Next Issue
Volume 26, May-2
Previous Issue
Volume 26, April-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
3.5
Journals
Sensors
Volume 26
Issue 9
sensors-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors, Volume 26, Issue 9 (May-1 2026) – 345 articles

Cover Story (view full-size image): Differentiating idiopathic pulmonary fibrosis (IPF) from autoimmune usual interstitial pneumonia (aUIP) remains a major clinical challenge due to overlapping radiological and histopathological features. In this study, we applied electronic nose (eNose) technology to analyze exhaled volatile organic compound (VOC) patterns (“breathprints”) in patients with fibrotic interstitial lung diseases. Using multivariate signal processing, including principal component analysis and linear discriminant analysis, we demonstrated that breath-derived metabolic signatures can discriminate IPF from aUIP with promising accuracy. These findings support breathomics as a non-invasive, rapid, and repeatable adjunct to multidisciplinary diagnostic evaluation, offering a novel systems-level approach to disease phenotyping in interstitial lung diseases. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
16 pages, 4977 KB  
Article
DGADiff: Decoupled Guide Attention with Diffusion Model for Portrait Stylization
by Yi Ren, Zihan Shen, Junchao Fan and Guanlun Guo
Sensors 2026, 26(9), 2915; https://doi.org/10.3390/s26092915 - 6 May 2026
Viewed by 880
Abstract
Diffusion-based models have substantially propelled the progress of portrait stylization. Nevertheless, the lack of clear supervisory signals often leads to pattern drift in the target portrait. To overcome this issue, we introduce DGADiff, a training-free stylization framework based on a diffusion model. Specifically, [...] Read more.
Diffusion-based models have substantially propelled the progress of portrait stylization. Nevertheless, the lack of clear supervisory signals often leads to pattern drift in the target portrait. To overcome this issue, we introduce DGADiff, a training-free stylization framework based on a diffusion model. Specifically, we first leverage prior knowledge from a pre-trained latent consistency model (LCM) to efficiently sample representative features from noisy image pairs. Next, we design a Decoupled Guide Attention Mechanism (DGA), that disentangles the U-Net attention into separate self-attention and masked-attention tracks, enabling accurate transfer of fine-grained facial style patterns. Extensive experiments verify that our DGADiff achieves favorable results across multiple metrics in content-to-style and style-to-content multi-domain tasks, demonstrating the effectiveness of spatial attention decoupling for portrait stylization. Full article
(This article belongs to the Section Intelligent Sensors)
►▼ Show Figures

Figure 1

27 pages, 9863 KB  
Article
Online Monitoring of Transformer Winding Faults Based on Pulse Coupling Injection
by Zetong Wang, Yuhan Zou, Junhao Ma, Zongnan Liu, Xinyu Peng, Tianran Zhang, Sizhe Xiang, Chenguo Yao and Shoulong Dong
Sensors 2026, 26(9), 2914; https://doi.org/10.3390/s26092914 - 6 May 2026
Viewed by 791
Abstract
Aiming at the problems with traditional transformer winding deformation detection, requiring power outages, low signal-to-noise ratios for online monitoring, and insufficient feature extraction, this paper proposes a live monitoring and intelligent diagnosis method based on pulse-coupled injection. At the hardware level, a semi-ring [...] Read more.
Aiming at the problems with traditional transformer winding deformation detection, requiring power outages, low signal-to-noise ratios for online monitoring, and insufficient feature extraction, this paper proposes a live monitoring and intelligent diagnosis method based on pulse-coupled injection. At the hardware level, a semi-ring capacitive coupling sensor is developed and designed, which realizes non-contact injection of high-frequency pulse signals and high-SNR extraction without a power outage. The reliability of the system under complex working conditions is verified by field experiments on multiple actual 110 kV transformers. At the algorithm level, an innovative MSCNN–Transformer–PGA deep composite model fused with prior electromagnetic physical knowledge is constructed and combined with the transformer equivalent circuit model. The model uses a multi-scale convolution to extract local details of frequency response signals, adopts Transformer to establish the global sequence dependence, and introduces a Physics-Guided Attention mechanism (PGA) to adaptively focus on the key fault physical frequency bands. The experimental results show that the proposed method effectively overcomes electromagnetic noise interference, and the fault classification accuracy of single-modal pulse frequency response data reaches 97.6%, providing a high-precision online monitoring solution for the safe operation and maintenance of transformers. Full article
(This article belongs to the Special Issue Advanced Sensing Applications for Fault Diagnosis and Reliability Analysis of Elector-Mechanical Systems)
►▼ Show Figures

Figure 1

24 pages, 6247 KB  
Article
Sensor-Based Fault Diagnosis and Prognosis of Neurophysiological States: A Transformer Autoencoder Approach to EEG Monitoring
by Jesús Jaime Moreno Escobar, Mauro Daniel Castillo Pérez, Erika Yolanda Aguilar del Villar and Hugo Quintana Espinosa
Sensors 2026, 26(9), 2913; https://doi.org/10.3390/s26092913 - 6 May 2026
Viewed by 705
Abstract
This study presents a sensor-based condition monitoring framework for the diagnosis and prognosis of neurophysiological states using electroencephalographic (EEG) signals. Leveraging a comparative deep learning architecture, we evaluate a baseline Variational Autoencoder against a Transformer-based Autoencoder to model latent representations of EEG dynamics [...] Read more.
This study presents a sensor-based condition monitoring framework for the diagnosis and prognosis of neurophysiological states using electroencephalographic (EEG) signals. Leveraging a comparative deep learning architecture, we evaluate a baseline Variational Autoencoder against a Transformer-based Autoencoder to model latent representations of EEG dynamics across three therapeutic phases: pre-intervention, during intervention, and post-intervention. The proposed methodology aligns with sensor-based fault diagnosis principles by treating deviations from stable neurophysiological states as diagnostic indicators and temporal phase transitions as markers of therapeutic stage progression. Using a dataset of 94 EEG sessions from six subjects with diverse neurological conditions, we demonstrate that the Transformer Autoencoder, through its self-attention mechanism, captures cross-band spectral relationships more effectively than the VAE, resulting in denser within-phase clusters and improved separation between therapeutic stages. Quantitative evaluation reveals small but statistically significant effects between pre- and during-intervention phases (ηpartial2=0.0388) and pre- and post-intervention phases (ηpartial2=0.0470), predominantly driven by delta, theta, beta, and gamma rhythms. These findings illustrate how sensor-based latent state monitoring can provide interpretable, data-driven insights for condition assessment and phase transition assessment between sessions in complex dynamic systems, with potential applicability beyond clinical domains to industrial condition monitoring and fault diagnosis tasks. The framework confirms that it offers qualitative indicators, rather than predictive clinical outputs. Full article
(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)
►▼ Show Figures

Figure 1

24 pages, 13233 KB  
Article
A Curriculum-Learning-Assisted MAPPO-Based Algorithm for Dynamic Spectrum Access and Anti-Jamming in UAV Swarms
by Xiaoze Yuan and Jiabao Wen
Sensors 2026, 26(9), 2912; https://doi.org/10.3390/s26092912 - 6 May 2026
Viewed by 943
Abstract
The utilization of drone swarms for cooperative missions is becoming increasingly prevalent. However, establishing high-concurrency and highly reliable communication links in complex environments remains a significant challenge. Existing methods based on traditional Medium Access Control (MAC) protocols struggle to cope with high-density collisions, [...] Read more.
The utilization of drone swarms for cooperative missions is becoming increasingly prevalent. However, establishing high-concurrency and highly reliable communication links in complex environments remains a significant challenge. Existing methods based on traditional Medium Access Control (MAC) protocols struggle to cope with high-density collisions, while conventional deep reinforcement learning (DRL) approaches often encounter convergence difficulties in non-stationary interference environments, leading to notable limitations in anti-jamming robustness and algorithmic efficiency. To tackle this problem, this paper proposes a dynamic access algorithm based on Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO). Specifically, we adopt a Centralized Training with Decentralized Execution (CTDE) architecture to enable implicit spectrum cooperation within the swarm. Notably, we design a three-stage progressive curriculum learning mechanism—basic collision avoidance, load balancing, and dynamic anti-jamming—coupled with a phased reward reshaping strategy, guiding the agents to progressively master intelligent frequency-hopping decisions in complex environments. Experimental results demonstrate that in simulated scenarios involving dynamic sweep jamming and high-load multi-drone communication, the proposed method significantly outperforms baseline models such as Carrier Sense Multiple Access (CSMA), random frequency hopping, and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in terms of normalized throughput, channel collision rate, and convergence speed. This research provides theoretical support and an algorithmic foundation for achieving highly reliable access in large-scale swarm data links under harsh environmental conditions. Full article
(This article belongs to the Section Intelligent Sensors)
►▼ Show Figures

Figure 1

22 pages, 13126 KB  
Article
A Multi-Modal Few-Shot Learning Framework for Foreign Object Segmentation in GIS Inspection
by Jiaxin Liu, Yexing Lang, Jianeng Tang, Qiang Li, Songting Yang and Songyi Dian
Sensors 2026, 26(9), 2911; https://doi.org/10.3390/s26092911 - 6 May 2026
Viewed by 697
Abstract
The reliable operation of Gas-Insulated Switchgear (GIS) is crucial for power system safety, yet automatic foreign object inspection within its cavities remains challenging due to low-light conditions and strong reflections. This paper proposes a multi-modal few-shot learning framework for high-precision foreign object segmentation [...] Read more.
The reliable operation of Gas-Insulated Switchgear (GIS) is crucial for power system safety, yet automatic foreign object inspection within its cavities remains challenging due to low-light conditions and strong reflections. This paper proposes a multi-modal few-shot learning framework for high-precision foreign object segmentation in GIS. To overcome imaging interference, we first establish a dual-light (visible and ultraviolet) image acquisition system and design a lightweight fusion network to adaptively integrate multi-modal features, enhancing scene representation. For the core few-shot segmentation task, we introduce a novel Multi-Similarity Guided Branch Network (MSBNet). This network employs a support-query dual-branch architecture to extract sample prototypes. It features an improved background similarity guidance mechanism to suppress base-class feature interference and a multi-similarity fusion module that synergistically integrates multi-level and multi-metric information, which significantly improves the continuity and boundary accuracy of the segmentation masks. Experiments on our GIS dataset demonstrate that, under extremely limited sample conditions, the proposed method rapidly adapts to unseen foreign object classes and substantially outperforms existing few-shot segmentation baselines. Full article
(This article belongs to the Section Sensing and Imaging)
►▼ Show Figures

Figure 1

21 pages, 6540 KB  
Article
HAPQ: A Hardware-Aware Pruning and Quantization Pipeline for Event-Based SNN Detection
by Zhengyinan Li and Jing Wu
Sensors 2026, 26(9), 2910; https://doi.org/10.3390/s26092910 - 6 May 2026
Viewed by 711
Abstract
Autonomous driving perception demands low latency, high temporal resolution, and stringent hardware efficiency. While event-based spiking neural networks (SNNs) offer bio-inspired sparse computation, their deployment on edge field-programmable gate arrays (FPGAs) is obstructed by irregular execution patterns and temporal state storage overhead. To [...] Read more.
Autonomous driving perception demands low latency, high temporal resolution, and stringent hardware efficiency. While event-based spiking neural networks (SNNs) offer bio-inspired sparse computation, their deployment on edge field-programmable gate arrays (FPGAs) is obstructed by irregular execution patterns and temporal state storage overhead. To address this, we propose HAPQ, a unified hardware-aware pruning and quantization pipeline for compact event-based object detection. Starting from an end-to-end adaptive sampling SNN detector (EAS-SNN), HAPQ conducts hardware-aware configuration search within discrete digital signal processor (DSP) and block RAM (BRAM) budgets, applies single-instruction-multiple-data (SIMD)-aligned structured pruning for computational regularity, and jointly quantizes synaptic weights and membrane potentials via a shift-friendly fixed-point recurrence. Evaluation on the Prophesee Gen1 dataset and an FPGA accelerator shows that HAPQ improves detection accuracy from 0.284 to 0.425 in mean average precision (mAP50:95) and achieves 0.722 mAP50. Hardware implementation reveals a reduction in lookup table (LUT) usage to 1680, complete DSP elimination, and a maximum operating frequency of 920.81 MHz at 0.630 W. These results confirm that effective temporal SNN deployment requires joint optimization of model architecture, state precision, and hardware-aligned workload organization. Full article
(This article belongs to the Special Issue AI/ML-Powered Intelligent IoT Systems with Smart Sensors for Next-Generation Edge Computing)
►▼ Show Figures

Figure 1

35 pages, 2353 KB  
Review
Machine Learning Applications with Sensors for Indoor Air Quality Research
by Cosmina-Mihaela Rosca and Adrian Stancu
Sensors 2026, 26(9), 2909; https://doi.org/10.3390/s26092909 - 6 May 2026
Viewed by 1056
Abstract
Nowadays, people spend over 80% of their lives indoors, which makes indoor air quality (IAQ) research important. The paper presents, firstly, a structured overview of publicly available IAQ datasets suitable for machine learning (ML) research, secondly, a comparative analysis of the reviewed datasets, [...] Read more.
Nowadays, people spend over 80% of their lives indoors, which makes indoor air quality (IAQ) research important. The paper presents, firstly, a structured overview of publicly available IAQ datasets suitable for machine learning (ML) research, secondly, a comparative analysis of the reviewed datasets, thirdly, an ML-oriented mapping between tasks and algorithms, to outline the algorithmic families that are most appropriate given the dataset structure and the prediction target, and fourthly, an investigation on IAQ–ML using custom-made solutions that include sensors for data acquisition. The methodology included an analysis of 1162 papers from the Web of Science, 1536 from Scopus, and 756 from IEEE Xplore, between 1 January 2020 and 31 December 2025, to capture recent trends in ML-based IAQ research. The findings show that linear regression (132 articles), Logistic regression (91), random forest—RF (77), Long Short-Term Memory—LSTM (77), Principal Component Analysis (63), and Elastic Net are the most popular among researchers. Most studies report accuracy over 90%, with maximum values of 99.37% for LSTM and 99.20% for RF. In the case of regression, the R2 values range between 82% and 98%, especially for CO2 and PM2.5 prediction. eXtreme Gradient Boosting or hybrid RF-LSTM architectures achieve R2 values of up to 99%. The IAQ public and private datasets analyzed for this study provide a strong foundation for transfer learning, but differences require careful preprocessing to ensure consistent comparisons and reliable conclusions. The distribution of articles by sensor type for IAQ parameters shows that linear regression remains the most widely used ML method (26 studies), followed by LSTM (19) and RF (18). The research results confirm that there is no universal algorithm for IAQ, and the quality and structure of the data contribute to the success of ML models. This study aims to be a foundation for the development of future intelligent IAQ monitoring systems. Full article
(This article belongs to the Special Issue Chemical Sensors for Air Pollutants: Where the Heck Are We!)
►▼ Show Figures

Figure 1

14 pages, 10929 KB  
Article
A High-Sensitivity Sweat Glucose Biosensor Enabled by an In Situ Grown NiFe PBA on Porous Pt/Ni/Au-SPE
by Huajie Shu, Qinglin Liu, Qianhui Wei, Changhui Mao, Feng Wei and Hailing Tu
Sensors 2026, 26(9), 2908; https://doi.org/10.3390/s26092908 - 6 May 2026
Viewed by 826
Abstract
As a promising class of catalysts for enzymatic glucose sensors, Prussian blue analogues (PBAs) exhibit exceptional biomimetic activity. However, their performance is often constrained by poor intrinsic conductivity, which typically limits their sensitivity. To address this limitation, this study presents an effective approach [...] Read more.
As a promising class of catalysts for enzymatic glucose sensors, Prussian blue analogues (PBAs) exhibit exceptional biomimetic activity. However, their performance is often constrained by poor intrinsic conductivity, which typically limits their sensitivity. To address this limitation, this study presents an effective approach using direct in situ growth of PBAs on the electrode substrates, which enables the effective integration of PBA-based electrochemical systems. A porous Ni framework was first electrodeposited onto a screen-printed gold electrode substrate, followed by the reduction of Pt onto the porous Ni. Subsequently, NiFe PBA was synthesized in situ using the porous Pt/Ni structure as a sacrificial template. Functionalized with glucose oxidase (GOx), the PBA/Pt/Ni biosensor exhibited excellent performance for glucose detection in buffer solution, with a high sensitivity of 262.6 μA mM−1·cm−2 and an ultra-low detection limit of 1.45 μM (calculated at a signal-to-noise ratio of 3, S/N = 3). Notably, its sensitivity corresponds to a two-fold enhancement relative to the electrodes modified with commercial Prussian blue using the conventional drop-casting method. Even when tested in human sweat samples, the biosensor achieved a high sensitivity of 236.4 μA mM−1·cm−2 and a linear detection range of 20–1000 μM, with the broad sensing range fully encompassing the typical physiological concentrations of glucose in human sweat. This excellent performance arises from the high specific surface area of the porous Pt/Ni structure and the tight connection between PBA and the sacrificial Ni anode. This research presents a promising design strategy for advanced, wearable, and non-invasive health-monitoring platforms. Full article
(This article belongs to the Section Biosensors)
►▼ Show Figures

Figure 1

20 pages, 5155 KB  
Article
Applying Monte Carlo Method for Straight-Line Model Sensor Calibration
by Pedro M. Ramos and Fernando M. Janeiro
Sensors 2026, 26(9), 2907; https://doi.org/10.3390/s26092907 - 6 May 2026
Viewed by 703
Abstract
Sensors are used in measurement systems to enable estimation of physical parameters. Their calibration is an essential requirement and to perform the overall system/sensor calibration, its input is changed while the output is measured. Parameters from an appropriate sensor model are then determined [...] Read more.
Sensors are used in measurement systems to enable estimation of physical parameters. Their calibration is an essential requirement and to perform the overall system/sensor calibration, its input is changed while the output is measured. Parameters from an appropriate sensor model are then determined from these measurements. If the model is a straight-line, a first-order least squares linear regression is commonly used to estimate the slope and offset—this is often called simple linear regression. However, this method is unable to consider uncertainty in the sensor/system input measurements. This paper reviews the possible methods to estimate the optimal straight-line parameters considering uncertainties in both input and output measurements. The Monte Carlo Method can deal with all types of uncertainties in each of the measurements, whether sensor inputs or outputs, and also take into account possible covariances of these measurements. A key aspect of this work is the application to the heteroscedastic case, where measurement uncertainties vary across observations. An MCM-based strategy is proposed to optimize the selection of new measurement input values to minimize the estimated slope uncertainty. This strategy is shown to significantly reduce, in the presented case, the number of required measurement values. Full article
(This article belongs to the Special Issue Intelligent Sensing Systems: From Design to IoT Integration)
►▼ Show Figures

Figure 1

27 pages, 4292 KB  
Article
VIRTUES: A Virtual Reality Multimodal Sensing Platform for Quantifying and Supporting Cross-Neurotype Collaboration
by Ashwaq Zaini Amat, Mahrukh Tauseef, Deeksha Adiani, Amy S. Weitlauf and Nilanjan Sarkar
Sensors 2026, 26(9), 2906; https://doi.org/10.3390/s26092906 - 6 May 2026
Viewed by 823
Abstract
Effective workplace collaboration is essential for productivity and creativity, yet achieving the necessary mutual understanding can be challenging, particularly involving individuals from different neurotypes. This work evaluates VIRTUES, a Virtual Reality (VR) platform designed to foster mutual understanding and collaborative behaviors between autistic [...] Read more.
Effective workplace collaboration is essential for productivity and creativity, yet achieving the necessary mutual understanding can be challenging, particularly involving individuals from different neurotypes. This work evaluates VIRTUES, a Virtual Reality (VR) platform designed to foster mutual understanding and collaborative behaviors between autistic and neurotypical individuals. VIRTUES integrates multimodal sensing (eye tracking, interaction logs, and transcribed speech) to objectively quantify five defined dimensions of collaboration while providing real-time, context-aware support through an embedded rule-based feedback mechanism. A user study involving 12 autistic–neurotypical pairs demonstrates that VIRTUES can assess and support collaborative efforts across different neurotypes. Through synchronized sensing data, we identified that Information Pooling serves as a critical driving factor for successful collaborative performance. These preliminary findings suggest that VIRTUES provides a foundation for exploring inclusive teamwork and may inform the design of future interventions to support neurodiverse social-technical skill acquisition. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human: 2nd Edition)
►▼ Show Figures

Figure 1

19 pages, 8660 KB  
Article
YOLOv12-VSD: A Transfer-Learning-Assisted Real-Time Detection Algorithm for Vehicle Surface Defects
by Haopu Liu, Dequn Zhao and Yu Li
Sensors 2026, 26(9), 2905; https://doi.org/10.3390/s26092905 - 6 May 2026
Viewed by 646
Abstract
Vehicle surface defect detection faces three core challenges: classification–localization inconsistency for boundary-sensitive defects, insufficient multi-scale feature response across defect sizes, and cross-scenario generalization degradation caused by domain shift among production lines. This paper proposes YOLOv12-VSD, an improved detection algorithm addressing these issues through [...] Read more.
Vehicle surface defect detection faces three core challenges: classification–localization inconsistency for boundary-sensitive defects, insufficient multi-scale feature response across defect sizes, and cross-scenario generalization degradation caused by domain shift among production lines. This paper proposes YOLOv12-VSD, an improved detection algorithm addressing these issues through coordinated modifications at three levels. An IoU-aware classification loss aligns classification confidence with localization quality. A reparameterized convolution module at the P4 feature level (P4-RepC3) enriches intermediate-layer directional feature diversity without increasing inference cost. A multi-scale spatial pyramid pooling–fast structure at the P5 feature level (P5-SPPF) expands the effective receptive field for large-area defects. A three-stage transfer learning framework comprising source-domain pretraining, target-domain adaptation, and low-learning-rate refinement is further designed to reduce domain shift with limited annotations. Experiments show that YOLOv12-VSD achieves a mean Average Precision at IoU threshold 0.50 (mAP@50) of 0.715, the highest among six comparison models, with only 6.1M parameters and 17.1 giga floating-point operations per second (GFLOPs). After three-stage transfer, mAP@50 improves from 0.531 to 0.652, with training duration reduced by 64%. Full article
(This article belongs to the Collection Sensors and Actuators for Intelligent Vehicles)
►▼ Show Figures

Figure 1

28 pages, 40845 KB  
Article
Multi-Scale Temporal Coordinate Attention Network with Peak-Aware Mechanism for Rolling Bearing Fault Diagnosis Under Low Signal-to-Noise Ratio Conditions
by Xin Zhang, Xinming Liu, Fan Chen, Quanlong Li, Li Zhang and Jiahao Tian
Sensors 2026, 26(9), 2904; https://doi.org/10.3390/s26092904 - 6 May 2026
Viewed by 646
Abstract
Intelligent fault diagnosis of rolling bearings under high-noise industrial conditions remains a significant challenge. Traditional attention-based deep learning models often rely on global average pooling, which may inadvertently smooth out high-frequency transient impulses essential for fault identification, potentially leading to degraded performance in [...] Read more.
Intelligent fault diagnosis of rolling bearings under high-noise industrial conditions remains a significant challenge. Traditional attention-based deep learning models often rely on global average pooling, which may inadvertently smooth out high-frequency transient impulses essential for fault identification, potentially leading to degraded performance in low signal-to-noise ratio (SNR) environments. To address this, we propose a Multi-Scale Temporal Coordinate Attention Network (MS-TCANet). The framework introduces a Peak-Aware Coordinate Attention (PACA) mechanism that combines max-pooling and average-pooling along directional coordinates. This dual-pooling design aims to better preserve transient impact features while maintaining a stable global representation, thereby mitigating the feature over-smoothing issue common in conventional attention modules. Additionally, an asymmetric multi-scale convolution block is incorporated to capture both short-term impacts and long-range periodic signatures. Experiments on three benchmark datasets (CWRU, Paderborn University, and XJTU-SY) indicate that the proposed MS-TCANet achieves favorable diagnostic accuracy compared to several representative and advanced methods, particularly under severe noise conditions (e.g., −10 dB SNR). t-SNE and Grad-CAM visualizations further suggest that the model can capture fault-related signatures more reliably than standard architectures in noisy environments. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
►▼ Show Figures

Figure 1

25 pages, 1539 KB  
Article
RFE-YOLO: A Lightweight Receptive Field-Enhanced Network for UAV Imagery Object Detection
by Yimo Peng and Xiangyu Ge
Sensors 2026, 26(9), 2903; https://doi.org/10.3390/s26092903 - 6 May 2026
Viewed by 826
Abstract
Object detection in unmanned aerial vehicle (UAV) remote sensing imagery remains a formidable challenge due to the diminutive scale of targets, complex background clutter, and extreme variability in target morphology. Standard convolutional neural networks typically suffer from irreversible fine-grained information loss during downsampling, [...] Read more.
Object detection in unmanned aerial vehicle (UAV) remote sensing imagery remains a formidable challenge due to the diminutive scale of targets, complex background clutter, and extreme variability in target morphology. Standard convolutional neural networks typically suffer from irreversible fine-grained information loss during downsampling, as strided operations discard critical spatial details essential for the localization of tiny objects. To address these issues, we propose RFE-YOLO, a lightweight receptive field-enhanced network specifically tailored for high-precision small object detection in UAV scenarios. First, the Cross-Scale Receptive Field Enhancement (CSRE) module is designed to mitigate intrinsic information loss by integrating space-to-depth convolution (SPD-Conv), which preserves spatial details by migrating them into the channel dimension. This module further employs an energy-based adaptive weight generation mechanism to distinguish target signals from environmental noise. Second, this paper proposes the C3k2-Dynamic Inception Mixer Block (C3k2-DIMB), which adaptively captures anisotropic features—such as slender vehicles—via dynamic kernel weighting and multi-shape inception kernels. Third, the Shuffled Upsampling for Resolution Enhancement (SURE) module is introduced to maintain spatial fidelity during resolution recovery, utilizing a channel shuffle mechanism to overcome information isolation. Finally, the Multi-feature Fusion Module (MFM) replaces conventional static concatenation with a dynamic softmax-based competition mechanism, effectively bridging the semantic gap between multi-level features while suppressing background distractors. Experimental results on the VisDrone dataset demonstrate that RFE-YOLO significantly enhances the representation capability for small objects. Specifically, the proposed model achieves a state-of-the-art mAP50 of 42.70%, representing a substantial 9.3% improvement over the baseline YOLO11n. Furthermore, our architecture maintains an exceptionally lightweight profile with only 1.91 M parameters, demonstrating that high-precision detection can be achieved through structural intelligence rather than excessive parameter scaling. This makes RFE-YOLO highly suitable for real-time inference on edge-deployed UAV platforms. Full article
(This article belongs to the Special Issue AI-Based Object Detection and Tracking in UAVs: Challenges and Research Directions—2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 14523 KB  
Article
Robust Control of Distribution Static Compensator in Self-Excited Induction Generator-Based Wind Energy Systems Under Sensor Failures and Abnormal Load Conditions
by Ali Sait Özer and Hulusi Karaca
Sensors 2026, 26(9), 2902; https://doi.org/10.3390/s26092902 - 6 May 2026
Viewed by 821
Abstract
Self-excited induction generators (SEIGs) used in wind energy systems suffer from poor voltage and frequency regulation due to varying active/reactive power demands of nonlinear and unbalanced loads. The distribution static compensator (DSTATCOM) provides an effective solution through reactive power support and harmonic mitigation. [...] Read more.
Self-excited induction generators (SEIGs) used in wind energy systems suffer from poor voltage and frequency regulation due to varying active/reactive power demands of nonlinear and unbalanced loads. The distribution static compensator (DSTATCOM) provides an effective solution through reactive power support and harmonic mitigation. However, its performance strongly depends on the robustness of the control algorithm against harmonics, load imbalance, and sensor-induced measurement errors such as DC offset, which degrade reference current generation. This study proposes an Advanced Dual Fourth-Order Generalized Integrator (ADFOGI)-based control algorithm to improve voltage and frequency regulation of SEIG–DSTATCOM systems under such adverse conditions. The proposed method inherently rejects DC offset components and enables accurate reference current generation even under severe harmonic distortion, load imbalance, and transient disturbances. The effectiveness of the approach is validated on an OPAL-RT real-time platform under three scenarios: nonlinear load, unbalanced nonlinear load, and one-phase open-circuit condition, where DC offset is intentionally introduced to emulate sensor errors. Under the most severe case, where load current THD reaches 16.23%, SEIG current THD is reduced to 3.71% and voltage THD to 1.66%. In all scenarios, harmonic levels remain below the IEEE-519-2022 limit of 5%, confirming the robustness and effectiveness of the proposed control strategy. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
►▼ Show Figures

Figure 1

22 pages, 3108 KB  
Article
Self-Information-Driven Gated Graph Convolutional Network for Occluded Person Re-Identification
by Wanran Guo, Jiake Meng, Yuan Xue, Yaxian Fan and Zhenyu Fang
Sensors 2026, 26(9), 2901; https://doi.org/10.3390/s26092901 - 6 May 2026
Viewed by 820
Abstract
Occluded person re-identification (Re-ID) aims to accurately match occluded pedestrian images against complete gallery images captured across multiple cameras, a task that is critical to public security and intelligent surveillance systems. Existing graph neural network (GNN)-based methods typically assign uniform aggregation weights to [...] Read more.
Occluded person re-identification (Re-ID) aims to accurately match occluded pedestrian images against complete gallery images captured across multiple cameras, a task that is critical to public security and intelligent surveillance systems. Existing graph neural network (GNN)-based methods typically assign uniform aggregation weights to all nodes, failing to reflect the inherent reliability difference between visible and occluded body regions, which allows noise from low-confidence nodes to propagate freely and corrupt the final pedestrian representation. To address this, we propose the Self-Information-Driven Gated Graph Convolutional Network (SI-GCN). Keypoint detection confidence scores are transformed into logarithmic self-information measures as uncertainty priors for a learnable gating mechanism. The proposed SIG module enables visible nodes to dominate information diffusion while occluded nodes absorb more from neighbors, achieving efficient feature updating. A dynamic confidence calibration (DCC) strategy further synchronizes node reliability estimates with feature evolution across successive GCN layers. Extensive experiments on six public benchmarks covering occluded, partial, and holistic Re-ID scenarios demonstrate that SI-GCN achieves state-of-the-art performance, with Rank-1 accuracy and mAP improvements of 1.2% and 0.9%, respectively, over the strongest baseline on the Occluded-REID dataset, demonstrating its strong potential for deployment in real-world public security and urban surveillance applications where occlusion is pervasive. Full article
(This article belongs to the Special Issue Deep Learning Technologies and Their Applications in Image Processing, Computer Vision, and Computational Intelligence)
►▼ Show Figures

Figure 1

17 pages, 3872 KB  
Article
Fusion-Based Semantic Segmentation and 3D Reconstruction Using Radar–LiDAR Point Clouds: A Comparative Evaluation of DeepLabv3 and FCN-ResNet Against Traditional Architectures
by John Paipa, Cristian Suancha and Eduardo A. Fernández
Sensors 2026, 26(9), 2900; https://doi.org/10.3390/s26092900 - 6 May 2026
Viewed by 622
Abstract
Reliable person segmentation with sparse 3D sensors degrades significantly under adverse atmospheric conditions. This work presents a controlled comparative evaluation of four segmentation architectures—U-Net, Mask R-CNN, DeepLabV3+, and FCN-ResNet—on a fused Radar–LiDAR dataset for binary person–background segmentation and applies a dual-domain evaluation procedure [...] Read more.
Reliable person segmentation with sparse 3D sensors degrades significantly under adverse atmospheric conditions. This work presents a controlled comparative evaluation of four segmentation architectures—U-Net, Mask R-CNN, DeepLabV3+, and FCN-ResNet—on a fused Radar–LiDAR dataset for binary person–background segmentation and applies a dual-domain evaluation procedure that formally links 2D pixel-level overlap (IoU, Dice) to 3D geometric fidelity (Chamfer distance, Completeness) through mask back-projection onto fused point clouds. Raw point clouds are rasterized into range–intensity grids enriched with Radar reflectivity; the predicted masks are then reprojected into 3D space and evaluated using Chamfer distance and Completeness under three controlled visibility conditions. U-Net achieves the highest 2D overlap (IoU = 0.82, Dice = 0.89), while DeepLabV3+ delivers the best 3D reconstruction fidelity (Chamfer = 0.021 m, Completeness = 93.4%) and the highest overall accuracy (97.9%). This dissociation between 2D overlap and 3D fidelity is explained by DeepLabV3+’s multi-scale Atrous Spatial Pyramid Pooling (ASPP), which reduces boundary fragmentation during back-projection; more than 70% of the Chamfer deviation across competing architectures originates at object contours. Mask R-CNN performs well when instances are clearly separated, and FCN-ResNet offers the lowest computational cost at reduced boundary precision. Radar–LiDAR fusion sustains an IoU within 3% of clear-weather performance under dense fog, whereas LiDAR-only inputs degrade by more than 12%. Due to the 12:1 background-to-person class imbalance, overlap-based metrics (IoU, Dice) are prioritized over raw accuracy in all reported comparisons. These results provide actionable deployment guidance and constitute a reproducible evaluation procedure for future sparse-sensor fusion studies, independently of the architectures evaluated. Full article
(This article belongs to the Special Issue Advances in Point Clouds for Sensing Applications)
►▼ Show Figures

Figure 1

13 pages, 1260 KB  
Article
An Exploratory Analysis of Postural Control in People with Type 2 Diabetes Mellitus Using a Smartphone IMU Sensor
by Trine Rolighed Thomsen, Sophia Pölhöšová, Asger Ahlmann Bech, Aksayan Arunanthy Mahalingasivam, Nicklas Højgaard-Hessellund Rasmussen and Anderson Souza Oliveira
Sensors 2026, 26(9), 2899; https://doi.org/10.3390/s26092899 - 6 May 2026
Viewed by 550
Abstract
Background: There is a growing need for highly accessible and simplified methods to track postural control in adults affected by neurodegenerative diseases. Therefore, the aim of this study was to assess the validity of smartphone-derived postural control analyses compared with traditional center-of-pressure (COP) [...] Read more.
Background: There is a growing need for highly accessible and simplified methods to track postural control in adults affected by neurodegenerative diseases. Therefore, the aim of this study was to assess the validity of smartphone-derived postural control analyses compared with traditional center-of-pressure (COP) measures in healthy adults and people with type 2 diabetes mellitus (T2DM). Methods: A total of 36 participants (21 controls, 15 T2DM) completed static postural testing during single- and double-leg stance, also with eyes open and eyes closed. Data from a smartphone attached to the lower back measured trunk acceleration (SP-ACC) concurrently with gold-standard center of pressure (COP). The root mean square (RMS) and movement velocity (MV) were extracted from both trunk acceleration and COP data. The effect of balance condition and groups were statistically evaluated using non-parametric statistical tests. Results: SP-ACC and COP metrics showed progressive sway increases with task difficulty in both groups (all p < 0.001). RMS-ACC demonstrated moderate-to-strong correlations with RMS-COP across conditions (r = 0.55–0.90). Compared with controls, the T2DM group exhibited significantly higher RMS-ACC in DLS-EC and SLS-EO (both p < 0.01) and higher MV-ACC in DLS-EO, SLS-EO, and SLS-EC (p = 0.04–<0.001), reflecting impaired postural control. Conclusions: Smartphone-based IMU assessments showed good agreement with COP analysis and detected condition-specific balance deficits in T2DM. These findings support smartphone-based IMU metrics as a promising tool for accessible and scalable balance screening in diabetes care. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion and Health Monitoring)
►▼ Show Figures

Figure 1

25 pages, 5684 KB  
Article
Wavelet-Based Health Monitoring Approach for Train Door Actuation Using Motor Current Analysis
by Yaojung Shiao, Premkumar Gadde and Manichandra Bollepelly
Sensors 2026, 26(9), 2898; https://doi.org/10.3390/s26092898 - 6 May 2026
Viewed by 590
Abstract
Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity [...] Read more.
Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article
(This article belongs to the Special Issue Intelligent Automatic Control Systems)
►▼ Show Figures

Figure 1

25 pages, 5542 KB  
Article
A General Finite Beam on Tensionless Foundation Model for Rail Track Characterization and Evaluation
by Hamoud H. Alshallaqi and Brett A. Story
Sensors 2026, 26(9), 2897; https://doi.org/10.3390/s26092897 - 5 May 2026
Viewed by 649
Abstract
Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail [...] Read more.
Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail–ballast–subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment–curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
►▼ Show Figures

Figure 1

13 pages, 2593 KB  
Article
Fingerprint Recognition Based on Molecular-Scale Conductance Response via Electrochemically Gated Quantum Tunnelling
by Zifan Wang, Long Yi, Ga Zhang, Xufei Ma, Ye Tian, Bintian Zhang, Xu Liu and Longhua Tang
Sensors 2026, 26(9), 2896; https://doi.org/10.3390/s26092896 - 5 May 2026
Viewed by 901
Abstract
Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To [...] Read more.
Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To address this, we propose an electrochemical-gate-controlled nanoscale tunnelling strategy that expands the two-electrode system to a three-electrode configuration via a tunable gate potential, enabling the differentiation of distinct molecules at near-single-molecule sensitivity. Scanning the gate potential under constant tunnelling bias modulates the alignment between molecular orbitals and the electrode Fermi level, altering the statistical characteristics of molecular tunnelling transport. Experimental results show that target molecules induce a bimodal distribution of tunnelling current (background and molecule-correlated channels), with the second peak exhibiting distinct gate potential dependence. Comparative analysis of ascorbic acid (AA), acetylcholine (ACh), and uric acid (UA) reveals unique trajectories of characteristic peaks with gate potential, forming an electrochemical gate response fingerprint. This gate-dependent conductance trajectory provides a novel statistical dimension for molecular recognition, enabling differentiation of distinct molecules. Full article
(This article belongs to the Special Issue Feature Papers in Electronic Sensors 2026)
►▼ Show Figures

Figure 1

30 pages, 29707 KB  
Article
Physics-Enhanced Orthogonal Sensing for Self-Supervised Anomaly Detection in Rolling Mills
by Yifan Wang, Bin Zheng, Yehan Feng and Xiong Chen
Sensors 2026, 26(9), 2895; https://doi.org/10.3390/s26092895 - 5 May 2026
Viewed by 904
Abstract
The rolling mill guiding system is a key component that affects the quality of steel products. However, due to the harsh on-site environment, there is usually a lack of effective online monitoring and early warning mechanisms. Moreover, in industrial environments, fault samples are [...] Read more.
The rolling mill guiding system is a key component that affects the quality of steel products. However, due to the harsh on-site environment, there is usually a lack of effective online monitoring and early warning mechanisms. Moreover, in industrial environments, fault samples are very scarce, making supervised artificial intelligence methods difficult to apply. This paper proposes a “physics-enhanced” orthogonal-sensing cyber-physical architecture that integrates hardware and software design. At the hardware level, an embedded orthogonal sensing layout (P⊥V) is designed to decouple drive-chain vibration from rolling-force fluctuations at the transducer level. At the algorithm level, the state monitoring of the guiding system is formulated as a self-supervised anomaly detection problem, and a two-branch network architecture is designed: one branch uses the CSD transformer to capture physical coupling characteristics, while the other branch uses VQ-VAE to extract operating-condition context. Experimental results on a dataset comprising real operational data and expert-validated synthetic fault scenarios show that the system achieves an AUC-ROC of 0.952 and a false alarm rate of 0.048 under a 95% TPR, with an end-to-end processing latency of approximately 8 ms per window and a system-level fault response time of approximately 108 ms, and thus meets the requirements of real-time industrial monitoring. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
►▼ Show Figures

Figure 1

16 pages, 3496 KB  
Article
A Four-Wavelength Flow-Through Fluorescence–Scatterometric Sensor That Allows for Real-Time Determination of Fat and Protein Content in Milk–Air Mixtures with High Accuracy
by Maxim E. Astashev, Dmitry N. Ignatenko, Elena A. Molkova, Ivan M. Gogolev, Andrey V. Onegov, Sergey Y. Smolentsev, Artem R. Khakimov, Semen S. Ruzin, Dmitry A. Budnikov, Dmitriy Yu. Pavkin and Sergey V. Gudkov
Sensors 2026, 26(9), 2894; https://doi.org/10.3390/s26092894 - 5 May 2026
Viewed by 1159
Abstract
(1) Background: Currently, there is a problem of prompt determination of fat and protein content in the milk–air mixture of milking machines. (2) Methods: A design of a sensor prototype is proposed, combining measurements of light scattering (scatterometry) and fluorescence (fluorometry) to determine [...] Read more.
(1) Background: Currently, there is a problem of prompt determination of fat and protein content in the milk–air mixture of milking machines. (2) Methods: A design of a sensor prototype is proposed, combining measurements of light scattering (scatterometry) and fluorescence (fluorometry) to determine the component composition of the milk–air mixture formed during milking. (3) Results: An optical and electronic circuit of a flow sensor has been developed, using four sources of optical radiation: blue, green and red semiconductor lasers (light scattering in milk) and a UV LED (milk fluorescence), as well as an axial photodiode array for recording the light scattering indicatrix and the fluorescence intensity of the milk–air mixture. The use of three laser sources in the scatterometric circuit allows for the determination of the fat content in milk with an error of 0.05%, which is better than all currently known analogs. The developed sensor enables the detection of counterfeit milk containing palm oil instead of milk fat. It operates reliably in a temperature range of 5–35 °C and at milk flow rates of up to 100 mL/sec. (4) Conclusions: The sensor is capable of transmitting real-time data on the fat and protein content of milk to an RS-232 serial port, enabling integration into milking robots and automated milking systems. Full article
(This article belongs to the Special Issue Robotic Systems for Future Farming)
►▼ Show Figures

Figure 1

38 pages, 4934 KB  
Article
Automated Ergonomic Risk Assessment of Wheelchair Users During Cabinet Interaction Using Vision-Based 3D Pose Estimation
by Yilin Xu, Ziqian Yang, Tao Sun and Jiachuan Ning
Sensors 2026, 26(9), 2893; https://doi.org/10.3390/s26092893 - 5 May 2026
Viewed by 990
Abstract
Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated [...] Read more.
Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated ergonomic risk assessment of wheelchair users during cabinet interaction. The proposed framework integrates YOLOv11 for human detection, MHFormer for monocular 3D pose reconstruction, and a fuzzy logic-enhanced RULA model for continuous ergonomic risk quantification from video-derived motion signals. To support model development and evaluation, we constructed a dedicated wheelchair cabinet-operation dataset comprising 30 participants, including 14 experienced wheelchair users and 16 trained simulation participants, across five representative cabinet-operation scenarios. The raw dataset contained approximately 5 h of RGB video and about 150,000 original frames. To reduce redundancy caused by highly similar consecutive frames and to mitigate overfitting risk, representative frames were sampled from the continuous video sequences, resulting in 10,000 images for annotation and model development. Based on the proposed framework, raw visual sensor signals are transformed into temporally continuous kinematic representations and ergonomic risk scores, enabling non-contact and real-time health-state interpretation in assistive living environments. The proposed method achieved an average joint-angle estimation RMSE of 7.5°, representing an approximately 60% reduction compared with a Kinect v2-based motion capture baseline (18.6°), which is widely used for low-cost ergonomic evaluation. In benchmark evaluation, the proposed method achieved 84% risk-classification accuracy with a Cohen’s kappa of 0.66, outperforming representative baseline approaches. The results further indicated that low revolving-door and low-drawer operations were associated with higher and more sustained ergonomic risk exposure than sliding-door interaction. These findings demonstrate that vision-based sensor signal analysis can provide an effective solution for intelligent health management, ergonomic monitoring, and perception-driven assessment in accessible and assistive autonomous living systems. Full article
(This article belongs to the Special Issue Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems)
►▼ Show Figures

Figure 1

19 pages, 9298 KB  
Article
Standalone RFID Access Control System with Data-Integrity Verification Capabilities
by Valentin Popa, Adrian I. Petrariu, Partemie M. Mutescu, Alexandru A. Maftei and Alexandru Lavric
Sensors 2026, 26(9), 2892; https://doi.org/10.3390/s26092892 - 5 May 2026
Viewed by 1191
Abstract
Today, access control systems are used in almost every institution and building. This is because they are an effective solution that provides a high level of security. There are many commercially available systems that provide security-related access features for buildings, including biometric options. [...] Read more.
Today, access control systems are used in almost every institution and building. This is because they are an effective solution that provides a high level of security. There are many commercially available systems that provide security-related access features for buildings, including biometric options. Most use a centralized architecture, where each building can be remotely controlled via an Internet connection. This paper presents a completely different system from those on the market, a decentralized system with clone-detection and data-integrity verification mechanisms that allows access to buildings. The overall architecture includes hardware encoding of the access system’s location, and access is granted based on information written to the RFID card by the card-issuing center. This allows the system to be easily reconfigured at the hardware level prior to installation in the access area. The proposed system uses a confidential RFID card data integrity algorithm that uses the card data and immutable UID to determine a checksum in order to validate the RFID card data. As a result, any unwanted modification of at least one bit invalidates the card and blocks access to the building. The system was implemented, validated, and extensively tested over a one-year period with no reported operational issues. Full article
(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)
►▼ Show Figures

Figure 1

24 pages, 25000 KB  
Article
A Real-Time SDR-Based Vehicular Scatterometer with Multi-Subband Coherent Synthesis
by Shijie Yang, Wei Guo, Caiyun Wang, Peng Liu, Te Wang, Zhenzhen Liang, Qing Xing, Xingming Zheng and Bingze Li
Sensors 2026, 26(9), 2891; https://doi.org/10.3390/s26092891 - 5 May 2026
Viewed by 1052
Abstract
Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order [...] Read more.
Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order of hundreds of MHz for fine range resolution, especially for systems requiring real-time onboard processing. To address this problem, this paper presents a vehicular, fully polarimetric, SDR-based scatterometer that achieves an equivalent wideband response by sequentially transmitting adjacent narrow subbands and coherently synthesizing them onboard. To enable real-time operation on a resource-limited field-programmable gate array/system-on-chip (FPGA/SoC) platform, we adopt a frequency-domain synthesis-pulse-compression pipeline that avoids interpolation and eliminates repeated matched filtering across subbands. A slot-based online phase calibration is performed within the settling window after each fast lock to estimate and compensate random local oscillator (LO) phase offsets, preserving coherent stitching. In addition, pulse repetition within each subband and coherent accumulation are integrated to improve the signal-to-noise ratio (SNR) under real-time throughput constraints. A Zynq-based implementation demonstrates deterministic onboard range-profile output, with a minimum processing latency of about 1.57 ms per frame. Loopback and outdoor experiments validate the equivalent 200 MHz bandwidth (five 40 MHz subbands), achieving approximately 0.75 m resolution and yielding sidelobe metrics consistent with the designed windowing, including a peak sidelobe ratio (PSLR) of −27.43 dB and an integrated sidelobe ratio (ISLR) of −12.38 dB. Field scans over farmland further show consistent σ0 trends across incidence angle and azimuth, indicating reliable onboard quantitative backscattering measurement. These results demonstrate that the proposed method provides a feasible solution for deterministic real-time equivalent wideband scatterometry on a low-cost SDR platform. Full article
(This article belongs to the Section Remote Sensors)
►▼ Show Figures

Figure 1

27 pages, 3261 KB  
Article
Adaptive Dual Reinforcement Learning for Hybrid Spatial–Temporal Networks in RIS-Assisted Indoor Localization (ADRL-HSTNet)
by Mostafa Mohamed, Ahmed Radi and Shady Zahran
Sensors 2026, 26(9), 2890; https://doi.org/10.3390/s26092890 - 5 May 2026
Viewed by 993
Abstract
Reconfigurable intelligent surface sensors (RISs) have emerged as a promising technology for enhancing wireless indoor localization by intelligently controlling signal propagation; however, extracting reliable localization fingerprints from RIS-assisted signals remains challenging due to multipath fading, environmental noise, and nonlinear spatial–temporal channel dynamics. To [...] Read more.
Reconfigurable intelligent surface sensors (RISs) have emerged as a promising technology for enhancing wireless indoor localization by intelligently controlling signal propagation; however, extracting reliable localization fingerprints from RIS-assisted signals remains challenging due to multipath fading, environmental noise, and nonlinear spatial–temporal channel dynamics. To address this, we propose an Adaptive Dual-Reinforcement Learning-Hybrid Spatial–Temporal Network (ADRL-HSTNet) for RIS-assisted indoor localization. The framework utilizes dual-channel RSSI and phase measurements, followed by noise filtering, normalization, and sliding-window segmentation prior to feature extraction. It then constructs enhanced representations through handcrafted feature extraction and multi-branch processing, including patch-based features, wavelet-domain representations, statistical descriptors, and multi-level segmentation masks. These heterogeneous inputs are encoded using lightweight transformer-based encoders to capture multiscale dependencies. A first reinforcement learning selector adaptively weights the most informative feature branches to produce a fused representation, which is further processed by spatial and temporal transformer modules. Their outputs are adaptively combined via a second reinforcement learning selector to obtain robust localization embedding. The model jointly performs classification, coordinate regression, and uncertainty estimation end-to-end. Experimental results across multiple RIS configurations outperformed the KAN, LSTM-KAN, and RHL-Net (compared against the proposed ADRL-HSTNet) baselines, achieving accuracies of 83.33%, 75.22%, 93.33%, and 88.89%, confirming the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue New Technologies in Wireless Communication System)
►▼ Show Figures

Figure 1

37 pages, 9968 KB  
Article
SETJiP: Spatial and Extra Temporal Jigsaw Puzzles for Video Anomaly Detection
by Liheng Shen, Tetsu Matsukawa and Einoshin Suzuki
Sensors 2026, 26(9), 2889; https://doi.org/10.3390/s26092889 - 5 May 2026
Viewed by 873
Abstract
Video Anomaly Detection (VAD) is commonly formulated as a one-class classification task. Global motion, with temporal variations across most pixels within an object-centric region, e.g., walking, is typically regular, whereas localized motion, e.g., waving, can be ambiguous. Decoupled spatial and temporal jigsaw puzzles [...] Read more.
Video Anomaly Detection (VAD) is commonly formulated as a one-class classification task. Global motion, with temporal variations across most pixels within an object-centric region, e.g., walking, is typically regular, whereas localized motion, e.g., waving, can be ambiguous. Decoupled spatial and temporal jigsaw puzzles (DSTJiP) is a self-supervised method that learns discriminative representations by predicting the original order of spatially and temporally shuffled patches. However, DSTJiP’s uniform sampling and equal weighting do not assign stronger supervision to global-motion examples within the temporal objective. Consequently, the temporal supervision allocated to global-motion examples may become insufficient across training-data regimes with varying proportions of these examples, deteriorating VAD performance. Nevertheless, excessively strengthening such supervision also degrades performance. To address these issues, we propose spatial and extra temporal jigsaw puzzles (SETJiP) with two RGB-only training schemes that provide stronger and more conservative temporal supervision for global-motion examples, respectively. One scheme strengthens temporal supervision on these examples via additional temporal jigsaw puzzles. The other does so more conservatively by upweighting their temporal jigsaw puzzles. Experiments on four VAD benchmarks show that both schemes improve on DSTJiP and remain highly competitive with state-of-the-art methods. Full article
(This article belongs to the Section Sensing and Imaging)
►▼ Show Figures

Figure 1

26 pages, 678 KB  
Article
Evaluating the Adversarial Robustness and Clinical Safety of Quantized Hierarchical Transformers for Edge-Based Malaria Microscopy
by Umar Hasan, Turki G. Alghamdi and Muhammad Ali Nayeem
Sensors 2026, 26(9), 2888; https://doi.org/10.3390/s26092888 - 5 May 2026
Cited by 1 | Viewed by 1043
Abstract
Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its [...] Read more.
Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its impact on clinical safety and security remains unexplored. This study presents an adversarial audit of quantized Vision Transformers for medical edge deployment. We evaluated a Swin-Tiny transformer against ViT-Tiny and MobileNetV3 baselines using a 27,558-image malaria dataset and an out-of-distribution (OOD) White Blood Cell dataset. Our findings redefine the “Quantization Shield” hypothesis. PTQ compresses the Swin model by 3.9× (to 27.89 MB) with a negligible 0.11% accuracy drop, maintaining statistical reliability on OOD tests. However, the hypothesized architectural resilience shatters under white-box Projected Gradient Descent (PGD) attacks. Despite robustness against single-step attacks, both MobileNetV3 and the INT8 Swin-Tiny collapse to 0.00% accuracy under iterative PGD. Conversely, the quantized Swin-Tiny resists black-box transfer attacks from a surrogate, maintaining 81.00% accuracy. We conclude that while quantized Vision Transformers meet mobile sensor constraints, integer quantization provides zero innate defense against targeted iterative perturbations, exposing a critical vulnerability in diagnostic IoT networks. Full article
(This article belongs to the Special Issue AI-Enabled Sensing Technology for Smart Healthcare and Precision Diagnosis)
►▼ Show Figures

Figure 1

13 pages, 7788 KB  
Article
Precision Gas Sensing Interface Circuit with Digital Potentiometer-Based Dynamic Gain Control
by Soon-Kyu Kwon and Hyeon-June Kim
Sensors 2026, 26(9), 2887; https://doi.org/10.3390/s26092887 - 5 May 2026
Viewed by 981
Abstract
This paper proposes a digital potentiometer-based adaptive gas sensor interface for stable detection without signal saturation under extreme environmental fluctuations. Conventional fixed-gain circuits often suffer from limited dynamic range, leading to data loss when severe baseline drifts exceed ADC input limits. To address [...] Read more.
This paper proposes a digital potentiometer-based adaptive gas sensor interface for stable detection without signal saturation under extreme environmental fluctuations. Conventional fixed-gain circuits often suffer from limited dynamic range, leading to data loss when severe baseline drifts exceed ADC input limits. To address this, we developed a real-time control algorithm that actively adjusts attenuator and amplifier gains, maintaining the ADC input voltage (VADC) near the common-mode voltage (VCM). Experimental results demonstrate that the interface remains stable even when the buffer voltage reaches 2.75 V, significantly surpassing the 1.2 V ADC limit. Sensor resistance data, reconstructed by inversely calculating updated circuit parameters, achieved high accuracy with a Mean Absolute Percentage Error (MAPE) of 1.628% and a maximum relative error under 4.8%. Consequently, this study proves that logically extending the physically limited ADC dynamic range enables high-precision gas sensing in diverse environments without requiring high-performance computing devices. This approach provides a cost-effective and robust solution for compact IoT-based gas monitoring systems. Full article
(This article belongs to the Special Issue Two-Dimensional (2D) Metal Oxide-Enabled Electrochemical and Gas Sensors)
►▼ Show Figures

Figure 1

16 pages, 4339 KB  
Article
A Scene Detection Complexity Metric for Infrared Small Target Detection
by Zhiyuan Huang and Zhiyong Zhang
Sensors 2026, 26(9), 2886; https://doi.org/10.3390/s26092886 - 5 May 2026
Viewed by 865
Abstract
Infrared small target detection is widely used in aerospace surveillance, maritime search and rescue, and military reconnaissance. However, the performance of detection algorithms is highly dependent on scene characteristics, and methods that perform well in simple backgrounds may degrade substantially in complex environments. [...] Read more.
Infrared small target detection is widely used in aerospace surveillance, maritime search and rescue, and military reconnaissance. However, the performance of detection algorithms is highly dependent on scene characteristics, and methods that perform well in simple backgrounds may degrade substantially in complex environments. Existing indicators, such as information entropy, average gradient, and peak signal-to-noise ratio, can reflect detection difficulty from individual perspectives, but they do not provide a unified measure that jointly considers target saliency, background complexity, and target–background coupling. To address this issue, this study proposes a scene detection complexity (SDC) metric for quantifying the difficulty of infrared small target detection. Six basic indicators are selected from three dimensions, namely target saliency, background complexity, and target–background coupling: statistical variance, target–background contrast, signal-to-clutter ratio, information entropy, structural similarity, and target size. After Min–Max normalization, objective weights are determined by combining the entropy weight method and principal component analysis, and the weighted indicators are fused into an SDC value in the range of [0,1]. Experiments on 100 test images selected from IRST640, MSISTD, SIRST-V2, and an infrared small-aircraft sequence dataset show that the proposed SDC achieves a Pearson linear correlation coefficient of 0.956 with subjective difficulty ratings and −0.902 with image-level detection scores obtained from seven representative algorithms. The results further indicate that traditional methods are more sensitive to increasing scene complexity, whereas deep-learning-based methods are comparatively more robust in complex backgrounds. The proposed SDC provides a unified and objective tool for performance evaluation, algorithm selection, and pre-assessment of scene difficulty in infrared small target detection. Full article
(This article belongs to the Section Remote Sensors)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-345
Previous Issue
Next Issue
Back to TopTop