
In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human–machine interaction interfaces. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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15 pages, 2428 KiB  
Article
Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging
by Hang Xue, Xiping Xu, Yang Yang, Dongmei Hu and Guocheng Niu
Sensors 2024, 24(6), 1855; https://doi.org/10.3390/s24061855 - 14 Mar 2024
Viewed by 447
Abstract
The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct [...] Read more.
The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct drying method. The hyperspectral imaging system was employed to capture the spectral images of corn seeds within the wavelength range of 1100–2498 nm. By utilizing seven preprocessing techniques, including moving average, S–G smoothing, baseline, normalization, SNV, MSC, and detrending, we preprocessed the spectral data and then established a PLSR model for comparison. The results show that the model established using the normalization preprocessing method has the best prediction performance. To remove spectral redundancy and simplify the prediction model, we utilized SPA, CASR, and UVE algorithms to extract feature wavelengths. Based on three algorithms (PLSR, PCR, and SVM), we constructed 12 predictive models. Upon evaluating these models, it was determined that the normalization-SPA-PLSR algorithm produced the most accurate prediction. This model boasts high RC2 and RP2 values of 0.9917 and 0.9914, respectively, along with low RMSEP and RMSECV values of 0.0343 and 0.0257, respectively, indicating its exceptional stability and predictive capabilities. This suggests that the model can precisely estimate the moisture content of maize seeds. The results showed that hyperspectral imaging technology provides technical support for rapid and non-destructive prediction of corn seed moisture content and new methods in seed quality evaluation. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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14 pages, 2237 KiB  
Article
Hologram Noise Model for Data Augmentation and Deep Learning
by Dániel Terbe, László Orzó, Barbara Bicsák and Ákos Zarándy
Sensors 2024, 24(3), 948; https://doi.org/10.3390/s24030948 - 01 Feb 2024
Viewed by 732
Abstract
This paper introduces a noise augmentation technique designed to enhance the robustness of state-of-the-art (SOTA) deep learning models against degraded image quality, a common challenge in long-term recording systems. Our method, demonstrated through the classification of digital holographic images, utilizes a novel approach [...] Read more.
This paper introduces a noise augmentation technique designed to enhance the robustness of state-of-the-art (SOTA) deep learning models against degraded image quality, a common challenge in long-term recording systems. Our method, demonstrated through the classification of digital holographic images, utilizes a novel approach to synthesize and apply random colored noise, addressing the typically encountered correlated noise patterns in such images. Empirical results show that our technique not only maintains classification accuracy in high-quality images but also significantly improves it when given noisy inputs without increasing the training time. This advancement demonstrates the potential of our approach for augmenting data for deep learning models to perform effectively in production under varied and suboptimal conditions. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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15 pages, 10083 KiB  
Article
Aberration Estimation for Synthetic Aperture Digital Holographic Microscope Using Deep Neural Network
by Hosung Jeon, Minwoo Jung, Gunhee Lee and Joonku Hahn
Sensors 2023, 23(22), 9278; https://doi.org/10.3390/s23229278 - 20 Nov 2023
Viewed by 652
Abstract
Digital holographic microscopy (DHM) is a valuable technique for investigating the optical properties of samples through the measurement of intensity and phase of diffracted beams. However, DHMs are constrained by Lagrange invariance, compromising the spatial bandwidth product (SBP) which relates resolution and field [...] Read more.
Digital holographic microscopy (DHM) is a valuable technique for investigating the optical properties of samples through the measurement of intensity and phase of diffracted beams. However, DHMs are constrained by Lagrange invariance, compromising the spatial bandwidth product (SBP) which relates resolution and field of view. Synthetic aperture DHM (SA-DHM) was introduced to overcome this limitation, but it faces significant challenges such as aberrations in synthesizing the optical information corresponding to the steering angle of incident wave. This paper proposes a novel approach utilizing deep neural networks (DNNs) for compensating aberrations in SA-DHM, extending the compensation scope beyond the numerical aperture (NA) of the objective lens. The method involves training a DNN from diffraction patterns and Zernike coefficients through a circular aperture, enabling effective aberration compensation in the illumination beam. This method makes it possible to estimate aberration coefficients from the only part of the diffracted beam cutoff by the circular aperture mask. With the proposed technique, the simulation results present improved resolution and quality of sample images. The integration of deep neural networks with SA-DHM holds promise for advancing microscopy capabilities and overcoming existing limitations. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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15 pages, 1221 KiB  
Article
Study of the Feasibility of Decoupling Temperature and Strain from a ϕ-PA-OFDR over an SMF Using Neural Networks
by Andrés Pedraza, Daniel del Río, Víctor Bautista-Juzgado, Antonio Fernández-López and Ángel Sanz-Andrés
Sensors 2023, 23(12), 5515; https://doi.org/10.3390/s23125515 - 12 Jun 2023
Cited by 6 | Viewed by 1025
Abstract
Despite several existing techniques for distributed sensing (temperature and strain) using standard Single-Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most decoupling techniques require special optical fibers and are difficult to implement with high-spatial-resolution distributed techniques, [...] Read more.
Despite several existing techniques for distributed sensing (temperature and strain) using standard Single-Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most decoupling techniques require special optical fibers and are difficult to implement with high-spatial-resolution distributed techniques, such as OFDR. Therefore, this work’s objective is to study the feasibility of decoupling temperature and strain out of the readouts of a phase and polarization analyzer OFDR (ϕ-PA-OFDR) taken over an SMF. For this purpose, the readouts will be subjected to a study using several machine learning algorithms, among them Deep Neural Networks. The motivation that underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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16 pages, 3961 KiB  
Article
Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
by Ahmad Elleathy, Faris Alhumaidan, Mohammed Alqahtani, Ahmed S. Almaiman, Amr M. Ragheb, Ahmed B. Ibrahim, Jameel Ali, Maged A. Esmail and Saleh A. Alshebeili
Sensors 2023, 23(11), 5015; https://doi.org/10.3390/s23115015 - 24 May 2023
Cited by 1 | Viewed by 1399
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 [...] Read more.
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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13 pages, 4860 KiB  
Article
Deep Learning-Based Speech Enhancement of an Extrinsic Fabry–Perot Interferometric Fiber Acoustic Sensor System
by Shiyi Chai, Can Guo, Chenggang Guan and Li Fang
Sensors 2023, 23(7), 3574; https://doi.org/10.3390/s23073574 - 29 Mar 2023
Cited by 2 | Viewed by 1285
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 [...] Read more.
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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Review

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18 pages, 10794 KiB  
Review
Recent Progress in MEMS Fiber-Optic Fabry–Perot Pressure Sensors
by Ye Chen, Dongqin Lu, Huan Xing, Haotian Ding, Junxian Luo, Hanwen Liu, Xiangxu Kong and Fei Xu
Sensors 2024, 24(4), 1079; https://doi.org/10.3390/s24041079 - 07 Feb 2024
Cited by 1 | Viewed by 922
Abstract
Pressure sensing plays an important role in many industrial fields; conventional electronic pressure sensors struggle to survive in the harsh environment. Recently microelectromechanical systems (MEMS) fiber-optic Fabry–Perot (FP) pressure sensors have attracted great interest. Here we review the basic principles of MEMS fiber-optic [...] Read more.
Pressure sensing plays an important role in many industrial fields; conventional electronic pressure sensors struggle to survive in the harsh environment. Recently microelectromechanical systems (MEMS) fiber-optic Fabry–Perot (FP) pressure sensors have attracted great interest. Here we review the basic principles of MEMS fiber-optic FP pressure sensors and then discuss the sensors based on different materials and their industrial applications. We also introduce recent progress, such as two-photon polymerization-based 3D printing technology, and the state-of-the-art in this field, e.g., sapphire-based sensors that work up to 1200 °C. Finally, we discuss the limitations and opportunities for future development. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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