Sensors

15 pages, 24344 KiB

Open AccessArticle

The Influence of Dimensional Parameters on the Characteristics of Magnetic Flux Concentrators Used in Tunneling Magnetoresistance Devices

by Ran Bi, Huiquan Zhang, Shi Pan, Xinting Liu, Ruiying Chen, Shilin Wu and Jun Hu

Sensors 2025, 25(15), 4739; https://doi.org/10.3390/s25154739 (registering DOI) - 31 Jul 2025

Abstract

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing [...] Read more.

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing magnetic flux concentrators (MFCs) offers an effective approach to enhance TMR sensitivity. In this study, the finite element method was employed to analyze the effects of different MFC geometric structures on the uniformity of the magnetic field in the air gap and the magnetic circuit gain (MCG). It was determined that the MCG of the MFC is not directly related to the absolute values of its parameters but rather to their ratios. Simulation analyses evaluated the impact of these parameter ratios on both the MCG and its spatial distribution uniformity, leading to the formulation of MFC design optimization principles. Building on these simulation-derived principles, several MFCs were fabricated using the 1J85 material, and an experimental platform was established to validate the simulation findings. The fabricated MFCs achieved an MCG of 7.325 times. Based on the previously developed TMR devices, a detection sensitivity of 2.46 nT/

\sqrt{Hz}

@1Hz was obtained. By optimizing parameter configurations, this work provides theoretical guidance for further enhancing the performance of TMR sensors in magnetic field measurements. Full article

(This article belongs to the Section Physical Sensors)

► Show Figures

Figure 1

13 pages, 1429 KiB

Open AccessArticle

Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule

by Meiyuan Miao, Chen Ye, Zhiping Xu, Laiding Zhao and Jiafeng Yao

Sensors 2025, 25(15), 4738; https://doi.org/10.3390/s25154738 (registering DOI) - 31 Jul 2025

Abstract

The endoscopic capsule is a miniaturized device used for medical diagnosis, which is less invasive compared to traditional gastrointestinal endoscopy and can reduce patient discomfort. However, it faces challenges in communication transmission, such as high power consumption, serious signal interference, and low data [...] Read more.

The endoscopic capsule is a miniaturized device used for medical diagnosis, which is less invasive compared to traditional gastrointestinal endoscopy and can reduce patient discomfort. However, it faces challenges in communication transmission, such as high power consumption, serious signal interference, and low data transmission rate. To address these issues, this paper proposes an optimized modulation scheme that is low-cost, low-power, and robust in harsh environments, aiming to improve its transmission rate. The scheme is analyzed in terms of the in-body channel. The analysis and discussion for the scheme in wireless body area networks (WBANs) are divided into three aspects: bit error rate (BER) performance, energy efficiency (EE), and spectrum efficiency (SE), and complexity. These correspond to the following issues: transmission rate, communication quality, and low power consumption. The results demonstrate that the optimized scheme is more suitable for improving the communication performance of endoscopic capsules. Full article

(This article belongs to the Special Issue Wireless Communication Technologies for Internet of Things and Wireless Sensor Networks)

18 pages, 2946 KiB

Open AccessArticle

A Preprocessing Pipeline for Pupillometry Signal from Multimodal iMotion Data

by Jingxiang Ong, Wenjing He, Princess Maglanque, Xianta Jiang, Lawrence M. Gillman, Ashley Vergis and Krista Hardy

Sensors 2025, 25(15), 4737; https://doi.org/10.3390/s25154737 (registering DOI) - 31 Jul 2025

Abstract

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack [...] Read more.

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack of standardized pipelines for managing pupillometry data on a multimodal platform. Preprocessing pupil data in multimodal platforms poses challenges like timestamp misalignment, missing data, and inconsistencies across multiple data sources. To address these challenges, the authors introduced a systematic preprocessing pipeline for pupil diameter measurements collected using iMotions 10 (version 10.1.38911.4) during an endoscopy simulation task. The pipeline involves artifact removal, outlier detection using advanced methods such as the Median Absolute Deviation (MAD) and Moving Average (MA) algorithm filtering, interpolation of missing data using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and mean pupil diameter calculation through linear regression, as well as normalization of mean pupil diameter and integration of the pupil diameter dataset with facial expression data. By following these steps, the pipeline enhances data quality, reduces noise, and facilitates the seamless integration of pupillometry other multimodal datasets. In conclusion, this pipeline provides a detailed and organized preprocessing method that improves data reliability while preserving important information for further analysis. Full article

(This article belongs to the Section Intelligent Sensors)

19 pages, 2913 KiB

Open AccessArticle

Radiation Mapping: A Gaussian Multi-Kernel Weighting Method for Source Investigation in Disaster Scenarios

by Songbai Zhang, Qi Liu, Jie Chen, Yujin Cao and Guoqing Wang

Sensors 2025, 25(15), 4736; https://doi.org/10.3390/s25154736 (registering DOI) - 31 Jul 2025

Abstract

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant [...] Read more.

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

(This article belongs to the Section Navigation and Positioning)

► Show Figures

Figure 1

13 pages, 769 KiB

Open AccessArticle

A Novel You Only Listen Once (YOLO) Deep Learning Model for Automatic Prominent Bowel Sounds Detection: Feasibility Study in Healthy Subjects

by Rohan Kalahasty, Gayathri Yerrapragada, Jieun Lee, Keerthy Gopalakrishnan, Avneet Kaur, Pratyusha Muddaloor, Divyanshi Sood, Charmy Parikh, Jay Gohri, Gianeshwaree Alias Rachna Panjwani, Naghmeh Asadimanesh, Rabiah Aslam Ansari, Swetha Rapolu, Poonguzhali Elangovan, Shiva Sankari Karuppiah, Vijaya M. Dasari, Scott A. Helgeson, Venkata S. Akshintala and Shivaram P. Arunachalam

Sensors 2025, 25(15), 4735; https://doi.org/10.3390/s25154735 (registering DOI) - 31 Jul 2025

Abstract

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low [...] Read more.

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis: 2nd Edition)

► Show Figures

Figure 1

21 pages, 4206 KiB

Open AccessArticle

A Semi-Automated RGB-Based Method for Wildlife Crop Damage Detection Using QGIS-Integrated UAV Workflow

by Sebastian Banaszek and Michał Szota

Sensors 2025, 25(15), 4734; https://doi.org/10.3390/s25154734 (registering DOI) - 31 Jul 2025

Abstract

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). [...] Read more.

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

(This article belongs to the Section Remote Sensors)

28 pages, 2841 KiB

Open AccessArticle

A Multi-Constraint Co-Optimization LQG Frequency Steering Method for LEO Satellite Oscillators

by Dongdong Wang, Wenhe Liao, Bin Liu and Qianghua Yu

Sensors 2025, 25(15), 4733; https://doi.org/10.3390/s25154733 (registering DOI) - 31 Jul 2025

Abstract

High-precision time–frequency systems are essential for low Earth orbit (LEO) navigation satellites to achieve real-time (RT) centimeter-level positioning services. However, subject to stringent size, power, and cost constraints, LEO satellites are typically equipped with oven-controlled crystal oscillators (OCXOs) as the system clock. The [...] Read more.

High-precision time–frequency systems are essential for low Earth orbit (LEO) navigation satellites to achieve real-time (RT) centimeter-level positioning services. However, subject to stringent size, power, and cost constraints, LEO satellites are typically equipped with oven-controlled crystal oscillators (OCXOs) as the system clock. The inherent long-term stability of OCXOs leads to rapid clock error accumulation, severely degrading positioning accuracy. To simultaneously balance multi-dimensional requirements such as clock bias accuracy, and frequency stability and phase continuity, this study proposes a linear quadratic Gaussian (LQG) frequency precision steering method that integrates a four-dimensional constraint integrated (FDCI) model and hierarchical weight optimization. An improved system error model is refined to quantify the covariance components (Σ₁₁, Σ₂₂) of the LQG closed-loop control system. Then, based on the FDCI model that explicitly incorporates quantization noise, frequency adjustment, frequency stability, and clock bias variance, a priority-driven collaborative optimization mechanism systematically determines the weight matrices, ensuring a robust tradeoff among multiple performance criteria. Experiments on OCXO payload products, with micro-step actuation, demonstrate that the proposed method reduces the clock error RMS to 0.14 ns and achieves multi-timescale stability enhancement. The short-to-long-term frequency stability reaches 9.38 × 10⁻¹³ at 100 s, and long-term frequency stability is 4.22 × 10⁻¹⁴ at 10,000 s, representing three orders of magnitude enhancement over a free-running OCXO. Compared to conventional PID control (clock bias RMS 0.38 ns) and pure Kalman filtering (stability 6.1 × 10⁻¹³ at 10,000 s), the proposed method reduces clock bias by 37% and improves stability by 93%. The impact of quantization noise on short-term stability (1–40 s) is contained within 13%. The principal novelty arises from the systematic integration of theoretical constraints and performance optimization within a unified framework. This approach comprehensively enhances the time–frequency performance of OCXOs, providing a low-cost, high-precision timing–frequency reference solution for LEO satellites. Full article

(This article belongs to the Section Remote Sensors)

► Show Figures

Figure 1

19 pages, 1370 KiB

Open AccessArticle

Airborne-Platform-Assisted Transmission and Control Separation for Multiple Access in Integrated Satellite–Terrestrial Networks

by Chaoran Huang, Xiao Ma, Xiangren Xin, Weijia Han and Yanjie Dong

Sensors 2025, 25(15), 4732; https://doi.org/10.3390/s25154732 (registering DOI) - 31 Jul 2025

Abstract

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) [...] Read more.

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

(This article belongs to the Special Issue Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing)

► Show Figures

Figure 1

40 pages, 18911 KiB

Open AccessArticle

Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration

by Shohreh Kia, Johannes B. Mayer, Erik Westphal and Benjamin Leiding

Sensors 2025, 25(15), 4731; https://doi.org/10.3390/s25154731 (registering DOI) - 31 Jul 2025

Abstract

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly [...] Read more.

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)

17 pages, 3595 KiB

Open AccessArticle

Sensor-Based Monitoring of Fire Precursors in Timber Wall and Ceiling Assemblies: Research Towards Smarter Embedded Detection Systems

by Kristian Prokupek, Chandana Ravikumar and Jan Vcelak

Sensors 2025, 25(15), 4730; https://doi.org/10.3390/s25154730 (registering DOI) - 31 Jul 2025

Abstract

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and [...] Read more.

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

(This article belongs to the Special Issue Advancements in Fire Detection Technologies: Innovations for Extreme Environments)

► Show Figures

Figure 1

30 pages, 7956 KiB

Open AccessReview

A Review of Multiscale Interaction Mechanisms of Wind–Leaf–Droplet Systems in Orchard Spraying

by Yunfei Wang, Zhenlei Zhang, Ruohan Shi, Shiqun Dai, Weidong Jia, Mingxiong Ou, Xiang Dong and Mingde Yan

Sensors 2025, 25(15), 4729; https://doi.org/10.3390/s25154729 (registering DOI) - 31 Jul 2025

Abstract

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent [...] Read more.

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)

17 pages, 726 KiB

Open AccessArticle

A Post-Quantum Public-Key Signcryption Scheme over Scalar Integers Based on a Modified LWE Structure

by Mostefa Kara, Mohammad Hammoudeh, Abdullah Alamri and Sultan Alamri

Sensors 2025, 25(15), 4728; https://doi.org/10.3390/s25154728 (registering DOI) - 31 Jul 2025

Abstract

To ensure confidentiality and integrity in the era of quantum computing, most post-quantum cryptographic schemes are designed to achieve either encryption or digital signature functionalities separately. Although a few signcryption schemes exist that combine these operations into a single, more efficient process, they [...] Read more.

To ensure confidentiality and integrity in the era of quantum computing, most post-quantum cryptographic schemes are designed to achieve either encryption or digital signature functionalities separately. Although a few signcryption schemes exist that combine these operations into a single, more efficient process, they typically rely on complex vector, matrix, or polynomial-based structures. In this work, a novel post-quantum public-key encryption and signature (PQES) scheme based entirely on scalar integer operations is presented. The proposed scheme employs a simplified structure where the ciphertext, keys, and core cryptographic operations are defined over scalar integers modulo n, significantly reducing computational and memory overhead. By avoiding high-dimensional lattices or ring-based constructions, the PQES approach enhances implementability on constrained devices while maintaining strong security properties. The design is inspired by modified learning-with-errors (LWE) assumptions, adapted to scalar settings, making it suitable for post-quantum applications. Security and performance evaluations, achieving a signcryption time of 0.0007 s and an unsigncryption time of 0.0011 s, demonstrate that the scheme achieves a practical balance between efficiency and resistance to quantum attacks. Full article

(This article belongs to the Section Intelligent Sensors)

► Show Figures

Figure 1

22 pages, 1350 KiB

Open AccessArticle

Optimization of Dynamic SSVEP Paradigms for Practical Application: Low-Fatigue Design with Coordinated Trajectory and Speed Modulation and Gaming Validation

by Yan Huang, Lei Cao, Yongru Chen and Ting Wang

Sensors 2025, 25(15), 4727; https://doi.org/10.3390/s25154727 (registering DOI) - 31 Jul 2025

Abstract

Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain–computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic [...] Read more.

Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain–computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency’s base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application. Full article

(This article belongs to the Special Issue EEG-Based Brain–Computer Interfaces: Research and Applications)

► Show Figures

Figure 1

24 pages, 4217 KiB

Open AccessArticle

Contact Load Measurement and Validation for Tapered Rollers in Wind Turbine Main Bearing

by Zhenggang Guo, Jingqi Yu, Wanxiu Hao and Yuming Niu

Sensors 2025, 25(15), 4726; https://doi.org/10.3390/s25154726 (registering DOI) - 31 Jul 2025

Abstract

Addressing the need for contact load detection in wind turbine main bearings during service, a roller contact load measurement method is proposed. An analytical model characterizes the contact load-to-inner bore strain mapping relationship. To overcome the inherent low sensitivity of direct bore strain [...] Read more.

Addressing the need for contact load detection in wind turbine main bearings during service, a roller contact load measurement method is proposed. An analytical model characterizes the contact load-to-inner bore strain mapping relationship. To overcome the inherent low sensitivity of direct bore strain measurement, bore-to-measurement-point sensitivity analysis was optimized. Multiple structurally optimized sensor brackets were designed to enhance strain measurement sensitivity, and their performance was comparatively evaluated via simulation. To mitigate sensitivity fluctuations caused by roller rotation phase variations, a strain–phase–load calculation method incorporating real-time phase compensation was developed and verified through simulation analysis. A dedicated roller contact load testing system was constructed and experimental validation was conducted. Results demonstrate 95% accuracy in contact load acquisition. This method accurately obtains roller contact loads in wind turbine main bearings, proving crucial for studying bearing mechanical behavior, predicting fatigue life, optimizing structural design, and enhancing reliability. Full article

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

► Show Figures

Figure 1

16 pages, 2174 KiB

Open AccessArticle

TwinFedPot: Honeypot Intelligence Distillation into Digital Twin for Persistent Smart Traffic Security

by Yesin Sahraoui, Abdessalam Mohammed Hadjkouider, Chaker Abdelaziz Kerrache and Carlos T. Calafate

Sensors 2025, 25(15), 4725; https://doi.org/10.3390/s25154725 (registering DOI) - 31 Jul 2025

Abstract

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we [...] Read more.

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article

(This article belongs to the Special Issue Vehicular Communications in 5G and 6G: Technologies, Architectures, and Challenges for Future Mobility)

► Show Figures

Figure 1

10 pages, 1468 KiB

Open AccessArticle

Noninvasive Mapping of Extracellular Potassium in Breast Tumors via Multi-Wavelength Photoacoustic Imaging

by Jeff Folz, Ahmad Eido, Maria E. Gonzalez, Roberta Caruso, Xueding Wang, Celina G. Kleer and Janggun Jo

Sensors 2025, 25(15), 4724; https://doi.org/10.3390/s25154724 (registering DOI) - 31 Jul 2025

Abstract

Elevated extracellular potassium (K⁺) in the tumor microenvironment (TME) of breast and other cancers is increasingly recognized as a critical factor influencing tumor progression and immune suppression. Current methods for noninvasive mapping of the potassium distribution in tumors are limited. Here, [...] Read more.

Elevated extracellular potassium (K⁺) in the tumor microenvironment (TME) of breast and other cancers is increasingly recognized as a critical factor influencing tumor progression and immune suppression. Current methods for noninvasive mapping of the potassium distribution in tumors are limited. Here, we employed photoacoustic chemical imaging (PACI) with a solvatochromic dye-based, potassium-sensitive nanoprobe (SDKNP) to quantitatively visualize extracellular potassium levels in an orthotopic metaplastic breast cancer mouse model, Ccn6-KO. Tumors of three distinct sizes (5 mm, 10 mm, and 20 mm) were imaged using multi-wavelength photoacoustic imaging at five laser wavelengths (560, 576, 584, 605, and 625 nm). Potassium concentration maps derived from spectral unmixing of the photoacoustic images at the five laser wavelengths revealed significantly increased potassium levels in larger tumors, confirmed independently by inductively coupled plasma mass spectrometry (ICP-MS). The PACI results matched ICP-MS measurements, validating PACI as a robust, noninvasive imaging modality for potassium mapping in tumors in vivo. This work establishes PACI as a promising tool for studying the chemical properties of the TME and provides a foundation for future studies evaluating the immunotherapy response through ionic biomarker imaging. Full article

(This article belongs to the Special Issue Advances in Photoacoustic Resonators and Sensors)

► Show Figures

Figure 1

19 pages, 12094 KiB

Open AccessArticle

Intelligent Active Suspension Control Method Based on Hierarchical Multi-Sensor Perception Fusion

by Chen Huang, Yang Liu, Xiaoqiang Sun and Yiqi Wang

Sensors 2025, 25(15), 4723; https://doi.org/10.3390/s25154723 (registering DOI) - 31 Jul 2025

Abstract

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control [...] Read more.

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

(This article belongs to the Special Issue Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport Equipment)

► Show Figures

Figure 1

10 pages, 1975 KiB

Open AccessCommunication

Measuring Asymmetric Ionic Current Waveform Through Micropores for Detecting Reduced Red Blood Cell Deformability Due to Plasmodium falciparum Infection

by Kazumichi Yokota, Ken Hirano, Kazuaki Kajimoto and Muneaki Hashimoto

Sensors 2025, 25(15), 4722; https://doi.org/10.3390/s25154722 (registering DOI) - 31 Jul 2025

Abstract

The mechanisms underlying reduced deformability of red blood cells (RBCs) in Plasmodium falciparum remain unclear. The decrease in RBC deformability associated with malarial infection was measured using ektacytometry, and only mean values were evaluated. In this study, we report the development of a [...] Read more.

The mechanisms underlying reduced deformability of red blood cells (RBCs) in Plasmodium falciparum remain unclear. The decrease in RBC deformability associated with malarial infection was measured using ektacytometry, and only mean values were evaluated. In this study, we report the development of a microfluidic sensing device that can evaluate decreased RBC deformability at the single-cell level by measuring ionic current waveforms through micropores. Using an in vitro culture system, we found that when RBC deformability was reduced by P. falciparum infection, ionic current waveforms changed. As RBC deformability decreased, waveforms became asymmetric. Computer simulations suggested that these waveform parameters are largely independent of RBC size and may represent a reliable indicator of diminished deformability. This novel microfluidic RBC deformability sensor allows for detailed single-cell analysis of malaria-associated deformability reduction, potentially aiding in elucidating its pathology. Full article

(This article belongs to the Special Issue Recent Advances in Microfluidic Sensing Devices)

► Show Figures

Figure 1

21 pages, 8731 KiB

Open AccessArticle

Individual Segmentation of Intertwined Apple Trees in a Row via Prompt Engineering

by Herearii Metuarea, François Laurens, Walter Guerra, Lidia Lozano, Andrea Patocchi, Shauny Van Hoye, Helin Dutagaci, Jeremy Labrosse, Pejman Rasti and David Rousseau

Sensors 2025, 25(15), 4721; https://doi.org/10.3390/s25154721 (registering DOI) - 31 Jul 2025

Abstract

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree [...] Read more.

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of

97 %

based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

(This article belongs to the Special Issue Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions)

► Show Figures

Figure 1

Journal Description

Sensors

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI