Next Issue
Volume 26, March-2
Previous Issue
Volume 26, February-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Volume 26
Issue 5
sensors-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors, Volume 26, Issue 5 (March-1 2026) – 338 articles

Cover Story (view full-size image): This study introduces a two-step approach to enhance the safety of unmanned aerial vehicles through advanced propeller damage detection. The first stage operates in real-time onboard the drone, utilizing an embedded machine learning model to analyze acoustic noise sampled by a MEMS microphone. This achieves an accuracy of over 99% in identifying potential faults. Upon detecting an anomaly, the drone is directed to a ground station equipped with a frequency-modulated continuous-wave radar system, constituting the second stage. This offers a high-precision, contactless diagnostic by measuring vibrational displacement to quantify the damage severity. By seamlessly integrating onboard edge processing with radar-based analysis, the proposed framework ensures reliable predictive maintenance for drone operations in complex environments. 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:
18 pages, 2772 KB  
Article
Microfiber Interferometric Sensor for Ultrasound Detection
by Xiuxin Wang, Jiwen Zhou, Shuojian Xiong Zheng, Zihao Wang, Bowen Tang and Hongzhong Li
Sensors 2026, 26(5), 1739; https://doi.org/10.3390/s26051739 - 9 Mar 2026
Viewed by 480
Abstract
By setting up ultrasonic fields in solid and liquid environments, the propagation characteristics of ultrasonic waves were investigated, and a sensing experiment device with related physical field settings was constructed. A comparison of results between multi-mode microfiber and single-mode fiber interferometric sensors found [...] Read more.
By setting up ultrasonic fields in solid and liquid environments, the propagation characteristics of ultrasonic waves were investigated, and a sensing experiment device with related physical field settings was constructed. A comparison of results between multi-mode microfiber and single-mode fiber interferometric sensors found that the multi-mode microfiber maintains the original ultrasonic waveform output and has much higher sensitivity than the single-mode fiber sensor. The sensor in the paper had a detection limit of approximately 540 Pa and a bandwidth of approximately 5 MHz. The photoacoustic experiment with the microfiber ultrasound sensor had the highest resolution, which is about 10 times that of a single-mode fiber sensor. In summary, the multi-mode microfiber interferometric sensor was applied to ultrasonic detection. Full article
(This article belongs to the Special Issue Ultrasound Imaging and Sensing for Nondestructive Testing)
►▼ Show Figures

Figure 1

22 pages, 871 KB  
Review
The Heart’s Electromagnetic Field in Emotions, Empathy and Human Connection: Biosensor-Derived Insights into Heart–Brain Axis Mechanisms and a Basis for Novel BioMagnetoTherapies
by Andreas Palantzas and Maria Anagnostouli
Sensors 2026, 26(5), 1738; https://doi.org/10.3390/s26051738 - 9 Mar 2026
Viewed by 1860
Abstract
The heart’s electromagnetic field (HEMF) represents the strongest magnetic signal in the human body and has been increasingly associated with processes related to the Heart–Brain Axis (HBA). The present review summarizes its biophysical basis along with current and emerging biosensing technologies. It examines [...] Read more.
The heart’s electromagnetic field (HEMF) represents the strongest magnetic signal in the human body and has been increasingly associated with processes related to the Heart–Brain Axis (HBA). The present review summarizes its biophysical basis along with current and emerging biosensing technologies. It examines hypotheses regarding interpersonal interactions and interactions with external fields, including geomagnetic activity, and reviews evidence linking the HEMF to autonomic activity and emotional states. It provides an overview of magnetic field-based therapeutics, introduced here as our own term “BioMagnetoTherapies” (BMT), underscoring their common objective of externally inducing, stabilizing or restoring coherence across the HBA. Collectively, it positions cardiac electromagnetic signals as both a measurable marker, key to HBA dynamics and related disorders, as well as a promising target for emerging biosensor- and BioMagneto-Therapeutics. Full article
(This article belongs to the Section Biosensors)
►▼ Show Figures

Figure 1

34 pages, 3357 KB  
Article
Sequence-Preserving Dual-FoV Defense for Traffic Sign and Light Recognition in Autonomous Vehicles
by Abhishek Joshi, Janhavi Krishna Koda and Abhishek Phadke
Sensors 2026, 26(5), 1737; https://doi.org/10.3390/s26051737 - 9 Mar 2026
Viewed by 458
Abstract
For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks [...] Read more.
For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax τ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article
(This article belongs to the Special Issue Sensing Technologies for Autonomous Driving and Intelligent Transportation Systems)
►▼ Show Figures

Figure 1

26 pages, 6031 KB  
Article
Real-Time Low-Cost Traffic Monitoring Based on Quantized Convolutional Neural Networks for the CNOSSOS-EU Noise Model
by Domenico Profumo, Gonzalo de León, Alessandro Monticelli, Luca Fredianelli and Gaetano Licitra
Sensors 2026, 26(5), 1736; https://doi.org/10.3390/s26051736 - 9 Mar 2026
Viewed by 395
Abstract
Accurate urban noise mapping requires granular traffic flow characterization aligned with specific acoustic models, such as CNOSSOS-EU. Existing monitoring solutions often lack the specific categorization capabilities, cost-effectiveness, or flexibility required for large-scale deployment in resource-constrained environments. To address this challenge, the present study [...] Read more.
Accurate urban noise mapping requires granular traffic flow characterization aligned with specific acoustic models, such as CNOSSOS-EU. Existing monitoring solutions often lack the specific categorization capabilities, cost-effectiveness, or flexibility required for large-scale deployment in resource-constrained environments. To address this challenge, the present study describes the development of a real-time multi-vehicle recognition system based on low-cost edge computing hardware, specifically a Raspberry Pi 4 coupled with a Coral TPU accelerator. The proposed methodology integrates a quantized YOLOv8 convolutional neural network (CNN) with a tracking algorithm to enable real-time detection and classification of vehicles into five distinct classes, allowing for precise aggregation according to CNOSSOS-EU standards. The model was trained on a proprietary dataset of 15,000 images and subjected to 8-bit post-training quantization to optimize inference speed. Experimental results demonstrate that the system achieves an inference speed of 14 FPS and a mean Average Precision (mAP@50) of 92.2% in daytime conditions, maintaining robust performance on embedded devices. In a real-world case study, the proposed system significantly outperformed a commercial traffic monitoring solution, achieving a weighted percentage error of just 6.6% compared to the commercial system’s 59.9%, effectively bridging the gap between manual counting accuracy (1.4% error) and automated efficiency. Full article
(This article belongs to the Special Issue Advanced Environmental Sensing Towards Acoustic Monitoring and Modeling: Applications and Challenges)
►▼ Show Figures

Figure 1

16 pages, 7395 KB  
Article
Wavelet-Assisted Adaptive EKF Phase Shift Estimation Approach for Motion-Induced Error Compensation
by Xin Lai, Qiushuo Yu and Zhenyi Chen
Sensors 2026, 26(5), 1735; https://doi.org/10.3390/s26051735 - 9 Mar 2026
Viewed by 351
Abstract
Phase-shifting profilometry (PSP) suffers from motion-induced phase-step variations in dynamic scenes. The breakdown of the fixed phase shift assumption results in issues such as ripples, distortions and accuracy decline in PSP systems. To reduce motion-induced phase errors, we propose a wavelet-assisted adaptive extended [...] Read more.
Phase-shifting profilometry (PSP) suffers from motion-induced phase-step variations in dynamic scenes. The breakdown of the fixed phase shift assumption results in issues such as ripples, distortions and accuracy decline in PSP systems. To reduce motion-induced phase errors, we propose a wavelet-assisted adaptive extended Kalman filter (WAEKF) to estimate varied pixel-wise phase shift. A wavelet-based strategy is presented to extract an initial spatial carrier frequency at each row from fringe patterns for EKF estimation. A state-space model employing the quadrature phase component and carrier frequency is established in this paper. The unknown phase shifts can be evaluated by using a forward–backward filter. Experiments show that the proposed method can acquire an accurate initial carrier frequency and phase shift map, which effectively reduces 3D reconstruction error and can be extended to N-step PSP systems. Full article
(This article belongs to the Section Electronic Sensors)
►▼ Show Figures

Figure 1

20 pages, 11684 KB  
Article
Adaptive Digital Twin Modeling with Control: Integration of Extended Kalman Filter-Based Recursive Sparse Nonlinear Identification with Model Predictive Control
by Jingyi Wang, Liang Cao, Yankai Cao and Bhushan Gopaluni
Sensors 2026, 26(5), 1734; https://doi.org/10.3390/s26051734 - 9 Mar 2026
Viewed by 380
Abstract
The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, diminishing model accuracy, and restricted interactive capabilities. Addressing these critical issues, this paper proposes [...] Read more.
The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, diminishing model accuracy, and restricted interactive capabilities. Addressing these critical issues, this paper proposes a comprehensive digital twin development framework that integrates digital twin identification, real-time model updating, and advanced process control. The proposed approach first identifies the offline digital twin model through the sparse identification of a nonlinear dynamics algorithm, reducing the digital twin development time while maintaining model fidelity. Then, the identified model is updated by the extended Kalman filter to mitigate the problem of diminishing accuracy. Finally, incorporating the latest updated model into the model predictive control facilitates the control inputs optimization and enhances the interactive capacity of digital twins. Through one industrial case study and two simulation examples, the advantages of the proposed algorithm are demonstrated. Full article
(This article belongs to the Special Issue Sensors for Smart Manufacturing: Advanced Sensing Solutions for Human-Centric, Inclusive and Resilient Industrial Systems)
►▼ Show Figures

Figure 1

24 pages, 3008 KB  
Article
POLD-YOLO: A Lightweight YOLO11-Based Algorithm for Insulator Defect Detection in UAV Aerial Images
by Bo Hu, Fanfan Wu, Pengchao Zhang, Jinkai Cui and Yingying Liu
Sensors 2026, 26(5), 1733; https://doi.org/10.3390/s26051733 - 9 Mar 2026
Viewed by 380
Abstract
Detecting small insulator defects in unmanned aerial vehicle (UAV) imagery remains challenging due to low resolution, complex backgrounds and scale variation, which degrade the performance of existing detectors. This study aims to develop a highly efficient and accurate model for real-time insulator defect [...] Read more.
Detecting small insulator defects in unmanned aerial vehicle (UAV) imagery remains challenging due to low resolution, complex backgrounds and scale variation, which degrade the performance of existing detectors. This study aims to develop a highly efficient and accurate model for real-time insulator defect inspection on resource-constrained UAV platforms. This paper proposes POLD-YOLO, a novel lightweight object detector based on YOLO11. The key innovations include: (1) A backbone enhanced by a PoolingFormer module and Channel-wise Gated Linear Units (CGLUs) to boost feature extraction efficiency; (2) An Omni-Dimensional Adaptive Downsampling (OD-ADown) module for multi-scale feature extraction with low complexity; (3) A Lightweight Shared Convolutional Detection Head (LSCD-Head) to minimize the number of parameters; (4) A Focaler-MPDIoU loss function to improve bounding box regression. Extensive experiments conducted on a self-built UAV insulator defect dataset show that POLD-YOLO achieves a state-of-the-art mAP@0.5 of 92.4%, outperforming YOLOv5n, YOLOv8n, YOLOv10n, and YOLO11n by 3.6%, 1.6%, 1.4%, and 1.6%, respectively. Notably, it attains this superior accuracy with only 1.55 million parameters and 3.8 GFLOPs. POLD-YOLO establishes a new Pareto front for accuracy-efficiency for onboard defect detection. It demonstrates great potential for automated power line inspection and can be extended to other real-time aerial vision tasks. Full article
(This article belongs to the Special Issue Vision Based Defect Detection in Power Systems)
►▼ Show Figures

Figure 1

20 pages, 2211 KB  
Article
Enhanced Secretary Bird Optimization Algorithm for Energy-Efficient Cluster Head Selection in Wireless Sensor Networks
by Ketty Siti Salamah, Dadang Gunawan and Ajib Setyo Arifin
Sensors 2026, 26(5), 1732; https://doi.org/10.3390/s26051732 - 9 Mar 2026
Viewed by 301
Abstract
Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, [...] Read more.
Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3–13% improvement in last node death (LND) compared with the standard SBOA. Full article
(This article belongs to the Special Issue Advances in Communication Protocols for Wireless Sensor Networks)
►▼ Show Figures

Figure 1

21 pages, 5982 KB  
Article
Evaluating Geostationary Satellite-Based Approaches for NDVI Gap Filling in Polar-Orbiting Satellite Observations
by Han-Sol Ryu, Sung-Joo Yoon, Jinyeong Kim and Tae-Ho Kim
Sensors 2026, 26(5), 1731; https://doi.org/10.3390/s26051731 - 9 Mar 2026
Viewed by 362
Abstract
The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite [...] Read more.
The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions. Full article
(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)
►▼ Show Figures

Figure 1

19 pages, 1253 KB  
Article
SFE-GAT: Structure-Feature Evolution Graph Attention Network for Motor Imagery Decoding
by Xin Gao, Guohua Cao and Guoqing Ma
Sensors 2026, 26(5), 1730; https://doi.org/10.3390/s26051730 - 9 Mar 2026
Viewed by 487
Abstract
Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide [...] Read more.
Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics. Full article
(This article belongs to the Section Sensing and Imaging)
►▼ Show Figures

Figure 1

18 pages, 9838 KB  
Article
Unlocking Roadside Carbon Sequestration Potential: Machine Learning Estimation of AGB in Highway Vegetation Belts Using GF-2 High-Resolution Imagery
by Weiwei Jiang, Heng Tu and Qin Wang
Sensors 2026, 26(5), 1729; https://doi.org/10.3390/s26051729 - 9 Mar 2026
Viewed by 322
Abstract
Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along [...] Read more.
Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along highways and other linear transport corridors remains comparatively underexplored despite its potentially important role as a carbon sink. Here, we integrate field-measured AGB samples with GF-2 high-resolution satellite imagery to evaluate the suitability of multiple remote-sensing predictors and machine-learning algorithms for estimating AGB in highway roadside vegetation. Six remote-sensing variables were used as predictors, including four vegetation indices (Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index (EVI), and Modified Soil-Adjusted Vegetation Index (MSAVI) and two-band ratios (B342 and B12/34). Five regression models—multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost)—were developed and systematically compared under both single-variable and multi-variable scenarios. Model performance was evaluated using five-fold cross-validation, with the coefficient of determination (R2) and the root mean square error (RMSE) as metrics of evaluation. The results indicate that the RF model under the multi-variable scenario achieved the best overall performance, with a training R2 of 0.83 and a testing RMSE of 0.84 kg·m−2, substantially outperforming the other linear and non-linear models. The optimal RF model was further applied to GF-2 imagery to produce a spatially explicit AGB map for a 32 km highway segment and a 30 m roadside buffer on both sides, yielding an estimated total aboveground biomass of 566.97 t for the corridor. These findings demonstrate that combining high-resolution remote sensing with machine-learning approaches can effectively improve AGB estimation for linear roadside vegetation systems, providing technical support for ecological monitoring, roadside greening management, and carbon accounting for transport infrastructure. Full article
(This article belongs to the Section Remote Sensors)
►▼ Show Figures

Figure 1

27 pages, 8552 KB  
Article
A Data-Constrained and Physics-Guided Conditional Diffusion Model for Electrical Impedance Tomography Image Reconstruction
by Xiaolei Zhang and Zhou Rong
Sensors 2026, 26(5), 1728; https://doi.org/10.3390/s26051728 - 9 Mar 2026
Viewed by 475
Abstract
Electrical impedance tomography (EIT) provides noninvasive, high-temporal-resolution imaging for medical and industrial applications. However, accurate image reconstruction remains challenging due to the severe ill-posedness and nonlinearity of the inverse problem, as well as the limited robustness of existing single-source learning-based methods in real [...] Read more.
Electrical impedance tomography (EIT) provides noninvasive, high-temporal-resolution imaging for medical and industrial applications. However, accurate image reconstruction remains challenging due to the severe ill-posedness and nonlinearity of the inverse problem, as well as the limited robustness of existing single-source learning-based methods in real measurement scenarios. To address these limitations, a data-constrained and physics-guided Multi-Source Conditional Diffusion Model (MS-CDM) is proposed for EIT image reconstruction. Unlike conventional conditional diffusion methods that rely on a single measurement or an image prior, MS-CDM utilizes boundary voltage measurements as data-driven constraints and incorporates coarse reconstructions as physics-guided structural priors. This multi-source conditioning strategy provides complementary guidance during the reverse diffusion process, enabling balanced recovery of fine boundary details and global topological consistency. To support this framework, a Hybrid Swin–Mamba Denoising U-Net is developed, combining hierarchical window-based self-attention for local spatial modeling with bidirectional state-space modeling for efficient global dependency capture. Extensive experiments on simulated datasets and three real EIT experimental platforms demonstrate that MS-CDM consistently outperforms state-of-the-art numerical, supervised, and diffusion-based methods in terms of reconstruction accuracy, structural consistency, and noise robustness. Moreover, the proposed model exhibits robust cross-system applicability without system-specific retraining under multi-protocol training, highlighting its practical applicability in diverse real-world EIT scenarios. Full article
(This article belongs to the Section Sensing and Imaging)
►▼ Show Figures

Figure 1

22 pages, 6170 KB  
Article
A Lightweight Net with Dual-Path Feature Enhancer and Bidirectional Gated Fusion for Cloud Detection
by Yan Mo, Puhui Chen, Shaowei Bai and Erbao Xiao
Sensors 2026, 26(5), 1727; https://doi.org/10.3390/s26051727 - 9 Mar 2026
Viewed by 298
Abstract
Cloud detection serves as a critical preprocessing step in remote sensing image processing and quantitative applications. However, prevailing deep learning-based models often depend on computationally intensive backbone networks to achieve high accuracy, which hinders their deployment in resource-constrained scenarios such as on-board processing [...] Read more.
Cloud detection serves as a critical preprocessing step in remote sensing image processing and quantitative applications. However, prevailing deep learning-based models often depend on computationally intensive backbone networks to achieve high accuracy, which hinders their deployment in resource-constrained scenarios such as on-board processing or edge computing. To bridge the trade-off between accuracy and efficiency, this paper introduces a lightweight network for cloud detection. The core innovations of our network are twofold: (1) a dual-path feature enhancer that operates at the front end to extract and fuse multi-scale features through a parallel architecture, significantly enriching feature diversity and representational capacity, thereby alleviating the need for a complex backbone, and (2) a bidirectional gated fusion module, which adaptively integrates multi-scale features from the dual-path feature enhancer with deep semantic features from the backbone decoder through a gated attention mechanism and dynamic convolution, thereby enhancing feature discriminability. Comprehensive experiments on the public HRC_WHU dataset demonstrate that the proposed model achieves a high overall accuracy of 96.31% and a mean intersection-over-union of 92.82%, with only 12.04 GFLOPs of computational cost, outperforming several state-of-the-art methods. These results validate that our approach effectively balances high detection performance with computational efficiency, offering a practical solution for real-time, lightweight cloud detection in high-resolution remote sensing imagery. Full article
(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems—2nd Edition)
►▼ Show Figures

Figure 1

30 pages, 3732 KB  
Article
StepsConnect: A Real-Time Step-Sensing Ambient Display System to Support Connectedness for Family Members Living Apart
by Rui Wang, Tianqin Lu, Feng Wang, Yuan Lu and Jun Hu
Sensors 2026, 26(5), 1726; https://doi.org/10.3390/s26051726 - 9 Mar 2026
Viewed by 555
Abstract
Physical separation between family members arises not only from life choices such as education and employment, but also from health-related constraints that limit physical co-presence. This paper presents StepsConnect, a real-time step-sensing-based ambient display system that transforms personal walking data into dynamic digital [...] Read more.
Physical separation between family members arises not only from life choices such as education and employment, but also from health-related constraints that limit physical co-presence. This paper presents StepsConnect, a real-time step-sensing-based ambient display system that transforms personal walking data into dynamic digital art, providing low-effort and non-intrusive presence cues for family members living apart. The system continuously captures step data via smartphones and renders them as spatial and embodied visual cues embedded in everyday environments. We conducted a 90 min laboratory study with 15 young adult–parent dyads, in which young adults engaged in a simulated work session while viewing real-time visualizations of their parents’ step activity. Young adults’ perceived connectedness was measured using the Inclusion of Other in the Self (IOS) scale and complemented with semi-structured interviews, while parents’ walking data were logged to provide an objective behavioral reference. Quantitative results indicated modest and heterogeneous changes in IOS scores at the group level, with individual variability across participants. Qualitative findings suggested that step-based visualizations primarily functioned as ambient reminders and cues of presence, supporting momentary relational awareness while remaining calm and non-intrusive within the workspace context. Walking data exhibited large variation across dyads, providing objective context for participants’ subjective experience of presence, although connectedness was not simply proportional to activity magnitude. The findings suggest that aesthetic step-based ambient visualization primarily supports momentary relational awareness rather than immediate shifts in stable closeness. By clarifying this distinction, the study advances understanding of how sensing-based digital art may function as a complementary presence layer in intergenerational contexts. Full article
(This article belongs to the Section Environmental Sensing)
►▼ Show Figures

Figure 1

13 pages, 1047 KB  
Article
Wide-Field Oxygen Permeability Measurement of Contact Lenses Using a Modified Polarographic Electrode Cell
by Wen-Hong Tong, Jing Liu, Jae-Yeon Pyo, Ki-Choong Mah, Seung-Jin Oh and Jae-Young Jang
Sensors 2026, 26(5), 1725; https://doi.org/10.3390/s26051725 - 9 Mar 2026
Viewed by 353
Abstract
Oxygen permeability (Dk) is a key parameter for evaluating the ability of contact lenses to supply oxygen to the cornea. Although the polarographic method has been standardized as a reference technique for Dk measurement, conventional polarographic electrode cells are limited to a narrow [...] Read more.
Oxygen permeability (Dk) is a key parameter for evaluating the ability of contact lenses to supply oxygen to the cornea. Although the polarographic method has been standardized as a reference technique for Dk measurement, conventional polarographic electrode cells are limited to a narrow central measurement area of approximately 4 mm in diameter, which may not adequately represent oxygen transport under actual wearing conditions. In this study, a modified polarographic electrode cell enabling wide-field oxygen permeability measurement over an expanded central area with a diameter of 11 mm was developed and evaluated under ISO 18369 measurement conditions. The performance of the proposed system was evaluated by comparing its accuracy, repeatability, and relative error with those of a conventional polarographic electrode cell using plano hydrogel contact lens samples with different uniform thicknesses. The Dk values obtained using the modified measurement cell did not show a statistically significant difference compared to those measured with the conventional measurement cell (t = 2.682, p = 0.055), and the relative error between the two systems was 1.93%, meeting the ISO acceptance criteria for the development of a new testing method. These results demonstrate that wide-field Dk measurement can be achieved without compromising reliability, providing a more representative and ISO-compliant approach for contact lens oxygen permeability evaluation. Full article
(This article belongs to the Section Sensors Development)
►▼ Show Figures

Figure 1

15 pages, 2171 KB  
Article
A Flexible Piezoresistive Sensor Based on ZnO/MWCNTs/PDMS Composite Foam with Overall Performance Trade-Offs
by Jun Zheng, Wenting Xu, Wen Ding, Yalong Li, Binyou Xie, Jinhui Xu, Kang Li, Liang Chen, Yan Fan and Songwei Zeng
Sensors 2026, 26(5), 1724; https://doi.org/10.3390/s26051724 - 9 Mar 2026
Cited by 1 | Viewed by 505
Abstract
The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response [...] Read more.
The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response and long-term stability. This paper employed a glucose-based sugar-templating method to fabricate a fine-pore (50 μm) foam structure complemented by a dual-filler strategy to enhance overall performance. A robust porous conductive network was constructed by embedding zinc oxide (ZnO) and multi-walled carbon nanotubes (MWCNTs) into a polydimethylsiloxane (PDMS) matrix. The resulting sensor exhibits outstanding piezoresistive properties, featuring a wide linear detection range (0–80% strain) and a high sensitivity of 9.02 kPa−1 within the 0–10 kPa pressure range. It demonstrates rapid response/recovery times of 50/70 ms and maintains stable output performance even after 5000 compression cycles at 300 kPa. The sensor also exhibits negligible environmental interference and excellent long-term stability. When attached to finger joints, feet soles, or the throat, the sensor enables functions such as finger bending recognition, race-walking violation discrimination, gait analysis, and vocal fold vibration recognition, thereby demonstrating its considerable potential for application in human–computer interaction and human motion detection. Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
►▼ Show Figures

Graphical abstract

37 pages, 5507 KB  
Article
Target Tissue Identification Based on Image Processing for Regulating Automatic Robotic Lung Biopsy Sampler: Onsite Phantom Validation
by Maria Monserrat Diaz-Hernandez, Gerardo Ramirez-Nava and Isaac Chairez
Sensors 2026, 26(5), 1723; https://doi.org/10.3390/s26051723 - 9 Mar 2026
Viewed by 431
Abstract
Cancer is one of the global health problems that affects millions of people every year. Biopsies are among the standard methods for detecting and confirming a cancer diagnosis. Performing this study manually poses several challenges due to tissue movement and the difficulty of [...] Read more.
Cancer is one of the global health problems that affects millions of people every year. Biopsies are among the standard methods for detecting and confirming a cancer diagnosis. Performing this study manually poses several challenges due to tissue movement and the difficulty of precisely locating the target, as is often the case in lung biopsies. This study presents the design and implementation of an autonomous image processing algorithm included in a closed-loop controller that drives the activity of a multi-degree-of-freedom (six) robotic manipulator that performs emulated tissue biopsies. A realistic lung motion emulator, based on a two-degree-of-freedom robotic device with a photon emitter (to simulate radiopharmaceutical identification of cancerous tissue), was used to test the proposed automatic biopsy collector. Applying image processing to detect cancer tissue enables the identification of the centroid and tumor boundaries. Using the detected centroid coordinates, the reference trajectory of the end effector (biopsy needle) was automatically determined. A finite-time convergent controller was implemented to guide the robotic manipulator’s motion towards the tumor position within a specified time window. The controller was evaluated using a digital twin representation of the entire robotic system and using an experimental device working on the simulated mobile tumor emulator. Evaluation of simulated tumor detection and reference trajectory tracking effectiveness was used to validate the operation of the proposed automatic robotic lung biopsy sampler. The application of the controller allows one to track the position of the emulated tumor with a deviation of 0.52 mm and a settling time of less than 1 s. Full article
(This article belongs to the Special Issue Sensor Technologies and Surgical Robotics in Medical, Biomedical, and Healthcare Systems)
►▼ Show Figures

Figure 1

21 pages, 4325 KB  
Article
Robotic Arm Trajectory Planning for Tunnel Lighting Cleaning Based on the CAW-PSO Algorithm
by Zhibin Yao, Taibo Song, Hui Li, Hongwei Zhang and Zhanlong Li
Sensors 2026, 26(5), 1722; https://doi.org/10.3390/s26051722 - 9 Mar 2026
Viewed by 366
Abstract
Tunnel lighting cleaning is of significant practical importance for improving driving safety. To address the low operational efficiency of tunnel lighting cleaning tasks, a trajectory planning method based on the chaotic adaptive whale–particle swarm optimization (CAW-PSO) algorithm is proposed. Taking the SIASUN GCR16-2000 [...] Read more.
Tunnel lighting cleaning is of significant practical importance for improving driving safety. To address the low operational efficiency of tunnel lighting cleaning tasks, a trajectory planning method based on the chaotic adaptive whale–particle swarm optimization (CAW-PSO) algorithm is proposed. Taking the SIASUN GCR16-2000 robotic arm as the research object, the trajectory is constructed using a 3-5-3 polynomial interpolation, with the objective of achieving time-optimal trajectory planning. In the CAW-PSO algorithm, a tent chaotic map is introduced to improve the quality of the population; a linearly decreasing inertia weight is designed to strike a balance between local and global search; dynamic learning factors are defined to strengthen the individual learning ability and global cognitive capability of particles; finally, the exploitation mechanism of the whale optimization algorithm is employed to avoid getting trapped in local optima and improve convergence accuracy. The simulation time is 3.661 s, a reduction of 69.94%. The experimental results yielded a mean relative error of 1.16%, indicating good agreement with the simulation results. The results of the simulation and experiment indicate that the CAW-PSO effectively reduces the motion time of the robotic arm, exhibiting superior applicability in trajectory planning for tunnel lighting cleaning robotic arms. Full article
(This article belongs to the Section Sensors and Robotics)
►▼ Show Figures

Figure 1

22 pages, 3908 KB  
Article
Physics-Topology-Anchored Learning: A Robust and Lightweight Framework for Time-Series Prediction and Anomaly Detection Under Data Scarcity
by Xuanhao Hua, Weiqi Yin, Libin Wang, Meng Ma, Jianfeng Yuan and Jing Zhang
Sensors 2026, 26(5), 1721; https://doi.org/10.3390/s26051721 - 9 Mar 2026
Viewed by 309
Abstract
Health monitoring of complex systems is critical for ensuring reliability and achieving cost-effective reusability. However, deploying deep learning models in this domain is impeded by two primary constraints: the scarcity of high-quality fault samples and the restricted computational resources available on-board. To address [...] Read more.
Health monitoring of complex systems is critical for ensuring reliability and achieving cost-effective reusability. However, deploying deep learning models in this domain is impeded by two primary constraints: the scarcity of high-quality fault samples and the restricted computational resources available on-board. To address these challenges, this paper proposes a Physics-Topology-Anchored Learning (PTAL) framework. The core innovation lies in the effective integration of physical inductive bias into the model architecture. Specifically, PTAL incorporates a predefined adjacency matrix, derived from the physical mechanism, as a structural prior. This design anchors the neural network to explicit physical causality, effectively constraining the hypothesis space and reducing the model’s dependency on large-scale data. Furthermore, by coupling this physics-informed structure with a lightweight recurrent attention mechanism, the model avoids the high computational overhead typical of generic large-scale networks. Experimental evaluations demonstrate that PTAL achieves a peak diagnostic accuracy of 97.8% and a low standard deviation of 0.1145, significantly outperforming baseline models in data-scarce regimes. The results confirm that the proposed model successfully leverages physical bias to maintain a favorable trade-off between diagnostic performance and computational efficiency, making it highly suitable for the resource-constrained environments of complex systems. Full article
(This article belongs to the Special Issue AI-Assisted Condition Monitoring and Fault Diagnosis)
►▼ Show Figures

Figure 1

27 pages, 2147 KB  
Article
Federated Learning with Assured Privacy and Reputation-Driven Incentives for Internet of Vehicles
by Jiayong Chai, Mo Chen, Wei Zhang, Xiaojuan Wang and Jiaming Song
Sensors 2026, 26(5), 1720; https://doi.org/10.3390/s26051720 - 9 Mar 2026
Viewed by 378
Abstract
Cross-domain data collaboration is a core requirement for the intelligent development of critical areas such as the Internet of Vehicles and intelligent transportation systems. In this scenario, vehicles and various sensors deployed roadside continuously generate massive amounts of time-series data, yet this data [...] Read more.
Cross-domain data collaboration is a core requirement for the intelligent development of critical areas such as the Internet of Vehicles and intelligent transportation systems. In this scenario, vehicles and various sensors deployed roadside continuously generate massive amounts of time-series data, yet this data often forms “data silos” due to privacy regulations and a lack of trust between collaborating entities. Existing integrated schemes combining “Federated Learning + Blockchain” have achieved a certain degree of process traceability and automated payments, but risks of gradient-level privacy leakage persist, and inflexible and delayed incentive mechanisms result in low participation quality. To systematically address these bottlenecks, this paper proposes the Federated Learning with Assured Privacy and Reputation-Driven Incentives (FLARE) architecture, whose core innovation lies in the native integration of cryptographic security and mechanism design theory. It includes the Secure and Faithfully Executed Gradient aggregation (SafeGrad) protocol, which integrates partial homomorphic encryption and zero-knowledge proofs to provide verifiable privacy guarantees for gradient contributions while enabling efficient secure aggregation, defending against inversion attacks at the source; alongside this, it includes the Economy-on-Chain incentive (EconChain) mechanism, which designs an on-chain economic system based on blockchain, achieving precise measurement and sustainable incentivization of training process contributions through fine-grained instant micro-rewards and a dynamic reputation model. Experiments show that, compared to baseline schemes, FLARE can effectively enhance node participation enthusiasm and contribution quality without compromising model accuracy, providing a new paradigm with both strong security and high vitality for the trusted and efficient circulation of data. Full article
(This article belongs to the Special Issue Communications and Networking Based on Artificial Intelligence)
►▼ Show Figures

Figure 1

16 pages, 995 KB  
Article
EEG and IMU Gait Signal Processing: A Comparative Assessment of the “Reza” Exponential Filter and Classical Filters
by Reza Pousti, Daniel M. Russell, Derek C. Monroe and Christopher K. Rhea
Sensors 2026, 26(5), 1719; https://doi.org/10.3390/s26051719 - 9 Mar 2026
Viewed by 474
Abstract
Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential “Reza” filter with these designs for neural and locomotor data. We analyzed an open-source mobile [...] Read more.
Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential “Reza” filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain–body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5–50 Hz, while IMU axes were averaged and band-pass filtered at 0.5–5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5–50 Hz; IMU:0.5–5 Hz). Repeated-measures ANOVAs tested the effect of filter types (Butterworth, Chebyshev I, elliptic, Reza) with Bonferroni-adjusted post hoc tests for the six pairwise filter comparisons (αadj = 0.0083). We reported partial eta-squared (ηp2) as the ANOVA effect size. For EEG, PSD did not differ among filters (p = 0.146), whereas SNR differed strongly (p<0.001): Chebyshev and elliptic yielded the highest mean SNR and did not differ from each other, while both exceeded Butterworth, Reza was the lowest. For IMU, both SNR (p< 0.001) and PSD (p< 0.001) differed: Reza produced the highest mean SNR (significantly exceeding elliptic and Chebyshev), while Butterworth exceeded Chebyshev; meanwhile, IMU PSD showed a clear ordering with Reza retaining the most motion-band power, followed by Butterworth, then Chebyshev, with elliptic retaining the least. These results showed that filter choice materially shapes EEG and gait outcomes. For EEG, Chebyshev maximized SNR, while elliptic and Reza maintained comparable fidelity. For IMU gait signals, Reza matched Butterworth for denoising and preserved more signal power. Therefore, filter choice should be guided by the target outcome (SNR vs. band power) rather than a single default design. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)
►▼ Show Figures

Figure 1

18 pages, 1901 KB  
Article
Distributed Event-Driven Serverless Platform for Multicluster IoT Environments
by Hyungwoo Ju, Jangwon Seo and Younghan Kim
Sensors 2026, 26(5), 1718; https://doi.org/10.3390/s26051718 - 9 Mar 2026
Viewed by 341
Abstract
In modern smart city and IoT environments, diverse sensors for traffic management, environmental monitoring, and energy systems continuously generate large volumes of heterogeneous events in real time. Efficiently processing these multi-source event streams requires a scalable and responsive computing architecture. However, many Kubernetes-hosted [...] Read more.
In modern smart city and IoT environments, diverse sensors for traffic management, environmental monitoring, and energy systems continuously generate large volumes of heterogeneous events in real time. Efficiently processing these multi-source event streams requires a scalable and responsive computing architecture. However, many Kubernetes-hosted serverless Function-as-a-Service (FaaS) deployments operate within a single administrative cluster and provide limited user-level control over dynamic multicluster placement based on heterogeneous event types and real-time resource conditions. To address these limitations, this study proposes a generalized event-driven FaaS architecture capable of efficiently processing multi-event streams across multicluster environments. The proposed architecture was implemented on Kubernetes-based testbed by integrating a multicluster orchestrator, an event-processing engine, a workflow execution layer, and a serverless platform. Evaluation using a smart city-inspired scenario demonstrates that the proposed platform provides improved load distribution characteristics and maintains higher workflow success rates under increasing workloads compared to the evaluated single-cluster baseline. This research provides a scalable design approach for serverless platforms that can meet real-time event processing requirements in IoT and smart city applications. Full article
(This article belongs to the Section Internet of Things)
►▼ Show Figures

Figure 1

16 pages, 4442 KB  
Article
Bistatic Radar with Quantum-Generated Noise Phase Manipulation and Non-Directional Antennas
by Nikolay Gueorguiev, Atanas Nachev, Ognyan Todorov, Tereza Trencheva and Gergana Chalakova
Sensors 2026, 26(5), 1717; https://doi.org/10.3390/s26051717 - 9 Mar 2026
Viewed by 361
Abstract
The development of bistatic noise radars is a promising contemporary direction in the field of radar technology. Two novel approaches are proposed in this study as further development of existing methods for their design. The first approach involves using a quantum-generated random number [...] Read more.
The development of bistatic noise radars is a promising contemporary direction in the field of radar technology. Two novel approaches are proposed in this study as further development of existing methods for their design. The first approach involves using a quantum-generated random number sequence for phase manipulation control, which is practically infinite in duration. This ensures additional electronic protection of the radar, since the phase manipulation control code will not repeat regardless of the duration of its operation. The second approach is related to the introduction of synchronized emissions from both antennas in a manner ensuring equality or controlled difference of their signals upon arrival at a predetermined point in space. This enables the formation of a controlled electromagnetic field. As a result, received-signal processing capabilities are improved, while additional electronic “stealth” is achieved by creating a fictitious electromagnetic center of the radar’s resultant radiation (i.e., an effective RF phase center of the resultant emission) and complicating the determination of antenna locations. A block diagram and general algorithm for information processing of a bistatic radar with quantum-generated noise phase manipulation and non-directional antennas are proposed in this study. Full article
(This article belongs to the Section Radar Sensors)
►▼ Show Figures

Figure 1

14 pages, 1230 KB  
Article
Discriminating Between Fallers and Non-Fallers Using Kinematic Data from the Heel2Toe™ Wearable Sensor
by Nancy E. Mayo, Ahmed Abou-Sharkh, Helen Dawes, Sarah J. Donkers, Chelsia Gillis, Krista Goulding, Edward Hill, Kedar Mate and Yosuke Tomita
Sensors 2026, 26(5), 1716; https://doi.org/10.3390/s26051716 - 9 Mar 2026
Viewed by 335
Abstract
Most falls occur while walking, making gait quality a logical therapeutic target. Many temporo-spatial variables have been implicated in increased fall risk, but these are dependent upon kinematic parameters of the joints involved in the gait cycle. The widespread availability of wearable sensors [...] Read more.
Most falls occur while walking, making gait quality a logical therapeutic target. Many temporo-spatial variables have been implicated in increased fall risk, but these are dependent upon kinematic parameters of the joints involved in the gait cycle. The widespread availability of wearable sensors has made the acquisition of kinematic data feasible, and those related to the ankle are most relevant, as they relate most closely to causes of falls, trips, slips, and mis-steps. The purpose of this study is to estimate the extent to which measures of ankle angular velocity (AV) during walking are associated with falls. This is a comparative study of ankle AV metrics between people who have or have not experienced a fall in the past year. Data came from experimental use of the Heel2Toe™ sensor in a variety of settings, including demonstrations and clinical research studies. The sample comprised 387 participants, of whom 68 (17.6%) self-reported falling in the past year. Logistic regression with a natural cubic spline with 3 degrees of freedom identified AV of the angle at heel strike to discriminate between fallers and non-fallers, and the regression parameters were used to propose an algorithm to estimate fall risk. Applying the algorithm to the existing data yielded a range of probabilities from 0.0480 to 0.7245 depending on age of the person assessed. Further testing of this algorithm in different samples is warranted. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
►▼ Show Figures

Figure 1

24 pages, 9290 KB  
Article
Robust Localization of Low-Velocity Impacts on Honeycomb Sandwich Panels via FBG Sensor Networks
by Zhengwen Zhou, Yibo Yang, Xin Xu, Kexia Peng, Yihong Han, Guangming Song, Jingtai Li, Zhe Lin and Liangjie Guo
Sensors 2026, 26(5), 1715; https://doi.org/10.3390/s26051715 - 9 Mar 2026
Viewed by 332
Abstract
Honeycomb sandwich panels are widely used in aerospace, yet they are vulnerable to low-velocity impacts. Implementing effective localization is challenging because, unlike single-layer structures, the multi-layer energy dissipation capabilities of honeycomb core induce rapid stress wave attenuation and reverberations, degrading signal quality. This [...] Read more.
Honeycomb sandwich panels are widely used in aerospace, yet they are vulnerable to low-velocity impacts. Implementing effective localization is challenging because, unlike single-layer structures, the multi-layer energy dissipation capabilities of honeycomb core induce rapid stress wave attenuation and reverberations, degrading signal quality. This paper designs a testing platform for low-velocity impact and proposes a template matching method based on wavelet denoising and error outlier weighting. This method is based on 16 FBG sensors uniformly arranged on the panel, dividing the panel into 25 × 25 grids, with five impacts in each grid forming a template library. Similarity matching is performed by calculating the Euclidean distance between the template library and test signals, combined with wavelet denoising and outlier weighting to compute the average localization accuracy. The results show that for a honeycomb panel measuring 500 mm × 500 mm × 20 mm, the basic method yields an average localization accuracy of 21.29 mm. When error outlier weighting is applied, the accuracy improves to 12.36 mm. Finally, by combining outlier weighting with Sym5 wavelet denoising, the average error is further reduced to 8.53 mm. These results demonstrate that the proposed method mitigates the effects of signal instability in honeycomb structures, providing a robust and precise solution for aerospace SHM where traditional methods fall short. Full article
(This article belongs to the Special Issue Advances in Fiber Bragg Grating (FBG) and Fiber Optic Sensor Technology)
►▼ Show Figures

Figure 1

55 pages, 3447 KB  
Article
A Microservices-Based Solution with Hybrid Communication for Energy Management in Smart Grid Environments
by Artur F. S. Veloso, José V. Reis, Jr. and Ricardo A. L. Rabelo
Sensors 2026, 26(5), 1714; https://doi.org/10.3390/s26051714 - 9 Mar 2026
Viewed by 486
Abstract
The increasing variability of residential demand, combined with the expansion of distributed generation and electric vehicles, has introduced new challenges to the stability of Smart Grids (SGs). Centralized management models lack the flexibility required to operate under these conditions, reinforcing the need for [...] Read more.
The increasing variability of residential demand, combined with the expansion of distributed generation and electric vehicles, has introduced new challenges to the stability of Smart Grids (SGs). Centralized management models lack the flexibility required to operate under these conditions, reinforcing the need for scalable and data-driven architectures. This study proposes an energy management solution based on microservices, supported by hybrid communication in Low Power Wide Area Networks (LPWAN), integrating Long Range Wide Area Network (LoRaWAN) and LoRaMESH to enhance connectivity, local resilience, and reliability in data acquisition for Internet of Things (IoT) and Demand Response (DR) applications. A prototype composed of a Smart Meter (SM), a Data Aggregation Point (DAP), and a Concentrator (CON) was evaluated in a controlled environment, achieving Packet Delivery Rates above 97%, an average RSSI of −92 dBm, and a Signal-to-Noise Ratio close to 9 dB, validating the robustness of the hybrid communication. At a larger scale, data from 5567 households in the Low Carbon London (LCL) project were used to generate representative Load Profiles (LPs) through seven aggregation and clustering techniques, consistently identifying the 18:00–21:00 interval as the critical peak, with demand reaching up to 42% above the daily average. Fourteen load shifting algorithms were evaluated, and the Hybrid Adaptive Algorithm based on Intention and Resilience (HAAIR), proposed in this work, achieved the best overall performance with a 1.83% peak reduction, US$65.40 in cost savings, a reduction of 60 kg of CO2, a Comfort Loss Index of 0.04, resilience of 9.5, and reliability of 0.98. The results demonstrate that the integration of hybrid LPWAN communication, modular microservice-based architecture, and adaptive DR strategies driven by Artificial Intelligence (AI) represents a promising pathway toward scalable, resilient, and energy-efficient SGs. Full article
(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications—2nd Edition)
►▼ Show Figures

Figure 1

30 pages, 14380 KB  
Article
An Explainable Intelligent Fault Diagnosis for Rotating Machinery via Multi-Source Information Fusion Under Noisy Environments and Small Sample Conditions
by Gaolei Mao, Jinhua Wang and Yali Sun
Sensors 2026, 26(5), 1713; https://doi.org/10.3390/s26051713 - 8 Mar 2026
Viewed by 447
Abstract
In modern industrial systems, the fault diagnosis of rotating machinery is crucial for ensuring safe equipment operation. However, practical fault data are often contaminated by noise, and the scarcity of samples across fault conditions makes effective feature extraction challenging. Moreover, single-sensor measurements provide [...] Read more.
In modern industrial systems, the fault diagnosis of rotating machinery is crucial for ensuring safe equipment operation. However, practical fault data are often contaminated by noise, and the scarcity of samples across fault conditions makes effective feature extraction challenging. Moreover, single-sensor measurements provide limited and incomplete information, further degrading the accuracy and reliability of diagnostic models. To address these challenges, this paper proposes an explainable intelligent fault diagnosis for rotating machinery via multi-source information fusion under noisy environments and small sample conditions. Firstly, a multi-sensor data intelligent fusion module (MSDIFM) is developed. It converts multi-sensor vibration signals into time–frequency maps via continuous wavelet transform (CWT). Pixel-level cross-channel fusion is then performed using a variance-driven dynamic weighting strategy to generate a unified fusion map, adaptively highlighting high information channels. Secondly, a multi-dimensional adaptive asymmetric soft-threshold residual shrinkage block (MASRSB) is proposed to implement differentiated and dynamic threshold control for positive and negative features, enhancing representation and discrimination capabilities. Thirdly, the multi-scale Swin Transformer (MSSwin-T) is designed. This module significantly enhances the model’s feature extraction capability by expanding multi-level receptive fields, strengthening key channel representations, and reinforcing cross-window feature interactions. Finally, to validate the effectiveness of the proposed method, experiments are conducted on both the Case Western Reserve University (CWRU) dataset and the self-created PT890 dataset. Results demonstrate that the proposed method exhibits outstanding diagnostic performance and robustness under noisy conditions and with small sample sizes. Full article
(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)
►▼ Show Figures

Figure 1

21 pages, 6737 KB  
Article
Research on Transmission Characteristics of Magnetic Couplers for Underwater Wireless Power Transfer Based on Prior Knowledge Input Neural Network
by Jixie Xie, Chong Zhu and Xi Zhang
Sensors 2026, 26(5), 1712; https://doi.org/10.3390/s26051712 - 8 Mar 2026
Viewed by 387
Abstract
Underwater wireless power transfer (UWPT) operates under special conditions, where the conductivity of seawater introduces eddy current losses, thereby reducing system efficiency. Meanwhile, the design parameters of magnetic couplers significantly influence their transmission characteristics. This paper proposes a fast and accurate neural network [...] Read more.
Underwater wireless power transfer (UWPT) operates under special conditions, where the conductivity of seawater introduces eddy current losses, thereby reducing system efficiency. Meanwhile, the design parameters of magnetic couplers significantly influence their transmission characteristics. This paper proposes a fast and accurate neural network prediction model for mutual inductance and losses of magnetic couplers based on mirror-method prior knowledge within a prior knowledge input (PKI) framework. The proposed model integrates a low-fidelity analytical model with data-driven learning to achieve high prediction accuracy while maintaining computational efficiency. Based on the developed model, the transmission characteristics of unipolar rectangular and bipolar DD magnetic couplers are systematically investigated. The results indicate that the rectangular couplers exhibit higher overall efficiency than the DD couplers, with a more monotonic variation in efficiency under design constraints. Owing to its structural characteristics, the DD couplers present an optimal current-carrying area ratio, which is approximately 0.85 within the parameter range. Experimental validation is conducted at a 1 kW power with outer dimensions of 200 mm × 250 mm. The optimal transfer efficiencies of the rectangular and DD couplers reach 97.33% and 96.19%, respectively. The experimental results show good agreement with both simulations and model predictions, demonstrating the reliability of the proposed method for UWPT magnetic coupler analysis. Full article
(This article belongs to the Section Electronic Sensors)
►▼ Show Figures

Figure 1

34 pages, 7889 KB  
Article
Bi-Level Simulation-Driven Optimization for Route Guidance in Disrupted Metro Networks via Hybrid Swarm Intelligence
by Xuanchuan Zheng, Yong Qin, Jianyuan Guo, Xuan Sun and Guofei Gao
Sensors 2026, 26(5), 1711; https://doi.org/10.3390/s26051711 - 8 Mar 2026
Viewed by 273
Abstract
Real-time route guidance during disruptions in urban rail transit systems requires rapidly providing effective strategies that simultaneously alleviate congestion and account for passengers’ travel time. This study proposes an optimization framework that considers travel time, congestion perception time, and information costs, incorporating a [...] Read more.
Real-time route guidance during disruptions in urban rail transit systems requires rapidly providing effective strategies that simultaneously alleviate congestion and account for passengers’ travel time. This study proposes an optimization framework that considers travel time, congestion perception time, and information costs, incorporating a Logit choice model with information bias to reflect passengers’ behavioral responses under disruptions. A bi-level simulation evaluation mechanism is employed to rapidly evaluate the objective functions under different guidance strategies, where a Physically Consistent Incremental Simulator, based on differential computation, achieves a 599-fold speedup while maintaining high fidelity with full-scale simulations (Pearson correlation > 0.96). A hybrid algorithm combining the Gray Wolf Optimizer and Adaptive Large Neighborhood Search is developed to solve the origin–destination level route guidance optimization problem. The algorithm embeds domain knowledge-based “destroy and repair” operators with a sequential repair mechanism to enable fast global search and precise local refinement. Case study results demonstrate that the framework reduces severely congested sections by 36%, shortens average travel time by 7.16 min, and improves solution quality by 12–30% over baseline algorithms. These findings confirm the practical applicability of integrating intelligent optimization with high-efficiency simulation for emergency route guidance in large-scale metro networks. Full article
(This article belongs to the Special Issue Applications of Advanced Sensors and Interoperability Technologies in Autonomous Transportation Systems)
►▼ Show Figures

Figure 1

18 pages, 1675 KB  
Article
Efficient Data Aggregation in Smart Grids: A Personalized Local Differential Privacy Scheme
by Haina Song, Jinhang Sun, Mengyao Wang, Nan Zhao, Fan Zhang and Hongzhang Liu
Sensors 2026, 26(5), 1710; https://doi.org/10.3390/s26051710 - 8 Mar 2026
Viewed by 324
Abstract
The rapid advancement of smart grids, while enhancing the efficiency of power systems, has also raised serious concerns regarding the privacy and security of end-users’ electricity consumption data. Traditional privacy protection methods struggle to meet users’ individualized privacy requirements and often lead to [...] Read more.
The rapid advancement of smart grids, while enhancing the efficiency of power systems, has also raised serious concerns regarding the privacy and security of end-users’ electricity consumption data. Traditional privacy protection methods struggle to meet users’ individualized privacy requirements and often lead to a significant decline in data aggregation accuracy. To address the core contradiction between personalized privacy protection and high-precision grid analytics, this paper proposes an efficient data aggregation scheme based on personalized local differential privacy (EDAS-PLDP) tailored for smart grids. The proposed scheme enables smart terminal users to autonomously select their privacy protection levels based on individual needs, thereby breaking the limitations of the traditional “one-size-fits-all” approach. To mitigate the accuracy loss caused by personalized perturbations, a mean square error-based weighted aggregation strategy is introduced at the gateway side. This strategy evaluates the data quality from groups with different privacy preferences and adjusts aggregation weights to optimize the estimation accuracy of the global mean electricity consumption. Extensive experimental results demonstrate that, compared to existing mainstream schemes, EDAS-PLDP achieves higher estimation accuracy under various distributions of privacy preferences, user scales, and data granularities, while exhibiting lower time consumption, making it suitable for resource-constrained smart grid environments. Furthermore, the scheme shows excellent robustness against false data injection attacks. In summary, EDAS-PLDP provides a balanced and efficient solution for reconciling personalized privacy protection with high-precision data utility in smart grids. Full article
(This article belongs to the Section Internet of Things)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-338
Previous Issue
Next Issue
Back to TopTop