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Advanced Signal and Image Processing Techniques for Sensor Applications

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 10640

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Special Issue Editors

Dr. Jesmin Farzana Khan

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Guest Editor

Department of Electrical and Computer Engineering, 307 Luther H. Foster Hall, Tuskegee University, 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

Dr. Mandoye Ndoye

E-Mail Website
Guest Editor

Department of Electrical and Computer Engineering, 301 Luther H. Foster Hall, Tuskegee University, Tuskegee, AL 36088, USA
Interests: sensor signal processing; image processing pattern recognition; machine learning; radar signal processing; intelligent infrastructure systems

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 100 words) can be sent to the Editorial Office for announcement on this website.

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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Published Papers (7 papers)

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Research

20 pages, 4330 KiB

Open AccessArticle

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 1 | Viewed by 487

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 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 KiB

Open AccessArticle

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

Viewed by 709

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 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 KiB

Open AccessArticle

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 1 | Viewed by 1344

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 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

4 N + 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 KiB

Open AccessArticle

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 28 | Viewed by 1430

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 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 KiB

Open AccessArticle

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 5 | Viewed by 1359

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, 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 KiB

Open AccessArticle

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 1580

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, 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 KiB

Open AccessArticle

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 2 | Viewed by 2709

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 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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