Sensors

32 pages, 3528 KB

Open AccessArticle

Characterization of a Time Transfer Channel Between a Narrow-Band Transponder on a GEO Satellite and a Ground-Based Station

by Ferran Valdes Crespi, Pol Barrull Costa, Angel Slavov, Matthias Weiß and Peter Knott

Sensors 2026, 26(5), 1515; https://doi.org/10.3390/s26051515 (registering DOI) - 27 Feb 2026

Abstract

Time synchronization and positioning of bistatic radar transceivers is required to coordinate and meaningfully merge the measurements made between them. It simultaneously allows the radar transceivers to change their position throughout time. Despite their acknowledged vulnerabilities, Global Navigation Satellite Systems (GNSSs) are the [...] Read more.

Time synchronization and positioning of bistatic radar transceivers is required to coordinate and meaningfully merge the measurements made between them. It simultaneously allows the radar transceivers to change their position throughout time. Despite their acknowledged vulnerabilities, Global Navigation Satellite Systems (GNSSs) are the preferred source for Positioning, Navigation and Timing (PNT) services. Because of these vulnerabilities however, research on possible signal sources to obtain alternative positioning, navigation and timing (A-PNT) is of interest. This present work proposes the use of a narrow-band transponder installed on a geostationary (GEO) satellite to be used as one anchor for a future time transfer. A channel calibration is made between the transceiver station and the chosen satellite. Diverse models are used to estimate the channel effects throughout the signal propagation path, estimate the time delay, and correct the measurements, accordingly. The available channel bandwidth on the proposed satellite is

2.7

kHz, limiting the accuracy of the time measurements. After integration of multiple pulses, a time accuracy of approximately

1 μ

s is obtained. The range measurements are compared against satellite positions propagated from publicly available two-line element sets (TLEs). The obtained results suggest that, after calibration, the expected accuracy and a good repeatability is obtained. Thus, making the QO-100 satellite a suitable anchor for the proposed technique. Full article

(This article belongs to the Section Communications)

17 pages, 962 KB

Open AccessArticle

ArmTenna: Two-Armed RFID Explorer for Dynamic Warehouse Management

by Abdussalam A. Alajami and Rafael Pous

Sensors 2026, 26(5), 1513; https://doi.org/10.3390/s26051513 (registering DOI) - 27 Feb 2026

Abstract

Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing [...] Read more.

Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing sensing pose or prioritizing inventory-driven frontiers, which can result in incomplete coverage and redundant traversal. This paper presents ArmTenna, an articulated mobile robotic platform that formulates RFID inventory exploration as an active perception problem. The system integrates dual 4-DOF robotic arms carrying directional UHF RFID antennas and a 2-DOF neck-mounted RGB-D camera, enabling adaptive interrogation of candidate regions. We propose a multi-modal frontier exploration framework that combines newly detected EPC tags, average RSSI values, and vision-based product detections into a composite utility function for goal selection. By embedding articulated antenna control directly into the frontier evaluation loop, the robot tightly couples sensing geometry with exploration decisions. Experimental validation with 150 tagged items across three separated warehouse zones shows that ArmTenna achieves up to 97% map coverage, compared to 72% for a baseline platform, while reducing missed-tag regions. These results demonstrate that integrating active sensing pose control with multi-modal frontier evaluation provides an effective and scalable solution for RFID-driven warehouse inventory automation. Full article

(This article belongs to the Special Issue Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control)

16 pages, 2899 KB

Open AccessArticle

ESO-Det: An Efficient Small Object Detector for Real-Time UAV Perception

by Haodong Deng, Song Zhou and Weidong Yang

Sensors 2026, 26(5), 1512; https://doi.org/10.3390/s26051512 (registering DOI) - 27 Feb 2026

Abstract

Object detection in aerial drone imagery has attracted increasing attention in Unmanned Aerial Vehicle(UAV) sensing applications. However, small objects occupying limited image regions, with large scale variations and similar background interference, make it challenging to perceive them. Meanwhile, the constrained computing power of [...] Read more.

Object detection in aerial drone imagery has attracted increasing attention in Unmanned Aerial Vehicle(UAV) sensing applications. However, small objects occupying limited image regions, with large scale variations and similar background interference, make it challenging to perceive them. Meanwhile, the constrained computing power of the onboard platform imposes requirements on the speed and efficiency of the algorithm. In this paper, we propose an efficient object detection network for real-time UAV perception named ESO-Det. Our approach introduces three key innovations: (1) Dense Cross-branch Complementary Module, a lightweight model that dynamically integrates semantic and spatial information to improve the network’s understanding of scene details. (2) Large-Kernel Context Integration Module, a module that expands receptive fields to effectively aggregate multi-scale contextual information. (3) Lightweight Selective Aggregation Module, a model selectively aggregates fused multi-scale features through different functional branches. Extensive experiments demonstrate that the proposed method achieves higher performance than representative existing approaches while maintaining real-time processing capability. The results show that our method is suitable for real-time UAV object detection. Full article

(This article belongs to the Section Sensing and Imaging)

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29 pages, 3115 KB

Open AccessArticle

Acoustic Detection of Insects in Stored Products in the Presence of Strong Ambient Noise

by Daniel Kadyrov, Alexander Sutin, Nikolay Sedunov, Alexander Sedunov and Hady Salloum

Sensors 2026, 26(5), 1511; https://doi.org/10.3390/s26051511 (registering DOI) - 27 Feb 2026

Abstract

Acoustic detection methods offer a non-destructive alternative to manual inspection for identifying insect infestations in stored products, but their performance is compromised by ambient noise in operational environments. This study presents an enhanced detection algorithm for the Acoustic Stored Product Insect Detection System [...] Read more.

Acoustic detection methods offer a non-destructive alternative to manual inspection for identifying insect infestations in stored products, but their performance is compromised by ambient noise in operational environments. This study presents an enhanced detection algorithm for the Acoustic Stored Product Insect Detection System (A-SPIDS) that enables reliable single-insect detection in the presence of strong external noise. The platform’s physical noise isolation achieved an average attenuation of 45

d

B

above 2000

Hz

. Spectral analysis revealed that insect signals dominate over ambient noise, generating insect-like impulses in the high-frequency band, enabling optimization of the Normalized Signal Pulse Amplitude (NSPA) detection metric to the 1565

Hz

–6000

Hz

frequency band, resulting in 99.4% detection accuracy at 80

d

B

A ambient noise levels. The external microphone was leveraged to identify and remove noise-generated impulses from internal piezoelectric sensor recordings, achieving 100% detection with zero false alarms across the recorded dataset featuring species Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor in oatmeal, rice, wheat, and corn products at noise levels exceeding 100

d

B

A. Full article

(This article belongs to the Section Electronic Sensors)

21 pages, 20489 KB

Open AccessArticle

Semantic–Physical Sensor Fusion for Safe Physical Human–Robot Interaction in Dual-Arm Rehabilitation

by Disha Zhu, Xuefeng Wang and Shaomei Shang

Sensors 2026, 26(5), 1510; https://doi.org/10.3390/s26051510 (registering DOI) - 27 Feb 2026

Abstract

A safe physical human–robot interaction (pHRI) in rehabilitation requires reliable perception and low-latency decision making under heterogeneous and unreliable sensor inputs. This paper presents a multimodal sensor-fusion-based safety framework that integrates physical state estimation, semantic information fusion, and an edge-deployed large language model [...] Read more.

A safe physical human–robot interaction (pHRI) in rehabilitation requires reliable perception and low-latency decision making under heterogeneous and unreliable sensor inputs. This paper presents a multimodal sensor-fusion-based safety framework that integrates physical state estimation, semantic information fusion, and an edge-deployed large language model (LLM) for real-time pHRI safety control. A dynamics-based virtual sensing method is introduced to estimate internal joint torques from external force–torque measurements, achieving a normalized mean absolute error of

18.5 %

in real-world experiments. An asynchronous semantic state pool with a time-to-live mechanism is designed to fuse visual, force, posture, and human semantic cues while maintaining robustness to sensor delays and dropouts. Based on structured multimodal tokens, an instruction-tuned edge LLM outputs discrete safety decisions that are further mapped to continuous compliant control parameters. The framework is trained using a hybrid dataset consisting of limited real-world samples and LLM-augmented synthetic data, and evaluated on unseen real and mixed-condition scenarios. Experimental results show reliable detection of safety-critical events with a low emergency misdetection rate, while maintaining an end-to-end decision latency of approximately 223 ms on edge hardware. Real-world experiments on a rehabilitation robot demonstrate effective responses to impacts, user instability, and visual occlusions, indicating the practical applicability of the proposed approach for real-time pHRI safety monitoring. Full article

(This article belongs to the Section Biomedical Sensors)

21 pages, 744 KB

Open AccessArticle

Activity Recognition from Daily-Life Sounds Using Unsupervised Learning with Dirichlet Multinomial Mixture Models

by Ken Sadohara and Natsuki Miyata

Sensors 2026, 26(5), 1509; https://doi.org/10.3390/s26051509 (registering DOI) - 27 Feb 2026

Abstract

To support ambient assisted living for the elderly living alone, we investigate a method for recognizing daily activities from household sounds. To reduce the cost of building an activity-recognition model, we adopt an unsupervised learning approach based on a Dirichlet multinomial mixture model. [...] Read more.

To support ambient assisted living for the elderly living alone, we investigate a method for recognizing daily activities from household sounds. To reduce the cost of building an activity-recognition model, we adopt an unsupervised learning approach based on a Dirichlet multinomial mixture model. The model represents the generative process of neural audio codec codes conditioned on latent activities. We further extend the model to handle multiple streams of codes corresponding to different sound directions. This extension enables the formation of more accurate activity clusters, partly because code occurrence patterns exhibit burstiness. The proposed approach is expected to serve as a key component for constructing an activity recognition system that requires minimal labeled data and a small number of user inquiries. Full article

(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)

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24 pages, 842 KB

Open AccessArticle

Eigenvalue Adjustment-Based STAP in Airborne MIMO Radar Under Limited Snapshots

by Chao Xu, Qizhen Feng, Zhao Wang, Dingding Li and Di Song

Sensors 2026, 26(5), 1508; https://doi.org/10.3390/s26051508 (registering DOI) - 27 Feb 2026

Abstract

The covariance matrix performs a vital role for space-time adaptive processing (STAP) in airborne multiple-input multiple-output (MIMO) radar. As is known, the clutter-plus-noise covariance matrix (CPNCM), reflecting the statistical characteristics of radar echo, is a key component for MIMO-STAP. Commonly, an ideal CPNCM [...] Read more.

The covariance matrix performs a vital role for space-time adaptive processing (STAP) in airborne multiple-input multiple-output (MIMO) radar. As is known, the clutter-plus-noise covariance matrix (CPNCM), reflecting the statistical characteristics of radar echo, is a key component for MIMO-STAP. Commonly, an ideal CPNCM is impossible to obtain, and it must be estimated with sufficient snapshots. According to the RMB rule, MIMO-STAP requires many snapshots since MIMO radar has a high degree-of-freedom (DoF) due to its orthogonal transmit waveform. However, this is hard to satisfy in practice. This paper develops a novel covariance matrix estimation method under limited snapshots in airborne MIMO-STAP radar. Motivated by the random matrix theory, the proposed method enhances the CPNCM estimation by noise and clutter sample eigenvalues adjustment (EA). Concretely, the sample eigenvalues of noise are adjusted as noise power, and the ones of clutter are adjusted through minimizing the radar output power. Then, with the sample eigenvectors and adjusted sample eigenvalues, an effective CPNCM is formulated, and EA-MIMO-STAP is implemented reliably. Multiple experiments demonstrate that EA-MIMO-STAP has superior performance and robustness. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

27 pages, 3788 KB

Open AccessArticle

A Study on the Continuous and Discrete Wavelet Transform-Based Lithium-Ion Battery Fire Prediction Sensor Technology

by Wen-Cheng Jin, Chang-Won Kang, Soon-Hyung Lee and Yong-Sung Choi

Sensors 2026, 26(5), 1507; https://doi.org/10.3390/s26051507 (registering DOI) - 27 Feb 2026

Abstract

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs [...] Read more.

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

(This article belongs to the Section Electronic Sensors)

34 pages, 3350 KB

Open AccessArticle

Seconds Matter: Rapid Non-Contact Monitoring of Heart and Respiratory Rate from Face Videos

by Taha Khan, Péter Pál Boda, Annette Björklund and Stefan Malmberg

Sensors 2026, 26(5), 1506; https://doi.org/10.3390/s26051506 (registering DOI) - 27 Feb 2026

Abstract

Accurate, non-contact vital-sign monitoring promises a scalable alternative to conventional sensors, yet low signal quality and long recording times have limited real-life adoption. We present a dual-modality system that combines Eulerian video magnified remote photoplethysmography (rPPG) from facial videos with optical flow-based shoulder [...] Read more.

Accurate, non-contact vital-sign monitoring promises a scalable alternative to conventional sensors, yet low signal quality and long recording times have limited real-life adoption. We present a dual-modality system that combines Eulerian video magnified remote photoplethysmography (rPPG) from facial videos with optical flow-based shoulder tracking to estimate heart rate (HR) and respiratory rate (RR) from ultra-short 15 s recordings. With 200 participants, each providing 2 videos, 387 videos passed strict usability criteria, excluding flicker, blur, occlusion, and low illumination. For 15 s recordings, the HR estimates reached 98.5% accuracy within a ±10 beats per minute tolerance (MAE = 3.25, RMSE = 4.88, r = 0.93; p < 0.05) and the RR estimates achieved 98.4% accuracy within a ±5 respirations per minute tolerance (MAE = 0.69, RMSE = 0.87, r = 0.90; p < 0.05), exceeding prior studies that required 30 to 60 s recording lengths. Computational analysis on a standard home computer confirmed feasibility, with near real-time performance achievable on optimized hardware. By integrating complementary modalities and rigorous video quality control, the system overcomes low-SNR challenges, delivering high-fidelity, clinically validated vital signs monitoring. These results establish a robust, scalable, and precise framework for clinical and home care, demonstrating that accurate, contact-free HR and RR monitoring can now be achieved in seconds, making rapid, real-life vital signs assessment practical and accessible. Full article

(This article belongs to the Special Issue Systems for Contactless Monitoring of Vital Signs)

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25 pages, 24156 KB

Open AccessArticle

MLCANet: Multi-Level Composite Attention-Guided Network for Non-Homogeneous Image Dehazing in Adverse Weather Conditions

by Yongsheng Qiu

Sensors 2026, 26(5), 1505; https://doi.org/10.3390/s26051505 (registering DOI) - 27 Feb 2026

Abstract

Image dehazing is a challenging ill-posed problem in low-level computer vision tasks, requiring the restoration of high-quality, haze-free images from complex and foggy conditions. Deep learning-based dehazing methods struggle to effectively remove non-homogeneous fog distributions due to the uneven and dense nature of [...] Read more.

Image dehazing is a challenging ill-posed problem in low-level computer vision tasks, requiring the restoration of high-quality, haze-free images from complex and foggy conditions. Deep learning-based dehazing methods struggle to effectively remove non-homogeneous fog distributions due to the uneven and dense nature of fog patches, making it difficult to clear real-world fog variations. A key challenge for non-homogeneous image dehazing algorithms is efficiently capturing the spatial distribution of haze in areas with varying fog densities while restoring fine image details. To address these challenges, we propose MLCANet, a multi-level composite attention-guided network for non-homogeneous image dehazing. MLCANet mitigates the impact of uneven haze areas through two main components: the Multi-level Composite Attention Generation Network (MCAGN) and the Dehazed Image Reconstruction Network (DIRN). The MCAGN integrates channel attention (CA), spatial attention (SA), and multi-scale pixel attention (MSPA) to capture haze features at different spatial scales. The DIRN, based on a decoder-encoder architecture, combines multi-scale dilated convolutions and deformable convolutions to restore fine image details more flexibly and efficiently. Extensive qualitative and quantitative experiments, along with ablation studies, demonstrate the effectiveness and feasibility of this method for non-homogeneous image dehazing. Full article

(This article belongs to the Special Issue Image Processing and Visual Recognition for Adverse Weather Sensing and Monitoring)

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20 pages, 5414 KB

Open AccessArticle

Agreement-Based Validation of ISOMETRO for Upper-Limb Isometric Tension Measurements

by José Luis González-Montesinos, Jorge del Rosario Fernández-Santos, David Jiménez-Pavón, Alejandro Sánchez-Delgado, Rubén Aragón-Martín, Juan Manuel Escudier-Vázquez and Vanesa España-Romero

Sensors 2026, 26(5), 1504; https://doi.org/10.3390/s26051504 (registering DOI) - 27 Feb 2026

Abstract

Muscular fitness is a key component of health and athletic performance, and isometric strength is a widely used indicator. This study reports an agreement-based validation of the Isometric Strength Measurement Device (ISOMETRO) for upper-limb isometric tension measurements under controlled laboratory conditions. Twenty-one healthy [...] Read more.

Muscular fitness is a key component of health and athletic performance, and isometric strength is a widely used indicator. This study reports an agreement-based validation of the Isometric Strength Measurement Device (ISOMETRO) for upper-limb isometric tension measurements under controlled laboratory conditions. Twenty-one healthy young amateur rock climbers (11 men and 10 women) performed four upper-limb tensile tests (shoulder adduction at 90°, shoulder adduction at 60°, shoulder extension at 90°, and elbow extension at 90°). Agreement with an independent criterion device was evaluated using a force plate, while a series-connected load cell was used as an internal consistency check of the measurement chain. Linear mixed-effects models showed that ISOMETRO strongly predicted force plate values (β = 0.999, SE = 0.002, p < 0.001), with a marginal R² > 0.99. Bland–Altman analysis indicated negligible bias (−0.08 N) and narrow limits of agreement (−4.97 to 4.81 N), and concordance was excellent (CCC ≥ 0.996). The series-connected load cell comparison also showed near-unity agreement (β = 0.998, SE = 0.003, p < 0.001), supporting internal measurement chain integrity. These findings support excellent agreement between ISOMETRO and force plate measurements for upper-limb tensile isometric testing along the vertical axis in young amateur rock climbers under controlled laboratory conditions. However, given the specific sample characteristics and the strictly vertical laboratory configuration, these results should not be generalized to other populations, joint angles, force directions, or non-laboratory environments without further validation. Further studies are needed to confirm performance in more diverse contexts and to establish reliability for repeated-measurement applications. Full article

(This article belongs to the Special Issue Smart Sensors and Sensing Technologies for Biomedical Engineering)

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24 pages, 5395 KB

Open AccessArticle

An Advanced 3D Model of Vascularized Epithelial Ovarian Cancer in a Tumor-on-a-Chip System Based on Multi-Cell Culture

by Magdalena Flont, Agnieszka Żuchowska, Oliwia Tadko, Joanna Konopka, Paulina Musolf, Agnieszka Gnyszka, Patrycja Baranowska and Elżbieta Jastrzębska

Sensors 2026, 26(5), 1503; https://doi.org/10.3390/s26051503 (registering DOI) - 27 Feb 2026

Abstract

Epithelial ovarian cancer (EOC) is a highly lethal malignancy characterized by significant heterogeneity and poor prognosis due to late-stage diagnosis and chemotherapy resistance. Traditional two-dimensional (2D) models fail to replicate the complexity of the tumor microenvironment (TME), necessitating the development of advanced in [...] Read more.

Epithelial ovarian cancer (EOC) is a highly lethal malignancy characterized by significant heterogeneity and poor prognosis due to late-stage diagnosis and chemotherapy resistance. Traditional two-dimensional (2D) models fail to replicate the complexity of the tumor microenvironment (TME), necessitating the development of advanced in vitro systems. Here, we present a novel microfluidic tumor-on-a-chip (ToC) system that accurately models key features of EOC, including heterogeneity and vascularization. The developed cellular model was evaluated for functionality. It was demonstrated that endothelial cells of blood vessels within a collagen matrix successfully migrated toward the cancerous tissue, while the multicellular and multilayered tumor construct secreted pro-angiogenic factors. Additionally, long-term culture conditions induced inflammatory responses, mimicking in vivo tumor progression. This innovative platform enables precise investigations into EOC biology, angiogenesis, and TME interactions. Furthermore, it holds significant potential for drug screening, assessing therapeutic efficacy, and advancing personalized oncology approaches. Full article

(This article belongs to the Section Biosensors)

21 pages, 4407 KB

Open AccessArticle

An Intelligent Pressurized Thigh Band for Muscular Assistance and Multi-Mode Activity Recognition

by Wenda Wang, Wenbin Jiang, Yang Yu, Wei Dong, Hui Dong, Yongzhuo Gao, Dongmei Wu and Weiqi Lin

Sensors 2026, 26(5), 1502; https://doi.org/10.3390/s26051502 (registering DOI) - 27 Feb 2026

Abstract

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were [...] Read more.

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were conducted to evaluate the effects of different air bladder pressure conditions on metabolic rate and muscle activity. Simultaneously, pneumatic data corresponding to six common activities were collected, and a lightweight deep learning model was developed to enable high-precision motion classification. Finally, the model was deployed to an embedded platform to demonstrate its application potential. Results indicate that appropriate air bladder pressure significantly reduces quadriceps muscle activation and average metabolic cost. Furthermore, the deep learning model achieved 99.17% accuracy in recognizing the six activities and was successfully deployed to the embedded platform. This study validates the effectiveness of the intelligent pressurized thigh band in improving locomotor performance under static pressures and demonstrates the potential of air bladder pressure variations as a proxy indicator for movement intent for future closed-loop control. Full article

(This article belongs to the Special Issue Sensing Technology and Wearables for Physical Activity)

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20 pages, 8164 KB

Open AccessArticle

Targetless LiDAR–Camera Extrinsic Calibration via Class-Agnostic Boundary Mask Alignment and SPSA-Based Optimization

by Han-You Jeong, Woo-Hyuk Son, Dong-Wook Shin, Kyuna Cho, Minwoo Chee and Tae (Tom) Oh

Sensors 2026, 26(5), 1501; https://doi.org/10.3390/s26051501 (registering DOI) - 27 Feb 2026

Abstract

Targetless LiDAR–camera extrinsic calibration remains challenging due to unreliable cross-modal correspondences and sensitivity to initialization. We present a targetless extrinsic calibration framework based on class-agnostic boundary mask alignment in a shared image-plane representation. This scheme first constructs consistent LiDAR–camera mask pairs from image-plane [...] Read more.

Targetless LiDAR–camera extrinsic calibration remains challenging due to unreliable cross-modal correspondences and sensitivity to initialization. We present a targetless extrinsic calibration framework based on class-agnostic boundary mask alignment in a shared image-plane representation. This scheme first constructs consistent LiDAR–camera mask pairs from image-plane depth and intensity projections of LiDAR data and camera images. It then obtains robust initial pose candidates through bounded rotation-only global initialization and refines them using a computationally efficient stochastic gradient approximation to estimate the optimal extrinsic parameters. Experiments on the KITTI benchmark demonstrate a superior accuracy–runtime trade-off compared with a segmentation-based global optimization baseline, while real-world driving tests confirm stable cross-modal alignment under vibration and inter-modal timing jitter. Full article

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19 pages, 4992 KB

Open AccessArticle

Research on Denoising Methods for Laser Doppler Blood Flow Signals Based on Time-Domain Noise Perception and DWT

by Quanxin Sun, Jie Duan, Hui Guo and Aoyan Guo

Sensors 2026, 26(5), 1500; https://doi.org/10.3390/s26051500 (registering DOI) - 27 Feb 2026

Abstract

Addressing the challenges of composite noise (speckle noise, thermal noise, and random pulse interference) and non-stationarity in laser Doppler flow (LDF) signal processing, as well as the technical limitation of traditional threshold methods in balancing noise suppression and signal fidelity, this study proposes [...] Read more.

Addressing the challenges of composite noise (speckle noise, thermal noise, and random pulse interference) and non-stationarity in laser Doppler flow (LDF) signal processing, as well as the technical limitation of traditional threshold methods in balancing noise suppression and signal fidelity, this study proposes an adaptive denoising algorithm integrating temporal noise perception and discrete wavelet transform (DWT). A composite noise model is first established to characterize the interference. The signal undergoes a five-level DWT decomposition, where a local energy detection mechanism distinguishes signal-dominant from noise-dominant regions. An SNR-driven dynamic thresholding strategy is generated by combining inter-layer adaptive allocation with coefficient-level local weighting, followed by processing with an improved smoothing function to effectively suppress reconstruction artifacts. Simulations at a 1 dB input signal-to-noise ratio (SNR) yielded a 15.45 dB output SNR and a 0.05634 root mean square error (RMSE), outperforming traditional wavelet methods and modern benchmarks such as local variance and variational mode decomposition (VMD). Applied to a practical signal from an isolated vascular phantom with an initial SNR of

- 1.04

dB, the algorithm achieved a 13.86 dB output SNR and a 0.00258 RMSE. Results confirm the algorithm’s effectiveness for high-fidelity waveform capture in complex noise environments, offering a robust solution for vascular hemodynamic monitoring Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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12 pages, 1956 KB

Open AccessArticle

Experimental Development of XR Enteral Feeding Function for an Endotracheal Suctioning Training Environment Simulator

by Noriyo Colley, Shunsuke Komizunai, Atsuko Sato, Takanori Ishikawa, Mayumi Kouchiyama, Kazue Fujimoto, Toshiko Nasu, Ryosuke Nishima, Aiko Shiota, Eri Murata, Yumi Matsuda and Shinji Ninomiya

Sensors 2026, 26(5), 1499; https://doi.org/10.3390/s26051499 (registering DOI) - 27 Feb 2026

Abstract

Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding [...] Read more.

Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding simulator (ESTE-TF) that integrates sensor-derived performance metrics and two biological-reaction presentation modalities (projection mapping and tablet display). Methods: In Experiment 1, nursing students completed pre- and post-experience questionnaires assessing perceived learning across seven domains, alongside sensor-based measurements of feeding-start timing, dosing-rate characteristics, and total procedure time. Experiment 2 employed a tablet-based version with four learning items assessed for students and post-experience evaluations collected from registered nurses. Participants also compared the two XR presentation methods. Results: Students demonstrated perceived learning gains of small-to-large magnitude across both experiments (Experiment 1: d = 0.455–0.974; Experiment 2: d = 0.014–0.886), with wide 95% confidence intervals reflecting the exploratory nature of this pilot work. Sensor-derived data showed greater dosing-rate variability and longer procedure times among students than nurses. Participants reported that projection mapping offered a more embodied experience, whereas tablet displays provided clearer visibility. Conclusions: These findings indicate the feasibility and preliminary educational potential of integrating sensing technologies with XR-based biological-reaction presentation for gastrostomy-feeding training. Given the small samples and non-validated measures, results should be interpreted as exploratory. Future research will refine sensor accuracy, establish standardized performance metrics, and evaluate learning outcomes using validated instruments and controlled study designs. Full article

(This article belongs to the Special Issue Transforming Healthcare with Smart Sensing and Machine Learning)

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16 pages, 1329 KB

Open AccessArticle

Quantification of Tongue Motor Dysfunction in Amyotrophic Lateral Sclerosis Using a Smartphone-Based Task and Deep Learning

by Pedro S. Rocha, Duarte Folgado, Vasco A. Conceição, Miguel Oliveira Santos and Mamede de Carvalho

Sensors 2026, 26(5), 1498; https://doi.org/10.3390/s26051498 (registering DOI) - 27 Feb 2026

Abstract

Background: Bulbar dysfunction is a major complication of amyotrophic lateral sclerosis (ALS). This study aimed to develop and validate a simple, smartphone-based task for the objective assessment of tongue movements and to examine their association with clinical variables. Methods: 37 ALS patients and [...] Read more.

Background: Bulbar dysfunction is a major complication of amyotrophic lateral sclerosis (ALS). This study aimed to develop and validate a simple, smartphone-based task for the objective assessment of tongue movements and to examine their association with clinical variables. Methods: 37 ALS patients and 20 age- and sex-matched controls performed a tongue lateralization task, recorded with a smartphone. A deep-learning U-Net++-based model was used for segmentation and feature extraction. The frequency and maximum amplitude of tongue movements were quantified. Clinical measures included the ALS Functional Rating Scale-revised (ALSFRS-r) bulbar sub-scores, tongue fasciculations, jaw jerk, and tongue “spasticity”. Between-group differences and associations between tongue metrics and clinical features were assessed. Results: The U-Net++-based model achieved robust segmentation performance. Patients showed lower tongue movement frequency than controls (0.14 vs. 0.40, t = −9.58, p < 0.001). Normalized frequency was associated with dysarthria (t = −3.13, p = 0.003) but not dysphagia (t = −1.05, p = 0.30). Normalized frequency (t = 2.77, p = 0.009) and tongue “spasticity” (t = −2.57, p = 0.015) were both associated with speech performance in a multiple-regression model (R = 0.51, adjusted R² = 0.43). Conclusions: Our method provides an objective, minimally invasive measure of bulbar function in ALS, which correlates with clinical ratings and may detect subtle impairments not captured by standard assessments. This approach offers a promising tool for remote monitoring and may support more effective disease management. Full article

(This article belongs to the Section Physical Sensors)

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42 pages, 3268 KB

Open AccessArticle

LITO: Lemur-Inspired Task Offloading for Edge–Fog–Cloud Continuum Systems

by Asma Almulifi and Heba Kurdi

Sensors 2026, 26(5), 1497; https://doi.org/10.3390/s26051497 (registering DOI) - 27 Feb 2026

Abstract

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static [...] Read more.

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static heuristics, which lack adaptability to dynamic conditions, or on metaheuristic optimizers, which often incur high computational overhead and centralized coordination. This paper proposes LITO, a lemur-inspired task offloading algorithm for edge, fog, and cloud continuum systems that models the infrastructure as a social system in which computing nodes assume distinct roles that mirror lemur social hierarchies. Building on an abstracted model of lemur group behavior, LITO incorporates two key lemur-inspired mechanisms: an energy-aware task assignment mechanism based on sun basking, a thermoregulation behavior in which lemurs seek favorable warm spots, mapped here to selecting energetically efficient execution nodes, and a cooperative scheduling policy based on huddling, group clustering under stress, mapped here to sharing load among overloaded nodes. These mechanisms are combined with a continual supervised policy-learning layer with contextual bandit feedback that refines offloading decisions from online feedback. The resulting multi-objective formulation jointly minimizes energy consumption and deadline violations while maximizing resource utilization and throughput under high-load conditions in the edge and fog segment of the continuum. Simulations under diverse workload regimes and task complexities show that LITO outperforms representative multi-objective offloading baselines in terms of energy consumption, resource utilization, latency, Service Level Agreement (SLA) violations, and throughput in congested scenarios. Full article

(This article belongs to the Section Internet of Things)

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21 pages, 2612 KB

Open AccessArticle

Modeling the Geometry–Acoustics Dependence in Photoacoustic Resonators: A Toroidal Case Study

by Enza Panzardi, Anna Lo Grasso, Valerio Vignoli and Ada Fort

Sensors 2026, 26(5), 1496; https://doi.org/10.3390/s26051496 - 27 Feb 2026

Abstract

In this work we investigate the behavior of a toroidal photoacoustic resonator to provide compact, physics-guided analytical relationships that link its geometry to two key parameters: resonance frequency and quality factor. Finite-element data are combined with reduced-order analytical models to refine a corrected [...] Read more.

In this work we investigate the behavior of a toroidal photoacoustic resonator to provide compact, physics-guided analytical relationships that link its geometry to two key parameters: resonance frequency and quality factor. Finite-element data are combined with reduced-order analytical models to refine a corrected toroidal-resonance frequency model that accounts for effective propagation length and thermo-viscous effects. For the quality factor, a simple law motivated by a boundary-layer dissipation model is proposed. Derived models are validated by experimental tests performed using three 3D printed toroidal resonators in different sizes. Experimental results confirm the prediction both for the first and third resonance frequencies with an average relative error below 1%, outperforming cylindrical and uncorrected baseline models available in the literature. The results also confirm the predicted trend of the quality factor with respect to the torus’s minor radius, highlighting a direct relationship between the cross-sectional area and acoustic losses, which governs the balance between stored acoustic energy and thermo-viscous dissipation. Overall, the framework provides quick, interpretable design rules that reduce dependence on extensive finite-element method simulation campaigns for first-pass estimation of resonant behavior during the early design phase and guiding the optimization of high-performance PAS devices while preserving accuracy. Full article

(This article belongs to the Special Issue Advancements in Photoacoustic Sensors for Gas Detection and Air Quality Assessment)

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16 pages, 2616 KB

Open AccessArticle

Long-Range Source Localization in the Deep Sea Using Adaptive FDSL with a Few-Element Array

by Jingwen Yin, Haklim Ko and Hojun Lee

Sensors 2026, 26(5), 1495; https://doi.org/10.3390/s26051495 - 27 Feb 2026

Abstract

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying [...] Read more.

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying frequency-difference processing to both the measured field and the replica field. However, conventional FDSL typically relies on large-aperture arrays with numerous elements, resulting in high deployment costs and bulky systems. Furthermore, it exhibits limited resolution and elevated sidelobes. These limitations are exacerbated under reduced element counts and low signal-to-noise ratio (SNR) conditions. To improve performance under low SNR and small-array configurations, this paper proposes the FDSL-MVDR and FDSL-MUSIC methods by deriving adaptive weight vectors based on the frequency-difference covariance structure and redefining the ambiguity surface. Numerical simulations in a deep-sea Munk environment (source range 195 km, depth 1000 m) using a 15-element vertical line array demonstrate that the adaptive FDSL methods outperform conventional FDSL in terms of peak sharpness and sidelobe suppression. FDSL-MUSIC achieves approximately 100% localization success at SNR = −5 dB, a 4 dB improvement over conventional FDSL. Performance analyses under representative environmental mismatches indicate that the adaptive FDSL methods maintain robust localization performance and high-resolution characteristics in complex deep-sea environments. These results validate the feasibility of high-precision deep-sea localization using a few-element array. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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19 pages, 4436 KB

Open AccessArticle

Development of a 3D-Printed Capacitive Sensor for Soil Water Content Estimation Using Nickel-Based Conductive Paint

by Alessandro Comegna, Shawkat B. M. Hassan and Antonio Coppola

Sensors 2026, 26(5), 1494; https://doi.org/10.3390/s26051494 - 27 Feb 2026

Abstract

Understanding hydrological, agricultural, and environmental processes in soils relies on accurately measuring volumetric water content (θ), matric potential (h), and hydraulic conductivity (K). These parameters are fundamental for quantifying plant-available water, optimizing irrigation scheduling in precision agriculture, modeling watershed [...] Read more.

Understanding hydrological, agricultural, and environmental processes in soils relies on accurately measuring volumetric water content (θ), matric potential (h), and hydraulic conductivity (K). These parameters are fundamental for quantifying plant-available water, optimizing irrigation scheduling in precision agriculture, modeling watershed responses, and studying the impacts of climate change in complex ecosystems. Among these parameters, θ is truly indispensable, as it represents the primary indicator of the water status of soils and a prerequisite for interpreting the other hydraulic variables. In recent years, capacitive sensors have become one of the most widely adopted technologies for θ estimation, owing to their favorable balance between accuracy, robustness, and affordability. These sensors infer soil moisture by measuring dielectric permittivity of soils, which is strongly governed by water content, making them particularly suitable for distributed monitoring and IoT-based environmental applications. The present study aimed to develop a low-cost capacitive sensor for θ estimation. This sensor can be made using 3D printing technology combined with conductive, nickel-based paint, which (once applied on the 3D-printed guides) forms the capacitive electrode. The capacitive component operates at an operational frequency of 60 MHz. The system was subjected to a rigorous testing protocol, including calibration and validation phases under laboratory conditions using three soils of different textures. Its performance was specifically compared with the time-domain reflectometry (TDR) technique, which is widely recognized in Soil Physics and Soil Hydrology as the reference method for θ estimation due to its reliability and accuracy. These tests confirmed the effective performance of the proposed sensor, which overall exhibited good reliability within the selected validation range, corresponding to a θ range of 0 to 0.40 cm³/cm³. Full article

(This article belongs to the Section Smart Agriculture)

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17 pages, 4515 KB

Open AccessArticle

Lightweight, Compact, and High-Sensitivity Passive Fourier Transform Infrared Spectroscopy-Based Gas Detection System

by Xiangning Lu, Min Huang, Wenbin Ge, Lulu Qian, Zhanchao Wang, Yan Sun, Jinlin Chen and Wei Han

Sensors 2026, 26(5), 1493; https://doi.org/10.3390/s26051493 - 27 Feb 2026

Abstract

With the intensification of environmental pollution and the increasingly prominent problem of industrial harmful gas emissions, existing mainstream gas detection technologies still have obvious limitations in terms of real-time performance, non-contact capability, detection accuracy, and multi-component identification. To address this demand, this paper [...] Read more.

With the intensification of environmental pollution and the increasingly prominent problem of industrial harmful gas emissions, existing mainstream gas detection technologies still have obvious limitations in terms of real-time performance, non-contact capability, detection accuracy, and multi-component identification. To address this demand, this paper proposes a lightweight and compact gas detection system based on passive Fourier Transform Infrared Spectroscopy (FTIR). The system innovatively integrates an improved parallel pendulum mirror interferometer and a low-noise signal preprocessing module, and simultaneously presents a novel oversampling method fusing equal time, equal optical path difference, and digital filtering, which effectively enhances the operational stability and sampling accuracy of the spectrometer. The system features excellent platform adaptability and can be flexibly mounted on various operation carriers. Combined with a two-dimensional rotating platform and an inertial navigation module, its monitoring range and application scenarios can be further expanded. Indoor sensitivity test results show that the detection limit of the system for sulfur hexafluoride (SF₆) is less than 20 ppm; flight tests under real-world scenarios have successfully achieved accurate detection of SF₆ gas, fully verifying the practical application effectiveness of the system. Based on the comprehensive results of indoor and outdoor tests, the system demonstrates core technical advantages of high sensitivity, strong flexibility, and excellent real-time performance. It is expected to be widely applied in gas monitoring tasks across multiple fields such as industrial safety monitoring, ecological environment monitoring, and transportation support in the future. Full article

(This article belongs to the Section Physical Sensors)

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24 pages, 10647 KB

Open AccessArticle

Spatio-Temporal Feature Fusion for Anti-UAV Detection: Integrating Inter-Frame Dynamics and Appearance

by Yake Zhang, Xiaoxi Fu, Yunfeng Zhou, Xiaojun Guo, Bei Sun, Yinglong Wang and Yongping Zhai

Sensors 2026, 26(5), 1492; https://doi.org/10.3390/s26051492 - 27 Feb 2026

Abstract

In order to improve the detection capability of low-slow-small UAV targets in complex backgrounds, this paper introduces a novel method that combines spatio-temporal information, which includes (1) an improved YOLO detector for small UAV detection, (2) a motion target detection module, and (3) [...] Read more.

In order to improve the detection capability of low-slow-small UAV targets in complex backgrounds, this paper introduces a novel method that combines spatio-temporal information, which includes (1) an improved YOLO detector for small UAV detection, (2) a motion target detection module, and (3) an integrated combination strategy for static and dynamic judgment. We firstly provided an improved YOLOv11 static detection method by combining SPD Conv, BiFPN and a detect header for high-resolution layers, and then designed a dynamic target-detection algorithm which helps the YOLO method capture minor movement features, finally introducing a fusing strategy of static detection and dynamic judgment. The experimental results on small UAV datasets, including various sky, mountain and building backgrounds, have shown that the proposed approach increases Precision, Recall, and mAP50 by 12.1%, 29.5%, and 29.6%, respectively, compared with the baseline YOLO11 detector. The proposed MSM-YOLO achieves Precision, Recall, and mAP50 of 94%, 92%, and 86.3%, enabling the effective detection of small UAV targets in complex scenarios. Moreover, the ablation experiments also proved the effectiveness of each module. The proposed method was further deployed in a redesigned RK3588 embedded system, achieving 100 fps after optimized process, and it has shown effectiveness and practicality in further air-to-air UAV detection applications. Full article

(This article belongs to the Section Sensors and Robotics)

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16 pages, 3246 KB

Open AccessArticle

Chemical Heterogeneity Assessment of Authentic Edible Bird’s Nests Using Multimodal FTIR Spectroscopy: A Foundation for Future Authentication Strategies

by Dung Manh Ho, Agnieszka M. Banas, Krzysztof Banas, Utkarsh Mali and Mark B. H. Breese

Sensors 2026, 26(5), 1491; https://doi.org/10.3390/s26051491 - 27 Feb 2026

Abstract

Edible Bird’s Nest (EBN) is a highly prized food product, making it a frequent target for economic adulteration. Consequently, robust quality assurance is paramount to protect consumers and ensure market integrity. A significant barrier to effective quality control, however, is an incomplete understanding [...] Read more.

Edible Bird’s Nest (EBN) is a highly prized food product, making it a frequent target for economic adulteration. Consequently, robust quality assurance is paramount to protect consumers and ensure market integrity. A significant barrier to effective quality control, however, is an incomplete understanding of the natural chemical variability within authentic EBN. This variability, influenced by factors such as geographical origin, bird species, and post-harvest processing, can confound analytical measurements and complicate the definition of a standard reference. This study provides an existence proof in a defined cohort, characterizing microscale chemical heterogeneity in authentic A. fuciphagus EBN. We employed a multi-modal Fourier Transform Infrared (FTIR) spectroscopy approach, integrating transmission, macro-attenuated total reflectance (ATR), and high-resolution micro-ATR chemical imaging. A diverse set of validated, authentic EBN samples was analyzed using unsupervised Principal Component Analysis (PCA) to explore the data structure. Our results reveal significant and previously unquantified spectral heterogeneity, particularly in protein and glycoprotein-related regions. In our cohort, the chemical signatures of authentic EBN do not collapse to a single, uniform profile but span a broad, multi-dimensional continuum. This inherent variability presents a critical challenge for conventional quality control methods that rely on simplistic, single-spectrum standards, which may lead to the misclassification of genuine products. By establishing a robust chemical baseline for the authentic class, this work provides the foundational data essential for developing next-generation authentication models capable of reliably distinguishing this natural variance from deliberate adulteration. Full article

(This article belongs to the Special Issue Recent Advances in Infrared Spectroscopy and Imaging as Sensing Techniques)

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28 pages, 12993 KB

Open AccessArticle

The 12 November 2025 Ugly Duckling Geomagnetic Storm: From the Sun to the Earth

by Yury Yasyukevich, Ekaterina Danilchuk, Aleksandr Beletsky, Egor Borvenko, Aleksandr Chernyshov, Victor Fainshtein, Vera Ivanova, Denis Khabituev, Marina Kravtsova, Alexey Oinats, Sergey Olemskoy, Artem Padokhin, Konstantin Ratovsky, Valery Sdobnov, Artem Vesnin, Anna Yasyukevich and Sergey Yazev

Sensors 2026, 26(5), 1490; https://doi.org/10.3390/s26051490 - 27 Feb 2026

Abstract

The 12 November 2025 G4 geomagnetic storm—the third most intense of solar cycle 25—was triggered by a complex shock-ICME (interplanetary coronal mass ejection) structure as a result of three ICMEs and driven shocks that arrived on 11–12 November. The main enhancement in the [...] Read more.

The 12 November 2025 G4 geomagnetic storm—the third most intense of solar cycle 25—was triggered by a complex shock-ICME (interplanetary coronal mass ejection) structure as a result of three ICMEs and driven shocks that arrived on 11–12 November. The main enhancement in the interplanetary magnetic field occurred in the sheath region behind the shock driven by the second ICME. The Dst index reached −217 nT (the SYM-H index reached −254 nT) and the maximum Kp index was 9-. To comprehensively analyze the causes of the storm and its complex effects on near-Earth space, we used a multi-instrumental data set, involving data from satellite missions (ACE, SDO, PROBA2), GNSS networks, ionosondes, optical instruments, high-frequency radars (SuperDARN-like), and cosmic ray monitors. The auroral oval expanded equatorward (down to ~35° N in America). We recorded a super equatorial plasma bubble that almost reached the auroral oval boundary. The equatorial anomaly crests intensified, exceeding 175 TECU, and shifted poleward (8–10°). At mid-latitudes, the F2 layer critical frequency exhibited a strong negative disturbance (−50%) during the main phase, followed by an unusually prolonged and intense positive phase (+100%). GPS Precise Point Positioning errors increased to 2–3 m at high latitudes and in regions affected by the equatorial bubble. The event also featured a Forbush decrease and ground-level enhancement (GLE 77 according to the database hosted by the University of Oulu) associated with the X5.1 solar flare. The results underscore the complex chain of processes from solar storm to geomagnetic and ionospheric responses, highlighting the risks to satellite-based navigation and communication systems. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Space Electromagnetic Environments)

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20 pages, 629 KB

Open AccessArticle

A Hybrid Approach to Universal Intrusion Detection Systems for Automotive Security

by Md Rezanur Islam, Mahdi Sahlabadi, Munkhdelgerekh Batzorig and Kangbin Yim

Sensors 2026, 26(5), 1489; https://doi.org/10.3390/s26051489 - 27 Feb 2026

Abstract

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all intrusion detection system (IDS) is challenging because each vehicle has a unique data profile. This is due to the complex and dynamic nature of the data [...] Read more.

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all intrusion detection system (IDS) is challenging because each vehicle has a unique data profile. This is due to the complex and dynamic nature of the data generated by vehicles regarding their model, driving style, test environment, and firmware update. To address this issue, a universal IDS has been developed that can be applied to all types of vehicles without the need for customization. Unlike conventional IDSs, the universal IDS can adapt to data distribution shifts caused by changes in driving style, vehicle platform, or firmware updates. In this study, a new hybrid approach has been developed, combining Pearson correlation with deep learning techniques. This approach has been tested using data obtained from four distinct mechanical and electronic vehicles, including Tesla, Sonata, and two Kia models. The data has been combined into two frequency datasets, and wavelet transformation has been employed to convert them into the frequency domain, enhancing generalizability. Additionally, a statistical method based on independent rule-based systems using Pearson correlation has been utilized to improve system performance. The system has been compared with eight different IDSs, three of which utilize the universal approach, while the remaining five are based on conventional techniques. The accuracy of each system has been evaluated through benchmarking, and the results demonstrate that the hybrid system effectively detects intrusions in various vehicle models. Full article

(This article belongs to the Special Issue Security and Privacy in Connected and Autonomous Vehicles)

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14 pages, 3021 KB

Open AccessArticle

Development and Validation of a Digitizer-Based TCSPC System for Scintillation Decay Time Analysis via an Extended Convolution Model

by Qianqian Zhou, Zhijie Yang, Wenhui Li, Juncheng Liang and Wuyun Xiao

Sensors 2026, 26(5), 1488; https://doi.org/10.3390/s26051488 - 27 Feb 2026

Abstract

The development of high-fidelity digital twins for scintillation spectrometer detectors demands precise experimental characterization of timing parameters. This work presents a comprehensive solution comprising a digitizer-based time-correlated single-photon counting (TCSPC) system and an extended convolution model for decay time analysis. We introduce a [...] Read more.

The development of high-fidelity digital twins for scintillation spectrometer detectors demands precise experimental characterization of timing parameters. This work presents a comprehensive solution comprising a digitizer-based time-correlated single-photon counting (TCSPC) system and an extended convolution model for decay time analysis. We introduce a physics-driven calibration principle, validating the system response against an independent physical benchmark to ensure fidelity. The proposed convolution model advances beyond the conventional model by incorporating additional parameters to account for scintillator-induced timing broadening and delay, thereby decoupling this effect from instrumental response. The model’s descriptive power was statistically validated through its application to fast scintillators, while its physical accuracy was robustly confirmed through the precise extraction of typical decay times from slow scintillators. This methodology establishes a reliable workflow from measurement to parameterization, directly supplying the decoupled inputs required for the digital twins of scintillation detectors. Full article

(This article belongs to the Section Optical Sensors)

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20 pages, 4751 KB

Open AccessArticle

Improving ORB-SLAM3 Accuracy in Dynamic Scenes with YOLO11 Segmentation

by Renata Raffaine Villegas, Anselmo Rafael Cukla, Gabriel Alejandro Tarnowski, Guillermo Mudry, Sergio Omar Lapczuk, Ely Carneiro de Paiva and Daniel Fernando Tello Gamarra

Sensors 2026, 26(5), 1487; https://doi.org/10.3390/s26051487 - 27 Feb 2026

Abstract

Traditional Visual SLAM systems, like ORB-SLAM3, often lose accuracy in dynamic environments. This work presents YOLO11-ORB-SLAM3, an enhancement to ORB-SLAM3 for dynamic scenarios, which integrates a YOLO11-based instance segmentation module to detect and exclude dynamic features from the tracking process. The system is [...] Read more.

Traditional Visual SLAM systems, like ORB-SLAM3, often lose accuracy in dynamic environments. This work presents YOLO11-ORB-SLAM3, an enhancement to ORB-SLAM3 for dynamic scenarios, which integrates a YOLO11-based instance segmentation module to detect and exclude dynamic features from the tracking process. The system is designed to work with stereo and RGB-D cameras, and its performance was evaluated on challenging dynamic sequences of the public TUM RGB-D dataset, and also through real-world experiments on a mobile robot using a stereo camera to highlight its robustness and viability for real robotic applications. Experimental results demonstrate that the proposed system outperforms the original ORB-SLAM3, reducing the error by 93% in the public TUM dataset while preserving computational efficiency. Full article

(This article belongs to the Topic Navigation and Positioning System: Opportunities and Obstacles)

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17 pages, 876 KB

Open AccessArticle

Transformer-Enhanced Localization via Adaptive PDP Representation Under Dynamic Bandwidths

by Lei Cao, Tianqi Xiang, Weiyan Chen, Yicheng Wang, Yuehong Gao and Xin Zhang

Sensors 2026, 26(5), 1486; https://doi.org/10.3390/s26051486 - 27 Feb 2026

Abstract

Accurate wireless positioning has remained challenging under dynamic bandwidth conditions and outdoor multipath environments that are typical in Internet of Things (IoT) and autonomous aerial vehicle (AAV) applications. Conventional learning-based localization methods rely on bandwidth-specific channel state information (CSI) representations, which causes the [...] Read more.

Accurate wireless positioning has remained challenging under dynamic bandwidth conditions and outdoor multipath environments that are typical in Internet of Things (IoT) and autonomous aerial vehicle (AAV) applications. Conventional learning-based localization methods rely on bandwidth-specific channel state information (CSI) representations, which causes the trained models to be inapplicable or less adaptive when the signal bandwidth differs from that used during training. To overcome this limitation, a unified and neural network-oriented framework is proposed, which constructs bandwidth-adaptive power delay profile (PDP) representations for learning-based models. A PDP preprocessing scheme through adaptive zero-padding and oversampled IFFT of heterogeneous CSI is introduced to generate dimension-consistent and delay-aligned neural network inputs. To enhance robustness, a sub-band-sliced PDP representation is developed to enhance model robustness, where each bandwidth is divided into equal-width sub-bands whose PDPs are independently processed and organized as Transformer tokens. A dedicated Transformer is designed to get the location estimation from PDPs of multi-access points. Simulation results have demonstrated that the proposed preprocessing-PDP-plus-Transformer framework achieves superior cross-bandwidth generalization and localization accuracy, compared to analytical and learning-based baselines. Full article

(This article belongs to the Special Issue Channel Characterization and Modeling for Future Wireless Communication Systems)

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19 pages, 1999 KB

Open AccessArticle

A Small-Sample Fault Diagnosis Method for High-Voltage Circuit Breaker Spring Mechanisms Based on Multi-Source Feature Fusion and Stacking Ensemble Learning

by Xining Li, Hanyan Xiao, Ke Zhao, Lei Sun, Tianxin Zhuang, Haoyan Zhang and Hongwei Mei

Sensors 2026, 26(5), 1485; https://doi.org/10.3390/s26051485 - 26 Feb 2026

Abstract

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking [...] Read more.

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Corrosion Monitoring)

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