Sensors

19 pages, 4477 KB

Open AccessArticle

Non-Contact Heart Rate Variability Monitoring with FMCW Radar via a Novel Signal Processing Algorithm

by Guangyu Cui, Yujie Wang, Xinyi Zhang, Jiale Li, Xinfeng Liu, Bijie Li, Jiayi Wang and Quan Zhang

Sensors 2025, 25(17), 5607; https://doi.org/10.3390/s25175607 - 8 Sep 2025

Abstract

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable [...] Read more.

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Full article

(This article belongs to the Section Biomedical Sensors)

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28 pages, 12692 KB

Open AccessArticle

In-Orbit Optimal Safe Formation Control for Surrounding an Unknown Huge Target with Specific Structure by Using Relative Sensors Only

by Bosong Wei, Cong Li, Zhaohui Dang and Xiaokui Yue

Sensors 2025, 25(17), 5606; https://doi.org/10.3390/s25175606 - 8 Sep 2025

Abstract

The issue of in-orbit optimal safe surrounding control for service satellite (SSat) formation against a huge unknown target satellite (TSat) with specific structures is solved by using relative measurements only, and an optimal cooperative safe surrounding (OCSS) hybrid controller achieving both target tracking [...] Read more.

The issue of in-orbit optimal safe surrounding control for service satellite (SSat) formation against a huge unknown target satellite (TSat) with specific structures is solved by using relative measurements only, and an optimal cooperative safe surrounding (OCSS) hybrid controller achieving both target tracking (TT) and configuration tracking (CT) is proposed corresponding to the two equal sub-objectives. Facing the challenges caused by incomplete information of the TSat, by using relative measurements only, the initial-condition-free boundaries are constructed by an arctan-based state transformation to directly constrain the target tracking error to perform prescribed transient and steady-state behaviors. Based on the shared TT control law, optimal collision-free CT controllers for all SSats are further solved via a nonzero-sum differential game, where the collision threat from all SSats and target structures are modeled by a novel circumscribed-sphere model. Finally, the effectiveness and advantages of the proposed OCSS control technique is verified by simulation results. Full article

(This article belongs to the Section Sensors and Robotics)

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24 pages, 4006 KB

Open AccessArticle

Online Centralized MPC for Lane Merging in Vehicle Platoons

by Shila Alizadehghobadi, Mukesh Singhal and Reza Ehsani

Sensors 2025, 25(17), 5605; https://doi.org/10.3390/s25175605 - 8 Sep 2025

Abstract

In the context of autonomous vehicles, proper lane merging is critical as it can reduce the traffic bottleneck and lead to safer road transportation. To obtain a collision-free and efficient lane merging, advanced control algorithms need to be designed to smoothly coordinate multiple [...] Read more.

In the context of autonomous vehicles, proper lane merging is critical as it can reduce the traffic bottleneck and lead to safer road transportation. To obtain a collision-free and efficient lane merging, advanced control algorithms need to be designed to smoothly coordinate multiple vehicles to form a platoon. Model predictive control (MPC) is such a controller capable of forecasting future states of multiple vehicles by optimizing their control inputs while satisfying the constraints. Prior MPC-based studies mostly utilized offline planning with a precomputed lookup table of feasible maneuvers to model lane merging. Although these model designs reduce the online computational load, they lack flexibility, as they rely on predefined scenarios and cannot easily adapt to dynamic or unpredictable situations. In this study, we present a centralized MPC framework capable of online trajectory tracking under dynamic constraints and disturbances, for collision-free operation in tightly spaced multi-vehicle platoons. To evaluate the flexibility of our online algorithm, we examine the role of prediction horizon—the time window over which future states are forecasted—and platoon size in determining both the feasibility and efficiency of merging maneuvers. Our results reveal that there exists an optimal prediction horizon at which braking and acceleration can be minimized, thereby reducing energy consumption by 35–40%. Additionally, we observe that increasing the prediction horizon beyond the minimum required for feasibility can alter the vehicle sequence in the platoon. Capturing the changes in vehicle sequence (e.g., who leads or yields) when prediction horizon varies, is a consequence of online trajectory optimization. This vehicle sequence change cannot be captured by offline planning that relies on precomputed look-up table maneuvers. We also found that as the number of vehicles increases, the minimum feasible prediction horizon increases significantly. Full article

(This article belongs to the Section Vehicular Sensing)

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15 pages, 3118 KB

Open AccessCommunication

Two-Stage Marker Detection–Localization Network for Bridge-Erecting Machine Hoisting Alignment

by Lei Li, Zelong Xiao and Taiyang Hu

Sensors 2025, 25(17), 5604; https://doi.org/10.3390/s25175604 - 8 Sep 2025

Abstract

To tackle the challenges of complex construction environment interference (e.g., lighting variations, occlusion, and marker contamination) and the demand for high-precision alignment during the hoisting process of bridge-erecting machines, this paper presents a two-stage marker detection–localization network tailored to hoisting alignment. The proposed [...] Read more.

To tackle the challenges of complex construction environment interference (e.g., lighting variations, occlusion, and marker contamination) and the demand for high-precision alignment during the hoisting process of bridge-erecting machines, this paper presents a two-stage marker detection–localization network tailored to hoisting alignment. The proposed network adopts a “coarse detection–fine estimation” phased framework; the first stage employs a lightweight detection module, which integrates a dynamic hybrid backbone (DHB) and dynamic switching mechanism to efficiently filter background noise and generate coarse localization boxes of marker regions. Specifically, the DHB dynamically switches between convolutional and Transformer branches to handle features of varying complexity (using depthwise separable convolutions from MobileNetV3 for low-level geometric features and lightweight Transformer blocks for high-level semantic features). The second stage constructs a Transformer-based homography estimation module, which leverages multi-head self-attention to capture long-range dependencies between marker keypoints and the scene context. By integrating enhanced multi-scale feature interaction and position encoding (combining the absolute position and marker geometric priors), this module achieves the end-to-end learning of precise homography matrices between markers and hoisting equipment from the coarse localization boxes. To address data scarcity in construction scenes, a multi-dimensional data augmentation strategy is developed, including random homography transformation (simulating viewpoint changes), photometric augmentation (adjusting brightness, saturation, and contrast), and background blending with bounding box extraction. Experiments on a real bridge-erecting machine dataset demonstrate that the network achieves detection accuracy (mAP) of 97.8%, a homography estimation reprojection error of less than 1.2 mm, and a processing frame rate of 32 FPS. Compared with traditional single-stage CNN-based methods, it significantly improves the alignment precision and robustness in complex environments, offering reliable technical support for the precise control of automated hoisting in bridge-erecting machines. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision Sensing Technologies in Engineering Applications and Digital Twins)

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20 pages, 1817 KB

Open AccessArticle

DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and Test-Time Adaptation

by Christian Weihsbach, Christian N. Kruse, Alexander Bigalke and Mattias P. Heinrich

Sensors 2025, 25(17), 5603; https://doi.org/10.3390/s25175603 - 8 Sep 2025

Abstract

Applying pre-trained medical deep learning segmentation models to out-of-domain images often yields predictions of insufficient quality. In this study, we propose using a robust generalizing descriptor, along with augmentation, to enable domain-generalized pre-training and test-time adaptation, thereby achieving high-quality segmentation in unseen domains. [...] Read more.

Applying pre-trained medical deep learning segmentation models to out-of-domain images often yields predictions of insufficient quality. In this study, we propose using a robust generalizing descriptor, along with augmentation, to enable domain-generalized pre-training and test-time adaptation, thereby achieving high-quality segmentation in unseen domains. In this study, five different publicly available datasets, including 3D CT and MRI images, are used to evaluate segmentation performance in out-of-domain scenarios. The settings include abdominal, spine, and cardiac imaging. Domain-generalized pre-training on source data is used to obtain the best initial performance in the target domain. We introduce a combination of the generalizing SSC descriptor and GIN intensity augmentation for optimal generalization. Segmentation results are subsequently optimized at test time, where we propose adapting the pre-trained models for every unseen scan using a consistency scheme with the augmentation–descriptor combination. The proposed generalized pre-training and subsequent test-time adaptation improve model performance significantly in CT to MRI cross-domain prediction for abdominal (+46.2 and +28.2 Dice), spine (+72.9), and cardiac (+14.2 and +55.7 Dice) scenarios (p < 0.001). Our method enables the optimal, independent use of source and target data, successfully bridging domain gaps with a compact and efficient methodology. Full article

(This article belongs to the Collection Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing)

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19 pages, 2548 KB

Open AccessArticle

Random Access Preamble Design for 6G Satellite–Terrestrial Integrated Communication Systems

by Min Hua, Zhongqiu Wu, Cong Zhang, Zeyang Xu, Xiaoming Liu and Wen Zhou

Sensors 2025, 25(17), 5602; https://doi.org/10.3390/s25175602 - 8 Sep 2025

Abstract

Satellite–terrestrial integrated communication systems (STICSs) are envisioned to provide ubiquitous, seamless connectivity in next-generation (6G) wireless communication networks for massive-scale Internet of Things (IoT) deployments. This global coverage extends beyond densely populated areas to remote regions (e.g., polar zones, open oceans, deserts) and [...] Read more.

Satellite–terrestrial integrated communication systems (STICSs) are envisioned to provide ubiquitous, seamless connectivity in next-generation (6G) wireless communication networks for massive-scale Internet of Things (IoT) deployments. This global coverage extends beyond densely populated areas to remote regions (e.g., polar zones, open oceans, deserts) and disaster-prone areas, supporting diverse IoT applications, including remote sensing, smart cities, intelligent agriculture/forestry, environmental monitoring, and emergency reporting. Random access signals, which constitute the initial transmission from access IoT devices to base station for unscheduled transmissions or network entry in terrestrial networks (TNs), encounter significant challenges in STICSs due to inherent satellite characteristics: wide coverage, large-scale access, substantial round-trip delay, and high carrier frequency offset (CFO). Consequently, conventional TN preamble designs based on Zadoff–Chu (ZC) sequences, as used in 4G LTE and 5G NR systems, are unsuitable for direct deployment in 6G STICSs. This paper first analyzes the challenges in adapting terrestrial designs to STICSs. It then proposes a CFO-resistant preamble design specifically tailored for STICSs and details its detection procedure. Furthermore, a dedicated root set selection algorithm for the proposed preambles is presented, generating an expanded pool of random access signals to meet the demands of increasing IoT device access. The developed analytical framework provides a foundation for performance analysis of random access signals in 6G STICSs. Full article

(This article belongs to the Special Issue 5G/6G Networks for Wireless Communication and IoT)

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31 pages, 5528 KB

Open AccessArticle

Gradient-Based Time-Extended Potential Field Method for Real-Time Path Planning in Infrastructure-Based Cooperative Driving Systems

by Jakyung Ko and Inchul Yang

Sensors 2025, 25(17), 5601; https://doi.org/10.3390/s25175601 - 8 Sep 2025

Abstract

This study proposes a real-time path generation method called the Gradient-based Time-extended Potential Field (GT-PF) for cooperative autonomous driving environments. The proposed approach models the road environment and dynamic obstacles as a time-variant potential field and generates safe and feasible paths by tracing [...] Read more.

This study proposes a real-time path generation method called the Gradient-based Time-extended Potential Field (GT-PF) for cooperative autonomous driving environments. The proposed approach models the road environment and dynamic obstacles as a time-variant potential field and generates safe and feasible paths by tracing the negative gradient of the field, which corresponds to the direction of steepest descent. In contrast to conventional sampling-based or optimization-based methods, the proposed PF framework enables lightweight computation and continuous trajectory generation in spatiotemporal domains. Furthermore, a velocity-oriented bias is introduced in the PF formulation to ensure that the generated paths satisfy the vehicle’s kinematic constraints and desired cruising behavior. The effectiveness of the proposed method is verified through comparative simulations against a sampling-based Rapidly exploring Random Tree (RRT) planner. Results demonstrate that the GT-PF approach exhibits superior performance in terms of runtime efficiency and safety. The system is particularly suitable for RSU (Roadside Unit)-based infrastructure control in real-time traffic environments. Future work includes the extension to complex urban scenarios, integration with multi-agent planning frameworks, and deployment in sensor-fused cooperative perception systems. Full article

(This article belongs to the Section Vehicular Sensing)

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33 pages, 4751 KB

Open AccessArticle

U-ResNet, a Novel Network Fusion Method for Image Classification and Segmentation

by Wenkai Li, Zhe Gao and Yaqing Song

Sensors 2025, 25(17), 5600; https://doi.org/10.3390/s25175600 - 8 Sep 2025

Abstract

Image classification and segmentation are important tasks in computer vision. ResNet and U-Net are representative networks for image classification and image segmentation, respectively. Although many scholars used to fuse these two networks, most integration focuses on image segmentation with U-Net, overlooking the capabilities [...] Read more.

Image classification and segmentation are important tasks in computer vision. ResNet and U-Net are representative networks for image classification and image segmentation, respectively. Although many scholars used to fuse these two networks, most integration focuses on image segmentation with U-Net, overlooking the capabilities of ResNet for image classification. In this paper, we propose a novel U-ResNet structure by combining U-Net’s convolution–deconvolution structure (UBlock) with ResNet’s residual structure (ResBlock) in a parallel manner. This novel parallel structure achieves rapid convergence and high accuracy in image classification and segmentation while also efficiently alleviating the vanishing gradient problem. Specifically, in the UBlock, the pixel-level features of both high- and low-resolution images are extracted and processed. In the ResBlock, a Selected Upsampling (SU) module was introduced to enhance performance on low-resolution datasets, and an improved Efficient Upsampling Convolutional Block (EUCB*) with a Channel Shuffle mechanism was added before the output of the ResBlock to enhance network convergence. Features from both the ResBlock and UBlock were merged for better decision making. This architecture outperformed the state-of-the-art (SOTA) models in both image classification and segmentation tasks on open-source and private datasets. Functions of individual modules were further verified via ablation studies. The superiority of the proposed U-ResNet displays strong feasibility and potential for advanced cross-paradigm tasks in computer vision. Full article

(This article belongs to the Section Sensing and Imaging)

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19 pages, 5858 KB

Open AccessArticle

An Improved Extended Wavenumber Domain Imaging Algorithm for Ultra-High-Resolution Spotlight SAR

by Gui Wang, Yao Gao and Weidong Yu

Sensors 2025, 25(17), 5599; https://doi.org/10.3390/s25175599 - 8 Sep 2025

Abstract

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error [...] Read more.

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error introduced by stop-and-go assumption. To this end, this paper proposes an improved extended wavenumber domain imaging algorithm for ultra-high-resolution SAR to systematically address the imaging quality degradation caused by these challenges. In the proposed algorithm, the one-step motion compensation method is employed to compensate for the errors caused by orbital curvature through range-dependent envelope shift interpolation and phase function correction. Then, the interpolation based on modified Stolt mapping is performed, thereby facilitating effective separation of the range and azimuth focusing. Finally, the residual range cell migration correction is applied to eliminate range position errors, followed by azimuth compression to achieve high-precision focusing. Both simulation and spaceborne data experiments are performed to verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Remote Sensing: Challenges and Future Direction)

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25 pages, 5440 KB

Open AccessArticle

Fast Path Planning for Kinematic Smoothing of Robotic Manipulator Motion

by Hui Liu, Yunfan Li, Zhaofeng Yang and Yue Shen

Sensors 2025, 25(17), 5598; https://doi.org/10.3390/s25175598 - 8 Sep 2025

Abstract

The Rapidly-exploring Random Tree Star (RRT*) algorithm is widely applied in robotic manipulator path planning, yet it does not directly consider motion control, where abrupt changes may cause shocks and vibrations, reducing accuracy and stability. To overcome this limitation, this paper proposes the [...] Read more.

The Rapidly-exploring Random Tree Star (RRT*) algorithm is widely applied in robotic manipulator path planning, yet it does not directly consider motion control, where abrupt changes may cause shocks and vibrations, reducing accuracy and stability. To overcome this limitation, this paper proposes the Kinematically Smoothed, dynamically Biased Bidirectional Potential-guided RRT* (KSBB-P-RRT*) algorithm, which unifies path planning and motion control and introduces three main innovations. First, a fast path search strategy on the basis of Bi-RRT* integrates adaptive sampling and steering to accelerate exploration and improve efficiency. Second, a triangle-inequality-based optimization reduces redundant waypoints and lowers path cost. Third, a kinematically constrained smoothing strategy adapts a Jerk-Continuous S-Curve scheme to generate smooth and executable trajectories, thereby integrating path planning with motion control. Simulations in four environments show that KSBB-P-RRT* achieves at least 30% reduction in planning time and at least 3% reduction in path cost, while also requiring fewer iterations compared with Bi-RRT*, confirming its effectiveness and suitability for complex and precision-demanding applications such as agricultural robotics. Full article

(This article belongs to the Section Sensors and Robotics)

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20 pages, 3509 KB

Open AccessArticle

FM-Net: A New Method for Detecting Smoke and Flames

by Jingwu Wang, Yuan Yao, Yinuo Huo and Jinfu Guan

Sensors 2025, 25(17), 5597; https://doi.org/10.3390/s25175597 - 8 Sep 2025

Abstract

Aiming at the core problem of high false and missed alarm rate and insufficient interference resistance of existing smoke and fire detection algorithms in complex scenes, this paper proposes a target detection network based on improved feature pyramid structure. By constructing a Context [...] Read more.

Aiming at the core problem of high false and missed alarm rate and insufficient interference resistance of existing smoke and fire detection algorithms in complex scenes, this paper proposes a target detection network based on improved feature pyramid structure. By constructing a Context Guided Convolutional Block instead of the traditional convolutional operation, the detected target and the surrounding environment information are fused with secondary features while reconfiguring the feature dimensions, which effectively solves the problem of edge feature loss in the down-sampling process. The Poly Kernel Inception Block is designed, and a multi-branch parallel network structure is adopted to realize multi-scale feature extraction of the detected target, and the collaborative characterization of the flame profile and smoke diffusion pattern is realized. In order to further enhance the logical location sensing ability of the target, a Manhattan Attention Mechanism Unit is introduced to accurately capture the spatial and temporal correlation characteristics of the flame and smoke by establishing a pixel-level long-range dependency model. Experimental tests are conducted using a self-constructed high-quality smoke and fire image dataset, and the results show that, compared with the existing typical lightweight smoke and fire detection models, the present algorithm has a significant advantage in detection accuracy, and it can satisfy the demand for real-time detection. Full article

(This article belongs to the Section Sensor Networks)

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20 pages, 1307 KB

Open AccessArticle

Designand Implementation of a Novel Wideband HF Communication System Based on NC-OFDM and Probabilistic Shaping

by Rifei Yang, Yong Bai and Zhuhua Hu

Sensors 2025, 25(17), 5596; https://doi.org/10.3390/s25175596 - 8 Sep 2025

Abstract

This paper proposes a novel wideband high-frequency (HF) communication system supporting video transmission based on non-contiguous orthogonal frequency division multiplexing (NC-OFDM) and probabilistic shaping (PS). The HF spectrum is currently very crowded; to find a free continuous frequency band around 500 KHz for [...] Read more.

This paper proposes a novel wideband high-frequency (HF) communication system supporting video transmission based on non-contiguous orthogonal frequency division multiplexing (NC-OFDM) and probabilistic shaping (PS). The HF spectrum is currently very crowded; to find a free continuous frequency band around 500 KHz for video transmission is almost impossible. So this paper investigates how to exploit spectrum holes in the HF band with NC-OFDM technology. We propose a transmission scheme over a wideband HF channel modeled by the Institute for Telecommunication Sciences (ITS) channel model with valid bandwidth up to 1 MHz. In order to improve the reliability of proposed scheme, this paper further investigates the probabilistic shaping-based coding modulation. Simulation results show that the designed wideband HF NC-OFDM communication system can meet the data rate required for video transmission. In addition, the probabilistic shaping-based coding modulation provides a significant performance improvement over uncoded systems and the probabilistic shaping offers an extra 0.6 dB shaping gain in the wideband HF channel compared to equal probability constellation systems. Full article

(This article belongs to the Special Issue Integrated Sensing and Communications (ISAC): Opportunities and Challenges)

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20 pages, 1906 KB

Open AccessArticle

Research on the Directional Measurement Method of Three-Dimensional Electric Field Intensity Components of the Atmosphere Based on the Geographic Coordinate System in the Airborne Model

by Wei Zhao, Zhizhong Li and Haitao Zhang

Sensors 2025, 25(17), 5595; https://doi.org/10.3390/s25175595 - 8 Sep 2025

Viewed by 87

Abstract

Due to the difficulty of achieving absolutely precise coaxial installation of measurement components, a coaxial error will occur between the measurement axis of the sensor and that of the electronic compass when conducting the measurement of the airborne atmospheric electric field intensity based [...] Read more.

Due to the difficulty of achieving absolutely precise coaxial installation of measurement components, a coaxial error will occur between the measurement axis of the sensor and that of the electronic compass when conducting the measurement of the airborne atmospheric electric field intensity based on the geographic coordinate system under the airborne mode of unmanned aerial vehicles. This error leads to less satisfactory measurement accuracy of the atmospheric electric field intensity components based on the geographic coordinate system. In this study, the angle between the measurement axis of the sensor and that of the compass is set as the parameter to be identified, and a modified model for three-dimensional electric field orientation decomposition based on geographic coordinates is constructed. In view of the characteristics of the nonlinear equations of this model, an algorithm based on tansig–Nonlinear Dynamic Inertia Weight Adaptive Particle Swarm Optimization (NDIWAPSO) is proposed to solve the modified model, successfully addressing the problem of insufficient measurement accuracy of the electric field intensity components in the geographic coordinate system caused by the coaxial error. The experimental results show that the parameters of the three-dimensional electric field orientation decomposition modified model can be accurately identified by the algorithm proposed in this paper, improving the measurement accuracy of the atmospheric electric field intensity components based on the geographic coordinate system and laying a necessary foundation for lightning warning. Full article

(This article belongs to the Section Physical Sensors)

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23 pages, 4487 KB

Open AccessArticle

Lightweight Anonymous Authentication for IoT: A Taxonomy and Survey of Security Frameworks

by Jian Zhong, Sheng He, Zhicai Liu and Ling Xiong

Sensors 2025, 25(17), 5594; https://doi.org/10.3390/s25175594 - 8 Sep 2025

Viewed by 55

Abstract

The resource-constrained nature of Internet of Things (IoT) devices necessitates authentication mechanisms built upon lightweight cryptographic primitives, such as symmetric key algorithms and hash functions. In response to demands for user anonymity and forward secrecy, numerous innovative authentication schemes have emerged. This work [...] Read more.

The resource-constrained nature of Internet of Things (IoT) devices necessitates authentication mechanisms built upon lightweight cryptographic primitives, such as symmetric key algorithms and hash functions. In response to demands for user anonymity and forward secrecy, numerous innovative authentication schemes have emerged. This work presents a systematic review of these state-of-the-art approaches. We introduce a structured classification by synthesizing the field into nine distinct sub-frameworks, each focused on either user anonymity or forward secrecy. These are then integrated into two general frameworks that provide both properties. Our analysis illuminates the design principles, security guarantees, and performance trade-offs inherent to each framework. Building on this classification, we comparatively evaluate the security features and performance metrics of 45 representative schemes. Ultimately, this work seeks to enhance the understanding of current challenges and foster further advancement in IoT security. Full article

(This article belongs to the Section Internet of Things)

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13 pages, 637 KB

Open AccessArticle

Influence of Sex and Body Size on the Validity of the Microsoft Kinect for Frontal Plane Knee Kinematics During Landings

by Jillian Neufeld, Vital Nwaokoro and Derek N. Pamukoff

Sensors 2025, 25(17), 5593; https://doi.org/10.3390/s25175593 - 8 Sep 2025

Viewed by 86

Abstract

Three-dimensional (3D) motion capture is inaccessible, and the Microsoft Kinect is an alternative to measure surrogates of knee valgus that may contribute to anterior cruciate ligament (ACL) injury risk. We evaluated the influence of sex and body size on the agreement between methods. [...] Read more.

Three-dimensional (3D) motion capture is inaccessible, and the Microsoft Kinect is an alternative to measure surrogates of knee valgus that may contribute to anterior cruciate ligament (ACL) injury risk. We evaluated the influence of sex and body size on the agreement between methods. A total of 40 (10 per sex and BMI group) participants were included. The Kinect and motion capture measured knee ankle separation ratio (KASR) and knee abduction angles (KAAs). Intraclass correlation coefficients (ICCs) evaluated agreement between methods. 2 (sex) by 2 (BMI) by 2 (method) ANOVA compared kinematics between groups. Agreement between methods was moderate-to-good for KASR (initial contact ICCs 0.667–0.86; peak flexion ICCs 0.766–0.882). Agreement for KAA was low-to-moderate (initial contact ICCs 0.128–0.575; peak flexion ICCs 0.315–0.760). There was a BMI-by-method interaction for KASR at initial contact (p < 0.01) and a main effect of method (p < 0.01). There were BMI-by-method interactions for KAA (initial contact p > 0.01; peak knee flexion p < 0.01). The high BMI group had greater KAAs than the low BMI group, but only using motion capture. The Kinect is an alternative for measuring KASR, but not KAA. The high BMI group had greater KAAs than the low BMI group, but only when measured with motion capture. Full article

(This article belongs to the Special Issue Sensor-Based Methods for Kinematics, Kinetics, and Physiology in Motion: Applications and Metrological Characterization)

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15 pages, 1786 KB

Open AccessArticle

Application of Gaussian SVM Flame Detection Model Based on Color and Gradient Features in Engine Test Plume Images

by Song Yan, Yushan Gao, Zhiwei Zhang and Yi Li

Sensors 2025, 25(17), 5592; https://doi.org/10.3390/s25175592 - 8 Sep 2025

Viewed by 161

Abstract

This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation [...] Read more.

This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation system—captured plume images for analysis. To detect abnormal flame phenomena in the plume, a Gaussian support vector machine (SVM) model was developed using image features that were derived from both color and gradient information. Six representative frames containing visible flames were selected from a single test failure video. These images were segmented in the YCbCr color space using the k-means clustering algorithm to distinguish flame and non-flame pixels. A 10-dimensional feature vector was constructed for each pixel and then reduced to five dimensions using the Maximum Relevance Minimum Redundancy (mRMR) method. The reduced vectors were used to train the Gaussian SVM model. The model achieved a 97.6% detection accuracy despite being trained on a limited dataset. It has been successfully applied in multiple subsequent engine tests, and it has proven effective in detecting ablation-related anomalies. By combining real-world sensor data acquisition with intelligent image-based analysis, this work enhances the monitoring capabilities in rocket engine development. Full article

(This article belongs to the Special Issue Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition)

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18 pages, 4180 KB

Open AccessArticle

The Modified Scaled Adaptive Daqrouq Wavelet for Biomedical Non-Stationary Signals Analysis

by Khaled Daqrouq and Rania A. Alharbey

Sensors 2025, 25(17), 5591; https://doi.org/10.3390/s25175591 - 8 Sep 2025

Viewed by 253

Abstract

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the [...] Read more.

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the specific characteristics of the signal, allowing it to outperform conventional wavelet methods. The system reaches adaptability through three core methods featuring gradient-dependent scale adjustments for fast transient detection and smooth regions, and instantaneous frequency monitoring achieved by a combination of STFT and Hilbert transforms and an iterative error reduction process using gradient descent and genetic algorithms. Continuous Wavelet Transform (CWT) combined with Discrete Wavelet Transform (DWT) extracts features from ECG and speech signals. Throughout this process, MSADW maintains great time precision to detect transients as well as maintain sensitivity for the audio’s base stability. Testing MSADW in practical use reveals its superior performance because it detects R-peaks accurately within 0.01 s through zero-crossing methods, which combine P/T-wave detection with effective ECG signal segmentation and noise-free reconstructed speech (

MSE: 1.17 \times 10^{- 31}

). The localized parameterization framework of MSADW, enabled by feedback refinement, fulfills missing aspects in biomedical signal evaluation and creates space for low-cost real-time evaluation methods for medical devices and arrhythmia and ischemic detection platforms. The theoretical backbone for MSADW establishes itself because this work shows how wavelet analysis can transition toward managing non-stationary and noise-prone domains. Full article

(This article belongs to the Special Issue Biosignal Sensing Analysis (EEG, EMG, ECG, PPG) (2nd Edition))

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6 pages, 159 KB

Open AccessEditorial

Advances in Magnetic Sensors and Their Applications

by Nicholas Sammut and Marco Calvi

Sensors 2025, 25(17), 5590; https://doi.org/10.3390/s25175590 - 8 Sep 2025

Viewed by 268

Abstract

Magnetic sensors are fundamental to a vast array of scientific and technological endeavours, permeating disciplines from fundamental physics [...] Full article

(This article belongs to the Special Issue Advances in Magnetic Sensors and Their Applications)

20 pages, 3802 KB

Open AccessArticle

Research on Takeover Safety of Intelligent Vehicles Based on Accident Scenarios in Real-Vehicle Testing

by Pingfei Li, Meiling Zhou, Chang Xu, He Li, Wenhao Hu, Zhengping Tan, Lingyun Xiao, Xiaojun Mou and Hao Feng

Sensors 2025, 25(17), 5589; https://doi.org/10.3390/s25175589 - 7 Sep 2025

Viewed by 1068

Abstract

With the increasing emergence of intelligent vehicles, novel accident patterns have gradually emerged. In human–machine cooperative driving (HMCD) states, despite driving automation systems being capable of controlling lateral and longitudinal vehicle motions over extended periods, functional limitations persist in specific scenarios due to [...] Read more.

With the increasing emergence of intelligent vehicles, novel accident patterns have gradually emerged. In human–machine cooperative driving (HMCD) states, despite driving automation systems being capable of controlling lateral and longitudinal vehicle motions over extended periods, functional limitations persist in specific scenarios due to insufficient expected functionalities. When combined with risk factors, such as driver distraction, these limitations significantly elevate accident risks. This study investigated takeover safety through real vehicle testing in two typical accident scenarios: large-curvature curves and static obstacles. The key findings include the following: (1) in scenarios involving large curvature curves and static obstacles, vehicles are prone to lane departure and missed target detection, which are typical dangerous scenarios; (2) during the human–machine cooperative driving phase, the design of the driving automation system should focus on enhancing driver engagement in driving tasks, while in the autonomous driving phase, the vehicle’s early warning capabilities should be strengthened; (3) the takeover request for longitudinal control requires at least 4.12 s of driver reaction time, while the takeover request for lateral control requires at least 1.87 s. This study provides important theoretical and practical references for safety in designing assisted driving systems and the testing of hazardous scenarios. Full article

(This article belongs to the Section Vehicular Sensing)

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21 pages, 5241 KB

Open AccessArticle

Spatiotemporal Variation of Burnt Area Detected from High-Resolution Sentinel-2 Observation During the Post-Monsoon Fire Seasons of 2022–2024 in Punjab, India

by Ardhi Adhary Arbain and Ryoichi Imasu

Sensors 2025, 25(17), 5588; https://doi.org/10.3390/s25175588 - 7 Sep 2025

Viewed by 655

Abstract

Underestimation of PM_2.5 emissions from the agricultural sector persists as a major difficulty for air quality studies, partly because of underutilization of high-resolution observation platforms for constructing a global emissions inventory. Coarse-resolution products used for such purposes often miss fine-scale burnt areas [...] Read more.

Underestimation of PM_2.5 emissions from the agricultural sector persists as a major difficulty for air quality studies, partly because of underutilization of high-resolution observation platforms for constructing a global emissions inventory. Coarse-resolution products used for such purposes often miss fine-scale burnt areas created by stubble-burning practices, which are primary sources of agricultural PM_2.5 emissions. For this study, we used the high-resolution Sentinel-2 observations to examine the spatiotemporal variability of burnt areas in Punjab, a major hotspot of agricultural burning in India, during the post-monsoon fire season (October–December) in 2022–2024. The results highlight the Sentinel-2 capability of detecting more than 34,000 km² of burnt areas (approx. 68% of Punjab’s total area) as opposed to the less than 7000 km² (approx. 12% of Punjab’s total area) detected by MODIS. The study also reveals, in unprecedented detail, multi-annual spatial and temporal shifting of burning events from northern to central and southern Punjab. This detection discrepancy has led to marked disparities in estimated monthly emissions, with approximately 217.3 million tons of PM_2.5 emitted in October 2022 compared to 8.7 million tons found by EDGAR v.8.1. This underscores higher-resolution observation systems intended to support construction of a global PM_2.5 emissions inventory. Full article

(This article belongs to the Section Remote Sensors)

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25 pages, 69171 KB

Open AccessArticle

CrackNet-Weather: An Effective Pavement Crack Detection Method Under Adverse Weather Conditions

by Wei Wang, Xiaoru Yu, Bin Jing, Ziqi Tang, Wei Zhang, Shengyu Wang, Yao Xiao, Shu Li and Liping Yang

Sensors 2025, 25(17), 5587; https://doi.org/10.3390/s25175587 - 7 Sep 2025

Viewed by 307

Abstract

Accurate pavement crack detection under adverse weather conditions is essential for road safety and effective pavement maintenance. However, factors such as reduced visibility, background noise, and irregular crack morphology make this task particularly challenging in real-world environments. To address these challenges, we propose [...] Read more.

Accurate pavement crack detection under adverse weather conditions is essential for road safety and effective pavement maintenance. However, factors such as reduced visibility, background noise, and irregular crack morphology make this task particularly challenging in real-world environments. To address these challenges, we propose CrackNet-Weather, which is a robust and efficient detection method that systematically incorporates three key modules: a Haar Wavelet Downsampling Block (HWDB) for enhanced frequency information preservation, a Strip Pooling Bottleneck Block (SPBB) for multi-scale and context-aware feature fusion, and a Dynamic Sampling Upsampling Block (DSUB) for content-adaptive spatial feature reconstruction. Extensive experiments conducted on a challenging dataset containing both rainy and snowy weather demonstrate that CrackNet-Weather significantly outperforms mainstream baseline models, achieving notable improvements in mean Average Precision, especially for low-contrast, fine, and irregular cracks. Furthermore, our method maintains a favorable balance between detection accuracy and computational complexity, making it well suited for practical road inspection and large-scale deployment. These results confirm the effectiveness and practicality of CrackNet-Weather in addressing the challenges of real-world pavement crack detection under adverse weather conditions. Full article

(This article belongs to the Special Issue Health Monitoring and Maintenance of Road Pavements Using Emerging Sensing Technologies)

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17 pages, 7707 KB

Open AccessArticle

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

by Methun Kamruzzaman, Jorge S. Salinas, Hemanth Kolla, Kenneth L. Sale, Uma Balakrishnan and Kunal Poorey

Sensors 2025, 25(17), 5586; https://doi.org/10.3390/s25175586 - 7 Sep 2025

Viewed by 302

Abstract

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, [...] Read more.

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics. Full article

(This article belongs to the Special Issue Recent Advances in Wearable and Non-Invasive Sensors)

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19 pages, 7767 KB

Open AccessArticle

Compilation of Load Spectrum of Loader Working Device and Application in Fatigue Life Prediction

by Xiaohua Shi, Wenming Guo, Jiyang Wang, Gang Li and Hao Lu

Sensors 2025, 25(17), 5585; https://doi.org/10.3390/s25175585 - 7 Sep 2025

Viewed by 303

Abstract

During the working process of the wheel loader, the repeated cycle of the shoveling and unloading process will produce an impact, so the loader is under a cyclic load for a long time, which leads to the frequent failure of its main parts. [...] Read more.

During the working process of the wheel loader, the repeated cycle of the shoveling and unloading process will produce an impact, so the loader is under a cyclic load for a long time, which leads to the frequent failure of its main parts. In this study, a new way of compiling the load spectrum of the loader’s working device and its application in fatigue life prediction is proposed. Through experimental data collection and preprocessing, the force of the cylinder block and hinge contact is corrected by mapping and inertia, which accurately reflects the actual force of the loader. The whole life cycle load spectrum is compiled by using the rainflow counting method and the extrapolation coefficient, and the test efficiency is optimized with the low-amplitude load omission method. By combining finite element analysis with material S-N curves using nCode DesignLife (version 11.1) and ANSYS Workbench frameworks (version 2024 R2), this research accurately predicts the fatigue life of the loader’s working unit and identifies key failure areas. The prediction results are consistent with the actual feedback data, and the accuracy of the method is verified. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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19 pages, 9786 KB

Open AccessArticle

Maize Kernel Batch Counting System Based on YOLOv8-ByteTrack

by Ran Li, Qiming Liu, Miao Wang, Yuchen Su, Chen Li, Mingxiong Ou and Lu Liu

Sensors 2025, 25(17), 5584; https://doi.org/10.3390/s25175584 - 7 Sep 2025

Viewed by 357

Abstract

In recent years, the application of deep learning technology in the field of food engineering has developed rapidly. As an essential food raw material and processing target, the number of kernels per maize plant is a critical indicator for assessing crop growth and [...] Read more.

In recent years, the application of deep learning technology in the field of food engineering has developed rapidly. As an essential food raw material and processing target, the number of kernels per maize plant is a critical indicator for assessing crop growth and predicting yield. To address the challenges of frequent target ID switching, high falling speed, and the limited accuracy of traditional methods in practical production scenarios for maize kernel falling count, this study designs and implements a real-time kernel falling counting system based on a Convolutional Neural Network (CNN). The system captures dynamic video streams of kernel falling using a high-speed camera and innovatively integrates the YOLOv8 object detection framework with the ByteTrack multi-object tracking algorithm to establish an efficient and accurate kernel trajectory tracking and counting model. Experimental results demonstrate that the system achieves a tracking and counting accuracy of up to 99% under complex falling conditions, effectively overcoming counting errors caused by high-speed motion and object occlusion, and significantly enhancing robustness. This system combines high intelligence with precision, providing reliable technical support for automated quality monitoring and yield estimation in food processing production lines, and holds substantial application value and prospects for widespread adoption. Full article

(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)

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20 pages, 1309 KB

Open AccessArticle

A Multidimensional Matrix Completion Method for 2-D DOA Estimation with L-Shaped Array

by Haoyue Zhang, Junpeng Shi, Zhihui Li and Shuyun Shi

Sensors 2025, 25(17), 5583; https://doi.org/10.3390/s25175583 - 7 Sep 2025

Viewed by 386

Abstract

This paper focuses on two-dimensional (2-D) direction-of-arrival (DOA) estimation for an L-shaped array. While recent studies have explored sparse methods for this problem, most exploit only the cross-correlation matrix, neglecting self-correlation information and resulting accuracy degradation. We propose a multidimensional matrix completion method [...] Read more.

This paper focuses on two-dimensional (2-D) direction-of-arrival (DOA) estimation for an L-shaped array. While recent studies have explored sparse methods for this problem, most exploit only the cross-correlation matrix, neglecting self-correlation information and resulting accuracy degradation. We propose a multidimensional matrix completion method that employs joint sparsity and redundant correlation information embedded in the covariance matrix to reconstruct a structured matrix compactly coupling the two DOA parameters. A semidefinite program problem formulated via covariance fitting criteria is proved equivalent to the atomic norm minimization framework. The alternating direction method of multipliers is designed to reduce computational costs. Numerical results corroborate the analysis and demonstrate the superior estimation accuracy, identifiability, and resolution of the proposed method. Full article

(This article belongs to the Section Radar Sensors)

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17 pages, 861 KB

Open AccessArticle

MS-UNet: A Hybrid Network with a Multi-Scale Vision Transformer and Attention Learning Confusion Regions for Soybean Rust Fungus

by Tian Liu, Liangzheng Sun, Qiulong Wu, Qingquan Zou, Peng Su and Pengwei Xie

Sensors 2025, 25(17), 5582; https://doi.org/10.3390/s25175582 - 7 Sep 2025

Viewed by 446

Abstract

Soybean rust, caused by the fungus Phakopsora pachyrhizi, is recognized as the most devastating disease affecting soybean crops worldwide. In practical applications, performing accurate Phakopsora pachyrhizi segmentation (PPS) is essential for elucidating the morphodynamics of soybean rust, thereby facilitating effective prevention strategies [...] Read more.

Soybean rust, caused by the fungus Phakopsora pachyrhizi, is recognized as the most devastating disease affecting soybean crops worldwide. In practical applications, performing accurate Phakopsora pachyrhizi segmentation (PPS) is essential for elucidating the morphodynamics of soybean rust, thereby facilitating effective prevention strategies and advancing research on related soybean diseases. Despite its importance, studies focusing on PPS-related datasets and the automatic segmentation of Phakopsora pachyrhizi remain limited. To address this gap, we propose an efficient semantic segmentation model named MS-UNet (Multi-Scale Confusion UNet Network). In the hierarchical Vision Transformer (ViT) module, the feature maps are down-sampled to reduce the lengths of the keys (K) and values (V), thereby minimizing the computational complexity. This design not only lowers the resource demands of the transformer but also enables the network to effectively capture multi-scale and high-resolution features. Additionally, depthwise separable convolutions are employed to compensate for positional information, which alleviates the difficulty the ViT faces in learning robust positional encodings, especially for small datasets. Furthermore, MS-UNet dynamically generates labels for both hard-to-segment and easy-to-segment regions, compelling the network to concentrate on more challenging locations and improving its overall segmentation capability. Compared to the existing state-of-the-art methods, our approach achieves a superior performance in PPS tasks. Full article

(This article belongs to the Section Electronic Sensors)

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15 pages, 6210 KB

Open AccessArticle

Multi-Temporal Remote Sensing Image Matching Based on Multi-Perception and Enhanced Feature Descriptors

by Jinming Zhang, Wenqian Zang and Xiaomin Tian

Sensors 2025, 25(17), 5581; https://doi.org/10.3390/s25175581 - 7 Sep 2025

Viewed by 381

Abstract

Multi-temporal remote sensing image matching plays a crucial role in tasks such as detecting changes in urban buildings, monitoring agriculture, and assessing ecological dynamics. Due to temporal variations in images, significant changes in land features can lead to low accuracy or even failure [...] Read more.

Multi-temporal remote sensing image matching plays a crucial role in tasks such as detecting changes in urban buildings, monitoring agriculture, and assessing ecological dynamics. Due to temporal variations in images, significant changes in land features can lead to low accuracy or even failure when matching results. To address these challenges, in this study, a remote sensing image matching framework is proposed based on multi-perception and enhanced feature description. Specifically, the framework consists of two core components: a feature extraction network that integrates multiple perceptions and a feature descriptor enhancement module. The designed feature extraction network effectively focuses on key regions while leveraging depthwise separable convolutions to capture local features at different scales, thereby improving the detection capabilities of feature points. Furthermore, the feature descriptor enhancement module optimizes feature point descriptors through self-enhancement and cross-enhancement phases. The enhanced descriptors not only extract the geometric information of the feature points but also integrate global contextual information. Experimental results demonstrate that, compared to existing remote sensing image matching methods, our approach maintains a strong matching performance under conditions of angular and scale variation. Full article

(This article belongs to the Section Remote Sensors)

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12 pages, 3135 KB

Open AccessArticle

Validity and Reliability of a Novel AI-Based System in Athletic Performance Assessment: The Case of DeepSport

by Burakhan Aydemir, Muhammed Talha Aydoğan, Emre Boz, Murat Kul, Fatih Kırkbir and Abdullah Bora Özkara

Sensors 2025, 25(17), 5580; https://doi.org/10.3390/s25175580 - 7 Sep 2025

Viewed by 477

Abstract

This study aimed to examine the validity and reliability of the AI-based DeepSport application by comparing its outcomes with those from the reference device, OptoJump. The primary dependent variables measured were jump height and anaerobic power during vertical jump assessments. Twelve elite male [...] Read more.

This study aimed to examine the validity and reliability of the AI-based DeepSport application by comparing its outcomes with those from the reference device, OptoJump. The primary dependent variables measured were jump height and anaerobic power during vertical jump assessments. Twelve elite male basketball players voluntarily participated in the study (age = 21.53 ± 1.14 years; sports experience = 6.47 ± 1.01 years). DeepSport uses AI-based image processing from standard cameras, while OptoJump uses optical sensor technology. Both DeepSport and OptoJump systems were utilized to assess participants’ Countermovement Jump (CMJ) and Squat Jump (SJ) performances. A G*Power (version 3.1.9.7) analysis determined the required sample size, adopting a 95% confidence level, 90% test power, and an effect size of 0.25. Validity assessments were conducted using Bland-Altman plots and ordinary least products (OLP) regression analysis, while reliability was evaluated through intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM), and smallest detectable change (SDC) analyses. DeepSport showed excellent reliability in CMJ and SJ tests with ICC values > 0.90, and CV ranged between 2.12% and 4.95%. Results were consistent with OptoJump, showing no significant differences according to t-test results (p > 0.05). Bland–Altman analyses indicated no systematic bias and random distribution. These findings confirm that both DeepSport and OptoJump devices demonstrate high reliability and consistency, suggesting their validity and reliability for use in athlete performance assessments by coaches and athletes. Full article

(This article belongs to the Section Intelligent Sensors)

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33 pages, 16564 KB

Open AccessArticle

Design and Implementation of an Off-Grid Smart Street Lighting System Using LoRaWAN and Hybrid Renewable Energy for Energy-Efficient Urban Infrastructure

by Seyfettin Vadi

Sensors 2025, 25(17), 5579; https://doi.org/10.3390/s25175579 - 6 Sep 2025

Viewed by 1278

Abstract

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid [...] Read more.

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

(This article belongs to the Topic Human-Centered Sustainable Buildings: Data-Driven Insights, Smart Sensing Technologies, Renewable Energy Integration, and Emerging Technologies)

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29 pages, 1761 KB

Open AccessArticle

5G High-Precision Positioning in GNSS-Denied Environments Using a Positional Encoding-Enhanced Deep Residual Network

by Jin-Man Shen, Hua-Min Chen, Hui Li, Shaofu Lin and Shoufeng Wang

Sensors 2025, 25(17), 5578; https://doi.org/10.3390/s25175578 - 6 Sep 2025

Viewed by 1052

Abstract

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source [...] Read more.

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

(This article belongs to the Section Navigation and Positioning)

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