Sensors

20 pages, 5650 KiB

Open AccessArticle

The In-Plane Deformation and Free Vibration Analysis of a Rotating Ring Resonator of a Gyroscope with Evenly Distributed Mass Imperfections

by Dongsheng Zhang and Shuming Li

Sensors 2025, 25(15), 4764; https://doi.org/10.3390/s25154764 (registering DOI) - 1 Aug 2025

Abstract

A rotating imperfect ring resonator of the gyroscope is modeled by a rotating thin ring with evenly distributed point masses. The free response of the rotating ring structure at constant speed is investigated, including the steady elastic deformation and wave response. The dynamic [...] Read more.

A rotating imperfect ring resonator of the gyroscope is modeled by a rotating thin ring with evenly distributed point masses. The free response of the rotating ring structure at constant speed is investigated, including the steady elastic deformation and wave response. The dynamic equations are formulated by using Hamilton’s principle in the ground-fixed coordinates. The coordinate transformation is applied to facilitate the solution of the steady deformation, and the displacements and tangential tension for the deformation are calculated by the perturbation method. Employing Galerkin’s method, the governing equation of the free vibration is casted in matrix differential operator form after the separation of the real and imaginary parts with the inextensional assumption. The natural frequencies are calculated through the eigenvalue analysis, and the numerical results are obtained. The effects of the point masses on the natural frequencies of the forward and backward traveling wave curves of different orders are discussed, especially on the measurement accuracy of gyroscopes for different cases. In the ground-fixed coordinates, the frequency splitting results in a crosspoint of the natural frequencies of the forward and backward traveling waves. The finite element method is applied to demonstrate the validity and accuracy of the model. Full article

(This article belongs to the Section Physical Sensors)

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Graphical abstract

25 pages, 2860 KiB

Open AccessReview

Multimodal Sensing-Enabled Large Language Models for Automated Emotional Regulation: A Review of Current Technologies, Opportunities, and Challenges

by Liangyue Yu, Yao Ge, Shuja Ansari, Muhammad Imran and Wasim Ahmad

Sensors 2025, 25(15), 4763; https://doi.org/10.3390/s25154763 (registering DOI) - 1 Aug 2025

Abstract

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal [...] Read more.

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 4517 KiB

Open AccessArticle

A Method Integrating the Matching Field Algorithm for the Three-Dimensional Positioning and Search of Underwater Wrecked Targets

by Huapeng Cao, Tingting Yang and Ka-Fai Cedric Yiu

Sensors 2025, 25(15), 4762; https://doi.org/10.3390/s25154762 (registering DOI) - 1 Aug 2025

Abstract

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching [...] Read more.

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

(This article belongs to the Special Issue Sensor Fusion in Positioning and Navigation)

15 pages, 3678 KiB

Open AccessArticle

Virtual Signal Processing-Based Integrated Multi-User Detection

by Dabao Wang and Zhao Li

Sensors 2025, 25(15), 4761; https://doi.org/10.3390/s25154761 (registering DOI) - 1 Aug 2025

Abstract

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, [...] Read more.

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

(This article belongs to the Section Intelligent Sensors)

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22 pages, 2449 KiB

Open AccessArticle

Tracking Consensus for Nonlinear Multi-Agent Systems Under Asynchronous Switching and Undirected Topology

by Shanyan Hu and Mengling Wang

Sensors 2025, 25(15), 4760; https://doi.org/10.3390/s25154760 (registering DOI) - 1 Aug 2025

Abstract

This paper investigates the tracking consensus of nonlinear multi-agent systems under undirected topology, considering asynchronous switching caused by delays between communication topology switching and controller switching. First, by using the properties of undirected topology graphs, the controller design process is simplified. Then, to [...] Read more.

This paper investigates the tracking consensus of nonlinear multi-agent systems under undirected topology, considering asynchronous switching caused by delays between communication topology switching and controller switching. First, by using the properties of undirected topology graphs, the controller design process is simplified. Then, to address asynchronous delays during topology switching, the system operation is divided into synchronized and delayed modes based on the status of the controller and topology. Every operating mode has a corresponding control strategy. To alleviate the burden of communication and computation, an event-triggered mechanism (ETM) is introduced to reduce the number of controller updates. By constructing an augmented Lyapunov function that incorporates both matching and mismatching periods, sufficient conditions ensuring system stability are established. The required controller based on the dynamic ETM is obtained by solving Linear Matrix Inequalities (LMIs). Finally, a simulation example is conducted to verify its effectiveness. Full article

(This article belongs to the Special Issue Wireless Communication Technologies for Internet of Things and Wireless Sensor Networks)

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23 pages, 3467 KiB

Open AccessArticle

Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data

by David Orlando Salazar Torres, Diyar Altinses and Andreas Schwung

Sensors 2025, 25(15), 4759; https://doi.org/10.3390/s25154759 (registering DOI) - 1 Aug 2025

Abstract

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals [...] Read more.

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 3271 KiB

Open AccessArticle

Mobile App–Induced Mental Fatigue Affects Strength Asymmetry and Neuromuscular Performance Across Upper and Lower Limbs

by Andreas Stafylidis, Walter Staiano, Athanasios Mandroukas, Yiannis Michailidis, Lluis Raimon Salazar Bonet, Marco Romagnoli and Thomas I. Metaxas

Sensors 2025, 25(15), 4758; https://doi.org/10.3390/s25154758 (registering DOI) - 1 Aug 2025

Abstract

This study aimed to investigate the effects of mental fatigue on physical and cognitive performance (lower-limb power, isometric and handgrip strength, and psychomotor vigilance). Twenty-two physically active young adults (12 males, 10 females; M_age = 20.82 ± 1.47) were randomly assigned to [...] Read more.

This study aimed to investigate the effects of mental fatigue on physical and cognitive performance (lower-limb power, isometric and handgrip strength, and psychomotor vigilance). Twenty-two physically active young adults (12 males, 10 females; M_age = 20.82 ± 1.47) were randomly assigned to either a Mental Fatigue (MF) or Control group (CON). The MF group showed a statistically significant (p = 0.019) reduction in non-dominant handgrip strength, declining by approximately 2.3 kg (about 5%), while no such change was observed in the CON group or in dominant handgrip strength across groups. Reaction time (RT) was significantly impaired following the mental fatigue protocol: RT increased by 117.82 ms, representing an approximate 46% longer response time in the MF group (p < 0.001), whereas the CON group showed a smaller, non-significant increase of 32.82 ms (~12% longer). No significant differences were found in squat jump performance, indicating that lower-limb explosive power may be less affected by acute mental fatigue. These findings demonstrate that mental fatigue selectively impairs fine motor strength and cognitive processing speed, particularly reaction time, while gross motor power remains resilient. Understanding these effects is critical for optimizing performance in contexts requiring fine motor control and sustained attention under cognitive load. Full article

(This article belongs to the Special Issue Sensing Human Cognitive Factors)

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25 pages, 2859 KiB

Open AccessArticle

Feature-Based Normality Models for Anomaly Detection

by Hui Yie Teh, Kevin I-Kai Wang and Andreas W. Kempa-Liehr

Sensors 2025, 25(15), 4757; https://doi.org/10.3390/s25154757 (registering DOI) - 1 Aug 2025

Abstract

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important [...] Read more.

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

(This article belongs to the Special Issue Innovative Approaches to Cybersecurity for IoT and Wireless Networks)

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14 pages, 2795 KiB

Open AccessArticle

Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data

by Jiazhi Dai, Mario Rotea and Nasser Kehtarnavaz

Sensors 2025, 25(15), 4756; https://doi.org/10.3390/s25154756 (registering DOI) - 1 Aug 2025

Abstract

Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus [...] Read more.

Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating. Full article

(This article belongs to the Special Issue Sensors Technology Applied in Power Systems and Energy Management)

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14 pages, 21956 KiB

Open AccessArticle

Evaluating Image Quality Metrics as Loss Functions for Image Dehazing

by Rareș Dobre-Baron, Adrian Savu-Jivanov and Cosmin Ancuți

Sensors 2025, 25(15), 4755; https://doi.org/10.3390/s25154755 (registering DOI) - 1 Aug 2025

Abstract

The difficulty and manual nature of procuring human evaluators for ranking the quality of images affected by various types of degradations, and of those cleaned up by developed algorithms, has lead to the widespread adoption of automated metrics, like the Peak Signal-to-Noise Ratio [...] Read more.

The difficulty and manual nature of procuring human evaluators for ranking the quality of images affected by various types of degradations, and of those cleaned up by developed algorithms, has lead to the widespread adoption of automated metrics, like the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM). However, disparities between rankings given by these metrics and those given by human evaluators have encouraged the development of improved image quality assessment (IQA) metrics that are a better fit for this purpose. These methods have been previously used solely for quality assessments and not as objectives in the training of neural networks for high-level vision tasks, despite the potential improvements that may come about by directly optimizing for desired metrics. This paper examines the adequacy of ten recent IQA metrics, compared with standard loss functions, within two trained dehazing neural networks, with observed broad improvement in their performance. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

20 pages, 4782 KiB

Open AccessArticle

Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring

by Jiangfeng Li, Jiahao Qin, Kaimin Kang, Mingzhi Liang, Kunpeng Liu and Xiaohua Ding

Sensors 2025, 25(15), 4754; https://doi.org/10.3390/s25154754 (registering DOI) - 1 Aug 2025

Abstract

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology [...] Read more.

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. Full article

(This article belongs to the Section Navigation and Positioning)

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13 pages, 1717 KiB

Open AccessArticle

High-Performance Hydrogen Gas Sensor Based on Pd-Doped MoS₂/Si Heterojunction

by Enyu Ma, Zihao Xu, Ankai Sun, Shuo Yang and Jianyu Jiang

Sensors 2025, 25(15), 4753; https://doi.org/10.3390/s25154753 (registering DOI) - 1 Aug 2025

Abstract

High-performance hydrogen gas sensors have gained considerable interest for their crucial function in reducing H₂ explosion risk. Although MoS₂ has good potential for chemical sensing, its application in hydrogen detection at room temperature is limited by slow response and incomplete recovery. [...] Read more.

High-performance hydrogen gas sensors have gained considerable interest for their crucial function in reducing H₂ explosion risk. Although MoS₂ has good potential for chemical sensing, its application in hydrogen detection at room temperature is limited by slow response and incomplete recovery. In this work, Pd-doped MoS₂ thin films are deposited on a Si substrate, forming Pd-doped MoS₂/Si heterojunctions via magnetron co-sputtering. The incorporation of Pd nanoparticles significantly enhances the catalytic activity for hydrogen adsorption and facilitates more efficient electron transfer. Owing to its distinct structural characteristics and sharp interface properties, the fabricated Pd-doped MoS₂/Si heterojunction device exhibits excellent H₂ sensing performance under room temperature conditions. The gas sensor device achieves an impressive sensing response of ~6.4 × 10³% under 10,000 ppm H₂ concentration, representing a 110% improvement compared to pristine MoS₂. Furthermore, the fabricated heterojunction device demonstrates rapid response and recovery times (24.6/12.2 s), excellent repeatability, strong humidity resistance, and a ppb-level detection limit. These results demonstrate the promising application prospects of Pd-doped MoS₂/Si heterojunctions in the development of advanced gas sensing devices. Full article

(This article belongs to the Special Issue 2D Materials for Advanced Sensing Technology)

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23 pages, 3580 KiB

Open AccessArticle

Distributed Collaborative Data Processing Framework for Unmanned Platforms Based on Federated Edge Intelligence

by Siyang Liu, Nanliang Shan, Xianqiang Bao and Xinghua Xu

Sensors 2025, 25(15), 4752; https://doi.org/10.3390/s25154752 (registering DOI) - 1 Aug 2025

Abstract

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this [...] Read more.

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning Techniques for Intelligent Sensing Applications)

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24 pages, 23817 KiB

Open AccessArticle

Dual-Path Adversarial Denoising Network Based on UNet

by Jinchi Yu, Yu Zhou, Mingchen Sun and Dadong Wang

Sensors 2025, 25(15), 4751; https://doi.org/10.3390/s25154751 (registering DOI) - 1 Aug 2025

Abstract

Digital image quality is crucial for reliable analysis in applications such as medical imaging, satellite remote sensing, and video surveillance. However, traditional denoising methods struggle to balance noise removal with detail preservation and lack adaptability to various types of noise. We propose a [...] Read more.

Digital image quality is crucial for reliable analysis in applications such as medical imaging, satellite remote sensing, and video surveillance. However, traditional denoising methods struggle to balance noise removal with detail preservation and lack adaptability to various types of noise. We propose a novel three-module architecture for image denoising, comprising a generator, a dual-path-UNet-based denoiser, and a discriminator. The generator creates synthetic noise patterns to augment training data, while the dual-path-UNet denoiser uses multiple receptive field modules to preserve fine details and dense feature fusion to maintain global structural integrity. The discriminator provides adversarial feedback to enhance denoising performance. This dual-path adversarial training mechanism addresses the limitations of traditional methods by simultaneously capturing both local details and global structures. Experiments on the SIDD, DND, and PolyU datasets demonstrate superior performance. We compare our architecture with the latest state-of-the-art GAN variants through comprehensive qualitative and quantitative evaluations. These results confirm the effectiveness of noise removal with minimal loss of critical image details. The proposed architecture enhances image denoising capabilities in complex noise scenarios, providing a robust solution for applications that require high image fidelity. By enhancing adaptability to various types of noise while maintaining structural integrity, this method provides a versatile tool for image processing tasks that require preserving detail. Full article

(This article belongs to the Section Sensing and Imaging)

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20 pages, 4569 KiB

Open AccessArticle

Lightweight Vision Transformer for Frame-Level Ergonomic Posture Classification in Industrial Workflows

by Luca Cruciata, Salvatore Contino, Marianna Ciccarelli, Roberto Pirrone, Leonardo Mostarda, Alessandra Papetti and Marco Piangerelli

Sensors 2025, 25(15), 4750; https://doi.org/10.3390/s25154750 (registering DOI) - 1 Aug 2025

Abstract

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly [...] Read more.

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

(This article belongs to the Special Issue Secure and Decentralised IoT Systems)

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15 pages, 4258 KiB

Open AccessArticle

Complex-Scene SAR Aircraft Recognition Combining Attention Mechanism and Inner Convolution Operator

by Wansi Liu, Huan Wang, Jiapeng Duan, Lixiang Cao, Teng Feng and Xiaomin Tian

Sensors 2025, 25(15), 4749; https://doi.org/10.3390/s25154749 (registering DOI) - 1 Aug 2025

Abstract

Synthetic aperture radar (SAR), as an active microwave imaging system, has the capability of all-weather and all-time observation. In response to the challenges of aircraft detection in SAR images due to the complex background interference caused by the continuous scattering of airport buildings [...] Read more.

Synthetic aperture radar (SAR), as an active microwave imaging system, has the capability of all-weather and all-time observation. In response to the challenges of aircraft detection in SAR images due to the complex background interference caused by the continuous scattering of airport buildings and the demand for real-time processing, this paper proposes a YOLOv7-MTI recognition model that combines the attention mechanism and involution. By integrating the MTCN module and involution, performance is enhanced. The Multi-TASP-Conv network (MTCN) module aims to effectively extract low-level semantic and spatial information using a shared lightweight attention gate structure to achieve cross-dimensional interaction between “channels and space” with very few parameters, capturing the dependencies among multiple dimensions and improving feature representation ability. Involution helps the model adaptively adjust the weights of spatial positions through dynamic parameterized convolution kernels, strengthening the discrete strong scattering points specific to aircraft and suppressing the continuous scattering of the background, thereby alleviating the interference of complex backgrounds. Experiments on the SAR-AIRcraft-1.0 dataset, which includes seven categories such as A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and others, show that the mAP and mRecall of YOLOv7-MTI reach 93.51% and 96.45%, respectively, outperforming Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8. Compared with the basic YOLOv7, mAP is improved by 1.47%, mRecall by 1.64%, and FPS by 8.27%, achieving an effective balance between accuracy and speed, providing research ideas for SAR aircraft recognition. Full article

(This article belongs to the Section Radar Sensors)

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20 pages, 5647 KiB

Open AccessArticle

Research on the Improved ICP Algorithm for LiDAR Point Cloud Registration

by Honglei Yuan, Guangyun Li, Li Wang and Xiangfei Li

Sensors 2025, 25(15), 4748; https://doi.org/10.3390/s25154748 (registering DOI) - 1 Aug 2025

Abstract

Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most [...] Read more.

Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most engineering and industrial measurement applications, the accuracy and density of LiDAR point clouds are highly dependent on laser scanners, leading to significant variability that critically affects registration quality. Key factors influencing point cloud accuracy include scanning distance, incidence angle, and the surface characteristics of the target. Notably, in short-range scanning scenarios, incidence angle emerges as the dominant error source. Building on this insight, this study systematically investigates the relationship between scanning incidence angles and point cloud quality. We propose an incident-angle-dependent weighting function for point cloud observations, and further develop an improved weighted Iterative Closest Point (ICP) registration algorithm. Experimental results demonstrate that the proposed method achieves approximately 30% higher registration accuracy compared to traditional ICP algorithms and a 10% improvement over Faro SCENE’s proprietary solution. Full article

(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)

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11 pages, 6279 KiB

Open AccessCommunication

Low-Profile Broadband Filtering Antennas for Vehicle-to-Vehicle Applications

by Shengtao Chen and Wang Ren

Sensors 2025, 25(15), 4747; https://doi.org/10.3390/s25154747 (registering DOI) - 1 Aug 2025

Abstract

This paper proposes a compact, broadband, and low-profile filtering antenna designed for Sub-6 GHz communication. By applying characteristic mode analysis to the radiating elements, the operational mechanism of the antenna is clearly elucidated. The current cancellation among different radiating elements results in two [...] Read more.

This paper proposes a compact, broadband, and low-profile filtering antenna designed for Sub-6 GHz communication. By applying characteristic mode analysis to the radiating elements, the operational mechanism of the antenna is clearly elucidated. The current cancellation among different radiating elements results in two radiation nulls in the primary radiation direction, effectively enhancing the filtering effect. The antenna achieves a wide operational bandwidth (

S_{11} \leq - 10

dB) of 35.9% (4.3–6.4 GHz), making it highly suitable for Sub-6 GHz communication systems. Despite its compact size of 25 × 25 mm², the antenna consistently maintains stable broadside radiation patterns, with a peak gain of 6.14 dBi and a minimal gain fluctuation of less than 1 dBi at 4.6–6.45 GHz. This design ensures reliable and robust communication performance for V2V systems operating in the designated frequency band. Full article

(This article belongs to the Section Communications)

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21 pages, 3864 KiB

Open AccessArticle

Sub-MHz EMAR for Non-Contact Thickness Measurement: How Ultrasonic Wave Directivity Affects Accuracy

by Alexander Siegl, David Auer, Bernhard Schweighofer, Andre Hochfellner, Gerald Klösch and Hannes Wegleiter

Sensors 2025, 25(15), 4746; https://doi.org/10.3390/s25154746 (registering DOI) - 1 Aug 2025

Abstract

Electromagnetic acoustic resonance (EMAR) is a well-established non-contact method for ultrasonic thickness measurement, typically operated at frequencies above 1 MHz using an electromagnetic acoustic transducer (EMAT). This study successfully extends EMAR into the sub-MHz range, allowing supply voltages below 60 V and thus [...] Read more.

Electromagnetic acoustic resonance (EMAR) is a well-established non-contact method for ultrasonic thickness measurement, typically operated at frequencies above 1 MHz using an electromagnetic acoustic transducer (EMAT). This study successfully extends EMAR into the sub-MHz range, allowing supply voltages below 60 V and thus offering safer and more cost-effective operation. Experiments were conducted on copper blocks approximately 20 mm thick, where a relative thickness accuracy of better than 0.2% is obtained. Regarding this result, the research identifies a critical design principle: Stable thickness resonances and subsequently accurate thickness measurement are achieved when the ratio of ultrasonic wavelength to EMAT track width (

λ

/w) falls below 1. This minimizes the excitation and interactions with structural eigenmodes, ensuring consistent measurement reliability. To support this, the study introduces a system-based model to simulate the EMAR method. The model provides detailed insights into how wave propagation affects the accuracy of EMAR measurements. Experimental results align well with the simulation outcome and confirm the feasibility of EMAR in the sub-MHz regime without compromising precision. These findings highlight the potential of low-voltage EMAR as a safer, cost-effective, and highly accurate approach for industrial ultrasonic thickness measurements. Full article

(This article belongs to the Special Issue Electromagnetic Sensing and Its Applications)

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