Sensors

26 pages, 7411 KB

Open AccessArticle

Energy Transfer Mechanism of Hard-Roof Hydraulic Fracturing in Goaf-Side Working Face Based on Microseismic-Driven Damage Model

by Rupei Zhang, Siyuan Gong, Wu Cai, Hui Li and Yuanhang Qiu

Sensors 2026, 26(11), 3566; https://doi.org/10.3390/s26113566 - 3 Jun 2026

Abstract

Directional long-borehole hydraulic fracturing is an important technique for controlling rockbursts induced by hard roofs. Its effectiveness depends primarily on whether fracturing-induced damage can modify the roof-bearing structure and thereby regulate stress concentration and elastic strain energy accumulation in the coal-rock mass ahead [...] Read more.

Directional long-borehole hydraulic fracturing is an important technique for controlling rockbursts induced by hard roofs. Its effectiveness depends primarily on whether fracturing-induced damage can modify the roof-bearing structure and thereby regulate stress concentration and elastic strain energy accumulation in the coal-rock mass ahead of the working face. However, existing numerical simulations commonly rely on predefined weakened zones or empirical parameter reduction, which makes it difficult to represent the spatial heterogeneity and mechanical evolution of rock damage during field hydraulic fracturing. Taking the 2803 goaf-side working face in Hetaoyu Coal Mine as the engineering background, this study proposes a microseismic-data-driven method for characterizing hydraulic fracturing-induced damage and incorporates it into a FLAC3D finite-difference model. The stress field, elastic strain energy field, and damage distribution ahead of the working face are compared under non-fractured and hydraulically fractured conditions. In the proposed method, the energy of fracturing-induced microseismic events is converted into the Benioff strain of numerical zones according to the attenuation law of microseismic wave propagation, and the corresponding rock damage variable is then calculated using a Weibull damage model. The fracturing-damaged rock mass is further represented by weakening the elastic modulus, cohesion, and friction angle, together with the stochastic generation of strongly damaged zones. The results show that, without hydraulic fracturing, the hard roof maintains a strong, continuous bearing capacity, resulting in a continuous lateral abutment stress concentration zone and a high elastic strain energy accumulation zone ahead of the working face and near the goaf-side boundary. After hydraulic fracturing, a patchy and locally connected high-damage weakening zone forms in the target roof strata. This damaged zone cuts the original continuous load-transfer structure through which the hard roof concentrates load toward the goaf side, reduces the extent of high-stress and high-energy zones in the coal seam, and induces an asymmetric adjustment of the dominant mining-induced energy release zone from the goaf side toward the solid-coal side. These simulation results agree well with the field observation that microseismic activity is mainly concentrated near the roadway on the solid-coal side. The study indicates that the rockburst-control mechanism of directional long-borehole hydraulic fracturing is not limited to simple overall stress dissipation. A key finding is that the fracturing-induced heterogeneous damage zone effectively interrupts the continuous load-transfer and energy-storage paths on the goaf side. This induces an asymmetric spatial redistribution of the mining-induced energy field from the goaf side toward the solid-coal side, thereby mitigating the high static-load and high-energy-storage state ahead of the working face. Full article

(This article belongs to the Special Issue Feature Papers in “Environmental Sensing” Section 2026)

21 pages, 9092 KB

Open AccessArticle

Prior-Knowledge-Guided Graph Attention Network for Fault Diagnosis of Engine Valve Clearance

by Mingyu Li, Jingqian Wen, Xiaonan Yang, Yaoguang Hu, Xinlong Li and Zhongjie Shi

Sensors 2026, 26(11), 3565; https://doi.org/10.3390/s26113565 - 3 Jun 2026

Abstract

Fault diagnosis of diesel engines is a critical task in the operation and maintenance of complex equipment. Diesel engine fault diagnosis technology based on deep learning has seen widespread development due to its powerful feature learning and fault classification capabilities. However, traditional data-driven [...] Read more.

Fault diagnosis of diesel engines is a critical task in the operation and maintenance of complex equipment. Diesel engine fault diagnosis technology based on deep learning has seen widespread development due to its powerful feature learning and fault classification capabilities. However, traditional data-driven deep learning models cannot explicitly uncover relationships between signals, which hinders better fault information capture. Therefore, this paper proposes a diesel-engine valve-clearance fault diagnosis method driven by a combination of knowledge and data. Firstly, the original signals are converted into graph data with a topological structure based on the spatiotemporal relationships of events occurring within the cylinder, thereby uncovering the intrinsic structural information of the samples. Then, the graph structure is input into a graph convolutional attention network to extract features and learn fault patterns. Valve fault experiments were conducted on a diesel engine test bench, and the results indicate that the proposed knowledge and data-driven deep learning fault diagnosis model achieves better diagnostic performance and clearer interpretability compared to traditional data-driven deep learning fault diagnosis models, and it still has a relatively high accuracy in a diagnostic environment with scarce data. Full article

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

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29 pages, 2590 KB

Open AccessArticle

DAIS-MQTT: A Distributed MQTT Communication Method Based on Intelligent QoS Routing and Hierarchical Collaboration

by Mengjia Lian, Wanda Yin, Anying Chai, Ping Huang, Yunpeng Sun and Enqiu He

Sensors 2026, 26(11), 3564; https://doi.org/10.3390/s26113564 - 3 Jun 2026

Abstract

The continuous growth of IIoT systems has significantly increased the number of connected devices and message interactions, creating higher requirements for communication mechanisms in terms of scalability and adaptability under dynamic network environments. Although MQTT is widely used for its lightweight communication, its [...] Read more.

The continuous growth of IIoT systems has significantly increased the number of connected devices and message interactions, creating higher requirements for communication mechanisms in terms of scalability and adaptability under dynamic network environments. Although MQTT is widely used for its lightweight communication, its traditional centralized broker architecture limits scalability and fault tolerance in large-scale data transmission, reducing system scalability and fault tolerance. Additionally, static QoS configuration is difficult to adapt to dynamic environmental changes, resulting in high end-to-end latency and limited system throughput. To address these issues, this paper proposes a distributed MQTT communication method based on intelligent QoS routing and hierarchical collaboration (DAIS-MQTT). This method designs a network routing algorithm based on a hierarchical tree structure (LCN), which effectively addresses the scalability limitation of centralized proxies by enabling multi-level proxy collaboration and self-recovery from faults. At the same time, it proposes a QoS routing algorithm based on intelligent decision trees (IQR), which jointly optimizes proxy selection and QoS levels to dynamically adapt to changes in the network environment, thereby solving the problem of insufficient adaptability in static QoS configurations. Experimental results show that compared with the traditional MQTT-based communication method, the DAIS-MQTT method reduces the average message delay by 29.9%, increases system throughput by 28.2%, and maintains a reliable transmission rate of 98.7% in unreliable network environments, making it suitable for high-dynamic and large-scale IIoT communication scenarios. Full article

(This article belongs to the Special Issue Industrial IoT Systems and Networks)

24 pages, 1333 KB

Open AccessPerspective

Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework

by Jean Mapinduzi, Kim Daniels, Oyéné Kossi, Jonas Verbrugghe and Bruno Bonnechère

Sensors 2026, 26(11), 3563; https://doi.org/10.3390/s26113563 - 3 Jun 2026

Abstract

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of [...] Read more.

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

24 pages, 3964 KB

Open AccessArticle

Cooperative Tracking of Vessel Trajectory by Multi-Static Passive Stations Using an MC-RMPF

by Bingzhuo Liu, Lingqi Kong and Panlong Wu

Sensors 2026, 26(11), 3562; https://doi.org/10.3390/s26113562 - 3 Jun 2026

Abstract

Traditional maritime vessel tracking methods based on multi-static passive radar stations typically process all available observations, leading to substantial computational overhead and estimation variance. Furthermore, discrepancies in refresh rates and noise levels among stations often cause significant jumps in estimated positions between updates, [...] Read more.

Traditional maritime vessel tracking methods based on multi-static passive radar stations typically process all available observations, leading to substantial computational overhead and estimation variance. Furthermore, discrepancies in refresh rates and noise levels among stations often cause significant jumps in estimated positions between updates, resulting in trajectory discontinuities. To mitigate these issues, this paper introduces a multi-station cooperative vessel tracking framework based on a motion-constrained resample–move particle filter (MC-RMPF). In the proposed method, systematic resampling is first used to alleviate particle degeneracy, and a markov chain monte carlo (MCMC) move step is subsequently applied to rejuvenate the resampled particles under vessel-motion feasibility constraints. Additionally, a distributed detection network is constructed using directional data from multiple stations, dynamically selecting optimal observation subsets to balance localization accuracy with computational load. The experimental results demonstrate that, compared to the baseline methods, our method reduces the Root Mean Square Error and Circular Error Probability of position tracking by 23.5% and 21.7%, respectively. It exhibits strong reliability in challenging scenarios such as target maneuvers and temporary observation loss. Full article

(This article belongs to the Section Radar Sensors)

24 pages, 1494 KB

Open AccessArticle

No More False Alert: Contrastive Learning for Predicting Health Deterioration from Imbalanced Care Records

by Haru Kaneko and Sozo Inoue

Sensors 2026, 26(11), 3561; https://doi.org/10.3390/s26113561 - 3 Jun 2026

Abstract

In this paper, we propose an outcome-based contrastive loss for imbalanced binary classification to alert to next-day health deterioration using care records and meteorological data. Long-term care facilities maintain daily care and observation records to monitor the health of older adults. Such objective [...] Read more.

In this paper, we propose an outcome-based contrastive loss for imbalanced binary classification to alert to next-day health deterioration using care records and meteorological data. Long-term care facilities maintain daily care and observation records to monitor the health of older adults. Such objective records are particularly valuable when sudden deterioration occurs, enabling timely coordination with medical institutions. Predicting deterioration one day in advance could provide care staff with an actionable window to intensify observation and adjust care plans (e.g., scheduling additional vital checks or increasing fluid intake monitoring). This could potentially reduce emergency transports and ease the burden on already understaffed care facilities. However, for such predictions to be useful in practice, false positives must be suppressed. Because deterioration events are rare, class imbalance generates an excess of false positives, causing alert fatigue and increasing the risk that actual events go unnoticed. To address these challenges, we propose an outcome-based contrastive loss that contrasts actual deteriorating samples against false alarms conditioned on mini-batch prediction outcomes. The proposed loss contracts same-label pairs to shape local structure within each ground-truth label. The loss also separates actual deteriorating samples from false alarms among samples predicted as deteriorating, thereby directly reducing unnecessary alerts. As a result, compared with random oversampling with standard cross-entropy, the proposed model improved precision from 3.97% to 12.94% (+8.97 percentage points), while limiting the F1-score decrease to 0.71 percentage points (from 7.28% to 6.57%). Pair-design ablations and UMAP projections supported this mechanism by indicating clearer separation between actually deteriorating and false-alarm samples in the learned representation space. These results suggest a viable direction for alert systems that produce fewer unnecessary alerts, reducing alert fatigue and supporting more reliable deterioration detection in care settings. Full article

(This article belongs to the Section Biomedical Sensors)

29 pages, 1139 KB

Open AccessArticle

Blind Device Detection via Extended Sparsity Estimation-OMP in Grant-Free NOMA-IoT

by Nur Andini, Andriyan Bayu Suksmono, Joko Suryana and Koredianto Usman

Sensors 2026, 26(11), 3560; https://doi.org/10.3390/s26113560 - 3 Jun 2026

Abstract

Grant-free non-orthogonal multiple access (NOMA) enables communication without a scheduling process. Base station (BS) must detect active users without knowing their number, a challenge that also occurs in grant-free NOMA–Internet of Things (IoT). Device detection in grant-free NOMA-IoT can be considered as signal [...] Read more.

Grant-free non-orthogonal multiple access (NOMA) enables communication without a scheduling process. Base station (BS) must detect active users without knowing their number, a challenge that also occurs in grant-free NOMA–Internet of Things (IoT). Device detection in grant-free NOMA-IoT can be considered as signal reconstruction in compressive sensing (CS). To address this limitation, we propose extended sparsity estimation- orthogonal matching pursuit (ESE-OMP) to detect active devices in single measurement vector (SMV) and multiple measurement vector (MMV) problems for grant-free NOMA-IoT systems, a reconstruction method in CS that operates without prior knowledge of the sparsity level, which corresponds to the number of active devices. The algorithm iteratively detects active devices by monitoring the absolute difference in

l_{1}

-norm of successive residuals, terminating when the change falls below a predefined threshold

ε

. ESE-OMP is evaluated under various grant-free NOMA-IoT systems, irregular low-density spreading-orthogonal frequency division multiplexing (LDS-OFDM), regular LDS-OFDM, and pattern division multiple access (PDMA) systems. When the signal-to-noise ratio (SNR) is 10 dB for the SMV problem with static active device composition, the regular LDS-OFDM system achieves a bit error rate (BER) of

2.95 \times 10^{- 4}

, while irregular LDS-OFDM and PDMA systems achieve BERs of

3.78 \times 10^{- 3}

and

1.79 \times 10^{- 2}

, respectively. The smaller the number of active devices, the better the performance of ESE-OMP. Full article

(This article belongs to the Special Issue Wireless Communication and Networking for loT)

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31 pages, 782 KB

Open AccessArticle

Cluster Target Tracking Based on Multi-Sensor Adaptive GLMB Filter

by Zheng Zhang, Daozhi Wei and Xirui Xue

Sensors 2026, 26(11), 3559; https://doi.org/10.3390/s26113559 - 3 Jun 2026

Abstract

In complex detection environments, unknown detection probability and clutter rate hinder accurate tracking of cluster targets. To address this issue, this paper proposes a novel multi-sensor adaptive generalized labeled multi-Bernoulli (MS-AGLMB) filter. Specifically, we consider interactions among cluster members and adopt a virtual [...] Read more.

In complex detection environments, unknown detection probability and clutter rate hinder accurate tracking of cluster targets. To address this issue, this paper proposes a novel multi-sensor adaptive generalized labeled multi-Bernoulli (MS-AGLMB) filter. Specifically, we consider interactions among cluster members and adopt a virtual leader–follower model to describe cluster kinematics. Given unknown environmental parameters, we employ an adaptive cardinalized probability hypothesis density (CPHD) filter to estimate the detection probability and clutter rate in real time. Furthermore, we use Gibbs sampling to efficiently truncate GLMB association hypotheses, obtaining the posterior density and solving the multi-sensor measurement partitioning problem. A joint prediction and update strategy enables simultaneous estimation of target trajectories, detection probability, clutter rate, and cluster structure. Simulation results demonstrate that the proposed algorithm achieves greater robustness in scenarios with time-varying detection probability and clutter rate, outperforming comparison filters in cluster target tracking. Full article

(This article belongs to the Section Physical Sensors)

25 pages, 2289 KB

Open AccessArticle

Superpixel Random Selection Random Walk Multi-Branch Depthwise Convolutional Neural Network for Hyperspectral Image Classification

by Kai Zhang, Xinwei Jiang and Zhihua Cai

Sensors 2026, 26(11), 3558; https://doi.org/10.3390/s26113558 - 3 Jun 2026

Abstract

Convolutional neural networks (CNNs) and training-free CNN variants have been successfully applied to hyperspectral image (HSI) processing and analysis. Training-free CNNs have shown promising feature extraction performance, which could effectively address the issue of typical CNNs being highly parameterized; however, inevitable noise and [...] Read more.

Convolutional neural networks (CNNs) and training-free CNN variants have been successfully applied to hyperspectral image (HSI) processing and analysis. Training-free CNNs have shown promising feature extraction performance, which could effectively address the issue of typical CNNs being highly parameterized; however, inevitable noise and redundancy in the randomly selected training-free convolutional kernels often leads to unsatisfactory performance. To address this issue, we propose Superpixel Random Selection Random Walk Multi-Branch Depthwise Convolutional Neural Network (SRSRWMD-CNN). Specifically, we propose a novel training-free convolutional neural network characterized by inter-layer multi-scale integration and intra-layer grouping. Various superpixels groups are first generated through multi-scale superpixel segmentation algorithms, then the predetermined number of superpixels are randomly sampled from these groups to serve as training-free convolution kernels. This mechanism enables adaptive computation of HSI feature maps without costly model training in the feature extraction stage, allowing the network to effectively capture a multi-scale spectral–spatial feature representation. Additionally, we propose a multi-branch depthwise convolution strategy that mitigates feature learning errors while significantly enhancing feature representation capabilities. A random walk strategy is employed to expand the receptive field and enhance the robustness of the training-free convolution kernels. Finally, the multi-scale spectral–spatial features are concatenated with the multiple convolutional stages to fuse salient shallow and deep features for accurate HSI classification. Extensive experiments demonstrate that the proposed method achieves superior performance compared to state-of-the-art algorithms. Full article

(This article belongs to the Special Issue High-Frequency Spectroscopy and Imaging: Techniques and Applications)

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36 pages, 28001 KB

Open AccessArticle

GeoFusion-3D: Multi-Scale Geomorphic Feature Fusion for Landslide Scar Detection Using UAV-Mounted LiDAR

by Abhudaya Shrivastava, Shelly Gupta and Zoran Obradovic

Sensors 2026, 26(11), 3557; https://doi.org/10.3390/s26113557 - 3 Jun 2026

Abstract

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the [...] Read more.

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

23 pages, 1229 KB

Open AccessReview

Sensing Techniques in Virtual Reality for Human Interaction: A Bibliometric Analysis

by Antonio del Bosque, Pablo Fernández-Arias and Diego Vergara

Sensors 2026, 26(11), 3556; https://doi.org/10.3390/s26113556 - 3 Jun 2026

Abstract

Virtual reality (VR) has emerged as a key technology for immersive human–computer interaction, where sensing systems are essential for enabling natural, adaptive, and multisensory experiences. However, the scientific landscape of sensing techniques in VR remains fragmented across disciplines, lacking a comprehensive and integrative [...] Read more.

Virtual reality (VR) has emerged as a key technology for immersive human–computer interaction, where sensing systems are essential for enabling natural, adaptive, and multisensory experiences. However, the scientific landscape of sensing techniques in VR remains fragmented across disciplines, lacking a comprehensive and integrative perspective. In this study, a bibliometric and science mapping analysis was conducted to systematically evaluate research trends, structures, and developments in sensing technologies for VR-based human interaction. A dataset of 2259 peer-reviewed articles (2005–2025) retrieved from Scopus and Web of Science was analyzed. The results indicate a steady growth in scientific production (5.37% annual growth rate) and a highly collaborative research environment, structured around a limited core of journals and dominated by leading countries such as China (18.0%) and the United States (17.8%). Conceptual and thematic analyses reveal a transition toward human-centered and interaction-driven approaches, with increasing emphasis on multimodal, wearable, and physiological sensing technologies. At the same time, areas such as haptic and tactile feedback appear comparatively less represented within the analyzed thematic structures. The analyzed bibliometric trends indicate increasing thematic convergence between sensing technologies, materials science, and intelligent systems within VR research, with growing research interest in integrated and multimodal sensing approaches. Full article

(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human: 2nd Edition)

23 pages, 1422 KB

Open AccessArticle

A Coarse-to-Fine Framework for Oil–Water Interface Measurement in Small-Caliber Transparent Test Tubes

by Bo Zhou, Yang Zhou, Jigang Zou, Zhandong Lv, Weijie Zhang, Ruihan Wang and Shengwei Meng

Sensors 2026, 26(11), 3555; https://doi.org/10.3390/s26113555 - 3 Jun 2026

Abstract

Accurate oil–water interface measurement in small transparent test tubes is important for subsequent volume readout in laboratory analysis. However, manual observation and conventional vision-based methods are easily affected by illumination variation, wall stains, and bubbles, while deep learning detectors alone usually provide only [...] Read more.

Accurate oil–water interface measurement in small transparent test tubes is important for subsequent volume readout in laboratory analysis. However, manual observation and conventional vision-based methods are easily affected by illumination variation, wall stains, and bubbles, while deep learning detectors alone usually provide only coarse semantic perception. To address this issue, a coarse-to-fine framework is proposed for robust oil–water interface measurement. In the coarse stage, YOLOv8n is used to provide semantic constraints for subsequent processing. In the fine stage, a Fisher-discriminative chromatic-weighted brightness feature is constructed from RGB information, where the RGB weights are derived from the Fisher criterion to enhance oil–water chromatic separability rather than using fixed grayscale or empirical channel weights. This feature is then fused with a SobelY-based vertical-gradient feature to improve interface localization. A stain-aware row-aggregation strategy with effective-pixel compensation is further introduced to suppress artefact interference. The validated interface position is finally converted into a volume readout, with additional correction for bubble-induced bias. The framework was validated on sampled frames from a complete shale-oil core pressing process conducted under mixed-lighting conditions. Stage-wise evaluation and ablation results indicate that the proposed design improves readout stability under stains, bubbles, and illumination variation, achieving a mean absolute error of 0.0159 mL and keeping the maximum error below 0.03 mL in the current experimental setup. Full article

(This article belongs to the Section Industrial Sensors)

28 pages, 18068 KB

Open AccessArticle

EAGLE-DET: Edge-Aware Global–Local Enhancement for Small Object Detection in UAV Aerial Imagery

by Yimeng Tao, Yan Ding, Bo Mo, Bozhi Zhang, Chunbo Zhao and Dawei Li

Sensors 2026, 26(11), 3554; https://doi.org/10.3390/s26113554 - 3 Jun 2026

Abstract

Small object detection in UAV aerial imagery poses significant challenges due to sparse pixel representation and ambiguous object boundaries. Through systematic analysis, we identify three critical degradation stages during forward propagation in deep detection networks: edge attenuation during feature extraction, semantic conflict during [...] Read more.

Small object detection in UAV aerial imagery poses significant challenges due to sparse pixel representation and ambiguous object boundaries. Through systematic analysis, we identify three critical degradation stages during forward propagation in deep detection networks: edge attenuation during feature extraction, semantic conflict during feature fusion, and detail loss during feature reconstruction. Existing methods address these stages in isolation or implicitly, lacking collaborative and stage-aware repair strategies. To address this issue, we propose EAGLE-DET, a novel detection framework based on sparse multi-scale attention and refined transformation. Specifically, the framework comprises three core modules: (1) the Cross-stage Multi-resolution Edge Enhancement Network (CMENet), which preserves small object edge representations via adaptive high-low frequency decomposition; (2) the Attention-guided Multi-scale Feature Fusion Network (AMFFN), which resolves cross-scale semantic conflicts through pyramidal sparse attention and multi-scale spatial decoupling; (3) the Enhanced Upsampling with Channel Bridging and Spatial Coordination module (EUCBSC), which recovers spatial detail fidelity via bidirectional channel shift mixing. Extensive experiments on three benchmark datasets—VisDrone-2019, UAVDT, and DOTA1.0—demonstrate the effectiveness of EAGLE-DET, which achieves improvements of 4.5% AP50 and 2.9% AP50:95 on VisDrone-2019 over the baseline, while maintaining inference at 71.7 FPS, achieving an optimal accuracy–efficiency trade-off. Full article

(This article belongs to the Section Navigation and Positioning)

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52 pages, 9613 KB

Open AccessReview

Recent Advancements in Digital Management and Monitoring of Mine Waste: Sensors, Characterization, and Predictive Modeling—A Review

by Tianqi Li, Feven Desta and Mike Buxton

Sensors 2026, 26(11), 3553; https://doi.org/10.3390/s26113553 - 3 Jun 2026

Abstract

Mining activities generate substantial volumes of solid waste materials during exploration and processing. These residuals pose environmental and geotechnical concerns due to their large spatial footprints and associated risks but may also contain potentially valuable resources. These characteristics highlight the necessity and opportunity [...] Read more.

Mining activities generate substantial volumes of solid waste materials during exploration and processing. These residuals pose environmental and geotechnical concerns due to their large spatial footprints and associated risks but may also contain potentially valuable resources. These characteristics highlight the necessity and opportunity of effective management and monitoring strategies. In recent years, a diverse range of technologies and methods have been applied to characterize mine waste compositions and analyze their spatial–temporal variability. These include remote sensing systems, ground-based sensors, and advanced data-driven methods. Despite the rapid advancement, the existing literature provides limited insight into the critical evaluation of how these techniques are applied in practice. This review systematically examines peer-reviewed journal articles published between 2021 and 2024 to highlight the state of the art in characterization, modeling, and monitoring techniques for mine waste. The review identifies recent trends, key gaps, advantages, and limitations of these techniques. The summary suggests that mining companies and research communities are increasingly adopting innovative technologies, transitioning from conventional methods to more sustainable practices. However, it also reveals ongoing challenges and persistent limitations. Further efforts, such as real-time monitoring capabilities, are required to achieve full implementation and integration across the industry and academia. Full article

(This article belongs to the Section Intelligent Sensors)

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62 pages, 16802 KB

Open AccessReview

Infrared Imaging for Autonomous Power Inspection: A Review from Detector to System Integration

by Yingye Guo, Yuxi Du, Run Mao, Yongyin Zhao and Junxiong Guo

Sensors 2026, 26(11), 3552; https://doi.org/10.3390/s26113552 - 3 Jun 2026

Abstract

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, [...] Read more.

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

(This article belongs to the Section Sensing and Imaging)

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21 pages, 10668 KB

Open AccessArticle

SpecBEV: An End-to-End BEV 3D Object Detection Algorithm Based on Frequency-Domain Analysis and Geometric Alignment

by Yu Lin and Shijie Jia

Sensors 2026, 26(11), 3551; https://doi.org/10.3390/s26113551 - 3 Jun 2026

Abstract

This paper proposes SpecBEV, an enhanced multi-view 3D object detection framework for autonomous driving using bird’s-eye-view (BEV) representations. Compared with LiDAR-based methods, multi-camera perception offers higher cost-effectiveness and flexibility. However, existing end-to-end BEV detectors suffer from illumination variations, occlusions, and cross-view inconsistencies during [...] Read more.

This paper proposes SpecBEV, an enhanced multi-view 3D object detection framework for autonomous driving using bird’s-eye-view (BEV) representations. Compared with LiDAR-based methods, multi-camera perception offers higher cost-effectiveness and flexibility. However, existing end-to-end BEV detectors suffer from illumination variations, occlusions, and cross-view inconsistencies during feature projection and fusion. These issues often introduce redundant background activations and geometric misalignment in the BEV space, leading to missed detections, false positives, and unstable localization. To address them, we introduce a frequency-prior spatial attention module (SA-Freq). It utilizes fixed discrete cosine transform (DCT) bases to model the multi-band responses of BEV features and produce spatial attention weights that suppress redundant activations and enhance target-related regions. We further design a cross-view feature alignment module (CFA) to ensure consistency between single-view BEV features and the fused BEV representation, thereby reducing geometric inconsistency and improving localization stability. Experiments on the nuScenes validation set demonstrate that SpecBEV achieves 0.3856 in mAP and 0.4871 in NDS. Compared with the BEVDet baseline, it yields an absolute gain of 0.1028 (36.35% relative improvement) in mAP and an absolute gain of 0.1371 (39.17% relative improvement) in NDS, which validates the effectiveness of the proposed method. Full article

(This article belongs to the Section Vehicular Sensing)

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15 pages, 26707 KB

Open AccessArticle

PdCu@rGO-based Electrochemical Sensor for Rapid Detection of Catechol

by Xiaoying Shen, Muyu Yan, Qiongya Wan, Ming Li, Xuefeng Wang, Pengcheng Xu and Yongheng Zhu

Sensors 2026, 26(11), 3550; https://doi.org/10.3390/s26113550 - 3 Jun 2026

Abstract

Catechol, a prevalent phenolic pollutant in food products, poses a significant threat to food safety, necessitating the development of rapid and sensitive detection methods. To overcome the limitations of conventional analytical techniques, such as expensive equipment and operational complexity, electrochemical sensors have gained [...] Read more.

Catechol, a prevalent phenolic pollutant in food products, poses a significant threat to food safety, necessitating the development of rapid and sensitive detection methods. To overcome the limitations of conventional analytical techniques, such as expensive equipment and operational complexity, electrochemical sensors have gained considerable attention owing to their rapid response and facile miniaturization. However, the rational design of sensing materials that exhibit both high sensitivity and selectivity remains a significant challenge. Herein, a series of PdCu bimetallic nanoparticles supported on reduced graphene oxide (PdCu@rGO) composites with varying Pd/Cu molar ratios was synthesized via a one-step liquid-phase reduction method. Owing to the synergistic electronic effects between Pd and Cu and the high electrical conductivity of the rGO support, the resulting nanocomposites exhibited excellent electrocatalytic activity toward catechol oxidation. At the optimal Pd/Cu molar ratio of 1:2, the fabricated Pd₁Cu₂@rGO/SPE sensor demonstrated a broad linear range of 0.5–500 μM, a low limit of detection of 200 nM (S/N = 3), good repeatability (RSD = 4.9%), and robust anti-interference capability. Furthermore, the proposed sensor was successfully applied to the detection of catechol in spiked green tea and fruit juice samples without complex pretreatment, achieving satisfactory recoveries of 91.0–101.4% and 98.6–104.8%, respectively. This work provides a reliable platform for the rapid, on-site screening of catechol in food matrices and offers valuable experimental insights into the rational design of bimetallic alloy–graphene heterostructures. Full article

(This article belongs to the Section Chemical Sensors)

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32 pages, 5222 KB

Open AccessArticle

A High-Precision Anti-Jamming Algorithm Based on Newton-Iteration-Enhanced Three-Spectral-Line RIFE with Real-Time Implementation

by Xinhua Tang and Yiming Wang

Sensors 2026, 26(11), 3549; https://doi.org/10.3390/s26113549 - 3 Jun 2026

Abstract

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This [...] Read more.

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This paper proposes a Newton-iteration-enhanced three-spectral-line RIFE algorithm implemented on a heterogeneous FPGA platform (Zynq-7000 SoC). The method performs coarse frequency estimation using the three-spectral-line RIFE to mitigate FFT fence effects, followed by Newton-based quadratic refinement, enabling high estimation accuracy with reduced FFT size. A fast–slow loop architecture is adopted, where the FPGA (PL) performs real-time interference suppression and the ARM (PS) handles system control and parameter updates. Experimental results show that, under static interference, the proposed method achieves a 10.9 dB improvement over direct estimation algorithms. Under chirp interference, it significantly outperforms both direct estimation and conventional iterative methods. In GNSS closed-loop tests, the proposed approach extends the anti-jamming margin to 82 dB J/S. Overall, the proposed method effectively balances estimation accuracy and processing latency, providing a practical solution for GNSS anti-jamming in high-dynamic environments. Full article

(This article belongs to the Special Issue Signal Processing for Satellite Navigation and Wireless Localization)

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20 pages, 37775 KB

Open AccessArticle

Spatiotemporal Evolution and Drivers of Highway Surface Deformation Based on SBAS-InSAR and Geodetector

by Zhaoyang Chen, Jin Li, Xu Zhang and Junwei Bi

Sensors 2026, 26(11), 3548; https://doi.org/10.3390/s26113548 - 3 Jun 2026

Abstract

To address the lack of long-term, wide-area surface deformation observations along the geologically complex Dangxiong–Yangbajing section of the G6 Expressway in the frozen-ground region of the Qinghai–Tibet Plateau, where conventional monitoring is insufficient, we applied Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) [...] Read more.

To address the lack of long-term, wide-area surface deformation observations along the geologically complex Dangxiong–Yangbajing section of the G6 Expressway in the frozen-ground region of the Qinghai–Tibet Plateau, where conventional monitoring is insufficient, we applied Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to retrieve surface deformation within a 2.0 km corridor on both sides of the highway from 24 November 2021 to 26 December 2024, and to characterize the spatiotemporal evolution of deformation. We then integrated eight explanatory factors (slope, surface roughness, distance to rivers, distance to faults, surface soil moisture, precipitation, land surface temperature (LST), and fractional vegetation cover (FVC)). Geodetector was used to quantify their explanatory power and spatial heterogeneity with respect to deformation. The results show pronounced spatially uneven settlement along this highway segment, with maximum annual settlement rates exceeding −45 mm/a. Five settlement centers were identified, including two major pavement subsidence zones. Distance to faults and soil moisture showed higher single-factor explanatory power, whereas FVC, precipitation, and LST also contributed to deformation heterogeneity. Interaction detection further indicated that the interactions between fault-related conditions with vegetation, soil moisture, precipitation, and LST substantially enhanced the explanatory power, suggesting that the deformation pattern was associated with multi-factor coupling rather than a single dominant environmental factor. These findings demonstrate the utility of integrating SBAS-InSAR with Geodetector analysis for corridor-scale highway deformation assessment and provide a remote sensing basis for targeted hazard assessment and risk mitigation for highways in frozen-ground environments. Full article

(This article belongs to the Special Issue AI-Enhanced Remote Sensing and InSAR for Geoscience Monitoring and Modeling)

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