Sensors

37 pages, 10225 KiB

Open AccessArticle

Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach

by Georgian Simion, Adrian Filipescu, Dan Ionescu and Adriana Filipescu

Sensors 2025, 25(2), 591; https://doi.org/10.3390/s25020591 - 20 Jan 2025

Viewed by 635

Abstract

This paper deals with a “digital twin” (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber–physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled [...] Read more.

This paper deals with a “digital twin” (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber–physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC. In addition to the connection via Profibus of the four PLCs, related to the WSs, to the main PLC, there are also the connections of other devices to the local networks, LAN Profinet and LAN Ethernet. There are the connections to the Internet, Cloud and Virtual Private Network (VPN) via WAN Ethernet by open platform communication unified architecture (OPC-UA). The overall system follows a DT approach that enables task planning through augmented reality (AR) and uses virtual reality (VR) for visualization through Synchronized Hybrid Petri Net (SHPN) simulation. Timed Petri Nets (TPNs) are used to control the processes within the MPS’s workstations. Continuous Petri Nets (CPNs) handle the movement of the MCPRS. Task planning in AR enables users to interact with the system in real time using AR technology to visualize and plan tasks. SHPN in VR is a combination of TPNs and CPNs used in the virtual representation of the system to synchronize tasks between the MPS and MCPRS. The workpiece (WP) visits stations successively as it is moved along the line for processing. If the processed WP does not pass the quality test, it is taken from the last WS and is transported, by MCPRS, to the first WS where it will be considered for reprocessing or scrapping. Full article

(This article belongs to the Special Issue Advanced Intelligent Robotics and Autonomous Control Systems—Celebrating the 75th Birthday of Professor Imre J. Rudas)

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25 pages, 7034 KiB

Open AccessArticle

Diagnosis of Reverse-Connection Defects in High-Voltage Cable Cross-Bonded Grounding System Based on ARO-SVM

by Yuhao Ai, Bin Song, Shaocheng Wu, Yongwen Li, Li Lu and Linong Wang

Sensors 2025, 25(2), 590; https://doi.org/10.3390/s25020590 - 20 Jan 2025

Viewed by 495

Abstract

High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath [...] Read more.

High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath grounding fault. However, there is a paucity of research on the defect of the reverse direction between the inner core and the outer shield of the coaxial cable. Firstly, this paper performed a theoretical analysis of the sheath current in the reversed-connection state and established a simulation model for verification. The outcomes of the simulation demonstrate that there are significant variations in the amplitudes of the sheath current under different reversed-connection conditions. Consequently, a feature vector was devised based on the amplitude of the sheath current. The support vector machine (SVM) was then applied to diagnose the reversed-connection defects in the HV cable cross-bonded grounding system. The artificial rabbits optimization (ARO) algorithm was adopted to optimize the SVM model, attaining an impressively high diagnostic accuracy rate of 99.35%. The effectiveness and feasibility of the proposed algorithm are confirmed through the analysis and validation of the practical example. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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23 pages, 5215 KiB

Open AccessArticle

A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism

by Zhe Quan and Jun Sun

Sensors 2025, 25(2), 589; https://doi.org/10.3390/s25020589 - 20 Jan 2025

Viewed by 898

Abstract

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and [...] Read more.

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult. To address these issues, we use YOLOv8s as the basic framework and introduce a multi-level feature fusion algorithm. Additionally, we design an attention mechanism that links distant pixels to improve small object feature extraction. To address missed detections and inaccurate localization, we replace the detection head with a dynamic head, allowing the model to route objects to the appropriate head for final output. We also introduce Slideloss to improve the model’s learning of difficult samples and ShapeIoU to better account for the shape and scale of bounding boxes. Experiments on datasets like VisDrone2019 show that our method improves accuracy by nearly 10% and recall by about 11% compared to the baseline. Additionally, on the AI-TODv1.5 dataset, our method improves the mAP50 from 38.8 to 45.2. Full article

(This article belongs to the Section Remote Sensors)

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14 pages, 474 KiB

Open AccessCommunication

Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography

by Chun-Chi Chen, Song-Xian Lin and Hyundoo Jeong

Sensors 2025, 25(2), 588; https://doi.org/10.3390/s25020588 - 20 Jan 2025

Viewed by 463

Abstract

With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame [...] Read more.

With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss. Through a comparative analysis, this study offers insights into efficient timing correction techniques for enhancing HR estimation from rPPG, particularly suitable for edge-computing applications where low computational complexity is essential. Cubic interpolation can provide robust performance in reconstructing signals but requires higher computational resources, while linear and filter interpolation offer more efficient solutions. The proposed low-complexity timing correction methods improve the reliability of rPPG-based HR estimation, making it a more robust solution for real-world healthcare applications. Full article

(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing)

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15 pages, 27241 KiB

Open AccessArticle

Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining

by Liying Song, Zhiqiang Han, Hengyong Nie and Woon-Ming Lau

Sensors 2025, 25(2), 587; https://doi.org/10.3390/s25020587 - 20 Jan 2025

Viewed by 445

Abstract

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the [...] Read more.

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient’s hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the “first-hand” comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9–7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer. Full article

(This article belongs to the Section Biomedical Sensors)

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54 pages, 5783 KiB

Open AccessArticle

Characterization of RAP Signal Patterns, Temporal Relationships, and Artifact Profiles Derived from Intracranial Pressure Sensors in Acute Traumatic Neural Injury

by Abrar Islam, Amanjyot Singh Sainbhi, Kevin Y. Stein, Nuray Vakitbilir, Alwyn Gomez, Noah Silvaggio, Tobias Bergmann, Mansoor Hayat, Logan Froese and Frederick A. Zeiler

Sensors 2025, 25(2), 586; https://doi.org/10.3390/s25020586 - 20 Jan 2025

Viewed by 573

Abstract

Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between [...] Read more.

Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between intracranial pressure (ICP) and the pulse amplitude of ICP (AMP). RAP remains largely unexplored in cases of moderate to severe acute traumatic neural injury (also known as traumatic brain injury (TBI)). The goal of this work is to explore the general description of (a) RAP signal patterns and behaviors derived from ICP pressure transducers, (b) temporal statistical relationships, and (c) the characterization of the artifact profile. Methods: Different summary and statistical measurements were used to describe RAP’s pattern and behaviors, along with performing sub-group analyses. The autoregressive integrated moving average (ARIMA) model was employed to outline the time-series structure of RAP across different temporal resolutions using the autoregressive (p-order) and moving average orders (q-order). After leveraging the time-series structure of RAP, similar methods were applied to ICP and AMP for comparison with RAP. Finally, key features were identified to distinguish artifacts in RAP. This might involve leveraging ICP/AMP signals and statistical structures. Results: The mean and time spent within the RAP threshold ranges ([0.4, 1], (0, 0.4), and [−1, 0]) indicate that RAP exhibited high positive values, suggesting an impaired compensatory reserve in TBI patients. The median optimal ARIMA model for each resolution and each signal was determined. Autocorrelative function (ACF) and partial ACF (PACF) plots of residuals verified the adequacy of these median optimal ARIMA models. The median of residuals indicates that ARIMA performed better with the higher-resolution data. To identify artifacts, (a) ICP q-order, AMP p-order, and RAP p-order and q-order, (b) residuals of ICP, AMP, and RAP, and (c) cross-correlation between residuals of RAP and AMP proved to be useful at the minute-by-minute resolution, whereas, for the 10-min-by-10-min data resolution, only the q-order of the optimal ARIMA model of ICP and AMP served as a distinguishing factor. Conclusions: RAP signals derived from ICP pressure sensor technology displayed reproducible behaviors across this population of TBI patients. ARIMA modeling at the higher resolution provided comparatively strong accuracy, and key features were identified leveraging these models that could identify RAP artifacts. Further research is needed to enhance artifact management and broaden applicability across varied datasets. Full article

(This article belongs to the Special Issue Sensing Signals for Biomedical Monitoring)

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15 pages, 8136 KiB

Open AccessArticle

The Fluorescent Detection of Alkaline Phosphatase Based on Iron Nanoclusters and a Manganese Dioxide Nanosheet

by Liang Zhao, Xinyue Liu, Xinwen Zhang, Siyu Liu and Jiazhen Wu

Sensors 2025, 25(2), 585; https://doi.org/10.3390/s25020585 - 20 Jan 2025

Viewed by 496

Abstract

Fluorescent iron nanoclusters are emerging fluorescent nanomaterials. Herein, we synthesized hemoglobin-coated iron nanoclusters (Hb−Fe NCs) with a significant fluorescence emission peak at 615 nm and investigated the inner-filter effect of fluorescence induced by a manganese dioxide nanosheet (MnO₂ NS). The fluorescence quenching [...] Read more.

Fluorescent iron nanoclusters are emerging fluorescent nanomaterials. Herein, we synthesized hemoglobin-coated iron nanoclusters (Hb−Fe NCs) with a significant fluorescence emission peak at 615 nm and investigated the inner-filter effect of fluorescence induced by a manganese dioxide nanosheet (MnO₂ NS). The fluorescence quenching of Hb−Fe NCs by a MnO₂ NS can be significantly reversed by the addition of ascorbic acid. On the basis of fluorescent recovery by ascorbic acid, we proposed a system that consisted of Hb−Fe NCs, a MnO₂ NS and ascorbate phosphate, and the proposed system was successfully used for alkaline phosphatase (ALP) detection in the range of 0–20 μg/mL based on the significant fluorescence recovery achieved. Full article

(This article belongs to the Special Issue Fluorescence Sensors for Biological and Medical Applications)

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24 pages, 19605 KiB

Open AccessReview

Field-Programmable Gate Array (FPGA)-Based Lock-In Amplifier System with Signal Enhancement: A Comprehensive Review on the Design for Advanced Measurement Applications

by Jose Alejandro Galaviz-Aguilar, Cesar Vargas-Rosales, Francisco Falcone and Carlos Aguilar-Avelar

Sensors 2025, 25(2), 584; https://doi.org/10.3390/s25020584 - 20 Jan 2025

Viewed by 539

Abstract

Lock-in amplifiers (LIAs) are critical tools in precision measurement, particularly for applications involving weak signals obscured by noise. Advances in signal processing algorithms and hardware synthesis have enabled accurate signal extraction, even in extremely noisy environments, making LIAs indispensable in sensor applications for [...] Read more.

Lock-in amplifiers (LIAs) are critical tools in precision measurement, particularly for applications involving weak signals obscured by noise. Advances in signal processing algorithms and hardware synthesis have enabled accurate signal extraction, even in extremely noisy environments, making LIAs indispensable in sensor applications for healthcare, industry, and other services. For instance, the electrical impedance measurement of the human body, organs, tissues, and cells, known as bioelectrical impedance, is commonly used in biomedical and healthcare applications because it is non-invasive and relatively inexpensive. Also, due to its portability and miniaturization capabilities, it has great potential for the development of new point-of-care and portable testing devices. In this document, we highlight existing techniques for high-frequency resolution and precise phase detection in LIA reference signals from field-programmable gate array (FPGA) designs. A comprehensive review is presented under the key requirements and techniques for single- and dual-phase digital LIA architectures, where relevant insights are provided to address the LIAs’ digital precision in measurement system configurations. Furthermore, the document highlights a novel method to enhance the spurious-free dynamic range (SFDR), thereby advancing the precision and effectiveness of LIAs in complex measurement environments. Finally, we summarize the diverse applications of impedance measurement, highlighting the wide range of fields that can benefit from the design of high performance in modern measurement technologies. Full article

(This article belongs to the Special Issue Feature Review Papers in Physical Sensors)

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12 pages, 20046 KiB

Open AccessCommunication

Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm

by Mirza Muhammad Waqar, Heein Yang, Rahmi Sukmawati, Sung-Ho Chae and Kwan-Young Oh

Sensors 2025, 25(2), 583; https://doi.org/10.3390/s25020583 - 20 Jan 2025

Viewed by 455

Abstract

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR [...] Read more.

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives. Full article

(This article belongs to the Section Remote Sensors)

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30 pages, 4193 KiB

Open AccessArticle

A Piecewise Linearization Based Method for Crossed Frequency Admittance Matrix Model Calculation of Harmonic Sources

by Youhang Yang, Shaorong Wang, Mingming Shi and Xian Zheng

Sensors 2025, 25(2), 582; https://doi.org/10.3390/s25020582 - 20 Jan 2025

Viewed by 423

Abstract

The integration of large-scale power electronic equipment has intensified harmonic issues in power systems. Accurate harmonic models are fundamental for evaluating and mitigating harmonic problems, but existing models still exhibit deficiencies in harmonic mechanism, model complexity and accuracy. This work proposes a calculation [...] Read more.

The integration of large-scale power electronic equipment has intensified harmonic issues in power systems. Accurate harmonic models are fundamental for evaluating and mitigating harmonic problems, but existing models still exhibit deficiencies in harmonic mechanism, model complexity and accuracy. This work proposes a calculation method of crossed frequency admittance matrix (CFAM) analytical model based on piecewise linearization, aiming to achieve accurate modeling of phase-controlled power electronic harmonic sources. Firstly, the traditional CFAM model construction methods are introduced, and the shortcomings in harmonic modeling are discussed. Subsequently, the parameter-solving process of the CFAM analytical model based on piecewise linearization is proposed. This method improves the accuracy of harmonic modeling and simplifies the construction process of the analytical model. Furthermore, taking single-phase and three-phase bridge rectifiers as examples, CFAM analytical models under intermittent and continuous load current conditions are established, respectively, and the unified harmonic models for both conditions are summarized. Finally, case studies of rectifier harmonic sources under varying circuit control parameters and supply voltage distortions are conducted through Matlab/Simulink and experiments. The results demonstrate that the proposed method provides higher accuracy and more stable performance for harmonic current estimation compared with the traditional CFAM model and constant current source model. Full article

(This article belongs to the Special Issue Sensors, Systems and Methods for Power Quality Measurements)

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19 pages, 4718 KiB

Open AccessArticle

Normative Database of Spatiotemporal Gait Metrics Across Age Groups: An Observational Case–Control Study

by Lianne Mobbs, Vinuja Fernando, R. Dineth Fonseka, Pragadesh Natarajan, Monish Maharaj and Ralph J. Mobbs

Sensors 2025, 25(2), 581; https://doi.org/10.3390/s25020581 - 20 Jan 2025

Viewed by 654

Abstract

Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large [...] Read more.

Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics. By doing so, we aim to enhance the clinical utility of gait analysis in diagnosing and managing health conditions. Methods: We conducted an observational case–control study involving 313 healthy participants. The MetaMotionC IMU by Mbientlab Inc., equipped with a triaxial accelerometer, gyroscope, and magnetometer, was used to capture gait data. The IMU was placed at the sternal angle of each participant to ensure optimal data capture during a 50 m walk along a flat, unobstructed pathway. Data were collected through a Bluetooth connection to a smartphone running a custom-developed application and subsequently analysed using IMUGaitPY, a specialised version of the GaitPY Python package. Results: The data showed that gait speeds decrease with ageing for males and females. The fastest gait speed is observed in the 41–50 age group at

1.35 \pm 0.23 m / s

. Males consistently exhibit faster gait speeds than females across all age groups. Step length and cadence do not have clear trends with ageing. Gait speed and step length increase consistently with height, with the tallest group (191–200 cm) walking at an average speed of

1.49 \pm 0.12 m / s

, with an average step length of

0.91 \pm 0.05 m

. Cadence, however, decreases with increasing height, with the tallest group taking

103.52 \pm 5.04 s t e p s / m i n

on average. Conclusions: This study has established a comprehensive normative database for the spatiotemporal gait metrics of gait speed, step length, and cadence, highlighting the complexities of gait dynamics across age and sex groups and the influence of height. Our findings offer valuable reference points for clinicians to distinguish between healthy and pathological gait patterns, facilitating early detection and intervention for gait-related disorders. Moreover, this database enhances the clinical utility of gait analysis, supporting more objective diagnoses and assessments of therapeutic interventions. The normative database provides a valuable reference future research and clinical practice. It enables a more nuanced understanding of how gait evolves with age, gender, and physical stature, thus informing the development of targeted interventions to maintain mobility and prevent falls in older adults. Despite potential selection bias and the cross-sectional nature of the study, the insights gained provide a solid foundation for further longitudinal studies and diverse sampling to validate and expand upon these findings. Full article

(This article belongs to the Special Issue Innovative Applications of Wearable Sensors in Musculoskeletal Biomechanics and Rehabilitation)

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26 pages, 1683 KiB

Open AccessArticle

Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm

by Bambang Susilo, Abdul Muis and Riri Fitri Sari

Sensors 2025, 25(2), 580; https://doi.org/10.3390/s25020580 - 20 Jan 2025

Viewed by 603

Abstract

The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of [...] Read more.

The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture’s left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats. Full article

(This article belongs to the Section Internet of Things)

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16 pages, 11828 KiB

Open AccessArticle

A Precise Oxide Film Thickness Measurement Method Based on Swept Frequency and Transmission Cable Impedance Correction

by Yifan Li, Qi Xiao, Lisha Peng, Songling Huang and Chaofeng Ye

Sensors 2025, 25(2), 579; https://doi.org/10.3390/s25020579 - 20 Jan 2025

Viewed by 391

Abstract

Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current [...] Read more.

Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current testing (ECT) is commonly employed during poolside inspections due to its simplicity and ease of on-site implementation. The use of swept frequency technology can mitigate the impact of interference parameters and improve the measurement accuracy of ECT. However, as the nuclear assembly is placed in a pool for inspection, a cable several dozen meters in length is used to connect the ECT probe to the instrument. The measurement is affected by the transmission line and its effect is a function of the operating frequencies, resulting in errors for swept frequency measurements. This paper proposes a method for precisely measuring oxide film thickness based on the swept frequency technique and long transmission line impedance correction. The signals are calibrated based on a transmission line model of the cable, effectively eliminating the influence of the transmission cable. A swept frequency signal-processing algorithm is developed to separate the parameters and calculate oxide film thickness. To verify the feasibility of the method, measurements are conducted on fuel cladding samples with varying conductivities. It is found that the method can accurately assess oxide film thickness with varying conductivity. The maximum error is 3.42 μm, while the average error is 1.82 μm. The impedance correction reduces the error by 66%. The experimental results indicate that this method can eliminate the impact of long transmission cables, and the algorithm can mitigate the influence of material conductivity. This method can be utilized to measure oxide film thickness in nuclear power maintenance inspections following extensive testing and engineering optimization. Full article

(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)

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20 pages, 3347 KiB

Open AccessArticle

Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization

by Jannik Henkmann, Vittorio Memmolo and Jochen Moll

Sensors 2025, 25(2), 578; https://doi.org/10.3390/s25020578 - 20 Jan 2025

Viewed by 520

Abstract

This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the [...] Read more.

This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model. Starting from current state of the art in algorithms used for damage detection and localization, an AI-based technique is developed and validated on an experimental benchmark dataset before tiny ML implementation on a low-cost development board. A discussion of the need for a balance between the reduction in computational resources and increasing the precision of the models is also reported. It is shown that by extracting simple features of the signal, the models required to predict the damage locations can be significantly reduced in size while still having high accuracies of over 90%. In addition, it is possible to use these predictions to construct a fairly accurate heat map indicating the likely damage locations. Finally, a convenient edge/cloud visualization of the results can be achieved by simplifying the heat map. Full article

(This article belongs to the Special Issue Smart Sensing Technology for Structural Health Monitoring)

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15 pages, 3461 KiB

Open AccessArticle

The Effect of Electrical Stimulation Strength Training on Lower Limb Muscle Activation Characteristics During the Jump Smash Performance in Badminton Based on the EMS and EMG Sensors

by Xinyu Lin, Yimin Hu and Yi Sheng

Sensors 2025, 25(2), 577; https://doi.org/10.3390/s25020577 - 20 Jan 2025

Viewed by 481

Abstract

This study investigates the effects of electrical stimulation (EMS) combined with strength training on lower limb muscle activation and badminton jump performance, specifically during the “jump smash” movement. A total of 25 male badminton players, with a minimum of three years of professional [...] Read more.

This study investigates the effects of electrical stimulation (EMS) combined with strength training on lower limb muscle activation and badminton jump performance, specifically during the “jump smash” movement. A total of 25 male badminton players, with a minimum of three years of professional training experience and no history of lower limb injuries, participated in the study. Participants underwent three distinct conditions: baseline testing, strength training, and EMS combined with strength training. Each participant performed specific jump tests, including the jump smash and static squat jump, under each condition. Muscle activation was measured using electromyography (EMG) sensors to assess changes in the activation of key lower limb muscles. The EMS intervention involved targeted electrical pulses designed to stimulate both superficial and deep muscle fibers, aiming to enhance explosive strength and coordination in the lower limbs. The results revealed that the EMS + strength condition significantly improved performance in both the jump smash and static squat jump, as compared to the baseline and strength-only conditions (F = 3.39, p = 0.042; F = 3.67, p = 0.033, respectively). Additionally, increased activation of the rectus femoris (RF) was observed in the EMS + strength condition, indicating improved muscle recruitment and synchronization, likely due to the activation of fast-twitch fibers. No significant differences were found in the eccentric-concentric squat jump (F = 0.59, p = 0.561). The findings suggest that EMS, when combined with strength training, is an effective method for enhancing lower limb explosiveness and muscle activation in badminton players, offering a promising training approach for improving performance in high-intensity, explosive movements. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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16 pages, 3215 KiB

Open AccessArticle

Ground-Target Recognition Method Based on Transfer Learning

by Qiuzhan Zhou, Jikang Hu, Huinan Wu, Cong Wang, Pingping Liu and Xinyi Yao

Sensors 2025, 25(2), 576; https://doi.org/10.3390/s25020576 - 20 Jan 2025

Viewed by 392

Abstract

A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of [...] Read more.

A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs. Full article

(This article belongs to the Section Environmental Sensing)

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27 pages, 3101 KiB

Open AccessArticle

Development of a Sustainable Flexible Humidity Sensor Based on Tenebrio molitor Larvae Biomass-Derived Chitosan

by Ezekiel Edward Nettey-Oppong, Riaz Muhammad, Emmanuel Ackah, Hojun Yang, Ahmed Ali, Hyun-Woo Jeong, Seong-Wan Kim, Young-Seek Seok and Seung Ho Choi

Sensors 2025, 25(2), 575; https://doi.org/10.3390/s25020575 - 20 Jan 2025

Viewed by 619

Abstract

This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate [...] Read more.

This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate composite suitable for real-time, resistive-type humidity detection. Comprehensive characterization of the chitosan film was performed using Fourier-transform infrared (FTIR) spectroscopy, contact angle measurements, and tensile testing, which confirmed its chemical structure, wettability, and mechanical stability. The developed sensor exhibited a broad range of measurements from 6% to 97% relative humidity (RH), a high sensitivity of 2.43 kΩ/%RH, and a rapid response time of 18.22 s with a corresponding recovery time of 22.39 s. Moreover, the chitosan-based humidity sensor also demonstrated high selectivity for water vapor when tested against various volatile organic compounds (VOCs). The superior performance of the sensor is attributed to the structural properties of chitosan, particularly its ability to form reversible hydrogen bonds with water molecules. This mechanism was further elucidated through molecular dynamics simulations, revealing that the conductivity in the sensor is modulated by proton mobility, which operates via the Grotthuss mechanism under high-humidity and the packed-acid mechanism under low-humidity conditions. Additionally, the chitosan-based humidity sensor was further seamlessly integrated into an Internet of Things (IoT) framework, enabling wireless humidity monitoring and real-time data visualization on a mobile device. Comparative analysis with existing polymer-based resistive-type sensors further highlighted the superior sensing range, rapid dynamic response, and environmental sustainability of the developed sensor. This eco-friendly, biomass-derived, eco-friendly sensor shows potential for applications in environmental monitoring, smart agriculture, and industrial process control. Full article

(This article belongs to the Special Issue Humidity Sensors Based on Spectroscopy)

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26 pages, 8033 KiB

Open AccessArticle

Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period

by Seonjun Yoon and Hyunsoo Kim

Sensors 2025, 25(2), 574; https://doi.org/10.3390/s25020574 - 20 Jan 2025

Viewed by 415

Abstract

In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study [...] Read more.

In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques. The system utilizes YOLOv5 for detecting jack supports in both construction drawings and on-site images captured through wearable cameras, while optical character recognition (OCR) and natural language processing (NLP) extract installation and dismantling timelines from work orders. The proposed framework enables continuous monitoring and ensures compliance with retention periods by aligning on-site data with documented requirements. The analysis includes 23 jack supports monitored daily over 28 days under varying environmental conditions, including lighting changes and structural configurations. The results demonstrate that the system achieves an average detection accuracy of 94.1%, effectively identifying discrepancies and reducing misclassifications caused by structural similarities and environmental variations. To further enhance detection reliability, methods such as color differentiation, construction plan overlays, and vertical segmentation were implemented, significantly improving performance. This study validates the effectiveness of integrating visual and textual data sources in dynamic construction environments. The study supports the development of automated monitoring systems by improving accuracy and safety measures while reducing manual intervention, offering practical insights for future construction site management. Full article

(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems)

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19 pages, 2096 KiB

Open AccessArticle

Mixed-Effects Model to Assess the Effect of Disengagements on Speed of an Automated Shuttle with Sensors for Localization, Navigation, and Obstacle Detection

by Abhinav Grandhi, Ninad Gore and Srinivas S. Pulugurtha

Sensors 2025, 25(2), 573; https://doi.org/10.3390/s25020573 - 20 Jan 2025

Viewed by 461

Abstract

The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and [...] Read more.

The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection. A multi-level mixed-effects Gaussian regression model with a log-link function was employed to analyze the effect of disengagement events on the automated shuttle speed, while accounting for control variables such as roadway geometry, weather conditions, time-of-the-day, day-of-the-week, and number of intermediate stops. When these variables are controlled, disengagements significantly reduce the automated shuttle speed, with the expected log of speed decreasing by 0.803 units during such events. This reduction underscores the disruptive impact of disengagements on the automated shuttle’s performance. The analysis revealed substantial variability in the effect of disengagements across different route segments, suggesting that certain segments, likely due to varying traffic conditions, road geometries, and traffic control characteristics, pose greater challenges for autonomous navigation. By employing a multi-level mixed-effects model, this study provides a robust framework for quantifying the operational impact of disengagements. The findings serve as vital insights for advancing the reliability and safety of autonomous systems through targeted improvements in technology and infrastructure. Full article

(This article belongs to the Section Navigation and Positioning)

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16 pages, 2280 KiB

Open AccessArticle

Identifying the Primary Kinetic Factors Influencing the Anterior–Posterior Center of Mass Displacement in Barbell Squats: A Factor Regression Analysis

by Diwei Chen, Dong Sun, Fengping Li, Dongxu Wang, Zhanyi Zhou, Zixiang Gao and Yaodong Gu

Sensors 2025, 25(2), 572; https://doi.org/10.3390/s25020572 - 20 Jan 2025

Viewed by 486

Abstract

Background: Barbell squats are commonly used in strength training, but the anterior–posterior displacement of the Center of Mass (COM) may impair joint stability and increase injury risk. This study investigates the key factors influencing COM displacement during different squat modes.; Methods: This study [...] Read more.

Background: Barbell squats are commonly used in strength training, but the anterior–posterior displacement of the Center of Mass (COM) may impair joint stability and increase injury risk. This study investigates the key factors influencing COM displacement during different squat modes.; Methods: This study recruited 15 male strength training enthusiasts, who performed 60% of their one-repetition maximum (1RM) in the Front Barbell Squat (FBS), High Bar Back Squat (HBBS), and Low Bar Back Squat (LBBS). Joint moments at both the hip, knee, and ankle were collected using a motion capture system and force plates, and a factor regression analysis was conducted using SPSS.; Results: In the FBS, primary factors influencing COM displacement included right knee adduction–abduction (38.59%), knee flexion–extension (31.08%), and hip internal–external rotation (29.83%). In the HBBS, they were right ankle internal–external rotation (19.13%), hip flexion–extension (−19.07%), and left knee flexion–extension (19.05%). In the LBBS, the key factors were left knee adduction–abduction (27.82%), right ankle internal–external rotation (27.59%), and left ankle internal–external rotation (26.12%).; Conclusion: The study identifies key factors affecting COM displacement across squat modes, with knee flexion–extension being dominant in the FBS and hip moments more significant in the HBBS and LBBS. These findings have implications for optimizing squat training and injury prevention strategies. Full article

(This article belongs to the Special Issue Sensor Techniques and Methods for Sports Science)

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19 pages, 2271 KiB

Open AccessArticle

Sensorless Junction Temperature Estimation of Onboard SiC MOSFETs Using Dual-Gate-Bias-Triggered Third-Quadrant Characteristics

by Yansong Lu, Yijun Ding, Jia Li, Hao Yin, Xinlian Li, Chong Zhu and Xi Zhang

Sensors 2025, 25(2), 571; https://doi.org/10.3390/s25020571 - 20 Jan 2025

Viewed by 491

Abstract

Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters’ (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and [...] Read more.

Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters’ (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and increase costs, thereby limiting applications. Therefore, there is still a lack of cost-effective and sensorless thermal monitoring techniques. This paper proposes a high-efficiency datasheet-driven method for sensorless estimation utilizing the third-quadrant characteristics of MOSFETs. Without changing the existing hardware, the closure degree of MOS channels is controlled through a dual-gate bias (DGB) strategy to achieve reverse conduction in different patterns with body diodes. This method introduces a MOSFET operating current that TSEPs are equally sensitive to into the two-argument function, improving the complexity and accuracy. A two-stage current pulse is used to decouple the motor effect in various conduction modes, and the TSEP-combined temperature function is built dynamically by substituting the currents. Then, the junction temperature is estimated by the measured bus voltage and current. Its effectiveness was verified through spice model simulation and a test bench with a three-phase inverter. The average relative estimation error of the proposed method is below

7.2 %

in centigrade. Full article

(This article belongs to the Section Electronic Sensors)

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22 pages, 632 KiB

Open AccessArticle

Performance and Energy Consumption Analysis for UWSNs with Priority Scheduling Based on Access Probability and Wakeup Threshold

by Ning Li, Zhiyu Xiang, Liang Feng, Zhiqiang Gao, Jiaqi Liu and Haitao Gu

Sensors 2025, 25(2), 570; https://doi.org/10.3390/s25020570 - 19 Jan 2025

Viewed by 628

Abstract

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. [...] Read more.

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH). This approach aims to conserve energy while maintaining acceptable network latency levels. The wakeup threshold restricts the frequency of state switching for the CH, thereby reducing energy consumption. Meanwhile, the dynamic access probability regulates the influx of packets to mitigate system congestion based on current network conditions. Furthermore, to accommodate the network’s varied transmission demands, packets generated by sensor nodes (SNs) are categorized into two types according to their sensitivity to latency. A discrete−time queueing model with preemptive priority is then established to evaluate the performance of different packets and the CH. Numerical results show how different parameters affect network performance and demonstrate that the proposed mixed packet forwarding mechanism can effectively manage the trade−off between latency and energy consumption, outperforming the traditional mechanism within a specific range of parameters. Full article

(This article belongs to the Section Sensor Networks)

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14 pages, 4022 KiB

Open AccessArticle

A Label-Free Colorimetric Aptasensor for Flavokavain B Detection

by Sisi Ke, Ningrui Wang, Xingyu Chen, Jiangwei Tian, Jiwei Li and Boyang Yu

Sensors 2025, 25(2), 569; https://doi.org/10.3390/s25020569 - 19 Jan 2025

Viewed by 474

Abstract

Flavokavain B (FKB), a hepatotoxic chalcone from Piper methysticum (kava), has raised safety concerns due to its role in disrupting redox homeostasis and inducing apoptosis in hepatocytes. Conventional chromatographic methods for FKB detection, while sensitive, are costly and impractical for field applications. In [...] Read more.

Flavokavain B (FKB), a hepatotoxic chalcone from Piper methysticum (kava), has raised safety concerns due to its role in disrupting redox homeostasis and inducing apoptosis in hepatocytes. Conventional chromatographic methods for FKB detection, while sensitive, are costly and impractical for field applications. In this work, DNA aptamers were selected using the library-immobilized method and high-throughput sequencing. Three families of aptamers were obtained, and the best one named FKB-S showed a dissociation constant (K_D) of 280 nM using microscale thermophoresis. To demonstrate its practical utility, a rapid and label-free colorimetric aptasensor was developed based on aptamer-induced gold nanoparticle aggregation. This assay achieved a detection limit of 150 nM (43.46 ng/mL) and provided results within 10 min. Compared to traditional chromatographic methods, the aptasensor offers a simple, cost-effective, and equipment-free approach for on-site FKB detection, making it a promising tool for the quality control and safety monitoring of kava-based products in diverse environments. Full article

(This article belongs to the Special Issue Fluorescence Sensors for Biological and Medical Applications)

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14 pages, 9165 KiB

Open AccessArticle

Curvature Determination Method for Diverging Acoustic Lens of Underwater Acoustic Transducer

by Minze Li, Mingzhen Xin, Fanlin Yang, Yu Luo, Jinpeng Liu and Niuniu Wu

Sensors 2025, 25(2), 568; https://doi.org/10.3390/s25020568 - 19 Jan 2025

Viewed by 560

Abstract

Underwater acoustic transducers need to expand the coverage of acoustic signals as much as possible in most ocean explorations, and the directivity indicators of transducers are difficult to change after the device is packaged, which makes the emergence angle of the underwater acoustic [...] Read more.

Underwater acoustic transducers need to expand the coverage of acoustic signals as much as possible in most ocean explorations, and the directivity indicators of transducers are difficult to change after the device is packaged, which makes the emergence angle of the underwater acoustic transducer limited in special operating environments, such as polar regions, submarine volcanoes, and cold springs. Taking advantage of the refractive characteristics of sound waves propagating in different media, the directivity indicators can be controlled by installing an acoustic lens outside the underwater acoustic transducer. To increase the detection range of an underwater acoustic transducer in a specific marine environment, a curvature-determining method for the diverging acoustic lens of an underwater acoustic transducer is proposed based on the acoustic ray tracing theory. The relationship equation between the original directivity indicators of the underwater acoustic transducer and the emergence angle in the specific environment is constructed, and the slope of the acoustic lens at different positions of the underwater acoustic transducer is obtained by a progressive solution. Then, the least squares polynomial fitting of the acoustic lens slope at all the refractive positions is carried out to obtain the optimal curvature of the acoustic lens. Experiments are designed to verify the effectiveness of the curvature determination method for the diverging acoustic lens of an underwater acoustic transducer, and the directivity indicators of acoustic lenses under different materials and different marine environments are analyzed. The experimental results show that the acoustic lens can change the directivity of the underwater acoustic transducer without changing the acoustic unit array, and the curvature of the acoustic lens directly affects the directivity indicators after refraction, so the method proposed in this paper has important reference value for determining the optimal shape of the diverging acoustic lens. Full article

(This article belongs to the Section Navigation and Positioning)

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12 pages, 878 KiB

Open AccessCommunication

Depression Recognition Using Daily Wearable-Derived Physiological Data

by Xinyu Shui, Hao Xu, Shuping Tan and Dan Zhang

Sensors 2025, 25(2), 567; https://doi.org/10.3390/s25020567 - 19 Jan 2025

Viewed by 690

Abstract

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to [...] Read more.

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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20 pages, 7358 KiB

Open AccessArticle

Computer-Aided Supporting Models of Customized Crack Propagation Sensors for Analysis and Prototyping

by Paulina Kurnyta-Mazurek, Rafał Wrąbel and Artur Kurnyta

Sensors 2025, 25(2), 566; https://doi.org/10.3390/s25020566 - 19 Jan 2025

Viewed by 713

Abstract

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or [...] Read more.

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability. Due to those characteristics, it is necessary to develop a family of computer-aided supporting models for rapid prototyping and analysis of the new designs of sensors of various shapes and configurations, which this paper presents by use of simulation tools. For a prototyping of the sensor lay out, an algorithm is elaborated, based on an application created in LabVIEW 2022 software, which generates two spreadsheets formatted by the requirements of Autodesk Inventor 2014 and COMSOL Multiphysics 5.6 software, based on data entered by the user. As a result, a tailored-in-shape CCPS layout is prepared. A parametric model of the sensor is prepared in Autodesk Inventor software, which automatically changes its geometric dimensions after changing data in an MS Excel spreadsheet. Then, the generated layout is analyzed to obtain electromechanical characteristics for defined CCPS geometry and materials used in the COMSOL Multiphysics software. Another application is devoted to purely mechanical analysis. The graphical user interface (GUI) add-on based on the Abaqus 2018 software engine is prepared for advanced mechanical analysis simulations of sensor materials in selected loading scenarios. The GUI is used for entering material libraries and the selection of loading conditions and a type of specimen, while the results of the numerical analysis are delivered through Abaqus. The main advantage of the developed GUI is the capacity for personnel inexperienced in using the Abaqus environment to perform analysis. Some results of simulation tests carried out in both COMSOL Multiphysics as well as Abaqus software are delivered in this paper, using a predefined parametric sensor model. For example, using a rigid epoxy resin for an insulating layer shows a negligible difference in the level of strain compared to the structure during a simulated tensile test, specifically in the tested layer thickness range of up to 0.3 mm. However, during bending tests, an approx. 17% change in principal strain level can be observed through the top to bottom edge of the epoxy resin layer. The adopted methodology for carrying out simulation studies assumes the parallel use of a set of various computer-aided tools. This approach allows for taking advantage of individual software environments, which allows for expanding the scope of analyses and using the developed models and applications in further research activities. Full article

(This article belongs to the Special Issue Sensors and New Trends in Global Metrology)

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21 pages, 49659 KiB

Open AccessArticle

Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy

by Jinhua Liu, Yongsheng Shi, Dongjin Huang and Jiantao Qu

Sensors 2025, 25(2), 565; https://doi.org/10.3390/s25020565 - 19 Jan 2025

Viewed by 718

Abstract

The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft [...] Read more.

The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images. We first construct an EndoTissue dataset of soft tissue regions in endoscopic images and fine-tune the Segment Anything Model (SAM) based on EndoTissue to obtain a potent segmentation network. Given a sequence of monocular endoscopic images, this segmentation network can quickly obtain the tissue mask images. Additionally, we incorporate tissue masks into a dynamic scene reconstruction method called Tensor4D to effectively guide the reconstruction of 3D deformable soft tissues. Finally, we propose adopting the image enhancement model EDAU-Net to improve the quality of the rendered views. The experimental results show that our method can effectively focus on the soft tissue regions in the image, achieving higher fidelity in detail and geometric structural integrity in reconstruction compared to state-of-the-art algorithms. Feedback from the user study indicates high participant scores for our method. Full article

(This article belongs to the Collection Artificial Intelligence (AI) in Biomedical Imaging)

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14 pages, 5735 KiB

Open AccessArticle

Research on Fire Detection of Cotton Picker Based on Improved Algorithm

by Zhai Shi, Fangwei Wu, Changjie Han and Dongdong Song

Sensors 2025, 25(2), 564; https://doi.org/10.3390/s25020564 - 19 Jan 2025

Viewed by 347

Abstract

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is [...] Read more.

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article

(This article belongs to the Section Smart Agriculture)

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23 pages, 9291 KiB

Open AccessArticle

Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest

by Hamza Sonalcan, Enes Bilen, Bahar Ateş and Ahmet Çağdaş Seçkin

Sensors 2025, 25(2), 563; https://doi.org/10.3390/s25020563 - 19 Jan 2025

Viewed by 485

Abstract

In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort [...] Read more.

In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded. The collected IMU data underwent preprocessing and feature extraction, followed by the application of machine learning algorithms including KNN, decision tree, Random Forest, AdaBoost, and XGBoost. Among these, the XGBoost algorithm with a window size of 250 and a 75% overlap yielded the highest accuracy of 96.6%. The system demonstrated superior performance compared to other single-sensor systems, achieving an overall classification accuracy of 96.9%. This research contributes to the field by presenting a new dataset of basketball movements, comparing the effectiveness of various feature extraction and machine learning methods, and offering a scalable, efficient, and accurate action recognition system for basketball. Full article

(This article belongs to the Special Issue Inertial Measurement Units in Sport—2nd Edition)

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57 pages, 21747 KiB

Open AccessReview

Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview

by Paolo Visconti, Giuseppe Rausa, Carolina Del-Valle-Soto, Ramiro Velázquez, Donato Cafagna and Roberto De Fazio

Sensors 2025, 25(2), 562; https://doi.org/10.3390/s25020562 - 19 Jan 2025

Viewed by 776

Abstract

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, [...] Read more.

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. Full article

(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)

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