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21 pages, 6847 KiB

Open AccessArticle

Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature

by Xueyuan Li and Wenjing Shang

Sensors 2024, 24(17), 5664; https://doi.org/10.3390/s24175664 - 30 Aug 2024

Cited by 1 | Viewed by 554

Abstract

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective [...] Read more.

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUC_ODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects. Full article

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

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13 pages, 3534 KiB

Open AccessArticle

Enhancing Multichannel Fiber Optic Sensing Systems with IFFT-DNN for Remote Water Level Monitoring

by Erfan Dejband, Tan-Hsu Tan, Cheng-Kai Yao, En-Ming Chang and Peng-Chun Peng

Sensors 2024, 24(15), 4903; https://doi.org/10.3390/s24154903 - 29 Jul 2024

Cited by 1 | Viewed by 825

Abstract

This paper proposes a novel approach to enhance the multichannel fiber optic sensing systems by integrating an Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) to accurately predict sensor responses despite signals overlapping and crosstalk between sensors. The IFFT-DNN leverages both frequency and [...] Read more.

This paper proposes a novel approach to enhance the multichannel fiber optic sensing systems by integrating an Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) to accurately predict sensor responses despite signals overlapping and crosstalk between sensors. The IFFT-DNN leverages both frequency and time domain information, enabling a comprehensive feature extraction which enhances the prediction accuracy and reliability performance. To investigate the IFFT-DNN’s performance, we propose a multichannel water level sensing system based on Free Space Optics (FSO) to measure the water level at multiple points in remote areas. The experimental results demonstrate the system’s high precision, with a Mean Absolute Error (MAE) of 0.07 cm, even in complex conditions. Hence, this system provides a cost-effective and reliable remote water level sensing solution, highlighting its practical applicability in various industrial settings. Full article

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

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11 pages, 1018 KiB

Open AccessArticle

Reconstruction of Optical Properties in Turbid Media: Omitting the Need of the Collimated Transmission for an Integrating Sphere Setup

by Dongqin Ni, Niklas Karmann and Martin Hohmann

Sensors 2024, 24(15), 4807; https://doi.org/10.3390/s24154807 - 24 Jul 2024

Viewed by 639

Abstract

Currently, the most reliable approach to reconstruct optical properties, namely absorption coefficient, reduced scattering coefficient, scattering coefficient and asymmetry factor, of turbid media is through inverse Monte Carlo simulation. To determine these optical properties, three measurements are required: total transmission, total reflection and [...] Read more.

Currently, the most reliable approach to reconstruct optical properties, namely absorption coefficient, reduced scattering coefficient, scattering coefficient and asymmetry factor, of turbid media is through inverse Monte Carlo simulation. To determine these optical properties, three measurements are required: total transmission, total reflection and collimated transmission. However, the accurate determination of the collimated transmission is very difficult. To overcome the difficulty of measuring the collimated transmission, it is proposed to measure the total transmission and total reflection of the same sample with two different thicknesses instead. To prove this alternative solution, machine learning is used to prove that the repeated measurement for two different thicknesses carries all the necessary information. As a result, all four optical properties can be measured with high accuracy, particularly for interpolation problems where the test data fall within the range of the training data. For extrapolation problems, high accuracy can be achieved in the determination of at least the absorption coefficient, reduced scattering coefficient and scattering coefficient. Hence, these results allow that in the future, an easier and therefore more precise reconstruction of the optical properties is possible, potentially even with inverse Monte Carlo simulations as the current standard. Full article

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

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14 pages, 2665 KiB

Open AccessArticle

Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor

by Uriel Martinez-Hernandez, Gregory West and Tareq Assaf

Sensors 2024, 24(9), 2821; https://doi.org/10.3390/s24092821 - 28 Apr 2024

Viewed by 2554

Abstract

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing [...] Read more.

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing. Full article

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

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21 pages, 37955 KiB

Open AccessArticle

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 1260

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

Open AccessArticle

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 - 1 Apr 2024

Viewed by 1538

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

Open AccessArticle

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

Cited by 4 | Viewed by 1229

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

R_{C}^{2}

and

R_{P}^{2}

values of 0.9917 and 0.9914, respectively, along with low

R M S E P

and

R M S E C V

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

Open AccessArticle

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 - 1 Feb 2024

Viewed by 1683

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

Open AccessArticle

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 1118

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

Open AccessArticle

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 9 | Viewed by 1613

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

Open AccessArticle

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 2 | Viewed by 2061

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

Open AccessArticle

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 5 | Viewed by 1810

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

Jump to: Research

18 pages, 10794 KiB

Open AccessReview

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 - 7 Feb 2024

Cited by 3 | Viewed by 2701

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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