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Advanced Signal and Image Processing Techniques for Sensor Applications
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Advanced Signal and Image Processing Techniques for Sensor Applications

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

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 27055

Special Issue Editors

Dr. Jesmin Farzana Khan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Tuskegee University, 307 Luther H. Foster Hall, Tuskegee, AL 36088, USA
Interests: signal processing; image processing; pattern recognition; communications; power electronics; computer vision; machine learning; biomedical engineering; smart grids; RF; radar; remote sensing; hyper-spectral imaging; education
Special Issues, Collections and Topics in MDPI journals
Dr. Mandoye Ndoye

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Tuskegee University, 301 Luther H. Foster Hall, Tuskegee, AL 36088, USA
Interests: sensor signal processing; image processing pattern recognition; machine learning; radar signal processing; intelligent infrastructure systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advance of sensor technology, a vast and ever-growing amount of data in various domains and modalities are readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate, due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensor signal data, further enhancement of the acquired sensor signals, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is necessary. The processing of the data and the consequent extraction of useful information are also vital and included in the topics of this Special Issue.

This Special Issue of Sensors aims to highlight advances in the development, testing and application of signal and image-processing algorithms and techniques for all types of sensors and sensing methodologies. Experimental and theoretical results, as well as review papers, will also be considered.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced sensor characterization techniques;
  • Ambient assisted living;
  • Biomedical signal and image analysis;
  • Signal and image processing (e.g., deblurring, denoising, super-resolution);
  • Signal and image understanding (e.g., object detection and recognition, action recognition, semantic segmentation, novel feature extraction);
  • Internet of Things (IoT);
  • Machine learning (e.g., deep learning) in signal and image processing;
  • Radar signal processing;
  • Real-time signal and image processing algorithms and architectures (e.g., FPGA, DSP, GPU);
  • Remote sensing processing;
  • Sensor data fusion and integration;
  • Sensor error modelling and online calibration;
  • Smart environments and smart cities;
  • Wearable sensor signal processing and its applications.

We look forward to receiving your contributions.

Dr. Jesmin Farzana Khan
Dr. Mandoye Ndoye
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal processing
  • image processing
  • machine learning
  • wireless sensor networks
  • Internet of Things
  • deep neural networks
  • dictionary learning
  • compressive sensing
  • big data
  • brain–computer interface
  • artificial intelligence

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Related Special Issue

Published Papers (15 papers)

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Research

21 pages, 6938 KB  
Article
IllumiSIFT: A Cascade Framework for DoG Pyramid Learning in Darkness
by Dewan Fahim Noor, Mohammed Rashid Chowdhury and Sadia Sikder
Sensors 2026, 26(7), 2147; https://doi.org/10.3390/s26072147 - 31 Mar 2026
Viewed by 435
Abstract
In visual object recognition problems, low light exposure and low-quality images present significant challenges in navigation, surveillance, and image retrieval applications, where reliable feature detection is critical. Although recent deep learning–based image enhancement methods improve visual quality in the pixel domain, these improvements [...] Read more.
In visual object recognition problems, low light exposure and low-quality images present significant challenges in navigation, surveillance, and image retrieval applications, where reliable feature detection is critical. Although recent deep learning–based image enhancement methods improve visual quality in the pixel domain, these improvements often do not translate to downstream machine vision performance, as important local gradient structures required for stable key point detection are frequently suppressed. In this work, we propose IllumiSIFT, a task-driven dark image enhancement framework that focuses on preserving Scale-Invariant Feature Transform (SIFT) key points by directly learning the Difference-of-Gaussian (DoG) pyramid from low-light image inputs. Unlike conventional pixel-level recovery approaches, the proposed method employs a cascaded residual learning architecture to predict Gaussian-blurred representations at multiple scales, enabling the generation of enhanced DoG images that are inherently aligned with the SIFT detection process. Extensive experiments conducted on the CDVS, Oxford Buildings, and Paris datasets demonstrate that the proposed approach consistently outperforms state-of-the-art enhancement methods in downstream SIFT matching performance under severe low-light conditions. These results confirm that gradient-domain, task-aligned enhancement provides a more effective and practical solution for recognition-centric low-light imaging applications. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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19 pages, 4965 KB  
Article
APVCPC: An Adaptive Predicted Value Computation and Pixel Classification Framework for Reversible Data Hiding in Encrypted Images
by Yaomin Wang, Wenguang He, Gangqiang Xiong and Yuyun Chen
Sensors 2026, 26(5), 1636; https://doi.org/10.3390/s26051636 - 5 Mar 2026
Viewed by 392
Abstract
With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual [...] Read more.
With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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25 pages, 8157 KB  
Article
Enhancing Bone Conduction Sensor Signals via Self-Supervised Acoustic Priors and Key-Value Memory
by Changyan Zheng, Hao He, Xiaohu Fan, Lin Li, Yang Zhao, Ye Yan and Erwei Yin
Sensors 2026, 26(4), 1137; https://doi.org/10.3390/s26041137 - 10 Feb 2026
Viewed by 1020
Abstract
Bone conduction (BC) sensors naturally resist ambient noise, but the captured speech suffers from severe high-frequency attenuation due to the low-pass filtering characteristics of body tissue. To compensate for this hardware-induced information deficiency, we propose a time-domain framework leveraging highly generalized representations from [...] Read more.
Bone conduction (BC) sensors naturally resist ambient noise, but the captured speech suffers from severe high-frequency attenuation due to the low-pass filtering characteristics of body tissue. To compensate for this hardware-induced information deficiency, we propose a time-domain framework leveraging highly generalized representations from Self-Supervised Learning (SSL). Specifically, we employ a large-scale pre-trained SSL model to generate embeddings that function as robust acoustic priors. Subsequently, a Key-Value Memory module is integrated to bridge the sensor domain gap, enabling the retrieval of high-fidelity priors from BC queries in the absence of reference air conduction signals. These retrieved cues are then processed by a Gated Attention Projection and dynamically fused into the primary network’s bottleneck, effectively recovering the high-frequency harmonics attenuated by the physical transmission path and rectifying the spectral distortion inherent in BC signals. Experiments on the ABCS and ESMB datasets demonstrate that our method surpasses state-of-the-art baselines in both quality and efficiency. It achieves PESQ gains of over 51% and 73% relative to raw BC inputs, respectively, with a compact architecture optimized for real-world deployment. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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20 pages, 2482 KB  
Article
Compression-Efficient Feature Extraction Method for a CMOS Image Sensor
by Keiichiro Kuroda, Yu Osuka, Ryoya Iegaki, Ryuichi Ujiie, Hideki Shima, Kota Yoshida and Shunsuke Okura
Sensors 2026, 26(3), 962; https://doi.org/10.3390/s26030962 - 2 Feb 2026
Viewed by 625
Abstract
To address the power constraints of the emerging Internet of Things (IoT) era, we propose a compression-efficient feature extraction method for a CMOS image sensor that can extract binary feature data. This sensor outputs six-channel binary feature data, comprising three channels of binarized [...] Read more.
To address the power constraints of the emerging Internet of Things (IoT) era, we propose a compression-efficient feature extraction method for a CMOS image sensor that can extract binary feature data. This sensor outputs six-channel binary feature data, comprising three channels of binarized luminance signals and three channels of horizontal edge signals, compressed via a run length encoding (RLE) method. This approach significantly reduces data transmission volume while maintaining image recognition accuracy. The simulation results obtained using a YOLOv7-based model designed for edge GPUs demonstrate that our approach achieves a large object recognition accuracy (APL50) of 60.7% on the COCO dataset while reducing the data size by 99.2% relative to conventional 8-bit RGB color images. Furthermore, the image classification results using MobileNetV3 tailored for mobile devices on the Visual Wake Words (VWW) dataset show that our approach reduces data size by 99.0% relative to conventional 8-bit RGB color images and achieves an image classification accuracy of 89.4%. These results are superior to the conventional trade-off between recognition accuracy and data size, thereby enabling the realization of low-power image recognition systems. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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34 pages, 7175 KB  
Article
Hybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation
by Popphon Laon, Tanawit Sahavisit, Supavee Pourbunthidkul, Sarut Puangragsa, Pattharin Wichittrakarn, Pattarapong Phasukkit and Nongluck Houngkamhang
Sensors 2026, 26(2), 648; https://doi.org/10.3390/s26020648 - 18 Jan 2026
Viewed by 552
Abstract
Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into [...] Read more.
Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R2 values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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31 pages, 1884 KB  
Article
Achieving Robotic Data Efficiency Through Machine-Centric FDCT Vision Processing
by Yair Wiseman
Sensors 2026, 26(2), 518; https://doi.org/10.3390/s26020518 - 13 Jan 2026
Cited by 2 | Viewed by 490
Abstract
To enhance a robot’s capacity to perceive and interpret its environment, an advanced vision system tailored specifically for machine perception was developed, moving away from human-oriented visual processing. This system improves robotic functionality by incorporating algorithms optimized for how computerized devices process visual [...] Read more.
To enhance a robot’s capacity to perceive and interpret its environment, an advanced vision system tailored specifically for machine perception was developed, moving away from human-oriented visual processing. This system improves robotic functionality by incorporating algorithms optimized for how computerized devices process visual information. Central to this paper’s approach is an improved Fast Discrete Cosine Transform (FDCT) algorithm, customized for robotic systems, which enhances object and obstacle detection in machine vision. By prioritizing higher frequencies and eliminating less critical lower frequencies, the algorithm sharpens focus on essential details. Instead of adapting the data stream for human vision, the FDCT and quantization tables were adjusted to suit machine vision requirements, achieving a file size reduction to about one-third of the original while preserving highly relevant data for robotic processing. This innovative approach significantly improves robots’ ability to navigate complex environments, perform tasks such as object recognition, motion detection, and obstacle avoidance with greater accuracy and efficiency. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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44 pages, 2885 KB  
Article
Advancing SAR Target Recognition Through Hierarchical Self-Supervised Learning with Multi-Task Pretext Training
by Md Al Siam, Dewan Fahim Noor, Mandoye Ndoye and Jesmin Farzana Khan
Sensors 2026, 26(1), 122; https://doi.org/10.3390/s26010122 - 24 Dec 2025
Cited by 1 | Viewed by 1035
Abstract
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems face significant challenges due to limited labeled data availability and persistent domain gaps between synthetic and measured imagery. This paper presents a comprehensive self-supervised learning (SSL) framework that eliminates dependency on synthetic data while [...] Read more.
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems face significant challenges due to limited labeled data availability and persistent domain gaps between synthetic and measured imagery. This paper presents a comprehensive self-supervised learning (SSL) framework that eliminates dependency on synthetic data while achieving state-of-the-art performance through multi-task pretext training and extensive downstream classifier evaluation. We systematically evaluate our SSL framework across diverse downstream classifiers spanning different computational paradigms and architectural families. Our study encompasses traditional machine learning approaches (SVM, Random Forest, XGBoost, Gradient Boosting), deep convolutional neural networks (ResNet, U-Net, MobileNet, EfficientNet), and a generative adversarial network. We conduct extensive experiments using the SAMPLE dataset with rigorous evaluation protocols. Results demonstrate that SSL significantly improves SAR ATR performance, with SVM achieving 99.63% accuracy, ResNet18 reaching 97.40% accuracy, and Random Forest demonstrating 99.26% accuracy. Our multi-task SSL framework employs nine carefully designed pretext tasks, including geometric invariance, signal robustness, and multi-scale analysis. Cross-validation experiments validate the generalizability and robustness of our findings. Rigorous comparison with SimCLR baseline validates that task-based SSL outperforms contrastive learning for SAR ATR. This work establishes a new paradigm for SAR ATR that leverages inherent radar data structure without synthetic augmentation, providing practical guidelines for deploying SSL-based SAR ATR systems and a foundation for future domain-specific self-supervised learning research in remote sensing applications. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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12 pages, 978 KB  
Article
Automated Remote Detection of Falls Using Direct Reconstruction of Optical Flow Principal Motion Parameters
by Simeon Karpuzov, Stiliyan Kalitzin, Olga Georgieva, Alex Trifonov, Tervel Stoyanov and George Petkov
Sensors 2025, 25(18), 5678; https://doi.org/10.3390/s25185678 - 11 Sep 2025
Cited by 2 | Viewed by 1044
Abstract
Detecting and alerting for falls is a crucial component of both healthcare and assistive technologies. Wearable devices are vulnerable to damage and require regular inspection and maintenance. Manned video surveillance avoids these problems, but it involves constant labor-intensive attention and, in most cases, [...] Read more.
Detecting and alerting for falls is a crucial component of both healthcare and assistive technologies. Wearable devices are vulnerable to damage and require regular inspection and maintenance. Manned video surveillance avoids these problems, but it involves constant labor-intensive attention and, in most cases, may interfere with the privacy of the observed individuals. To address this issue, in this work we introduce and evaluate a novel approach for fully automated fall detection. The presented technique uses direct reconstruction of principal motion parameters, avoiding the computationally expensive full optical flow reconstruction and still providing relevant descriptors for accurate detections. Our method is systematically compared with state-of-the-art techniques. Comparisons of detection accuracy, computational efficiency, and suitability for real-time applications are presented. Experimental results demonstrate notable improvements in accuracy while maintaining a lower computational cost compared to traditional methods, making our approach highly adaptable for real-world deployment. The findings highlight the robustness and universality of our model, suggesting its potential for integration into broader surveillance technologies. Future directions for development will include optimization for resource-constrained environments and deep learning enhancements to refine detection precision. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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20 pages, 4330 KB  
Article
YOLO-WAD for Small-Defect Detection Boost in Photovoltaic Modules
by Yin Wang, Wang Yun, Gang Xie and Zhicheng Zhao
Sensors 2025, 25(6), 1755; https://doi.org/10.3390/s25061755 - 12 Mar 2025
Cited by 7 | Viewed by 2472
Abstract
The performance of photovoltaic modules determines the lifetime of solar cells; however, accurate detection remains a challenge when facing smaller defects. To address this problem, in this paper, we propose a YOLO-WAD model based on YOLOv10n. Firstly, we replace C2f (CSP bottleneck with [...] Read more.
The performance of photovoltaic modules determines the lifetime of solar cells; however, accurate detection remains a challenge when facing smaller defects. To address this problem, in this paper, we propose a YOLO-WAD model based on YOLOv10n. Firstly, we replace C2f (CSP bottleneck with two convolutions) with C2f-WTConv (CSP bottleneck with two convolutions–wavelet transform convolution) in the backbone network to enlarge the receptive field and better extract the features of small-target defects (hot spots). Secondly, an ASF structure is introduced in the neck, which effectively fuses the different levels of output features extracted by the backbone network and enhances the model’s ability to detect small objects. Subsequently, an additional detection layer is added to the neck, and C2f is replaced by C2f-EMA (CSP bottleneck with two convolutions–efficient multi-scale attention mechanism), which can redistribute feature weights and prioritize relevant features and spatial details across image channels to improve feature extraction. Finally, the DyHead (dynamic head) detection head is introduced, which enables comprehensive scale, spatial, and channel awareness. This greatly enhances the model’s ability to classify and localize small-target defects. The experimental results show that YOLO-WAD detects our dataset with an overall accuracy of 95.6%, with the small-target defect detection accuracy reaching 86.3%, which is 4.1% and 9.5% higher than YOLOv10n and current mainstream models, verifying the feasibility of our algorithm. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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22 pages, 15972 KB  
Article
Regeneration Filter: Enhancing Mosaic Algorithm for Near Salt & Pepper Noise Reduction
by Ratko M. Ivković, Ivana M. Milošević and Zoran N. Milivojević
Sensors 2025, 25(1), 210; https://doi.org/10.3390/s25010210 - 2 Jan 2025
Cited by 5 | Viewed by 1965
Abstract
This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected [...] Read more.
This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter’s performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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19 pages, 4585 KB  
Article
A Real-Time Adaptive Station Beamforming Strategy for Next Generation Phased Array Radio Telescopes
by Guoliang Peng, Lihui Jiang, Xiaohui Tao, Yan Zhang and Rui Cao
Sensors 2024, 24(14), 4723; https://doi.org/10.3390/s24144723 - 20 Jul 2024
Cited by 2 | Viewed by 2729
Abstract
The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient [...] Read more.
The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn’t require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4N+2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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36 pages, 8555 KB  
Article
Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions
by Serhii Vladov, Lukasz Scislo, Valerii Sokurenko, Oleksandr Muzychuk, Victoria Vysotska, Serhii Osadchy and Anatoliy Sachenko
Sensors 2024, 24(13), 4246; https://doi.org/10.3390/s24134246 - 29 Jun 2024
Cited by 64 | Viewed by 2636
Abstract
The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network [...] Read more.
The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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22 pages, 6987 KB  
Article
Comprehensive Separation Algorithm for Single-Channel Signals Based on Symplectic Geometry Mode Decomposition
by Xinyu Wang, Jin Zhao and Xianliang Wu
Sensors 2024, 24(2), 462; https://doi.org/10.3390/s24020462 - 11 Jan 2024
Cited by 9 | Viewed by 2428
Abstract
This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, [...] Read more.
This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, a comprehensive single-channel mixed signal separation algorithm was proposed based on the combination of Symplectic Geometry Mode Decomposition (SGMD) and the FastICA algorithm. First, SGMD-FastICA uses SGMD to decompose single-channel mixed signals, and then it uses the Pearson correlation coefficient to select the Symplectic Geometry Components that exhibit higher correlation coefficients with the mixed signals. Then, these components are expanded with the single-channel mixed signals into virtual multi-channel signals and input into the FastICA algorithm. The simulation results show that the SGMD algorithm could eliminate noise interference while keeping the raw time series unchanged, which is achievable through symplectic geometry similarity transformation during the decomposition of mixed signals. Comparative experiment results also show that compared with the EMD-FastICA and VMD-FastICA, the SGMD-FastICA algorithm has the best separation effect for single-channel mixed signals. The SGMD-FastICA algorithm represents an improved solution that addresses the limitations of the FastICA algorithm, enabling the direct separation of single-channel mixed signals, while also addressing the challenge of proper signal separation in noisy environments. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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19 pages, 4684 KB  
Article
Optimization of Deep Learning Parameters for Magneto-Impedance Sensor in Metal Detection and Classification
by Hoijun Kim, Hobyung Chae, Soonchul Kwon and Seunghyun Lee
Sensors 2023, 23(22), 9259; https://doi.org/10.3390/s23229259 - 18 Nov 2023
Cited by 2 | Viewed by 2446
Abstract
Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, [...] Read more.
Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, we propose and analyze a deep learning model based on recurrent neural networks (RNNs) optimized for the MI sensor, such that it can detect and classify data that are relatively irregular and diverse compared to the EMG and acoustic signals. Our proposed method combines the long short-term memory (LSTM) and gated recurrent unit (GRU) models to detect and classify metal objects from signals acquired by an MI sensor. First, we configured various layers used in RNN with a basic model structure and tested the performance of each layer type. In addition, we succeeded in increasing the accuracy by processing the sequence length of the input data and performing additional work in the prediction process. An MI sensor acquires data in a non-contact mode; therefore, the proposed deep learning approach can be applied to drone control, electronic maps, geomagnetic measurement, autonomous driving, and foreign object detection. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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22 pages, 9660 KB  
Article
Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation
by Mingqing Li, Yuqing Wang and Haijiang Sun
Sensors 2023, 23(20), 8424; https://doi.org/10.3390/s23208424 - 12 Oct 2023
Cited by 10 | Viewed by 5071
Abstract
In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to [...] Read more.
In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to such challenges, we propose a method for the correction of non-uniformity in single-frame infrared images based on noise separation in the wavelet domain. More specifically, we commence by decomposing the noisy image into distinct frequency components through wavelet transformation. Subsequently, we employ a clustering algorithm to extract high-frequency noise from the vertical components within the wavelet domain, concurrently employing a method of surface fitting to capture low-frequency noise from the approximate components within the wavelet domain. Ultimately, the restored image is obtained by subtracting the combined noise components. The experimental results demonstrate that the proposed method, when applied to simulated noisy images, achieves the optimal levels among seven compared methods in terms of MSE, PSNR, and SSIM metrics. After correction on three sets of real-world test image sequences, the average non-uniformity index is reduced by 75.54%. Moreover, our method does not impose significant computational overhead in the elimination of superimposed noise, which is particularly suitable for applications necessitating stringent requirements in both image quality and processing speed. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
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