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Enhanced Reaction Time Measurement System Based on 3D Accelerometer in Athletics
Modular IoT Sensing and Data Pipeline for Digital Twin–Enabled Real-Time Aircraft Structural Health Monitoring
Flexible Sensor Foil Based on Polymer Optical Waveguide for Haptic Assessment
A Codesign Framework for the Development of Next Generation Wearable Computing Systems
Comparative Analysis of the Accuracy and Robustness of the Leap Motion Controller 2

Journal Description

Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors, published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB), International Society for the Measurement of Physical Behaviour (ISMPB), Chinese Society of Micro-Nano Technology (CSMNT) and more are affiliated with Sensors and their members receive discounts on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, Targets, AI Sensors and IJMD.
Journal Cluster of Instruments and Instrumentation: Actuators, AI Sensors, Instruments, Metrology, Micromachines and Sensors.

Impact Factor: 3.5 (2024); 5-Year Impact Factor: 3.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

13 pages, 1260 KB

Open AccessArticle

An Exploratory Analysis of Postural Control in People with Type 2 Diabetes Mellitus Using a Smartphone IMU Sensor

by Trine Rolighed Thomsen, Sophia Pölhöšová, Asger Ahlmann Bech, Aksayan Arunanthy Mahalingasivam, Nicklas Højgaard-Hessellund Rasmussen and Anderson Souza Oliveira

Sensors 2026, 26(9), 2899; https://doi.org/10.3390/s26092899 (registering DOI) - 6 May 2026

Background: There is a growing need for highly accessible and simplified methods to track postural control in adults affected by neurodegenerative diseases. Therefore, the aim of this study was to assess the validity of smartphone-derived postural control analyses compared with traditional center-of-pressure (COP) [...] Read more.

Background: There is a growing need for highly accessible and simplified methods to track postural control in adults affected by neurodegenerative diseases. Therefore, the aim of this study was to assess the validity of smartphone-derived postural control analyses compared with traditional center-of-pressure (COP) measures in healthy adults and people with type 2 diabetes mellitus (T2DM). Methods: A total of 36 participants (21 controls, 15 T2DM) completed static postural testing during single- and double-leg stance, also with eyes open and eyes closed. Data from a smartphone attached to the lower back measured trunk acceleration (SP-ACC) concurrently with gold-standard center of pressure (COP). The root mean square (RMS) and movement velocity (MV) were extracted from both trunk acceleration and COP data. The effect of balance condition and groups were statistically evaluated using non-parametric statistical tests. Results: SP-ACC and COP metrics showed progressive sway increases with task difficulty in both groups (all p < 0.001). RMS-ACC demonstrated moderate-to-strong correlations with RMS-COP across conditions (r = 0.55–0.90). Compared with controls, the T2DM group exhibited significantly higher RMS-ACC in DLS-EC and SLS-EO (both p < 0.01) and higher MV-ACC in DLS-EO, SLS-EO, and SLS-EC (p = 0.04–<0.001), reflecting impaired postural control. Conclusions: Smartphone-based IMU assessments showed good agreement with COP analysis and detected condition-specific balance deficits in T2DM. These findings support smartphone-based IMU metrics as a promising tool for accessible and scalable balance screening in diabetes care. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion and Health Monitoring)

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25 pages, 5684 KB

Open AccessArticle

Wavelet-Based Health Monitoring Approach for Train Door Actuation Using Motor Current Analysis

by Yaojung Shiao, Premkumar Gadde and Manichandra Bollepelly

Sensors 2026, 26(9), 2898; https://doi.org/10.3390/s26092898 (registering DOI) - 6 May 2026

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity [...] Read more.

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article

(This article belongs to the Special Issue Intelligent Automatic Control Systems)

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25 pages, 5542 KB

Open AccessArticle

A General Finite Beam on Tensionless Foundation Model for Rail Track Characterization and Evaluation

by Hamoud H. Alshallaqi and Brett A. Story

Sensors 2026, 26(9), 2897; https://doi.org/10.3390/s26092897 (registering DOI) - 5 May 2026

Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail [...] Read more.

Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail–ballast–subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment–curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)

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13 pages, 903 KB

Open AccessArticle

Fingerprint Recognition Based on Molecular-Scale Conductance Response via Electrochemically Gated Quantum Tunnelling

by Zifan Wang, Long Yi, Ga Zhang, Xufei Ma, Ye Tian, Bintian Zhang, Xu Liu and Longhua Tang

Sensors 2026, 26(9), 2896; https://doi.org/10.3390/s26092896 (registering DOI) - 5 May 2026

Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To [...] Read more.

Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To address this, we propose an electrochemical-gate-controlled nanoscale tunnelling strategy that expands the two-electrode system to a three-electrode configuration via a tunable gate potential, enabling the differentiation of distinct molecules at near-single-molecule sensitivity. Scanning the gate potential under constant tunnelling bias modulates the alignment between molecular orbitals and the electrode Fermi level, altering the statistical characteristics of molecular tunnelling transport. Experimental results show that target molecules induce a bimodal distribution of tunnelling current (background and molecule-correlated channels), with the second peak exhibiting distinct gate potential dependence. Comparative analysis of ascorbic acid (AA), acetylcholine (ACh), and uric acid (UA) reveals unique trajectories of characteristic peaks with gate potential, forming an electrochemical gate response fingerprint. This gate-dependent conductance trajectory provides a novel statistical dimension for molecular recognition, enabling differentiation of distinct molecules. Full article

(This article belongs to the Special Issue Feature Papers in Electronic Sensors 2026)

30 pages, 29707 KB

Open AccessArticle

Physics-Enhanced Orthogonal Sensing for Self-Supervised Anomaly Detection in Rolling Mills

by Yifan Wang, Bin Zheng, Yehan Feng and Xiong Chen

Sensors 2026, 26(9), 2895; https://doi.org/10.3390/s26092895 - 5 May 2026

The rolling mill guiding system is a key component that affects the quality of steel products. However, due to the harsh on-site environment, there is usually a lack of effective online monitoring and early warning mechanisms. Moreover, in industrial environments, fault samples are [...] Read more.

The rolling mill guiding system is a key component that affects the quality of steel products. However, due to the harsh on-site environment, there is usually a lack of effective online monitoring and early warning mechanisms. Moreover, in industrial environments, fault samples are very scarce, making supervised artificial intelligence methods difficult to apply. This paper proposes a “physics-enhanced” orthogonal-sensing cyber-physical architecture that integrates hardware and software design. At the hardware level, an embedded orthogonal sensing layout (

P ⊥ V

) is designed to decouple drive-chain vibration from rolling-force fluctuations at the transducer level. At the algorithm level, the state monitoring of the guiding system is formulated as a self-supervised anomaly detection problem, and a two-branch network architecture is designed: one branch uses the CSD transformer to capture physical coupling characteristics, while the other branch uses VQ-VAE to extract operating-condition context. Experimental results on a dataset comprising real operational data and expert-validated synthetic fault scenarios show that the system achieves an AUC-ROC of 0.952 and a false alarm rate of 0.048 under a 95% TPR, with an end-to-end processing latency of approximately 8 ms per window and a system-level fault response time of approximately 108 ms, and thus meets the requirements of real-time industrial monitoring. Full article

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

16 pages, 3496 KB

Open AccessArticle

A Four-Wavelength Flow-Through Fluorescence–Scatterometric Sensor That Allows for Real-Time Determination of Fat and Protein Content in Milk–Air Mixtures with High Accuracy

by Maxim E. Astashev, Dmitry N. Ignatenko, Elena A. Molkova, Ivan M. Gogolev, Andrey V. Onegov, Sergey Y. Smolentsev, Artem R. Khakimov, Semen S. Ruzin, Dmitry A. Budnikov, Dmitriy Yu. Pavkin and Sergey V. Gudkov

Sensors 2026, 26(9), 2894; https://doi.org/10.3390/s26092894 - 5 May 2026

(1) Background: Currently, there is a problem of prompt determination of fat and protein content in the milk–air mixture of milking machines. (2) Methods: A design of a sensor prototype is proposed, combining measurements of light scattering (scatterometry) and fluorescence (fluorometry) to determine [...] Read more.

(1) Background: Currently, there is a problem of prompt determination of fat and protein content in the milk–air mixture of milking machines. (2) Methods: A design of a sensor prototype is proposed, combining measurements of light scattering (scatterometry) and fluorescence (fluorometry) to determine the component composition of the milk–air mixture formed during milking. (3) Results: An optical and electronic circuit of a flow sensor has been developed, using four sources of optical radiation: blue, green and red semiconductor lasers (light scattering in milk) and a UV LED (milk fluorescence), as well as an axial photodiode array for recording the light scattering indicatrix and the fluorescence intensity of the milk–air mixture. The use of three laser sources in the scatterometric circuit allows for the determination of the fat content in milk with an error of 0.05%, which is better than all currently known analogs. The developed sensor enables the detection of counterfeit milk containing palm oil instead of milk fat. It operates reliably in a temperature range of 5–35 °C and at milk flow rates of up to 100 mL/sec. (4) Conclusions: The sensor is capable of transmitting real-time data on the fat and protein content of milk to an RS-232 serial port, enabling integration into milking robots and automated milking systems. Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

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38 pages, 4934 KB

Open AccessArticle

Automated Ergonomic Risk Assessment of Wheelchair Users During Cabinet Interaction Using Vision-Based 3D Pose Estimation

by Yilin Xu, Ziqian Yang, Tao Sun and Jiachuan Ning

Sensors 2026, 26(9), 2893; https://doi.org/10.3390/s26092893 - 5 May 2026

Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated [...] Read more.

Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated ergonomic risk assessment of wheelchair users during cabinet interaction. The proposed framework integrates YOLOv11 for human detection, MHFormer for monocular 3D pose reconstruction, and a fuzzy logic-enhanced RULA model for continuous ergonomic risk quantification from video-derived motion signals. To support model development and evaluation, we constructed a dedicated wheelchair cabinet-operation dataset comprising 30 participants, including 14 experienced wheelchair users and 16 trained simulation participants, across five representative cabinet-operation scenarios. The raw dataset contained approximately 5 h of RGB video and about 150,000 original frames. To reduce redundancy caused by highly similar consecutive frames and to mitigate overfitting risk, representative frames were sampled from the continuous video sequences, resulting in 10,000 images for annotation and model development. Based on the proposed framework, raw visual sensor signals are transformed into temporally continuous kinematic representations and ergonomic risk scores, enabling non-contact and real-time health-state interpretation in assistive living environments. The proposed method achieved an average joint-angle estimation RMSE of 7.5°, representing an approximately 60% reduction compared with a Kinect v2-based motion capture baseline (18.6°), which is widely used for low-cost ergonomic evaluation. In benchmark evaluation, the proposed method achieved 84% risk-classification accuracy with a Cohen’s kappa of 0.66, outperforming representative baseline approaches. The results further indicated that low revolving-door and low-drawer operations were associated with higher and more sustained ergonomic risk exposure than sliding-door interaction. These findings demonstrate that vision-based sensor signal analysis can provide an effective solution for intelligent health management, ergonomic monitoring, and perception-driven assessment in accessible and assistive autonomous living systems. Full article

(This article belongs to the Special Issue Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems)

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19 pages, 9156 KB

Open AccessArticle

Standalone RFID Access Control System with Data-Integrity Verification Capabilities

by Valentin Popa, Adrian I. Petrariu, Partemie M. Mutescu, Alexandru A. Maftei and Alexandru Lavric

Sensors 2026, 26(9), 2892; https://doi.org/10.3390/s26092892 - 5 May 2026

Today, access control systems are used in almost every institution and building. This is because they are an effective solution that provides a high level of security. There are many commercially available systems that provide security-related access features for buildings, including biometric options. [...] Read more.

Today, access control systems are used in almost every institution and building. This is because they are an effective solution that provides a high level of security. There are many commercially available systems that provide security-related access features for buildings, including biometric options. Most use a centralized architecture, where each building can be remotely controlled via an Internet connection. This paper presents a completely different system from those on the market, a decentralized system with clone-detection and data-integrity verification mechanisms that allows access to buildings. The overall architecture includes hardware encoding of the access system’s location, and access is granted based on information written to the RFID card by the card-issuing center. This allows the system to be easily reconfigured at the hardware level prior to installation in the access area. The proposed system uses a confidential RFID card data integrity algorithm that uses the card data and immutable UID to determine a checksum in order to validate the RFID card data. As a result, any unwanted modification of at least one bit invalidates the card and blocks access to the building. The system was implemented, validated, and extensively tested over a one-year period with no reported operational issues. Full article

(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)

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24 pages, 24803 KB

Open AccessArticle

A Real-Time SDR-Based Vehicular Scatterometer with Multi-Subband Coherent Synthesis

by Shijie Yang, Wei Guo, Caiyun Wang, Peng Liu, Te Wang, Zhenzhen Liang, Qing Xing, Xingming Zheng and Bingze Li

Sensors 2026, 26(9), 2891; https://doi.org/10.3390/s26092891 - 5 May 2026

Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order [...] Read more.

Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order of hundreds of MHz for fine range resolution, especially for systems requiring real-time onboard processing. To address this problem, this paper presents a vehicular, fully polarimetric, SDR-based scatterometer that achieves an equivalent wideband response by sequentially transmitting adjacent narrow subbands and coherently synthesizing them onboard. To enable real-time operation on a resource-limited field-programmable gate array/system-on-chip (FPGA/SoC) platform, we adopt a frequency-domain synthesis-pulse-compression pipeline that avoids interpolation and eliminates repeated matched filtering across subbands. A slot-based online phase calibration is performed within the settling window after each fast lock to estimate and compensate random local oscillator (LO) phase offsets, preserving coherent stitching. In addition, pulse repetition within each subband and coherent accumulation are integrated to improve the signal-to-noise ratio (SNR) under real-time throughput constraints. A Zynq-based implementation demonstrates deterministic onboard range-profile output, with a minimum processing latency of about 1.57 ms per frame. Loopback and outdoor experiments validate the equivalent 200 MHz bandwidth (five 40 MHz subbands), achieving approximately 0.75 m resolution and yielding sidelobe metrics consistent with the designed windowing, including a peak sidelobe ratio (PSLR) of −27.43 dB and an integrated sidelobe ratio (ISLR) of −12.38 dB. Field scans over farmland further show consistent

σ_{0}

trends across incidence angle and azimuth, indicating reliable onboard quantitative backscattering measurement. These results demonstrate that the proposed method provides a feasible solution for deterministic real-time equivalent wideband scatterometry on a low-cost SDR platform. Full article

(This article belongs to the Section Remote Sensors)

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27 pages, 3261 KB

Open AccessArticle

Adaptive Dual Reinforcement Learning for Hybrid Spatial–Temporal Networks in RIS-Assisted Indoor Localization (ADRL-HSTNet)

by Mostafa Mohamed, Ahmed Radi and Shady Zahran

Sensors 2026, 26(9), 2890; https://doi.org/10.3390/s26092890 - 5 May 2026

Reconfigurable intelligent surface sensors (RISs) have emerged as a promising technology for enhancing wireless indoor localization by intelligently controlling signal propagation; however, extracting reliable localization fingerprints from RIS-assisted signals remains challenging due to multipath fading, environmental noise, and nonlinear spatial–temporal channel dynamics. To [...] Read more.

Reconfigurable intelligent surface sensors (RISs) have emerged as a promising technology for enhancing wireless indoor localization by intelligently controlling signal propagation; however, extracting reliable localization fingerprints from RIS-assisted signals remains challenging due to multipath fading, environmental noise, and nonlinear spatial–temporal channel dynamics. To address this, we propose an Adaptive Dual-Reinforcement Learning-Hybrid Spatial–Temporal Network (ADRL-HSTNet) for RIS-assisted indoor localization. The framework utilizes dual-channel RSSI and phase measurements, followed by noise filtering, normalization, and sliding-window segmentation prior to feature extraction. It then constructs enhanced representations through handcrafted feature extraction and multi-branch processing, including patch-based features, wavelet-domain representations, statistical descriptors, and multi-level segmentation masks. These heterogeneous inputs are encoded using lightweight transformer-based encoders to capture multiscale dependencies. A first reinforcement learning selector adaptively weights the most informative feature branches to produce a fused representation, which is further processed by spatial and temporal transformer modules. Their outputs are adaptively combined via a second reinforcement learning selector to obtain robust localization embedding. The model jointly performs classification, coordinate regression, and uncertainty estimation end-to-end. Experimental results across multiple RIS configurations outperformed the KAN, LSTM-KAN, and RHL-Net (compared against the proposed ADRL-HSTNet) baselines, achieving accuracies of 83.33%, 75.22%, 93.33%, and 88.89%, confirming the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue New Technologies in Wireless Communication System)

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37 pages, 9961 KB

Open AccessArticle

SETJiP: Spatial and Extra Temporal Jigsaw Puzzles for Video Anomaly Detection

by Liheng Shen, Tetsu Matsukawa and Einoshin Suzuki

Sensors 2026, 26(9), 2889; https://doi.org/10.3390/s26092889 - 5 May 2026

Video Anomaly Detection (VAD) is commonly formulated as a one-class classification task. Global motion, with temporal variations across most pixels within an object-centric region, e.g., walking, is typically regular, whereas localized motion, e.g., waving, can be ambiguous. Decoupled spatial and temporal jigsaw puzzles [...] Read more.

Video Anomaly Detection (VAD) is commonly formulated as a one-class classification task. Global motion, with temporal variations across most pixels within an object-centric region, e.g., walking, is typically regular, whereas localized motion, e.g., waving, can be ambiguous. Decoupled spatial and temporal jigsaw puzzles (DSTJiP) is a self-supervised method that learns discriminative representations by predicting the original order of spatially and temporally shuffled patches. However, DSTJiP’s uniform sampling and equal weighting do not assign stronger supervision to global-motion examples within the temporal objective. Consequently, the temporal supervision allocated to global-motion examples may become insufficient across training-data regimes with varying proportions of these examples, deteriorating VAD performance. Nevertheless, excessively strengthening such supervision also degrades performance. To address these issues, we propose spatial and extra temporal jigsaw puzzles (SETJiP) with two RGB-only training schemes that provide stronger and more conservative temporal supervision for global-motion examples, respectively. One scheme strengthens temporal supervision on these examples via additional temporal jigsaw puzzles. The other does so more conservatively by upweighting their temporal jigsaw puzzles. Experiments on four VAD benchmarks show that both schemes improve on DSTJiP and remain highly competitive with state-of-the-art methods. Full article

(This article belongs to the Section Sensing and Imaging)

26 pages, 680 KB

Open AccessArticle

Evaluating the Adversarial Robustness and Clinical Safety of Quantized Hierarchical Transformers for Edge-Based Malaria Microscopy

by Umar Hasan, Turki G. Alghamdi and Muhammad Ali Nayeem

Sensors 2026, 26(9), 2888; https://doi.org/10.3390/s26092888 - 5 May 2026

Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its [...] Read more.

Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its impact on clinical safety and security remains unexplored. This study presents an adversarial audit of quantized Vision Transformers for medical edge deployment. We evaluated a Swin-Tiny transformer against ViT-Tiny and MobileNetV3 baselines using a 27,558-image malaria dataset and an out-of-distribution (OOD) White Blood Cell dataset. Our findings redefine the “Quantization Shield” hypothesis. PTQ compresses the Swin model by 3.9× (to 27.89 MB) with a negligible 0.11% accuracy drop, maintaining statistical reliability on OOD tests. However, the hypothesized architectural resilience shatters under white-box Projected Gradient Descent (PGD) attacks. Despite robustness against single-step attacks, both MobileNetV3 and the INT8 Swin-Tiny collapse to 0.00% accuracy under iterative PGD. Conversely, the quantized Swin-Tiny resists black-box transfer attacks from a surrogate, maintaining 81.00% accuracy. We conclude that while quantized Vision Transformers meet mobile sensor constraints, integer quantization provides zero innate defense against targeted iterative perturbations, exposing a critical vulnerability in diagnostic IoT networks. Full article

(This article belongs to the Special Issue AI-Enabled Sensing Technology for Smart Healthcare and Precision Diagnosis)

13 pages, 7788 KB

Open AccessArticle

Precision Gas Sensing Interface Circuit with Digital Potentiometer-Based Dynamic Gain Control

by Soon-Kyu Kwon and Hyeon-June Kim

Sensors 2026, 26(9), 2887; https://doi.org/10.3390/s26092887 - 5 May 2026

This paper proposes a digital potentiometer-based adaptive gas sensor interface for stable detection without signal saturation under extreme environmental fluctuations. Conventional fixed-gain circuits often suffer from limited dynamic range, leading to data loss when severe baseline drifts exceed ADC input limits. To address [...] Read more.

This paper proposes a digital potentiometer-based adaptive gas sensor interface for stable detection without signal saturation under extreme environmental fluctuations. Conventional fixed-gain circuits often suffer from limited dynamic range, leading to data loss when severe baseline drifts exceed ADC input limits. To address this, we developed a real-time control algorithm that actively adjusts attenuator and amplifier gains, maintaining the ADC input voltage (V_ADC) near the common-mode voltage (V_CM). Experimental results demonstrate that the interface remains stable even when the buffer voltage reaches 2.75 V, significantly surpassing the 1.2 V ADC limit. Sensor resistance data, reconstructed by inversely calculating updated circuit parameters, achieved high accuracy with a Mean Absolute Percentage Error (MAPE) of 1.628% and a maximum relative error under 4.8%. Consequently, this study proves that logically extending the physically limited ADC dynamic range enables high-precision gas sensing in diverse environments without requiring high-performance computing devices. This approach provides a cost-effective and robust solution for compact IoT-based gas monitoring systems. Full article

(This article belongs to the Special Issue Two-Dimensional (2D) Metal Oxide-Enabled Electrochemical and Gas Sensors)

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16 pages, 4335 KB

Open AccessArticle

A Scene Detection Complexity Metric for Infrared Small Target Detection

by Zhiyuan Huang and Zhiyong Zhang

Sensors 2026, 26(9), 2886; https://doi.org/10.3390/s26092886 - 5 May 2026

Infrared small target detection is widely used in aerospace surveillance, maritime search and rescue, and military reconnaissance. However, the performance of detection algorithms is highly dependent on scene characteristics, and methods that perform well in simple backgrounds may degrade substantially in complex environments. [...] Read more.

Infrared small target detection is widely used in aerospace surveillance, maritime search and rescue, and military reconnaissance. However, the performance of detection algorithms is highly dependent on scene characteristics, and methods that perform well in simple backgrounds may degrade substantially in complex environments. Existing indicators, such as information entropy, average gradient, and peak signal-to-noise ratio, can reflect detection difficulty from individual perspectives, but they do not provide a unified measure that jointly considers target saliency, background complexity, and target–background coupling. To address this issue, this study proposes a scene detection complexity (SDC) metric for quantifying the difficulty of infrared small target detection. Six basic indicators are selected from three dimensions, namely target saliency, background complexity, and target–background coupling: statistical variance, target–background contrast, signal-to-clutter ratio, information entropy, structural similarity, and target size. After Min–Max normalization, objective weights are determined by combining the entropy weight method and principal component analysis, and the weighted indicators are fused into an SDC value in the range of

[0, 1]

. Experiments on 100 test images selected from IRST640, MSISTD, SIRST-V2, and an infrared small-aircraft sequence dataset show that the proposed SDC achieves a Pearson linear correlation coefficient of 0.956 with subjective difficulty ratings and

- 0.902

with image-level detection scores obtained from seven representative algorithms. The results further indicate that traditional methods are more sensitive to increasing scene complexity, whereas deep-learning-based methods are comparatively more robust in complex backgrounds. The proposed SDC provides a unified and objective tool for performance evaluation, algorithm selection, and pre-assessment of scene difficulty in infrared small target detection. Full article

(This article belongs to the Section Remote Sensors)

20 pages, 4202 KB

Open AccessArticle

Communication-Efficient Federated Learning with Dual-Sided Sparse Aggregation for Edge Sensing Systems

by He Zhao and Jingwei Li

Sensors 2026, 26(9), 2885; https://doi.org/10.3390/s26092885 - 5 May 2026

Distributed edge sensing systems, such as IoT monitoring nodes, wearable devices, and camera-based sensing terminals, continuously generate privacy-sensitive data that are costly to transmit to a central server. Federated learning (FL) provides a promising solution for collaborative model training without raw-data sharing; however, [...] Read more.

Distributed edge sensing systems, such as IoT monitoring nodes, wearable devices, and camera-based sensing terminals, continuously generate privacy-sensitive data that are costly to transmit to a central server. Federated learning (FL) provides a promising solution for collaborative model training without raw-data sharing; however, its practical deployment in edge sensing systems is challenged by non-IID local observations, limited uplink/downlink resources, and restricted on-device computation. To address these issues, this paper proposes a Dual-Sided Sparse Aggregation (DSSA) mechanism integrated with FedProx for resource-constrained edge sensing environments. In the proposed framework, the server prunes the global model after each communication round and transmits only the retained parameters, while clients update the complementary parameters and upload sparse local gradients. This fixed-structure sparse training strategy reduces bidirectional communication overhead and local computation cost, while FedProx improves robustness under heterogeneous data distributions. Experiments on CIFAR-10 and SVHN with varying non-IID degrees, pruning ratios, and hyperparameter settings show that the proposed method achieves a favorable resource-performance trade-off, reducing communication cost by up to 73.0% and computation cost by up to 34.9% while maintaining competitive accuracy. Under controlled benchmark settings, the proposed method demonstrates substantial resource savings compared with FedAvg, particularly in mildly heterogeneous scenarios, indicating a favorable benchmark-level resource-performance trade-off for resource-constrained edge sensing scenarios under the evaluated settings. Full article

(This article belongs to the Section Communications)

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27 pages, 6283 KB

Open AccessArticle

Robust Rear-View Human Tracking for Robotic Visual Sensing: A Spatiotemporal Prediction and Multi-Modal Fusion Approach

by Xu Jia, Jia Xie, Yongguo Li, Jintao Liang and Zengmin Zhang

Sensors 2026, 26(9), 2884; https://doi.org/10.3390/s26092884 - 5 May 2026

Rear-view human tracking and re-identification remain critical challenges for robotic visual sensing in unmanned vehicles, particularly under adverse weather conditions and severe occlusion. Conventional deep learning models often suffer from feature contamination and trajectory drift under dynamic illumination. To overcome these bottlenecks, we [...] Read more.

Rear-view human tracking and re-identification remain critical challenges for robotic visual sensing in unmanned vehicles, particularly under adverse weather conditions and severe occlusion. Conventional deep learning models often suffer from feature contamination and trajectory drift under dynamic illumination. To overcome these bottlenecks, we propose a lightweight tracking framework driven by spatiotemporal prediction and multimodal feature fusion. Specifically, an ego-motion-aware Kalman prediction mechanism maintains temporal continuity during complete occlusions. Upon target reappearance, a multi-factor descriptor—fusing color histograms with geometric constraints—is employed within a dynamic Mahalanobis search region. This is coupled with a specular-reflection-penalized adaptive learning rate (η_k) that actively freezes template updates during severe environmental degradation conditions. Evaluated on a custom Mecanum-wheeled robot, the proposed method achieves a peak precision of 94.2% and a tracking success rate of 93.4%. Extensive experiments in extreme rainy night scenarios demonstrate a 35% reduction in average tracking error, maintaining a Center Location Error (CLE) below 11 pixels. Furthermore, the system achieves a rapid target re-identification response of 72.83 ms during occlusion phases. Ultimately, this framework delivers a highly robust and real-time solution for autonomous navigation in complex dynamic environments. Full article

(This article belongs to the Section Sensors and Robotics)

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21 pages, 5583 KB

Open AccessArticle

A 33 GHz Conformal Phased-Array Radar with Linearly Constrained Minimum Variance Digital Beamforming, Circular- Polarization Filtering, and Neural-Network Micro-Doppler Classification for Counter-UAS Applications

by Michael Baginski

Sensors 2026, 26(9), 2883; https://doi.org/10.3390/s26092883 - 5 May 2026

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 [...] Read more.

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below

- 20

dB with main-beam peaks within

0.1

° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a

36 \times 36

impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70

λ

. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering

0

°–

60

° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

(This article belongs to the Special Issue Integrated Radar Sensing and Perception for Next-Generation Intelligent Systems)

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5 pages, 154 KB

Open AccessEditorial

Machine Learning for Intelligent and Adaptive Communication Systems: From Optimization to Emerging Paradigms

by Haeyoung Lee, Yichuang Sun and Oluyomi Simpson

Sensors 2026, 26(9), 2882; https://doi.org/10.3390/s26092882 - 5 May 2026

Machine learning (ML) has been increasingly considered for various communication applications, demonstrating promising feasibility and effectiveness in enhancing system intelligence, adaptability, and operational efficiency [...] Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

32 pages, 32711 KB

Open AccessArticle

Adaptive Control of the Redundant Axis of a Surgical Robot for Operating Room Workspace Optimization Using Reinforcement Learning

by Irati Renedo-Alonso, Juan A. Sánchez-Margallo, Nestor Arana-Arexolaleiba and Íñigo Elguea-Aguinaco

Sensors 2026, 26(9), 2881; https://doi.org/10.3390/s26092881 - 5 May 2026

Laparoscopy is one of the most widely used surgical techniques in clinical practice. However, its practice is associated with medium- and long-term musculoskeletal disorders in surgeons. In this context, robot-assisted surgery has emerged as a promising approach for mitigating ergonomic constraints while enhancing [...] Read more.

Laparoscopy is one of the most widely used surgical techniques in clinical practice. However, its practice is associated with medium- and long-term musculoskeletal disorders in surgeons. In this context, robot-assisted surgery has emerged as a promising approach for mitigating ergonomic constraints while enhancing control and precision during laparoscope manipulation. Despite these advances, existing research predominantly focuses on robotic control strategies, whereas the study of human–robot interaction in the operating room remains comparatively underexplored. This paper presents a proof-of-concept framework for workspace-aware posture adaptation in collaborative surgical robotics. The proposed approach combines vision-based human activity recognition with reinforcement learning to control the shoulder–elbow–wrist redundant angle of a seven-degree-of-freedom manipulator holding a laparoscope. Based on the detected interaction context, the system distinguishes between controlling, observing, cutting, and blocked states. During the observation and cutting phases, the controller allows the robot’s posture to be reconfigured so that it tilts away from the human operator while maintaining the position of the laparoscope; when the surgeon moves away, the robot gradually returns to its default configuration. Two reward formulations, dense and fuzzy, are compared. Real-world experiments show that both approaches learn the desired reflexive behavior, while the fuzzy reward yields improved training stability and more consistent real-system performance, increasing workspace availability around the surgeon. Full article

(This article belongs to the Special Issue Novel Actuation, Sensing, and Intelligent Control in Medical Robotic Systems)

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12 pages, 1497 KB

Open AccessArticle

Eye Drift Signal Analysis Caused by Goggle Slippage in vHIT Measurements: A Signal Processing Perspective

by Ha Ngoc Khoan, Le Ky Bien, Tran Thi Nhan and Tran Van Nghia

Sensors 2026, 26(9), 2880; https://doi.org/10.3390/s26092880 - 5 May 2026

This Technical Report presents a quantitative signal processing approach to analyze and correct eye drift during vestibulo-ocular reflex (VOR) measurements using the video Head Impulse Test (vHIT). The objective is to determine the extent of drift caused by goggle slippage—a technical artifact that [...] Read more.

This Technical Report presents a quantitative signal processing approach to analyze and correct eye drift during vestibulo-ocular reflex (VOR) measurements using the video Head Impulse Test (vHIT). The objective is to determine the extent of drift caused by goggle slippage—a technical artifact that can distort the VOR gain index. A total of 57 impulses were categorized into three protocols: Lateral, LARP, and RALP. For each impulse, peak head velocity and eye drift (estimated from the average velocity during the pre- and post-impulse rest periods) were extracted using a custom signal processing pipeline implemented in MATLAB R2020b and Python 3.11 64 bit. Results showed the highest drift in the RALP group (−7.41 deg/s) and the lowest in the LARP group (−3.08 deg/s). The correlation between head velocity and drift was most prominent in the RALP group (r > 0.7), highlighting the impact of stimulation direction on goggle stability. This study proposes a drift detection method to be integrated into VOR correction algorithms, thereby enhancing gain analysis and saccade detection in automated systems. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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