Sensors

21 pages, 4901 KB

Open AccessArticle

A Neuro-Inspired Rate-Encoded Descriptor for High-Speed Asynchronous Robotic Vision

by Shane Harrigan, Sonya Coleman, Dermot Kerr, Pratheepan Yogarajah, Chengdong Wu and Zheng Fang

Sensors 2026, 26(16), 5311; https://doi.org/10.3390/s26165311 (registering DOI) - 21 Aug 2026

Abstract

This paper presents the Post-Stimulus Time-Dependent Event Descriptor (P-TED), a novel “pure event” feature descriptor designed for neuromorphic vision data. Unlike conventional frame-based approaches or hybrid methods that transform event data into intermediate representations, P-TED operates directly on asynchronous event streams, thereby preserving [...] Read more.

This paper presents the Post-Stimulus Time-Dependent Event Descriptor (P-TED), a novel “pure event” feature descriptor designed for neuromorphic vision data. Unlike conventional frame-based approaches or hybrid methods that transform event data into intermediate representations, P-TED operates directly on asynchronous event streams, thereby preserving the intrinsic low-latency and high-temporal-resolution advantages of event-based sensors. The descriptor integrates two complementary feature sets: a motion feature vector, which aggregates spatial relationships within a Moore neighbourhood to quantify stimulus direction, and a pattern feature vector, which employs rate encoding to capture temporal excitation signatures. The efficacy of the P-TED framework is validated through three distinct experiments: object and character recognition (MNIST-DVS and CIFAR10-DVS), mobile robot movement analysis, and complex non-rigid robotic hand gesture recognition (RoShamBo). Experimental results demonstrate that the P-TED achieves a significant reduction in classification latency, requiring only 2.7 ms compared to the 10.3 ms recorded by the state-of-the-art Distribution-Aware Retinal Transform (DART) framework. Additionally, P-TED exhibits superior robustness in disambiguating symmetric and mirrored motions, as well as in maintaining stability under non-linear fluctuations in event density caused by changing scale. This work establishes P-TED as a high-speed, computationally efficient, and explainable solution for real-time neuromorphic robotic vision systems. Full article

(This article belongs to the Special Issue Event-Based Vision and Multimodal Sensor Fusion)

24 pages, 4913 KB

Open AccessArticle

Privacy-Preserving Head Pose Estimation System for Measuring Cervical Range of Motion

by Zhuofu Liu, Lichao Zhang, Gaohan Li and Peter W. McCarthy

Sensors 2026, 26(16), 5310; https://doi.org/10.3390/s26165310 (registering DOI) - 21 Aug 2026

Abstract

Cervical range of motion (CROM) has been used in research and clinically for assessing cervical health. Gold-standard goniometers tend to be cumbersome. However, Inertial Measurement Units (IMUs) or vision-based alternatives demand frequent calibration and/or costly hardware; moreover, the subject is aware of being [...] Read more.

Cervical range of motion (CROM) has been used in research and clinically for assessing cervical health. Gold-standard goniometers tend to be cumbersome. However, Inertial Measurement Units (IMUs) or vision-based alternatives demand frequent calibration and/or costly hardware; moreover, the subject is aware of being measured and there is a risk of breaching privacy. In response, we have developed a non-contact HPNet system for head pose estimation (HPE) that can use a rear-facing camera to quantify CROM accurately. A Re-parameterized Visual Geometry Group (RepVGG)-D2se model is employed as the backbone of the network, and a Spatial Feature Enhancement (SCFE) module is incorporated to improve feature extraction. HPNet was evaluated on the large-scale Carnegie Mellon University (CMU) Panoptic dataset, achieving a mean absolute error (MAE) of 3.48°, 3.22°and 3.34° for yaw, pitch and roll respectively. Inter-instrument reliability was excellent for all six cervical movements when compared with the research/clinical-grade CROM device, with intraclass correlation coefficients (ICCs) averaging 0.939. Bland–Altman plots confirmed close agreement between the two methods. Cervical movement trajectory curves further confirmed the concordance between the clinical device and our method. The system is fully automatic, requires only a rear-facing camera, effectively preserves patient privacy, and provides accurate cervical posture estimation. This technology may provide a basis for future applications in neck-disorder screening, remote health monitoring, and personalized musculoskeletal wellness management, although further task-specific clinical validation will be required. To date, HPNet has been validated primarily on a computer-based platform and has not yet been deployed on smartphones. Future work will focus on model lightweighting, mobile deployment, and cross-device adaptation to facilitate its practical implementation on mobile devices. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies to Facilitate Clinical/Medical Decision Making)

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26 pages, 9400 KB

Open AccessArticle

Around the Clock: Wearing a Flex-Printed trEEGrid Electrode Patch and Miniaturized Amplifier for Nearly 24 Hours Across Daytime and Overnight EEG Sessions

by Joanna E. M. Scanlon, Axel H. Winneke, Wiebke Pätzold and Karen Insa Wolf

Sensors 2026, 26(16), 5309; https://doi.org/10.3390/s26165309 (registering DOI) - 21 Aug 2026

Abstract

Long-term EEG measurements (over eight hours) can allow a deeper understanding of everyday life brain functioning. However, most EEG systems can only be used for short periods in controlled settings, due to limitations in comfort, signal quality of EEG electrode sensors and amplifier [...] Read more.

Long-term EEG measurements (over eight hours) can allow a deeper understanding of everyday life brain functioning. However, most EEG systems can only be used for short periods in controlled settings, due to limitations in comfort, signal quality of EEG electrode sensors and amplifier size. In this study, we measured brain activity during day and night on ten lab member participants using our new flex-printed trEEGrid patch electrodes and a miniaturized and modularized amplifier prototype. The patch was left on participants up to 24 h while they went about their day. EEG was recorded during several exploratory tasks and overnight. Daytime scenarios included auditory and visual oddball tasks, as well as a resting-state measurement and sudoku cognitive load task. Impedances were recorded throughout all tasks. Ag/AgCl ‘ring’ electrodes were used as comparison. Daytime tasks were performed twice (i.e., once in the afternoon and again the next morning), to assess signal quality changes over time. P3 oddball, resting state and workload-related spectral effects were observed on both days. Auditory N1 amplitudes were similar between the patch and ring electrodes. The system shows promising data quality over the nearly 24 h wearing time and allows new possibilities for daytime and sleep EEG studies. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems—2nd Edition)

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17 pages, 4969 KB

Open AccessArticle

An Ultrasensitive Electrochemical Biosensor for Nucleic Acid Detection Based on Silver Nanoflower-Stem-Loop Probes

by Yingying Yuan, Xiaoyu Lei, Fengyu Li, Yuchen Su, Bo Liu, Lei Luo and Hangyu Zhang

Sensors 2026, 26(16), 5308; https://doi.org/10.3390/s26165308 (registering DOI) - 21 Aug 2026

Abstract

Nucleic acids are critical biomarkers that provide essential information throughout disease progression, making their detection critical to early diagnosis of both infectious and non-infectious diseases. However, existing detection methods, including classical analytical techniques and even most reported biosensors, are often constrained by complex [...] Read more.

Nucleic acids are critical biomarkers that provide essential information throughout disease progression, making their detection critical to early diagnosis of both infectious and non-infectious diseases. However, existing detection methods, including classical analytical techniques and even most reported biosensors, are often constrained by complex procedures, high costs, and limited sensitivity, with the majority operating at the femtomolar level and failing to achieve single-molecule detection needed for early-stage diagnosis. Here, we report an electrochemical biosensor based on silver nanoflowers (AgNFs) integrated with stem-loop probes (SPs) for universal nucleic acid detection, using Norovirus RNA as a model target to validate the platform. The SPs serve as critical elements in a signal amplification system, converting target binding into a biotin–streptavidin recognition event, which leads to the accumulation of AgNFs-SP complexes on laser-induced graphene (LIG) electrodes and generates a strong electrochemical signal. Under optimized conditions with a 50 min hybridization incubation (total assay time ~60 min), the sensor exhibits a linear response to Norovirus RNA concentrations from 1 aM to 10 fM, with a measured detection limit of 1 aM, achieving single-molecule-level detection capability. For applications requiring faster turnaround, a 20 min hybridization incubation (~30 min total assay time) shifts the linear range to 0.1 fM–1 pM with a measured detection limit of 0.1 fM, offering more rapid quantification when the maximum sensitivity is not required. The proposed biosensor is cost-effective, amenable to miniaturization, and designed as a versatile platform adaptable to other nucleic acid targets by simply modifying the probe sequence, showing broad potential for early diagnosis of various diseases. Full article

(This article belongs to the Section Biosensors)

27 pages, 8740 KB

Open AccessArticle

Research on the High-Precision Position Datum Long Baseline Coordinate Transfer Strategy Using BDS-3 High- and Low-Frequency Signals

by Mingduan Zhou, Wenxuan Zhang, Haodong Cai, Zichun Wang, Shuzhan Xia, Qiao Song, Shiqi Lin and Lu Qin

Sensors 2026, 26(16), 5307; https://doi.org/10.3390/s26165307 (registering DOI) - 21 Aug 2026

Abstract

High-precision long-baseline coordinate transfer is essential for maintaining spatial reference frames, and the modernized multi-frequency signals of BDS-3 provide new opportunities for this task. However, existing long-baseline network solutions still rely mainly on legacy frequency combinations, and quantitative evidence for pure new-frequency BDS-3 [...] Read more.

High-precision long-baseline coordinate transfer is essential for maintaining spatial reference frames, and the modernized multi-frequency signals of BDS-3 provide new opportunities for this task. However, existing long-baseline network solutions still rely mainly on legacy frequency combinations, and quantitative evidence for pure new-frequency BDS-3 combinations in large-scale coordinate transfer remains limited. This study evaluates the applicability of BDS-3 high- and low-frequency signal combinations for long-baseline position datum transfer and investigates frequency-combination selection. Seven continuous stations were used to form 21 long baselines. Five dual-frequency schemes were tested, including four BDS-3 combinations, namely B1I/B3I, B1I/B2a, B1C/B2a, and B1C/B3I, and one GPS reference combination, L1/L5. Double-differenced ionosphere-free baseline processing and three-dimensional constrained network adjustment were applied. Performance was assessed using carrier-phase precision, normalized root mean square (NRMS), baseline vector quality, and point-transfer differences. The results show that the BDS-3 B1C/B2a new-frequency combination achieved the best overall consistency among the BDS-3 schemes, with an average high-frequency carrier-phase precision of 6.3 mm, a mean NRMS of 0.23, millimeter-level baseline vector formal-error RMS, and a 19.7 mm point difference at the unknown station DCMS. Given that the evaluation is based on seven consecutive days of observations, the long-term applicability of the proposed strategy requires further validation. Full article

(This article belongs to the Special Issue Advances in GNSS Signal Processing and Navigation—Third Edition)

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18 pages, 1217 KB

Open AccessArticle

Lightweight Monocular Depth Estimation with Local Feature Enhancement Modules and Guided Data Augmentation

by Jae-young Lee and Soon-kak Kwon

Sensors 2026, 26(16), 5306; https://doi.org/10.3390/s26165306 (registering DOI) - 21 Aug 2026

Abstract

Although research on lightweight monocular depth estimation models has been actively conducted, achieving real-time deployment on edge devices remains challenging due to limited computational resources and memory capacity. To address these limitations, we propose a lightweight model for self-supervised monocular depth estimation. We [...] Read more.

Although research on lightweight monocular depth estimation models has been actively conducted, achieving real-time deployment on edge devices remains challenging due to limited computational resources and memory capacity. To address these limitations, we propose a lightweight model for self-supervised monocular depth estimation. We cut the iterations of each feature-extracting block nearly in half, while our proposed Asymmetric Dilated Convolution module and a StarNext module compensate for the reduced model capacity. Specifically, the Asymmetric Dilated Convolution module captures horizontal and vertical structural features through asymmetric kernels, and the StarNext module fuses multi-scale features via element-wise multiplication. In model training, random cropping and scaling are applied for inducing the model to focus on localized object features. Additionally, we introduce a Disparity-guided Cutout technique based on the pre-inferred disparity map to randomly mask adjacent pixels. Simulation results on the KITTI dataset demonstrate that the proposed model reduces the number of parameters and GFLOPs by approximately 50% and 58%, respectively, without significant degradation in depth estimation accuracy compared to the baseline Lite-Mono. Furthermore, inference benchmarks on the Jetson Orin Nano platform demonstrate speedups of approximately 46.0%, 46.1%, 46.3%, and 52.0% across the MaxN, 25 W, 15 W, and 7 W power modes, respectively. Full article

(This article belongs to the Special Issue Sensor-Based Image Processing and Sensing Techniques for Enhanced Object Detection)

37 pages, 4256 KB

Open AccessArticle

Interpretable Graph–Temporal–Spectral Fusion for Precursor-Related Anomaly Detection in Underground Mine Sensor Networks

by Shuren Mao, Yunpei Liang and Quangui Li

Sensors 2026, 26(16), 5305; https://doi.org/10.3390/s26165305 (registering DOI) - 21 Aug 2026

Abstract

Underground coal mine safety monitoring relies on multi-source sensor networks, but abnormal-state detection remains challenging because methane, airflow, dust, and equipment-operation signals are non-stationary, heterogeneous, and constrained by ventilation and mining disturbances. This study proposes GasNet, an interpretable engineering-prior graph–temporal–spectral framework for precursor-related [...] Read more.

Underground coal mine safety monitoring relies on multi-source sensor networks, but abnormal-state detection remains challenging because methane, airflow, dust, and equipment-operation signals are non-stationary, heterogeneous, and constrained by ventilation and mining disturbances. This study proposes GasNet, an interpretable engineering-prior graph–temporal–spectral framework for precursor-related anomaly detection. Variables are organized into methane-related core sensors and environmental–operational modulation sensors. A directed sensor graph is constructed using ventilation causality, sensor deployment, and shearer-coupling relationships. GasNet then integrates a graph convolutional network for spatial–topological modeling, TimesNet for temporal–spectral pattern extraction, and cross-attention for adaptive feature fusion. An unsupervised reconstruction strategy identifies intervals deviating from learned normal production patterns. Field validation was conducted on the 31002 fully mechanized working face of the Xinyuan Coal Mine, where eight precursor-related abnormal intervals were annotated from monitoring data and field records. GasNet achieved a Precision of 0.881, a Recall of 1.000, an F1-score of 0.937, a false-alarm rate of 0.0017, and zero missed detections, with the highest F1-score among seven time-series baselines. Interpretability analysis further provided feature-fusion and sensor-time evidence for warning review. These results support the feasibility of GasNet for interpretable anomaly detection in the investigated working face. Full article

(This article belongs to the Section Sensor Networks)

14 pages, 1233 KB

Open AccessArticle

Structure-Aware Noise Scheduling for Fixed-View Visual Sensor Reconstruction

by Xingyu Lu and Zengshan Yao

Sensors 2026, 26(16), 5304; https://doi.org/10.3390/s26165304 (registering DOI) - 21 Aug 2026

Abstract

Fixed-view visual sensors require normal-image reconstruction that preserves structural detail while exposing a local deviation in a residual map. Standard denoising diffusion probabilistic models (DDPMs) use spatially uniform corruption. We study an image-derived, structure-aware noise schedule for reference-image reconstruction, where the input image [...] Read more.

Fixed-view visual sensors require normal-image reconstruction that preserves structural detail while exposing a local deviation in a residual map. Standard denoising diffusion probabilistic models (DDPMs) use spatially uniform corruption. We study an image-derived, structure-aware noise schedule for reference-image reconstruction, where the input image is available and the same importance map can be fixed in the forward and reverse processes. A matched three-seed diagnostic further isolates the effect of the semantic-training/gradient-reverse map substitution and of a lower bound on local noise. Map consistency recovers much of the unconditional FID loss, but neither it nor the attenuation floor yields a universal advantage over DDPM. The six-category MVTec AD residual study is likewise category dependent: the gradient/edge special case improves selected texture-localization outcomes but degrades several object-level outcomes. We therefore present the method as a reconstruction diagnostic, not as a competitive industrial anomaly detector or a universally superior generator. Full article

(This article belongs to the Section Intelligent Sensors)

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29 pages, 11764 KB

Open AccessArticle

Optimization Scheme for Hybrid RIS-Assisted ISAC System for Controllable Communication and Sensing

by Zhishuo Deng, Bo Li and Hehang Wang

Sensors 2026, 26(16), 5303; https://doi.org/10.3390/s26165303 - 21 Aug 2026

Abstract

Integrated sensing and communication (ISAC) systems are envisioned as a key enabler for next-generation wireless networks. To simultaneously achieve multi-function of communication and sensing for this system, a hybrid reconfigurable intelligent surface (RIS) comprising both active and passive reflecting elements is proposed. An [...] Read more.

Integrated sensing and communication (ISAC) systems are envisioned as a key enabler for next-generation wireless networks. To simultaneously achieve multi-function of communication and sensing for this system, a hybrid reconfigurable intelligent surface (RIS) comprising both active and passive reflecting elements is proposed. An index-wise importance score matrix and a factorized representation of complex reflection coefficients are optimized by introducing a communication-sensing controllable coefficient. A novel ISAC system is investigated, which exploits the low-power advantage of passive reflecting elements while retaining the signal amplification capability of active reflecting elements. In the multiple-input multiple-output (MIMO) communication networks, the RIS reflection coefficient matrix is adaptively optimized via a Riemannian Hessian-based method. At the same time, the transmit precoding matrix is obtained using an extended weighted minimum mean square error (WMMSE) and Lagrange multiplier methods. Compared with baseline schemes including active-only RIS, passive-only RIS, random-phase RIS, and non-RIS, the proposed scheme enables multi-mode adjustability of communication and sensing which can be easily transplanted in current ISAC system. Under the constraint of transmit power, it exhibits more robust performance on communication-sensing with varying number of RIS elements and different signal-to-noise ratios (SNRs). Full article

(This article belongs to the Section Communications)

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37 pages, 67321 KB

Open AccessArticle

Improving Autism Diagnosis Across Ages Using Eye-Tracking and Temporal Transformer Models

by Mohammed A. AlZain, Mahmoud Rokaya, Dalia I. Hemdan, Ibrahim Gad, Malik Almaliki and Elsayed Atlam

Sensors 2026, 26(16), 5302; https://doi.org/10.3390/s26165302 - 21 Aug 2026

Abstract

Variation in gaze behavior due to age is currently a considerable challenge in building reliable eye-tracking systems for Autism Spectrum Disorder (ASD) diagnosis. However, existing strategies often focus on static gaze representation or dataset-based information, which can lead to limited generalization of findings [...] Read more.

Variation in gaze behavior due to age is currently a considerable challenge in building reliable eye-tracking systems for Autism Spectrum Disorder (ASD) diagnosis. However, existing strategies often focus on static gaze representation or dataset-based information, which can lead to limited generalization of findings depending on developmental groups and heterogeneous recording conditions. In this paper, we present a temporal transformer-based system for ASD classification using eye-tracking sequences. This allows you to model gaze behavior as a structured temporal process in the context of contextual attention, as well as employing entropy-based modeling for various distributions of variability over time and temporal consistency constraints to capture sequential gaze dynamics related to ASD behavioral patterns. The framework was evaluated using public eye-tracking corpus containing temporally ordered gaze recordings from ASD and TD participants across age groups. Five sequential experiments on baseline classification, class-balancing analysis, cross-age evaluation, ablation analysis, and cross-dataset transfer learning were performed to conduct experiment-based evaluations. Model performed 0.91 in in-domain Area Under the Receiver Operating Characteristic Curve (AUC) and 0.81 in F1-score on the primary eye-tracking dataset. In the cross-dataset assessment stage, the framework presented a relatively stable performance, with an AUC of 0.85 and an average F1-score of 0.74, irrespective of differences in participant distributions and recording conditions. Ablation analysis also revealed that entropy regularization and temporal consistency mechanisms played a significant role in model stability and classification performance. The ablation analysis provides additional insight into the contribution of the proposed framework components beyond the overall classification performance. Removing the entropy-based regularization reduced the model’s ability to represent variability in gaze allocation, whereas removing the temporal-consistency regularization resulted in less stable sequence representations during learning. These observations indicate that the proposed components complement the transformer-based sequence encoder by improving representation stability and preserving diagnostically relevant temporal information. Rather than acting as independent classifiers, the regularization mechanisms serve as supporting constraints that enhance the quality and robustness of the learned temporal representations. The results indicate that temporally structured gaze modeling is more robust, interpretable, and general in comparison to static gaze representations. In summary, the presented framework can represent a scalable and developmentally appropriate approach to gaze-based ASD classification and support the implementation of trusted neurodevelopmental screening systems. Full article

(This article belongs to the Special Issue Integrated IoT and Sensing in Healthcare System)

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29 pages, 3497 KB

Open AccessArticle

BDC-YOLO: A Novel Architecture Coupling Dynamic Serpentine Convolutions with Bi-Level Routing Attention for Road Defect Detection

by Bo Yang, Hongli Sheng, Chen Geng, Chen Chen and Huiqing Lian

Sensors 2026, 26(16), 5301; https://doi.org/10.3390/s26165301 - 21 Aug 2026

Abstract

Accurate pavement distress identification is essential for infrastructure maintenance. However, prevailing models frequently underperform in complicated environments due to extreme scale variations, atypical defect geometries, and severe background noise. To mitigate these limitations, this study presents BDC-YOLO, an upgraded detection network built upon [...] Read more.

Accurate pavement distress identification is essential for infrastructure maintenance. However, prevailing models frequently underperform in complicated environments due to extreme scale variations, atypical defect geometries, and severe background noise. To mitigate these limitations, this study presents BDC-YOLO, an upgraded detection network built upon the YOLOv8 baseline. The proposed architecture structurally incorporates three specialized mechanisms: Bi-level Routing Attention (BRA) to isolate relevant target features from background artifacts; Dynamic Snake Convolution (DySnakeConv) to capture the topological characteristics of elongated and irregularly shaped cracks; and Content-Aware ReAssembly of FEatures (CARAFE) to minimize information degradation during upsampling and refine multi-scale feature fusion. Evaluated on the RDDChina dataset, BDC-YOLO demonstrates superior accuracy over the baseline and comparative state-of-the-art methods. Specifically, the framework yields a mAP_0.5 of 88.9%, representing an absolute gain of 4.7% against the standard YOLOv8 model while achieving an inference speed of 175.4 FPS. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Target Recognition and Remote Sensing Data Processing)

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18 pages, 7772 KB

Open AccessArticle

Hierarchically Structured V₂O₅/PANI Heterostructures for Room-Temperature Ammonia Sensing

by Chunmei Shangguan, Anan Xu, Fang Wang, Ying Li, Jiao Jia and Zhenchen Liu

Sensors 2026, 26(16), 5300; https://doi.org/10.3390/s26165300 - 21 Aug 2026

Abstract

Ammonia, a toxic and volatile pollutant commonly found in chemical industrial environments, requires reliable real-time detection to ensure industrial safety and effective environmental monitoring. Conventional gas sensors typically operate at elevated temperatures, resulting in high power consumption. Moreover, pure metal oxides and conductive [...] Read more.

Ammonia, a toxic and volatile pollutant commonly found in chemical industrial environments, requires reliable real-time detection to ensure industrial safety and effective environmental monitoring. Conventional gas sensors typically operate at elevated temperatures, resulting in high power consumption. Moreover, pure metal oxides and conductive polymers often suffer from significant aggregation and exhibit suboptimal sensing performance under ambient conditions, limiting their practical applications. In this study, hierarchical porous V₂O₅/PANI composites were synthesized via a straightforward one-step coprecipitation method combined with in situ polymerization. The interlaced architecture of polyaniline (PANI) and vanadium pentoxide (V₂O₅) effectively reduces structural aggregation and increases the availability of surface active sites. Furthermore, the synergistic interaction at the bi-phase interface significantly enhances charge carrier transport, leading to improved ammonia-sensing capabilities at room temperature. Notably, the composite containing 20% V₂O₅ demonstrated superior response, selectivity, and reproducibility toward 10 ppm NH₃. Due to its simple fabrication process and room-temperature operation without external heating, the developed V₂O₅/PANI composite sensor holds significant potential for practical applications in low-concentration ammonia detection under ambient conditions. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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23 pages, 2766 KB

Open AccessArticle

Cloud–Edge Collaborative Personalized Deployment of Knowledge Bases in Semantic Communications

by Kaixiang Yang, Yushen Han, Yikai Xu and Mingkai Chen

Sensors 2026, 26(16), 5299; https://doi.org/10.3390/s26165299 - 21 Aug 2026

Abstract

With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronization of background knowledge between the transmitter and receiver, making the semantic [...] Read more.

With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronization of background knowledge between the transmitter and receiver, making the semantic knowledge base (SKB) a critical cornerstone. However, effectively selecting appropriate content from massive cloud-based knowledge repositories for edge deployment remains a significant challenge. This paper conducts systematic research to address the key issues in the flow deployment of SKBs at the edge, including insufficient adaptation to personalized preferences, inadequate timeliness management, and the complexity of multi-objective optimization. First, a comprehensive system model is constructed, integrating user preferences, knowledge relevance, transceiver matching degree, and the Age of Information (AOI). Second, the Generative Adversarial Network (GAN)-assisted Preference-based Reinforcement Learning (GaPbRL) algorithm is proposed. The experimental results demonstrate that this method outperforms traditional schemes in terms of knowledge-base hit rate, transceiver matching degree, and algorithm convergence speed, while significantly reducing the overhead of manual fine-tuning. This study provides a robust framework for the personalized and efficient cloud–edge collaborative deployment of SKBs. Full article

(This article belongs to the Special Issue The Key Technologies for Wireless Communication, Computing and Sensing)

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22 pages, 5725 KB

Open AccessArticle

A Priority-Aware Multi-Agent Reinforcement Learning Framework for Collaborative Intelligent Sensing in Social IoT

by Jing Zhu

Sensors 2026, 26(16), 5298; https://doi.org/10.3390/s26165298 - 21 Aug 2026

Abstract

Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load conditions, mismatches between resource provisioning and sensing quality of experience (QoE) can significantly degrade [...] Read more.

Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load conditions, mismatches between resource provisioning and sensing quality of experience (QoE) can significantly degrade system performance in applications such as smart cities. To address this issue, this paper proposes a service priority-aware collaborative sensing support framework based on a joint next-generation passive optical network (NG-PON) and cooperative intelligent service-based radio access network (CIS-RAN) architecture. The framework enables edge AI-driven inference and distributed sensor collaboration in heterogeneous Social IoT environments. Service-slice-specific priority weights are assigned to optical network units (ONUs) and wavelengths according to the QoE requirements and latency sensitivity of sensing tasks, allowing dynamic wavelength tuning that prioritizes high-impact collaborative services. The utility of a centralized intelligent processing pool is formulated to achieve priority-consistent and efficient resource coordination under collaborative constraints. In addition, a multi-agent AI-driven optimization framework is employed to derive adaptive resource allocation strategies that incorporate service priorities while satisfying stringent service-level agreements (SLAs). Simulation results show that the proposed framework improves system-level proxy metrics, including total utility, wavelength satisfaction, and resource utilization, compared with representative baseline schemes. Full article

(This article belongs to the Special Issue Collaborative Intelligent Sensing for Social IoT)

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25 pages, 15896 KB

Open AccessArticle

Privacy-Preserving and Poisoning-Robust Federated Learning for Industrial IoT

by Huan Yin, Congwen Chen, Jingyi Zhang, Dian Yu and Shuanggen Liu

Sensors 2026, 26(16), 5297; https://doi.org/10.3390/s26165297 - 21 Aug 2026

Abstract

With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly [...] Read more.

With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency. Full article

(This article belongs to the Special Issue Cyber Security and Privacy in Internet of Things (IoT))

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16 pages, 3980 KB

Open AccessArticle

A Gear-Driven Plantar Energy Harvester with Integrated Self-Sensing for Human Locomotion Recognition

by Xinrui Wang, Weiqi Lin, Wenda Wang, Yang Yu, Moyue Cong, Yongzhuo Gao and Wei Dong

Sensors 2026, 26(16), 5296; https://doi.org/10.3390/s26165296 - 21 Aug 2026

Abstract

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a [...] Read more.

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a wedge–lever transmission and amplifies the rotational speed using a multistage gear train with a total transmission ratio of 12. A one-way bearing enables directional power transmission during loading and prevents reverse rotation during recovery. The generated voltage serves both as the electrical output and as the sensing signal for locomotion recognition. Human-subject experiments were conducted under six locomotion modes: walking at 1, 2 and 3 m/s; running; ascending; and descending. Voltage signals were sampled at 2000 Hz and segmented into overlapping sequences. A CNN–LSTM model was used to extract local waveform features and temporal dependencies from the nonstationary signals. The model achieved an overall recognition accuracy of 98.8%, with most errors occurring between ascending and descending. The results demonstrate that a single plantar device can simultaneously harvest biomechanical energy and provide motion-related information, offering a compact solution for integrated energy harvesting and self-sensing in wearable systems. Full article

(This article belongs to the Special Issue Self-Powered Wearable Sensors for Mobile Autonomous Systems and Human–Robot Interaction)

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24 pages, 5349 KB

Open AccessArticle

MarShip-DET: A Frequency-Aware Multi-Scale Fusion Algorithm for Ship Detection in Maritime Remote Sensing Imagery

by Keren Chen, Xufang Zhu, Zhikun Liu, Fuyan Zhao and Kang Wang

Sensors 2026, 26(16), 5295; https://doi.org/10.3390/s26165295 - 21 Aug 2026

Abstract

To address the challenges of multi-scale target variation, complex background interference, and insufficient feature fusion quality in maritime remote sensing ship detection, this paper proposes MarShip-DET, a frequency-aware multi-scale fusion detection algorithm based on YOLO11n. Three core modules are introduced: Channel-Decoupled Progressive Feature [...] Read more.

To address the challenges of multi-scale target variation, complex background interference, and insufficient feature fusion quality in maritime remote sensing ship detection, this paper proposes MarShip-DET, a frequency-aware multi-scale fusion detection algorithm based on YOLO11n. Three core modules are introduced: Channel-Decoupled Progressive Feature Extraction Module (CDPFEM), which employs asymmetric channel decoupling with dual-statistic channel attention and image-relative-position-encoded multi-head self-attention to enhance discriminative feature extraction; Edge-Aware Region Context Fusion Module (EARCFusion), which integrates learnable Sobel edge sensing and cross-attention correction to achieve precise foreground refinement; and Wavelet-guided Prototype Attention Module (WavePAM), which combines Haar wavelet frequency decomposition with prototype-guided spatial compression attention to strengthen deep semantic representation. Experiments on HRSC2016 demonstrate that MarShip-DET achieves an mAP50 of 94.9% and an mAP50-95 of 84.4%, improving by 3.7% and 4.3% over the baseline, respectively. Zero-shot experiments on HRSID and SSDD, including comparisons with YOLO11n, D-FINE-N, and YOLOv13n, provide additional evidence of cross-domain transferability under the evaluated protocol. Full article

(This article belongs to the Section Remote Sensors)

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35 pages, 6221 KB

Open AccessArticle

A Dim Space Target Detection and Track Association Method for Dense Stellar Backgrounds

by Cheng Jiang, Zhixia Yang, Zhongqi Ma, Chiming Tong and Jinshen Wang

Sensors 2026, 26(16), 5294; https://doi.org/10.3390/s26165294 - 21 Aug 2026

Abstract

In space target surveillance missions, effective detection of dim space targets remains a challenge due to dense star interference and the random nature of target motion. To quickly and accurately extract dim space targets from complex star backgrounds, this paper proposes a dim [...] Read more.

In space target surveillance missions, effective detection of dim space targets remains a challenge due to dense star interference and the random nature of target motion. To quickly and accurately extract dim space targets from complex star backgrounds, this paper proposes a dim space target detection and track association method for dense star backgrounds. This paper analyzes various features of the target and background from a combined spatiotemporal perspective, including three main stages. First, inter-frame registration is used to filter out bright stars, followed by connected domain post-processing, which simplifies the star map background while enhancing the signal-to-noise ratio of dim targets. Secondly, an image difference fusion coarse processing module is proposed. The reconstructed multi-impulse function is derived from the spectral phase difference to estimate the displacement parameters of different components, after which the image difference fusion is designed to obtain candidate targets. Third, a directional track association algorithm is designed, with the candidate targets as the center and the motion parameters as thresholds, narrowing the association range to a fan-shaped region. This enables fast detection of target tracks while removing excess false alarms. The experimental results on four datasets demonstrate that this method outperforms traditional baseline methods in terms of target detection and localization accuracy. Full article

(This article belongs to the Section Navigation and Positioning)

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14 pages, 2996 KB

Open AccessArticle

A Static and Dynamic Combined Center of Mass Measurement Method Based on Multi-View Vision

by Daojing Qu, Xuhao Zhang, Genyou Wei, Meibao Wang and Zhiyao Xiang

Sensors 2026, 26(16), 5293; https://doi.org/10.3390/s26165293 - 21 Aug 2026

Abstract

The position of the center of mass directly affects the attitude control and flight safety of moving bodies such as unmanned aerial vehicles (UAVs). Therefore, high-precision measurement of the center of mass is required. Existing methods require changing the posture of the measured [...] Read more.

The position of the center of mass directly affects the attitude control and flight safety of moving bodies such as unmanned aerial vehicles (UAVs). Therefore, high-precision measurement of the center of mass is required. Existing methods require changing the posture of the measured object multiple times. This introduces repeated positioning errors and suffers from poor equipment versatility. To address these issues, this paper proposes a static and dynamic combined measurement method for the center of mass based on multi-view vision. First, the relationship between the swing period and the pendulum length under the simple pendulum principle is analyzed. The basic principle of determining the direction of the center of mass using the line of gravity is also examined. Second, an under-constrained compound pendulum fixture is designed. A binocular vision system is used to track circular markers, perform FFT-based period verification, and fit the gravity line using singular value decomposition (SVD). Third, using a standard cubic iron block as the test object, the influence of pendulum length and swing angle on measurement accuracy is studied. Finally, experiments verify that the proposed method can obtain three-dimensional coordinates of the center of mass under a single suspension condition. The results show that with a pendulum length of 330 mm and an initial swing angle of 4°, the root mean square error of the center of mass measurement is 0.70 mm, and the maximum deviation over five repeated measurements is 1.45 mm. This method does not require repeated lifting or changes in posture. It can meet the need for in-situ, high-precision center of mass measurement of UAVs and other aircraft. Full article

(This article belongs to the Section Sensing and Imaging)

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