Sensors

34 pages, 2334 KB

Open AccessReview

Survey on Reconnaissance Autonomous Robotic Systems for Disaster Management

by Sahaj Sinha, Sinjae Lee and Saurabh Singh

Sensors 2026, 26(5), 1659; https://doi.org/10.3390/s26051659 (registering DOI) - 5 Mar 2026

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, [...] Read more.

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, and their performance in the field and lab. There has been clear progress in navigation, sensor fusion, and situational awareness. The main challenges which remain include the use of energy and standardization of benchmarks. This survey focuses exclusively on Unmanned Ground Vehicles (UGVs) for disaster reconnaissance, examining recent advances in hardware, software, and autonomy. The survey highlights the improvements in navigation, sensor fusion, and intelligence, and identifies remaining challenges such as energy limitations, robustness in harsh conditions, and the lack of standardized benchmarks. The analysis synthesizes findings from over 190 recent studies (2020–2025) in ground-based disaster robotics, providing a comprehensive overview of current capabilities and research gaps. It encapsulates all issues with their remedy for future disaster-response systems. Full article

(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)

26 pages, 2605 KB

Open AccessReview

Deep Learning-Based Channel Estimation Techniques Using IEEE 802.11p Protocol, Limitations of IEEE 802.11p and Future Directions of IEEE 802.11bd: A Review

by Saveeta Bai, Jeff Kilby and Krishnamachar Prasad

Sensors 2026, 26(5), 1658; https://doi.org/10.3390/s26051658 (registering DOI) - 5 Mar 2026

Abstract

Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources [...] Read more.

Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources and a fixed pilot structure, which degrade the performance and effectiveness of traditional estimation techniques, particularly in dynamic environments. Recent advances in deep learning offer significant potential for addressing these issues by improving estimation accuracy and modelling complex channel dynamics. Though deep learning-based methods introduce trade-offs in computational complexity and accuracy, these are crucial constraints in latency-sensitive V2V scenarios. This article presents a comprehensive review of deep learning-based channel estimation techniques, analysing methods for the IEEE 802.11p standard and critically examining their limitations in both classical and deep learning-based approaches. Additionally, the article highlights improvements introduced by IEEE 802.11bd, which features an enhanced pilot structure and advanced modulation schemes, providing a more robust framework for adaptive, efficient channel estimation. By identifying future research pathways that balance delay, complexity, and accuracy, an intelligent and effective transportation system can be established. Full article

(This article belongs to the Special Issue Information Management and Vehicle Scheduling for Intelligent Transportation Systems)

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16 pages, 5170 KB

Open AccessArticle

Computer Vision-Assisted Measurement of Ephemeral Gully Morphology Using a Portable Pin-Artboard Sensor

by Harnoordeep Singh Mann, Hitesh Bhogilal Vasava, Hamid Mohebzadeh, Mojtaba Naeimi, Naoya Kadota, Manjeet Singh, Prasad Daggupati and Asim Biswas

Sensors 2026, 26(5), 1657; https://doi.org/10.3390/s26051657 - 5 Mar 2026

Abstract

Soil erosion, particularly ephemeral gully (EG) erosion, poses a significant threat to agricultural sustainability and ecosystem health. Despite their substantial impact on soil degradation, EGs have been relatively understudied, primarily due to their temporary nature and the limitations of existing measurement techniques. This [...] Read more.

Soil erosion, particularly ephemeral gully (EG) erosion, poses a significant threat to agricultural sustainability and ecosystem health. Despite their substantial impact on soil degradation, EGs have been relatively understudied, primarily due to their temporary nature and the limitations of existing measurement techniques. This study introduces an integrated approach for quantifying and analyzing EGs, addressing the critical need for accurate and scalable measurement methods. Our methodology combines three key components: (1) an updated portable field tool (Gulliometer), which improves upon existing designs to enhance data collection in diverse field conditions; (2) a standardized image acquisition protocol that ensures consistent, high-quality data capture; and (3) an image processing technique leveraging easy repetitive analysis of gully cross-sections. Laboratory validation using known geometric shapes demonstrated the high precision of our methodology, with error rates below 1%. Field applications in two distinct locations in Ontario, Canada, further confirmed the practicality and effectiveness of our approach under varied environmental conditions. This approach not only advances our understanding of ephemeral gully erosion but also aids in the development of effective soil conservation strategies and informed decision-making in land management. Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2025)

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18 pages, 919 KB

Open AccessArticle

Development of a Machine Learning-Based Predictive Model and Clinically Oriented Web Application for 30-Day Mortality Following Cardiac Surgery

by Telmo Miguel-Medina, Susel Góngora Alonso, Isabel de la Torre Díez, Miriam Blanco Sáez, Hector Lazaro Arrechea Elissalt, Atenea Ruigómez Noriega and María Lourdes del Río Solá

Sensors 2026, 26(5), 1656; https://doi.org/10.3390/s26051656 (registering DOI) - 5 Mar 2026

Abstract

This study aimed to develop and validate a machine learning-based model for predicting 30-day mortality in cardiac surgery patients and to implement a functional, clinician-oriented web application that enables the real-time use of the model. A retrospective cohort of 325 cardiac surgery patients [...] Read more.

This study aimed to develop and validate a machine learning-based model for predicting 30-day mortality in cardiac surgery patients and to implement a functional, clinician-oriented web application that enables the real-time use of the model. A retrospective cohort of 325 cardiac surgery patients was analysed using supervised machine learning. After preprocessing and clinical feature selection, several models were trained and evaluated through cross-validation. XGBoost achieved the best results, with an AUC-ROC of 0.968, recall of 0.800, and Brier score of 0.058. To facilitate clinical usability, a web-based application was developed using StreamLit, enabling clinicians to input patient data and predict mortality in real time. The application includes SHAP-based explainability for each prediction, thereby ensuring model transparency. Preliminary feedback from clinicians indicated that the tool was intuitive and informative and showed potential for preoperative risk assessment. The integration of a robust ML (machine learning) model with a functional clinical application offers a practical tool for supporting decision-making in cardiac surgery. This combined approach enhances both accuracy and accessibility, which are key to real-world impacts. Future work will involve multicentre validation and user-centred refinement. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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17 pages, 1407 KB

Open AccessArticle

A Coherent Parameter Estimation Method for Distributed Coherent Jamming Systems

by Liang Qi and Jianjiang Zhou

Sensors 2026, 26(5), 1655; https://doi.org/10.3390/s26051655 - 5 Mar 2026

Abstract

Regarding the problem of the accurate estimation of coherent parameters for the distributed coherent jamming system (DCJS) in active radar applications, this paper first establishes a transmit–receive signal model of the DCJS in the presence of coherent parameter estimation errors. Then, it analyzes [...] Read more.

Regarding the problem of the accurate estimation of coherent parameters for the distributed coherent jamming system (DCJS) in active radar applications, this paper first establishes a transmit–receive signal model of the DCJS in the presence of coherent parameter estimation errors. Then, it analyzes and verifies that the generalized cross-correlation function weighting method causes a decrease in the estimation accuracy of coherent parameters due to whitening processing, which in turn impairs the synthesis efficiency of the DCJS. Finally, a coherent parameter estimation method based on frequency-domain feature matching is proposed. The weighting method based on frequency-domain feature matching can effectively preserve the intra-pulse features of signals, thereby improving the estimation accuracy of coherent parameters. The simulation results show that, compared with the existing algorithms, the proposed method improves the time delay estimation accuracy by 27.0% and the phase difference estimation accuracy by 8.3%. Full article

(This article belongs to the Section Electronic Sensors)

23 pages, 1036 KB

Open AccessArticle

Predefined-Time Super-Twisting Sliding Mode Control for Construction Robot with Arbitrary Initial Values

by Hong-Bo Ai, Xin-Rong He and Chun-Wu Yin

Sensors 2026, 26(5), 1654; https://doi.org/10.3390/s26051654 - 5 Mar 2026

Abstract

To tackle the practical engineering challenge that construction robots are required to track the reference trajectory completely and precisely, this study puts forward a control scheme based on the extended reference trajectory and develops a novel super-twisting sliding mode controller with predefined-time convergence [...] Read more.

To tackle the practical engineering challenge that construction robots are required to track the reference trajectory completely and precisely, this study puts forward a control scheme based on the extended reference trajectory and develops a novel super-twisting sliding mode controller with predefined-time convergence capability. First, the influence mechanism of fluid materials on construction robots and their trajectory tracking control features are explored, and the design approach for the extended reference trajectory is elaborated. Subsequently, a nonsingular sliding surface with predefined-time convergence is constructed, and a RBF neural network with convergent weight vectors is established to approximate the composite disturbances existing in the robot system. On the basis of the proposed predefined-time convergent super-twisting control theory, a super-twisting sliding mode controller tailored for construction robots is devised, and the predefined-time convergence performance of the closed-loop system is theoretically validated. Numerical simulation results indicate that the proposed algorithm can guarantee that the construction robot’s angles move accurately along the actual reference trajectory, with the angular tracking error achieving a precision of

3 \times 10^{- 6}

rad, thereby confirming the feasibility and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

15 pages, 8090 KB

Open AccessArticle

Adaptive Multi-Sensor Fusion Localization with Eigenvalue-Based Degradation Detection for Mobile Robots

by Weizu Huang, Long Xiang, Ruohao Chen, Sheng Xu and Qing Wang

Sensors 2026, 26(5), 1653; https://doi.org/10.3390/s26051653 - 5 Mar 2026

Abstract

Autonomous mobile robots require robust localization in complex and dynamic environments, where single-sensor solutions often fail due to accumulated drift or signal degradation. LiDAR–inertial odometry provides accurate short-term motion estimation, but suffers from long-term error accumulation, whereas RTK-GNSS offers absolute positioning that becomes [...] Read more.

Autonomous mobile robots require robust localization in complex and dynamic environments, where single-sensor solutions often fail due to accumulated drift or signal degradation. LiDAR–inertial odometry provides accurate short-term motion estimation, but suffers from long-term error accumulation, whereas RTK-GNSS offers absolute positioning that becomes unreliable under occlusion or multipath effects. To solve the above problems, this paper proposes an adaptive multi-sensor fusion positioning framework that dynamically fuses LiDAR, IMU, and RTK-GNSS data based on the real-time quality evaluation of sensors. The system uses the front-end tightly coupled LiDAR–IMU iterative extension Kalman filter (IEKF) as the core estimator and combines loop detection with incremental factor graph optimization to suppress long-term drift. In addition, a degradation detection method based on the minimum eigenvalue of the Jacobian matrix is proposed to identify unreliable matching constraints in real time. In order to avoid abrupt changes in positioning results caused by fluctuations in sensor data quality, the system adopts a smooth fusion strategy based on covariance weighting. Experiments on the KITTI benchmark and self-collected datasets demonstrate that the proposed method significantly improves localization accuracy and robustness compared with pure LiDAR-based approaches, achieving stable centimeter-level performance while maintaining real-time capability on embedded platforms. Full article

(This article belongs to the Section Sensors and Robotics)

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22 pages, 3288 KB

Open AccessArticle

An Intelligent Real-Time System for Sentence-Level Recognition of Continuous Saudi Sign Language Using Landmark-Based Temporal Modeling

by Adel BenAbdennour, Mohammed Mukhtar, Osama Almolike, Bilal A. Khawaja and Abdulmajeed M. Alenezi

Sensors 2026, 26(5), 1652; https://doi.org/10.3390/s26051652 - 5 Mar 2026

Abstract

A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) [...] Read more.

A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) and the scarcity of real-time, sentence-level translation systems. This paper presents a real-time system for sentence-level recognition of continuous SSL and direct mapping to natural spoken Arabic. The proposed system operates end-to-end on live video streams or pre-recorded content, extracting spatio-temporal landmark features using the MediaPipe Holistic framework. For classification, the input feature vector consists of 225 features derived from hand and body pose landmarks. These features are processed by a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset to perform direct sentence-level classification over a vocabulary of 50 continuous Arabic sign language sentences, supported by an idle-based segmentation mechanism that enables natural, uninterrupted signing. Experimental evaluation demonstrates robust generalization: under a Leave-One-Signer-Out (LOSO) cross-validation protocol, the model attains a mean sentence-level accuracy of 94.2%, outperforming the fixed signer-independent split baseline of 92.07%, while maintaining real-time performance suitable for interactive use. To enhance linguistic fluency, an optional post-recognition refinement stage is incorporated using a large language model (LLM), followed by text-to-speech synthesis to produce audible Arabic output; this refinement operates strictly as post-processing and is not included in the reported recognition accuracy metrics. The results demonstrate that direct sentence-level modeling, combined with landmark-based feature extraction and real-time segmentation, provides an effective and practical solution for continuous SSL sentence recognition in real-time. Full article

(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))

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26 pages, 46386 KB

Open AccessArticle

Predicting Car-Engine Manufacturing Quality with Multi-Sensor Data of Manufacturing Assembly Process

by Xinyu Yang, Qianxi Zhang, Junjie Bao, Xue Wang, Nengchao Wu, Qing Tao, Haijia Wu and Li Liu

Sensors 2026, 26(5), 1651; https://doi.org/10.3390/s26051651 - 5 Mar 2026

Abstract

Car engine quality control is fundamentally hindered by extremely high-dimensional, noisy, and imbalanced multi-sensor data. To overcome these challenges, this paper proposes an edge-deployable diagnostic and predictive framework. First, a Sparse Autoencoder (SAE) maps over 12,000 distributed manufacturing parameters into a robust latent [...] Read more.

Car engine quality control is fundamentally hindered by extremely high-dimensional, noisy, and imbalanced multi-sensor data. To overcome these challenges, this paper proposes an edge-deployable diagnostic and predictive framework. First, a Sparse Autoencoder (SAE) maps over 12,000 distributed manufacturing parameters into a robust latent space to filter instrumentation noise. Second, for defect classification, a Class-Specific Weighted Ensemble (CSWE) tackles extreme class imbalance by aggressively penalizing majority-class bias, improving defect interception recall by 7.72%. Third, for transient performance tracking, an Adaptive Regime-Switching Regression (ARSR) replaces manual phase selection with unsupervised regime routing to dynamically weight local experts, reducing relative prediction error by 12%. Rigorously validated across three diverse public datasets (NASA C-MAPSS, AI4I, SECOM) and a physical H4 engine assembly line, the framework achieves an ultra-low inference latency of

80 \pm 3

ms, practically reducing the engine rework rate by 7.2%. Full article

(This article belongs to the Section Industrial Sensors)

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23 pages, 13357 KB

Open AccessArticle

Lumina-4DGS: Illumination-Robust Four-Dimensional Gaussian Splatting for Dynamic Scene Reconstruction

by Xiaoqiang Wang, Qing Wang, Yang Sun and Shengyi Liu

Sensors 2026, 26(5), 1650; https://doi.org/10.3390/s26051650 - 5 Mar 2026

Abstract

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance [...] Read more.

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

(This article belongs to the Section Optical Sensors)

23 pages, 10789 KB

Open AccessArticle

Statistical Feature Engineering for Robot Failure Detection: A Comparative Study of Machine Learning and Deep Learning Classifiers

by Sertaç Savaş

Sensors 2026, 26(5), 1649; https://doi.org/10.3390/s26051649 - 5 Mar 2026

Abstract

Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive [...] Read more.

Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive comparison of machine learning and deep learning methods is conducted for the classification of robot execution failures using data acquired from force–torque sensors. Three different feature engineering approaches are proposed. The first is a Baseline approach that includes 90 raw time-series features. The second is the Domain-6 approach, which consists of 6 basic statistical features per sensor (36 in total). The third is the Domain-12 approach, which comprises 12 comprehensive statistical features per sensor (72 in total). The domain features include the mean, standard deviation, minimum, maximum, range, slope, median, skewness, kurtosis, RMS, energy, and IQR. In total, ten classification algorithms are evaluated, including eight machine learning methods and two deep learning models: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Trees (DT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM-LGBM), as well as a One-Dimensional Convolutional Neural Network (CNN-1D) and Long Short-Term Memory (LSTM). For traditional machine learning algorithms, 5 × 5 nested cross-validation is used, whereas for deep learning models, 5-fold cross-validation with a 20% validation split is employed. To ensure statistical reliability, all experiments are repeated over 30 independent runs. The experimental results demonstrate that feature engineering has a decisive impact on classification performance. In addition, regardless of the feature set, the highest accuracy (93.85% ± 0.90) is achieved by the Naive Bayes classifier using the Baseline features. The Domain-12 feature set provides consistent improvements across many algorithms, with substantial performance gains. The results are reported using accuracy, precision, recall, and F1-score metrics and are supported by confusion matrices. Finally, permutation feature importance analysis indicates that the skewness features of the Fx and Fy sensors are the most critical variables for failure detection. Overall, these findings show that time-domain statistical features offer an effective approach for robot failure classification. Full article

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

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23 pages, 5671 KB

Open AccessArticle

A Multi-Disciplinary Approach to the Identification and Characterization of Areas of Potential Damage in the Building Materials of Ancient Monuments

by Giuseppe Casula, Silvana Fais, Maria Giovanna Bianchi and Paola Ligas

Sensors 2026, 26(5), 1648; https://doi.org/10.3390/s26051648 - 5 Mar 2026

Abstract

Today, the integrated study of historic buildings and their associated artifacts through three-dimensional modelling has become essential. Non-destructive diagnostic techniques are crucial for thorough understanding of the state of conservation of artifacts and stone construction materials used in ancient times. Therefore, it is [...] Read more.

Today, the integrated study of historic buildings and their associated artifacts through three-dimensional modelling has become essential. Non-destructive diagnostic techniques are crucial for thorough understanding of the state of conservation of artifacts and stone construction materials used in ancient times. Therefore, it is extremely important to create digital copies that preserve the memory of the analysed forms while also allowing an understanding of the deterioration phenomena that affect historic artifacts, thus guiding restoration efforts. In this paper, the authors present the integrated application of non-destructive geomatic techniques such as terrestrial laser scanning (TLS) in synergy with close-range photogrammetry (CRP) methods, and their integration with non-destructive geophysical diagnostic methods such as ultrasonic indirect tests, ultrasonic transmission tomography, and electrical resistivity. These methods have been further enhanced by complementary petrographic analyses of the investigated building stone materials. The integrated and coordinated application of these non-destructive techniques allowed the creation of high-precision models of both the surface and interior of several artifacts from the Basilica of San Saturnino, the oldest church in Cagliari (Italy), dedicated to the city’s patron saint. Finally, this integrated study highlighted areas of deterioration of these artifacts due to atmospheric elements such as wind and rain, and anthropogenic phenomena such as atmospheric particulate matter from traffic and other manufacturing activities. Full article

(This article belongs to the Special Issue Advanced Sensing Technology in Structural Health Monitoring)

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26 pages, 2153 KB

Open AccessArticle

Path-Integrated Ultrasonic Attenuation Modeling for Concrete with Random Aggregates Based on Modified Waterman–Truell Theory

by Haoran Zheng, Chao Lu, Dongjie Zhou, Xuejun Jia, Xiang Lv, Laixin Gao and Guangming Zhang

Sensors 2026, 26(5), 1647; https://doi.org/10.3390/s26051647 - 5 Mar 2026

Abstract

Ultrasonic sensing is an effective tool for characterizing heterogeneous concrete structures, yet quantitative interpretation of ultrasonic attenuation remains challenging due to aggregate-induced multiple scattering and spatial non-uniformity. This study proposes a path-integrated ultrasonic attenuation modeling framework for concrete with random aggregates. A quasi-one-dimensional [...] Read more.

Ultrasonic sensing is an effective tool for characterizing heterogeneous concrete structures, yet quantitative interpretation of ultrasonic attenuation remains challenging due to aggregate-induced multiple scattering and spatial non-uniformity. This study proposes a path-integrated ultrasonic attenuation modeling framework for concrete with random aggregates. A quasi-one-dimensional discretized wave equation is coupled with a modified version of the Waterman–Truell effective medium theory, in which multiple scattering effects are corrected by incorporating a Percus–Yevick structure factor and a geometric equivalence scheme for non-spherical aggregates. By discretizing the propagation path into locally homogeneous layers, cumulative attenuation is evaluated through explicit path integration, allowing spatial variations in aggregate volume fraction to be captured. Low-frequency ultrasonic transmission experiments (25 kHz) are conducted using serially assembled concrete specimens with controlled aggregate contents. The results reveal pronounced path-dependent attenuation behavior governed by local aggregate distribution. Compared with classical and effective Waterman–Truell models, the proposed approach significantly improves prediction accuracy, achieving a mean absolute percentage error of 7.29%. The framework provides a physically interpretable and experimentally validated method for ultrasonic sensing of heterogeneous concrete, with potential applications in non-destructive evaluation and structural health monitoring of high-end concrete-based engineering structures. Full article

(This article belongs to the Special Issue Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure)

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23 pages, 6800 KB

Open AccessArticle

CGALS-YOLO: Vision-Based Sensing for Protective Equipment Wearing Compliance Detection in Underground Environments

by Chao Huang and Hongkang Huang

Sensors 2026, 26(5), 1646; https://doi.org/10.3390/s26051646 - 5 Mar 2026

Abstract

Reliable vision-based sensing of protective equipment wearing compliance is essential for safety monitoring in underground mining environments, where complex lighting conditions, similar background textures, and large variations in the scale of wearable items significantly degrade detection performance. To address these challenges, this study [...] Read more.

Reliable vision-based sensing of protective equipment wearing compliance is essential for safety monitoring in underground mining environments, where complex lighting conditions, similar background textures, and large variations in the scale of wearable items significantly degrade detection performance. To address these challenges, this study proposes a vision-based protective equipment wearing compliance detection method for underground personnel based on CGALS-YOLO. Traditional object detection models often introduce substantial redundant background information during multi-scale feature fusion, which weakens the perception of key wearing regions, particularly for small-scale targets. To alleviate this issue, a content-guided feature fusion (CGAFusion) module is incorporated into the neck of the YOLOv8 network, enabling adaptive fusion of same-scale multi-path features through the collaborative effects of channel, spatial, and pixel attention mechanisms. This design enhances target-related feature representation while suppressing background interference in complex underground scenes. Furthermore, to reduce parameter redundancy and improve cross-scale discrimination consistency in the detection head, a lightweight shared convolution detection (LSCD) structure is introduced. By employing cross-scale shared convolution parameters, group normalization, and scale-adaptive regression, the proposed model achieves a parameter reduction of approximately 23.9% while lowering computational complexity and maintaining stable multi-scale detection performance. Experimental results on an underground protective equipment wearing compliance dataset demonstrate that CGALS-YOLO improves detection accuracy by approximately 4.6% and recall by 3.1% compared with the baseline YOLOv8n, achieving an mAP@0.5 of 89.4%. These results validate the effectiveness and practical applicability of the proposed method for real-time vision-based safety monitoring in underground environments. Full article

(This article belongs to the Section Environmental Sensing)

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28 pages, 31519 KB

Open AccessArticle

A Directional Nearest Neighbor Distance-Based Algorithm for Signal Photon Extraction from Spaceborne Photon-Counting LiDAR in Shallow Waters

by Shibin Zhao, Zhenwei Shi, Tingting Jin, Boxue Huang, Xiaokai Li and Hui Long

Sensors 2026, 26(5), 1645; https://doi.org/10.3390/s26051645 - 5 Mar 2026

Abstract

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting data remains essential due to strong solar background and intrinsic [...] Read more.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting data remains essential due to strong solar background and intrinsic instrument noise. To address this challenge, this study proposes a novel photon denoising method, termed the Directional Nearest Neighbor Distance-based Algorithm (DNNDA), for robust extraction of signal photons from shallow-water ICESat-2 data. Unlike existing methods that rely heavily on density or terrain features and often degrade under high-noise conditions, DNNDA systematically exploits both scale-corrected spatial relationships and directional distribution characteristics of photons. By quantitatively characterizing the directional features of photon distributions and embedding this information into a density representation, DNNDA amplifies the density contrast between signal and noise photons, rendering the seafloor signal photons more distinct and easier to extract. An evaluation index was further designed to automate optimal parameter determination. Validation using multiple global ICESat-2 datasets demonstrates that DNNDA achieves superior seafloor photon extraction performance, with F1-scores exceeding 95%. Further regression analysis against high-precision CUDEM data in the Puerto Rico region yields root-mean-square errors below 0.57 m. By jointly correcting scale anisotropy and incorporating directional information, DNNDA enables reliable and adaptive signal photon extraction across local and global scales, providing a robust solution for shallow-water bathymetry in complex, high-noise environments. Full article

(This article belongs to the Section Optical Sensors)

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22 pages, 3617 KB

Open AccessArticle

Batteryless IoT Sensing Using Thermoelectric Energy Harvesting from Industrial Motor Waste Heat

by Kamil Bancik, Jaromir Konecny, Martin Stankus, Radim Hercik, Jiri Koziorek, Vytautas Markevičius, Darius Andriukaitis and Michal Prauzek

Sensors 2026, 26(5), 1644; https://doi.org/10.3390/s26051644 - 5 Mar 2026

Abstract

This study presents the design, implementation, and validation of a thermoelectric energy harvesting system that exploits waste heat from an industrial electric motor to power an autonomous wireless sensor device. The proposed prototype integrates a single thermoelectric generator directly onto the motor housing [...] Read more.

This study presents the design, implementation, and validation of a thermoelectric energy harvesting system that exploits waste heat from an industrial electric motor to power an autonomous wireless sensor device. The proposed prototype integrates a single thermoelectric generator directly onto the motor housing and leverages the built-in cooling fan to maintain a stable thermal gradient of approximately 4–5 °C. Under real factory conditions, the system harvested 6.17 J of energy over 9612 s, sustaining continuous operation and 41 successful Long Range (LoRa) data transmissions with a positive energy balance. Compared with related works, the prototype achieved competitive or superior performance while operating at a lower motor rating of 0.25 kW, highlighting its efficiency relative to system scale. Key innovations include a hybrid DC/DC conversion chain bridging ultra-low input voltages to modern microcontrollers, and an adaptive transmission strategy that ensures predictable energy management and reliable wireless communication. These results demonstrate the feasibility of battery-free sensing in industrial environments and underline the potential of thermoelectric harvesting as a cost-effective, maintenance-free, and environmentally responsible solution for predictive maintenance and Industry 4.0 applications. Full article

(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)

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14 pages, 2575 KB

Open AccessArticle

Typical Wind Shear Simulation and Detection Analysis Based on Coherent Doppler Wind Lidar

by Yuanyuan Wei, Jinlong Yuan, Chaoyong Chen, Tengfei Wu and Zikang Tong

Sensors 2026, 26(5), 1643; https://doi.org/10.3390/s26051643 - 5 Mar 2026

Abstract

To enhance the accuracy of wind shear identification by coherent Doppler wind lidar (CDWL), it is necessary to clarify the variation characteristics of CDWL detection results under typical airflow disturbance conditions. This study first numerically simulated typical wind shear fields and generated the [...] Read more.

To enhance the accuracy of wind shear identification by coherent Doppler wind lidar (CDWL), it is necessary to clarify the variation characteristics of CDWL detection results under typical airflow disturbance conditions. This study first numerically simulated typical wind shear fields and generated the Plane Position Indication (PPI) results of CDWL through coordinate projection. Then, it compared the performance of the double-slope algorithm and the least squares algorithm on wind shear identification from the PPI data. The results showed that for wind fields with significant peak characteristics, the double-slope algorithm can more sensitively identify wind shear near the peak, compared with the least square algorithm. In contrast, for wind fields with stable, continuous and linear gradient characteristics, the least squares algorithm can better suppress noise and fit the wind speed gradient changes. Finally, a self-developed long-range CDWL was used to conduct wind shear detection experiments at a plateau airport. After the CDWL beam position was calibrated, its data were compared with those from the anemometer. The “least square + double-slope” scheme was adopted to analyze the typical wind shear case, and the effectiveness and reliability of the identification scheme were verified in combination with an aircraft crew report. Full article

(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)

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Open AccessReview

Mushroom-Derived Hydrophobins for Antifouling and Interface Preservation in Chemosensors

by Nardos F. Bisrat, Bethany R. Finnefrock, Matthew D. Gacura, Longyan Chen and Davide Piovesan

Sensors 2026, 26(5), 1642; https://doi.org/10.3390/s26051642 - 5 Mar 2026

Abstract

Surface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms can lead to signal drift, reduced sensitivity, and shortened device lifetime. Conventional antifouling strategies rely primarily on synthetic hydrophilic [...] Read more.

Surface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms can lead to signal drift, reduced sensitivity, and shortened device lifetime. Conventional antifouling strategies rely primarily on synthetic hydrophilic polymer coatings, such as polyethylene glycol and polyvinylpyrrolidone, which are effective but face limitations related to long-term stability, thickness, and compatibility with surface-sensitive sensing modalities. In this review, we focus on hydrophobins derived from mushroom-forming and filamentous fungi as a bio-based alternative for antifouling and anti-wetting surface modification. Mushroom-derived hydrophobins are small amphiphilic proteins capable of spontaneous self-assembly into nanometer-scale films that modulate surface energy, wettability, and interfacial friction without requiring covalent functionalization. The current state of research on hydrophobin structure, classification, and self-assembly is reviewed, followed by a synthesis of reported antifouling and tribological behaviors relevant to medical and sensor-adjacent surfaces. Representative experimental observations are discussed to illustrate trends consistent with the literature, without establishing new performance benchmarks. The implications of mushroom-derived hydrophobin coatings for chemosensors and biosensors are examined, particularly with respect to signal stability, surface accessibility, and durability. Limitations and future research directions are outlined to support translation into practical sensing technologies. Full article

(This article belongs to the Special Issue Low-Cost Chemosensors for Applications in Environment, Health, Food, and Industry Process Control, 2nd Edition)

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19 pages, 2798 KB

Open AccessArticle

A High-Precision Cooperative Localization Method for UAVs Based on Multi-Condition Constraints

by Haiqiao Liu, Wen Jiang, Qing Long, Qijun Xia and Xiang Chen

Sensors 2026, 26(5), 1641; https://doi.org/10.3390/s26051641 - 5 Mar 2026

Abstract

Global Navigation Satellite Systems (GNSSs) often suffer from significant localization errors in signal-denied environments. Furthermore, the accuracy of multi-UAV cooperative localization is highly sensitive to the relative geometric configuration of the swarm. To address these challenges, this paper proposed a novel high-precision and [...] Read more.

Global Navigation Satellite Systems (GNSSs) often suffer from significant localization errors in signal-denied environments. Furthermore, the accuracy of multi-UAV cooperative localization is highly sensitive to the relative geometric configuration of the swarm. To address these challenges, this paper proposed a novel high-precision and robust cooperative localization method for UAVs. The proposed method comprised two key modules. First, based on the principle of minimizing the Geometric Dilution of Precision, we optimized both the quantity and geometric configuration of the UAV swarm to identify the top three optimal aerial formations. Second, we introduced Ground-Assisted Reference Stations or Unmanned Ground Vehicles to establish an air–ground cooperative localization system. By leveraging Time Difference of Arrival constraints, this system significantly enhanced localization accuracy and robustness. From this analysis, two optimal hybrid configurations were selected. Experimental results showed that while purely air-based geometric optimization enhanced horizontal coverage, it failed to effectively suppress Z-axis errors due to inadequate vertical baselines, with deviations consistently oscillating between 3.0 m and 5.0 m. Conversely, the introduction of edge-deployed ground reference stations reduced the Position Dilution of Precision to a remarkably low level of 0.75, effectively suppressing error divergence. This demonstrated that the proposed air–ground cooperative scheme outperformed traditional pure air-based swarm approaches in localization performance. These findings hold significant theoretical and practical value. Full article

(This article belongs to the Special Issue AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications)

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