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Volume 25
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Sensors, Volume 25, Issue 4 (February-2 2025) – 291 articles

Cover Story (view full-size image): The human growth hormone (hGH) is a polypeptide hormone synthesized and secreted by the anterior pituitary gland, with excess levels linked to acromegaly-inducing pituitary adenomas and deficiencies associated with conditions such as short stature and Turner’s syndrome. This study explores the real-time biosensing of hGH using a microfluidic optical biosensor based on Reflectometric Interference Fourier Transform Spectroscopy (RIFTS). The platform features a nanoporous anodic alumina (NAA) monolayer, fabricated via two-step anodization to achieve 30–35 nm pores, later widened to 45 nm for enhanced sensitivity. Surface functionalization further improves selectivity toward hGH. The system exhibits a linear detection range of 12.5–100 µg/mL with a detection limit of 10.6 µg/mL, offering a cost-effective, rapid, and portable biosensing solution for clinical and diagnostic applications. View this paper
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27 pages, 8017 KiB  
Article
Quantum Variational vs. Quantum Kernel Machine Learning Models for Partial Discharge Classification in Dielectric Oils
by José Miguel Monzón-Verona, Santiago García-Alonso and Francisco Jorge Santana-Martín
Sensors 2025, 25(4), 1277; https://doi.org/10.3390/s25041277 - 19 Feb 2025
Viewed by 328
Abstract
In this paper, electrical discharge images are classified using AI with quantum machine learning techniques. These discharges were originated in dielectric mineral oils and were detected by a high-resolution optical sensor. The captured images were processed in a Scikit-image environment to obtain a [...] Read more.
In this paper, electrical discharge images are classified using AI with quantum machine learning techniques. These discharges were originated in dielectric mineral oils and were detected by a high-resolution optical sensor. The captured images were processed in a Scikit-image environment to obtain a reduced number of features or qubits for later training of quantum circuits. Two quantum binary classification models were developed and compared in the Qiskit environment for four discharge binary combinations. The first was a quantum variational model (QVM), and the second was a conventional support vector machine (SVM) with a quantum kernel model (QKM). The execution of these two models was realized on three fault-tolerant physical quantum IBM computers. The novelty of this article lies in its application to a real problem, unlike other studies that focus on simulated or theoretical data sets. In addition, a study is carried out on the impact of the number of qubits in QKM, and it is shown that increasing the number of qubits in this model significantly improves the accuracy in the classification of the four binary combinations studied. In the QVM, with two qubits, an accuracy of 92% was observed in the first discharge combination in the three quantum computers used, with a margin of error of 1% compared to the simulation obtained on classical computers. Full article
(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)
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21 pages, 6937 KiB  
Article
A Quantitative Analysis Study on the Effects of Moisture and Light Source on FTIR Fingerprint Image Quality
by Manjae Shin, Seungbong Lee, Seungbin Baek, Sunghoon Lee and Sungmin Kim
Sensors 2025, 25(4), 1276; https://doi.org/10.3390/s25041276 - 19 Feb 2025
Viewed by 279
Abstract
The frustrated total internal reflection (FTIR) optical fingerprint scanning method is widely used due to its cost-effectiveness. However, fingerprint image quality is highly dependent on fingertip surface conditions, with moisture generally considered a degrading factor. Interestingly, a prior study reported that excessive moisture [...] Read more.
The frustrated total internal reflection (FTIR) optical fingerprint scanning method is widely used due to its cost-effectiveness. However, fingerprint image quality is highly dependent on fingertip surface conditions, with moisture generally considered a degrading factor. Interestingly, a prior study reported that excessive moisture may improve image quality, though their findings were based on qualitative observations, necessitating further quantitative analysis. Additionally, since the FTIR method relies on optical principles, image quality is also influenced by the wavelength of the light source. In this study, we conducted a preliminary clinical experiment to quantitatively analyze the impact of moisture levels on fingertips (wet, dry, and control) and light wavelengths (red, green, and blue) on FTIR fingerprint image quality. A total of 20 male and female participants with no physical impairments were involved. The results suggest that FTIR fingerprint image quality may improve under wet conditions and when illuminated with green and blue light sources compared to dry conditions and red light. Statistical evidence supports this consistent trend. However, given the limited sample size, the statistical validity and generalizability of these findings should be interpreted with caution. These insights provide a basis for optimizing fingerprint imaging conditions, potentially enhancing the reliability and accuracy of automatic fingerprint identification systems (AFIS) by reducing variations in individual fingerprint quality. Full article
(This article belongs to the Special Issue Biometrics-Based Authentication: Advancements and Real-World Implementations)
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29 pages, 34115 KiB  
Article
Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation
by Aoyang Bai, Hongyun Song, Yan Wu, Shurong Dong, Gang Feng and Hao Jin
Sensors 2025, 25(4), 1275; https://doi.org/10.3390/s25041275 - 19 Feb 2025
Viewed by 294
Abstract
Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention [...] Read more.
Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention Transformer Network), an advanced neural network specifically tailored for multichannel temporal tasks. SWCTNet integrates IMU and surface electromyography (sEMG) data through sliding window convolution and channel-time attention mechanisms, enabling the efficient extraction of temporal features. This model enables the prediction of muscle activation patterns and kinematics using exclusively IMU data. The experimental results demonstrate that the SWCTNet model achieves recognition accuracies ranging from 87.93% to 91.03% on public temporal datasets and an impressive 98% on self-collected datasets. Additionally, SWCTNet exhibits remarkable precision and stability in generative tasks: the normalized DTW distance was 0.12 for the normal group and 0.25 for the patient group when using the self-collected dataset. This study positions SWCTNet as an advanced tool for extracting musculoskeletal features from IMU data, paving the way for innovative applications in real-time monitoring and personalized rehabilitation at home. This approach demonstrates significant potential for long-term musculoskeletal function monitoring in non-clinical or home settings, advancing the capabilities of IMU-based wearable devices. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
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28 pages, 474 KiB  
Article
Security Performance Analysis of Downlink Double Intelligent Reflecting Surface Non-Orthogonal Multiple Access Network for Edge Users
by Nguyen Thai Anh, Nguyen Hoang Viet, Dinh-Thuan Do and Adão Silva
Sensors 2025, 25(4), 1274; https://doi.org/10.3390/s25041274 - 19 Feb 2025
Viewed by 184
Abstract
In this work, we study the security performance of a double intelligent reflecting surface non-orthogonal multiple access (DIRS-NOMA) wireless communication system supporting communication for a group of two NOMA users (UEs) at the edge, with the existence of an eavesdropping device (ED). We [...] Read more.
In this work, we study the security performance of a double intelligent reflecting surface non-orthogonal multiple access (DIRS-NOMA) wireless communication system supporting communication for a group of two NOMA users (UEs) at the edge, with the existence of an eavesdropping device (ED). We also assume that there is no direct connection between the BS and the UEs. From the proposed model, we compute closed-form expressions for the secrecy outage probability (SOP) and the average security rate (ASR) for each UE. After that, we discuss and analyze the system security performance according to the NOMA power allocation for each user and the number of IRS counter-emission elements. In addition, we analyze the SOP of both the considered DIRS-NOMA and conventional NOMA systems to demonstrate that DIRS-NOMA systems have much better security than conventional NOMA systems. Based on the analytical results, we develop an ASR optimization algorithm using the alternating optimization method, combining NOMA power allocation factor optimization and IRS passive beam optimization through the Lagrange double transform. The derived analytical expressions are validated through Monte Carlo simulations. Full article
(This article belongs to the Section Communications)
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15 pages, 1201 KiB  
Article
Effect of Difference of Sensory Modality in Cognitive Task on Postural Control During Quiet Stance
by Yusuke Sakaki, Naoya Hasegawa, Ami Kawata, Hiromasa Akagi, Minori Sawada and Hiroki Mani
Sensors 2025, 25(4), 1273; https://doi.org/10.3390/s25041273 - 19 Feb 2025
Viewed by 182
Abstract
Cognitive loads impact postural control; however, the specific influence of sensory modalities employed in cognitive tasks during motor-cognitive dual tasks remains unclear. This study investigated the distinct effects of visual and auditory cognitive tasks on static postural control while controlling for differences in [...] Read more.
Cognitive loads impact postural control; however, the specific influence of sensory modalities employed in cognitive tasks during motor-cognitive dual tasks remains unclear. This study investigated the distinct effects of visual and auditory cognitive tasks on static postural control while controlling for differences in task content. Twenty-five healthy young adults were instructed to maintain a quiet stance on a force plate under three cognitive task conditions: a single motor task (control), a paced visual serial addition task (visual), and a paced auditory serial addition task (auditory). Center of pressure (COP) displacements were measured, and both linear (e.g., sway area) and non-linear assessments of postural control were analyzed. Results revealed a significant reduction in sway area during cognitive tasks compared to the control condition. However, under the auditory condition, the power spectrum density of COP displacements in the moderate frequency band was significantly higher than those in the control and visual conditions, accompanied by a notable increase in the mean power frequency. These findings suggest that auditory cognitive load exerts a more significant effect on postural control than visual cognitive load during motor-cognitive dual tasks. This highlights the relevance of sensory modalities in cognitive loads for effective fall-risk assessment. Full article
(This article belongs to the Special Issue Wearable Sensors for Postural Stability and Fall Risk Analyses)
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18 pages, 1514 KiB  
Article
Contrastive Learning with Global and Local Representation for Mixed-Type Wafer Defect Recognition
by Shantong Yin, Yangkun Zhang and Rui Wang
Sensors 2025, 25(4), 1272; https://doi.org/10.3390/s25041272 - 19 Feb 2025
Viewed by 169
Abstract
Recognizing defect patterns in semiconductor wafer bin maps (WBMs) poses a critical challenge in the integrated circuit (IC) manufacturing industry. The accurate classification and segmentation of these defect patterns are of utmost significance as they are key to tracing the root causes of [...] Read more.
Recognizing defect patterns in semiconductor wafer bin maps (WBMs) poses a critical challenge in the integrated circuit (IC) manufacturing industry. The accurate classification and segmentation of these defect patterns are of utmost significance as they are key to tracing the root causes of defects, thereby reducing costs and enhancing both product efficiency and quality. As the manufacturing process grows in complexity, the WBM becomes intricate when multiple defect patterns coexist on a single wafer, making the recognition task increasingly complicated. In addition, traditional supervised learning methods require a large number of labeled samples, which is labor-intensive. In this paper, we present a self-supervised contrastive learning framework for the classification and segmentation of mixed-type WBM defect patterns. Our model incorporates a global module for contrastive learning that captures image-level representations, alongside a local module that targets the comprehension of regional details, which is helpful for the segmentation of defective patterns. Experimental results demonstrate that our model performs effectively in scenarios where there is a limited number of labeled examples and a wealth of unlabeled ones. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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26 pages, 25224 KiB  
Article
A Multi-Task Causal Knowledge Fault Diagnosis Method for PMSM-ITSF Based on Meta-Learning
by Ping Lan, Liguo Yao, Yao Lu and Taihua Zhang
Sensors 2025, 25(4), 1271; https://doi.org/10.3390/s25041271 - 19 Feb 2025
Viewed by 158
Abstract
In the process of diagnosing the inter-turn short circuit fault of the joint permanent magnet synchronous motor of an industrial robot, due to the small and sparse fault sample data, it is easy to misdiagnose, and it is difficult to quickly and accurately [...] Read more.
In the process of diagnosing the inter-turn short circuit fault of the joint permanent magnet synchronous motor of an industrial robot, due to the small and sparse fault sample data, it is easy to misdiagnose, and it is difficult to quickly and accurately evaluate the fault degree, lock the fault location, and track the fault causes. A multi-task causal knowledge fault diagnosis method for inter-turn short circuits of permanent magnet synchronous motors based on meta-learning is proposed. Firstly, the variation of parameters under the motor’s inter-turn short circuit fault is thoroughly investigated, and the fault characteristic quantity is selected. Comprehensive simulations are conducted using Simulink, Simplorer, and Maxwell to generate data under different inter-turn short circuit fault states; meanwhile, the sample data are accurately labeled. Secondly, the sample data are introduced into the learning network for training, and the multi-task synchronous diagnosis of the fault degree and position of the short circuit between turns is realized. Finally, the Neo4j database based on causality knowledge of motor inter-turn short circuit fault is constructed. Experiments show that this method can diagnose the fault location, fault degree, and fault cause of the motor with different voltage unbalanced degrees. The diagnosis accuracy of fault degree is 99.75 ± 0.25%, and the diagnosis accuracy of fault location and fault degree is 99.45 ± 0.21%. Full article
(This article belongs to the Special Issue Robot Swarm Collaboration in the Unstructured Environment)
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17 pages, 1463 KiB  
Article
Interpretable Probabilistic Identification of Depression in Speech
by Stavros Ntalampiras
Sensors 2025, 25(4), 1270; https://doi.org/10.3390/s25041270 - 19 Feb 2025
Viewed by 196
Abstract
Mental health assessment is typically carried out via a series of conversation sessions with medical professionals, where the overall aim is the diagnosis of mental illnesses and well-being evaluation. Despite its arguable socioeconomic significance, national health systems fail to meet the increased demand [...] Read more.
Mental health assessment is typically carried out via a series of conversation sessions with medical professionals, where the overall aim is the diagnosis of mental illnesses and well-being evaluation. Despite its arguable socioeconomic significance, national health systems fail to meet the increased demand for such services that has been observed in recent years. To assist and accelerate the diagnosis process, this work proposes an AI-based tool able to provide interpretable predictions by automatically processing the recorded speech signals. An explainability-by-design approach is followed, where audio descriptors related to the problem at hand form the feature vector (Mel-scaled spectrum summarization, Teager operator and periodicity description), while modeling is based on Hidden Markov Models adapted from an ergodic universal one following a suitably designed data selection scheme. After extensive and thorough experiments adopting a standardized protocol on a publicly available dataset, we report significantly higher results with respect to the state of the art. In addition, an ablation study was carried out, providing a comprehensive analysis of the relevance of each system component. Last but not least, the proposed solution not only provides excellent performance, but its operation and predictions are transparent and interpretable, laying out the path to close the usability gap existing between such systems and medical personnel. Full article
(This article belongs to the Special Issue Advances in Acoustic Sensors and Deep Audio Pattern Recognition)
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22 pages, 8135 KiB  
Article
Secondary Frequency Modulation Strategy for SiC Inverters Based on Periodic Spread Spectrum Modulation
by Yanfei Cao, Junjie Su, Yan Yan, Zhichen Lin and Tingna Shi
Sensors 2025, 25(4), 1269; https://doi.org/10.3390/s25041269 - 19 Feb 2025
Viewed by 235
Abstract
This paper aims to design the modulation strategy of SiC motor controller for vehicles with low EMI, low device loss and low voltage/current ripple. The equivalent evaluation model of input voltage, output current and switching loss of the inverter under periodic spread spectrum [...] Read more.
This paper aims to design the modulation strategy of SiC motor controller for vehicles with low EMI, low device loss and low voltage/current ripple. The equivalent evaluation model of input voltage, output current and switching loss of the inverter under periodic spread spectrum modulation strategy is constructed, and the quantitative relationship between each parameter of spread spectrum modulation and the three indicators is established. The input/output performance and loss level of the inverter under different spread spectrum modulation strategies are evaluated. On this basis, based on the carrier frequency distribution characteristics of periodic signal spread spectrum modulation, a “secondary frequency modulation” strategy is proposed to reduce the inverter-conducted EMI to a greater extent under the limited spread spectrum range. Experimental results show that compared with the single periodic signal spread spectrum modulation, the “secondary frequency modulation” strategy can reduce the peak value of inverter-conducted EMI to a greater extent without increasing the ripple and loss of the inverter. Full article
(This article belongs to the Section Electronic Sensors)
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16 pages, 8654 KiB  
Communication
Analysis of the Influence of the Dynamic Characteristics of an Optical Bench on Optical Mechanical System Imaging Under Vibration Conditions
by Yijian Wang, Ping Jia, Ping Wang, Zhongyu Liu, Yupeng Zhang and Lu Sun
Sensors 2025, 25(4), 1268; https://doi.org/10.3390/s25041268 - 19 Feb 2025
Viewed by 259
Abstract
The imaging processes of optoelectronic devices are affected by vibration in the transportation platform, which can cause image shaking and blurring. Nowadays, devices often solve problems of image shaking and blurring using motion rotors. However, there is relatively little research on the influence [...] Read more.
The imaging processes of optoelectronic devices are affected by vibration in the transportation platform, which can cause image shaking and blurring. Nowadays, devices often solve problems of image shaking and blurring using motion rotors. However, there is relatively little research on the influence of optical fixtures themselves under vibration conditions. This article analyzes the influence of sinusoidal vibrations on the MTF of an imaging process, pointing out the randomness of imaging effects under conditions of low-frequency vibration. To address the issue of low-frequency vibration effects, an analysis of the designs, and experimental verification, of a specific optical system mount were conducted to verify the influence of the mount’s own properties on imaging under random vibration conditions, providing a basis for the design of future optical mechanical systems. Full article
(This article belongs to the Section Optical Sensors)
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22 pages, 2587 KiB  
Article
Toward Convenient and Accurate IMU-Based Gait Analysis
by Mohamed Boutaayamou, Doriane Pelzer, Cédric Schwartz, Sophie Gillain, Gaëtan Garraux, Jean-Louis Croisier, Jacques G. Verly and Olivier Brüls
Sensors 2025, 25(4), 1267; https://doi.org/10.3390/s25041267 - 19 Feb 2025
Viewed by 314
Abstract
While inertial measurement unit (IMU)-based systems have shown their potential in quantifying medically significant gait parameters, it remains to be determined whether they can provide accurate and reliable parameters both across various walking conditions and in healthcare settings. Using an IMU-based system we [...] Read more.
While inertial measurement unit (IMU)-based systems have shown their potential in quantifying medically significant gait parameters, it remains to be determined whether they can provide accurate and reliable parameters both across various walking conditions and in healthcare settings. Using an IMU-based system we previously developed, with one IMU module on each subject’s heel, we quantify the gait parameters of 55 men and 46 women, all healthy and aged 40–65, in normal, dual-task, and fast walking conditions. We evaluate their intra-session reliability, and we establish a new reference database of such parameters showing good to excellent reliability. ICC(2,1) assesses relative reliability, while SEM% and MDC% assess absolute reliability. The reliability is excellent for all spatiotemporal gait parameters and the stride length (SL) symmetry ratio (ICC ≥ 0.90, SEM% ≤ 4.5%, MDC% ≤ 12.4%) across all conditions. It is good to excellent for the fast walking performance (FWP) indices of stride (Sr), stance (Sa), double-support (DS), and step (St) times; gait speed (GS); and the GS normalized to leg length (GSn1) and body height (GSn2) (ICC ≥ 0.91, |SEM%| ≤ 10.0%, |MDC%| ≤ 27.6%). Men have a higher swing time (Sw) and SL across all conditions. The following parameters are gender-independent: (1) Sa, DS, GSn1, GSn2; (2) the symmetry ratios of SL and GS, as well as Sa% and Sw% (representing Sa and Sw as percentages of Sr); and (3) the FWPs of Sr, Sa, Sw, DS, St, cadence, Sa% and Sw%. Our results provide reference values with new insights into gender FWP comparisons rarely reported in the literature. The advantages and reliability of our IMU-based system make it suitable in medical applications such as prosthetic evaluation, fall risk assessment, and rehabilitation. Full article
(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)
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27 pages, 1569 KiB  
Article
Federated Learning Framework for Real-Time Activity and Context Monitoring Using Edge Devices
by Rania A. Alharbey and Faisal Jamil
Sensors 2025, 25(4), 1266; https://doi.org/10.3390/s25041266 - 19 Feb 2025
Viewed by 236
Abstract
With the increasing need for effective elderly care solutions, this paper presents a novel federated learning-based system that uses smartphones as edge devices to monitor and enhance elderly care in real-time. In this system, elderly individuals carry smartphones equipped with Inertial Measurement Unit [...] Read more.
With the increasing need for effective elderly care solutions, this paper presents a novel federated learning-based system that uses smartphones as edge devices to monitor and enhance elderly care in real-time. In this system, elderly individuals carry smartphones equipped with Inertial Measurement Unit (IMU) sensors, including an accelerometer for activity recognition, a barometer for altitude detection, and a combination of the accelerometer, gyrometer, and magnetometer for location tracking. The smartphones continuously collect real-time data as the elderly individuals go about their daily routines. These data are processed locally on each device to train personalized models for activity recognition and contextual monitoring. The locally trained models are then sent to a federated server, where the FedAvg algorithm is used to aggregate model parameters, creating an improved global model. This aggregated model is subsequently distributed back to the smartphones, enhancing their activity recognition capabilities. In addition to model updates, information on the users’ location, altitude, and context is sent to the server to enable the continuous monitoring and tracking of the elderly. By integrating activity recognition with location and altitude data, the system provides a comprehensive framework for tracking and supporting the well-being of elderly individuals across diverse environments. This approach offers a scalable and efficient solution for elderly care, contributing to enhanced safety and overall quality of life. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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27 pages, 7952 KiB  
Article
Evaluation of Long-Term Performance of Six PM2.5 Sensor Types
by Karoline K. Barkjohn, Robert Yaga, Brittany Thomas, William Schoppman, Kenneth S. Docherty and Andrea L. Clements
Sensors 2025, 25(4), 1265; https://doi.org/10.3390/s25041265 - 19 Feb 2025
Viewed by 273
Abstract
From July 2019 to January 2021, six models of PM2.5 air sensors were operated at seven air quality monitoring sites across the U.S. in Arizona, Colorado, Delaware, Georgia, North Carolina, Oklahoma, and Wisconsin. Common PM sensor data issues were identified, including repeat [...] Read more.
From July 2019 to January 2021, six models of PM2.5 air sensors were operated at seven air quality monitoring sites across the U.S. in Arizona, Colorado, Delaware, Georgia, North Carolina, Oklahoma, and Wisconsin. Common PM sensor data issues were identified, including repeat zero measurements, false high outliers, baseline shift, varied relationships between the sensor and monitor, and relative humidity (RH) influences. While these issues are often easy to identify during colocation, they are more challenging to identify or correct during deployment since it is hard to differentiate between real pollution events and sensor malfunctions. Air sensors may exhibit wildly different performances even if they have the same or similar internal components. Commonly used RH corrections may still have variable bias by hour of the day and seasonally. Most sensors show promise in achieving the U.S. Environmental Protection Agency (EPA) performance targets, and the findings here can be used to improve their performance and reliability further. This evaluation generated a robust dataset of colocated air sensor and monitor data, and by making it publicly available along with the results presented in this paper, we hope the dataset will be an asset to the air sensor community in understanding sensor performance and validating new methods. Full article
(This article belongs to the Special Issue Recent Trends in Air Quality Sensing)
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19 pages, 2090 KiB  
Article
Predicting Perennial Ryegrass Cultivars and the Presence of an Epichloë Endophyte in Seeds Using Near-Infrared Spectroscopy (NIRS)
by Simone Vassiliadis, Kathryn M. Guthridge, Priyanka Reddy, Emma J. Ludlow, Inoka K. Hettiarachchige and Simone J. Rochfort
Sensors 2025, 25(4), 1264; https://doi.org/10.3390/s25041264 - 19 Feb 2025
Viewed by 235
Abstract
Perennial ryegrass is an important temperate grass used for forage and turf worldwide. It forms symbiotic relationships with endophytic fungi (endophytes), conferring pasture persistence and resistance to herbivory. Endophyte performance can be influenced by the host genotype, as well as environmental factors such [...] Read more.
Perennial ryegrass is an important temperate grass used for forage and turf worldwide. It forms symbiotic relationships with endophytic fungi (endophytes), conferring pasture persistence and resistance to herbivory. Endophyte performance can be influenced by the host genotype, as well as environmental factors such as seed storage conditions. It is therefore critical to confirm seed quality and purity before a seed is sown. DNA-based methods are often used for quality control purposes. Recently, near-infrared spectroscopy (NIRS) coupled with hyperspectral imaging was used to discriminate perennial ryegrass cultivars and endophyte presence in individual seeds. Here, a NIRS-based analysis of bulk seeds was used to develop models for discriminating perennial ryegrass cultivars (Alto, Maxsyn, Trojan and Bronsyn), each hosting a suite of eight to eleven different endophyte strains. Sub-sampling, six per bag of seed, was employed to minimize misclassification error. Using a nested PLS-DA approach, cultivars were classified with an overall accuracy of 94.1–98.6% of sub-samples, whilst endophyte presence or absence was discriminated with overall accuracies between 77.8% and 96.3% of sub-samples. Hierarchical classification models were developed to discriminate bulked seed samples quickly and easily with minimal misclassifications of cultivars (<8.9% of sub-samples) or endophyte status within each cultivar (<11.3% of sub-samples). In all cases, greater than four of the six sub-samples were correctly classified, indicating that innate variation within a bag of seeds can be overcome using this strategy. These models could benefit turf- and pasture-based industries by providing a tool that is easy, cost effective, and can quickly discriminate seed bulks based on cultivar and endophyte content. Full article
(This article belongs to the Special Issue Spectroscopy for Biochemical Imaging and Sensing)
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11 pages, 3024 KiB  
Article
Hydrogenated Amorphous Silicon Charge-Selective Contact Devices on a Polyimide Flexible Substrate for Dosimetry and Beam Flux Measurements
by Mauro Menichelli, Saba Aziz, Aishah Bashiri, Marco Bizzarri, Clarissa Buti, Lucio Calcagnile, Daniela Calvo, Mirco Caprai, Domenico Caputo, Anna Paola Caricato, Roberto Catalano, Massimo Cazzanelli, Roberto Cirio, Giuseppe Antonio Pablo Cirrone, Federico Cittadini, Tommaso Croci, Giacomo Cuttone, Giampiero de Cesare, Paolo De Remigis, Sylvain Dunand, Michele Fabi, Luca Frontini, Catia Grimani, Mariacristina Guarrera, Hamza Hasnaoui, Maria Ionica, Keida Kanxheri, Matthew Large, Francesca Lenta, Valentino Liberali, Nicola Lovecchio, Maurizio Martino, Giuseppe Maruccio, Giovanni Mazza, Anna Grazia Monteduro, Arianna Morozzi, Augusto Nascetti, Stefania Pallotta, Andrea Papi, Daniele Passeri, Maddalena Pedio, Marco Petasecca, Giada Petringa, Francesca Peverini, Pisana Placidi, Matteo Polo, Alberto Quaranta, Gianluca Quarta, Silvia Rizzato, Federico Sabbatini, Leonello Servoli, Alberto Stabile, Cinzia Talamonti, Jonathan Emanuel Thomet, Luca Tosti, Monica Setia Vasquez Mora, Mattia Villani, Richard James Wheadon, Nicolas Wyrsch and Nicola Zemaadd Show full author list remove Hide full author list
Sensors 2025, 25(4), 1263; https://doi.org/10.3390/s25041263 - 19 Feb 2025
Viewed by 272
Abstract
Hydrogenated amorphous silicon (a-Si:H) devices on flexible substrates are currently being studied for application in dosimetry and beam flux measurements. The necessity of in vivo dosimetry requires thin devices with maximal transparency and flexibility. For this reason, a thin (<10 µm) a-Si:H device [...] Read more.
Hydrogenated amorphous silicon (a-Si:H) devices on flexible substrates are currently being studied for application in dosimetry and beam flux measurements. The necessity of in vivo dosimetry requires thin devices with maximal transparency and flexibility. For this reason, a thin (<10 µm) a-Si:H device deposited on a thin polyimide sheet is a very valid option for this application. Furthermore, a-Si:H is a material that has an intrinsically high radiation hardness. In order to develop these devices, the HASPIDE (Hydrogenated Amorphous Silicon Pixel Detectors) collaboration has implemented two different device configurations: n-i-p type diodes and charge-selective contact devices.Charge-selective contact-based devices have been studied for solar cell applications and, recently, the above-mentioned collaboration has tested these devices for X-ray dose measurements. In this paper, the HASPIDE collaboration has studied the X-ray and proton response of charge-selective contact devices deposited on Polyimide. The linearity of the photocurrent response to X-ray versus dose-rate has been assessed at various bias voltages. The sensitivity to protons has also been studied at various bias voltages and the wide range linearity has been tested for fluxes in the range from 8.3 × 107 to 2.49 × 1010 p/(cm2 s). Full article
(This article belongs to the Special Issue Advances in Physical, Chemical, and Biosensors)
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25 pages, 7292 KiB  
Article
Flexible Optimal Control of the CFBB Combustion System Based on ESKF and MPC
by Lei Han, Lingmei Wang, Enlong Meng, Yushan Liu and Shaoping Yin
Sensors 2025, 25(4), 1262; https://doi.org/10.3390/s25041262 - 19 Feb 2025
Viewed by 228
Abstract
In order to deeply absorb the power generation of new energy, coal-fired circulating fluidized bed units are widely required to participate in power grid dispatching. However, the combustion system of the units faces problems such as decreased control performance, strong coupling of controlled [...] Read more.
In order to deeply absorb the power generation of new energy, coal-fired circulating fluidized bed units are widely required to participate in power grid dispatching. However, the combustion system of the units faces problems such as decreased control performance, strong coupling of controlled signals, and multiple interferences in measurement signals during flexible operation. To this end, this paper proposes a model predictive control (MPC) scheme based on the extended state Kalman filter (ESKF). This scheme optimizes the MPC control framework. The ESKF is used to filter the collected output signals and jointly estimate the state and disturbance quantities in real time, thus promptly establishing a prediction model that reflects the true state of the system. Subsequently, taking the minimum output signal deviation of the main steam pressure and bed temperature and the control signal increment as objectives, a coordinated receding horizon optimization is carried out to obtain the optimal control signal of the control system within each control cycle. Tracking, anti-interference, and robustness experiments were designed to compare the control effects of ESKF-MPC, ID-PI, ID-LADRC, and MPC. The research results show that, when the system parameters had a ±30% perturbation, the adjustment time range of the main steam pressure and bed temperature loops of this method were 770~1600 s and 460~1100 s, respectively, and the ITAE indicator ranges were 0.615 × 105~1.74 × 105 and 3.9 × 106~6.75 × 106, respectively. The overall indicator values were smaller and more concentrated, and the robustness was stronger. In addition, the test results of the actual continuous variable condition process of the unit show that, compared with the PI strategy, after adopting the ESKF-MPC strategy, the overshoot of the main steam pressure loop of the combustion system was small, and the output signal was stable; the fluctuation range of the bed temperature loop was small, and the signal tracking was smooth; the overall control performance of the system was significantly improved. Full article
(This article belongs to the Section Industrial Sensors)
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13 pages, 2814 KiB  
Article
Physical Activity in Pre-Ambulatory Children with Cerebral Palsy: An Exploratory Validation Study to Distinguish Active vs. Sedentary Time Using Wearable Sensors
by Julie M. Orlando, Beth A. Smith, Jocelyn F. Hafer, Athylia Paremski, Matthew Amodeo, Michele A. Lobo and Laura A. Prosser
Sensors 2025, 25(4), 1261; https://doi.org/10.3390/s25041261 - 19 Feb 2025
Viewed by 323
Abstract
Wearable inertial sensor technology affords opportunities to record the physical activity of young children in their natural environments. The interpretation of these data, however, requires validation. The purpose of this study was to develop and establish the criterion validity of a method of [...] Read more.
Wearable inertial sensor technology affords opportunities to record the physical activity of young children in their natural environments. The interpretation of these data, however, requires validation. The purpose of this study was to develop and establish the criterion validity of a method of quantifying active and sedentary physical activity using an inertial sensor for pre-ambulatory children with cerebral palsy. Ten participants were video recorded during 30 min physical therapy sessions that encouraged gross motor play activities, and the video recording was behaviorally coded to identify active and sedentary time. A receiver operating characteristic curve identified the optimal threshold to maximize true positive and minimize false positive active time for eight participants in the development dataset. The threshold was 0.417 m/s2 and was then validated with the remaining two participants; the percent of true positives and true negatives was 92.2 and 89.7%, respectively. We conclude that there is potential for raw sensor data to be used to quantify active and sedentary time in pre-ambulatory children with physical disability, and raw acceleration data may be more generalizable than the sensor-specific activity counts commonly reported in the literature. Full article
(This article belongs to the Special Issue Innovative Applications of Wearable Sensors in Musculoskeletal Biomechanics and Rehabilitation)
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15 pages, 5107 KiB  
Article
Feasibility Study of Photoelectrochemical Sensing of Glucose and Urea Using BiVO4 and BiVO4/BiOCl Photoanodes
by Monika Skruodiene, Jelena Kovger-Jarosevic, Irena Savickaja, Jurga Juodkazyte and Milda Petruleviciene
Sensors 2025, 25(4), 1260; https://doi.org/10.3390/s25041260 - 19 Feb 2025
Viewed by 250
Abstract
This study investigates the photoelectrochemical (PEC) performance of molybdenum-doped bismuth vanadate (Mo-doped BiVO4) and its heterojunction with the BiOCl layer in glucose and urea sensing. Photoelectrochemical analyses, including cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), revealed that the formation of [...] Read more.
This study investigates the photoelectrochemical (PEC) performance of molybdenum-doped bismuth vanadate (Mo-doped BiVO4) and its heterojunction with the BiOCl layer in glucose and urea sensing. Photoelectrochemical analyses, including cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), revealed that the formation of a heterojunction enhanced charge carrier separation. The impact of the interaction between the surface of the photoanode and analytes on sensing performance was systematically evaluated. Among the tested configurations, Mo-doped BiVO4 exhibited superior glucose sensing with a limit of detection (LOD) of 0.173 µM, while BiVO4/BiOCl demonstrated an LOD of 2.474 µM. In the context of urea sensing, Mo-doped BiVO4 demonstrated an LOD of 0.656 µM, while BiVO4/BiOCl exhibited an LOD of 0.918 µM. Notably, despite the enhanced PEC activity observed in heterostructured samples, Mo-doped BiVO4 exhibited superior sensing performance, attributable to good interaction with analytes. The photocurrent response trends—an increase with glucose concentration and a decrease with urea concentration—were attributed to oxidation and adsorption phenomena on the photoanode surface. These findings underscore the critical role of photoanode surface engineering in advancing PEC sensor technology, paving the way for more efficient environmental and biomedical applications. Full article
(This article belongs to the Special Issue Recent Advances in Photo(electro)chemical Sensing and Sensors)
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46 pages, 1856 KiB  
Article
A Numerical and Experimental Investigation of the Most Fundamental Time-Domain Input–Output System Identification Methods for the Normal Modal Analysis of Flexible Structures
by Şefika İpek Lök, Carmine Maria Pappalardo, Rosario La Regina and Domenico Guida
Sensors 2025, 25(4), 1259; https://doi.org/10.3390/s25041259 - 19 Feb 2025
Viewed by 259
Abstract
This paper deals with developing a comparative study of the principal time-domain system identification methods suitable for performing an experimental modal analysis of structural systems. To this end, this work focuses first on analyzing and reviewing the mathematical background concerning the analytical methods [...] Read more.
This paper deals with developing a comparative study of the principal time-domain system identification methods suitable for performing an experimental modal analysis of structural systems. To this end, this work focuses first on analyzing and reviewing the mathematical background concerning the analytical methods and the computational algorithms of interest for this study. The methods considered in the paper are referred to as the AutoRegressive eXogenous (ARX) method, the State-Space ESTimation (SSEST) method, the Numerical Algorithm for Subspace State-Space System Identification (N4SID), the Eigensystem Realization Algorithm (ERA) combined with the Observer/Kalman Filter Identification (OKID) method, and the Transfer Function ESTimation (TFEST) method. Starting from the identified models estimated through the methodologies reported in the paper, a set of second-order configuration-space dynamical models of the structural system of interest can also be determined by employing an estimation method for the Mass, Stiffness, and Damping (MSD) matrices. Furthermore, in practical applications, the correct estimation of the damping matrix is severely hampered by noise that corrupts the input and output measurements. To address this problem, in this paper, the identification of the damping matrix is improved by employing the Proportional Damping Coefficient (PDC) identification method, which is based on the use of the identified set of natural frequencies and damping ratios found for the case study analyzed in the paper. This work also revisits the critical aspects and pitfalls related to using the Model Order Reduction (MOR) approach combined with the Balanced Truncation Method (BTM) to reduce the dimensions of the identified state-space models. Finally, this work analyzes the performance of all the fundamental system identification methods mentioned before when applied to the experimental modal analysis of flexible structures. This is achieved by carrying out an experimental campaign based on the use of a vibrating test rig, which serves as a demonstrative example of a typical structural system. The complete set of experimental results found in this investigation is reported in the appendix of the paper. Full article
(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)
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25 pages, 2389 KiB  
Review
A Critical Analysis of Cooperative Caching in Ad Hoc Wireless Communication Technologies: Current Challenges and Future Directions
by Muhammad Ali Naeem, Rehmat Ullah, Sushank Chudhary and Yahui Meng
Sensors 2025, 25(4), 1258; https://doi.org/10.3390/s25041258 - 19 Feb 2025
Viewed by 279
Abstract
The exponential growth of wireless traffic has imposed new technical challenges on the Internet and defined new approaches to dealing with its intensive use. Caching, especially cooperative caching, has become a revolutionary paradigm shift to advance environments based on wireless technologies to enable [...] Read more.
The exponential growth of wireless traffic has imposed new technical challenges on the Internet and defined new approaches to dealing with its intensive use. Caching, especially cooperative caching, has become a revolutionary paradigm shift to advance environments based on wireless technologies to enable efficient data distribution and support the mobility, scalability, and manageability of wireless networks. Mobile ad hoc networks (MANETs), wireless mesh networks (WMNs), Wireless Sensor Networks (WSNs), and Vehicular ad hoc Networks (VANETs) have adopted caching practices to overcome these hurdles progressively. In this paper, we discuss the problems and issues in the current wireless ad hoc paradigms as well as spotlight versatile cooperative caching as the potential solution to the increasing complications in ad hoc networks. We classify and discuss multiple cooperative caching schemes in distinct wireless communication contexts and highlight the advantages of applicability. Moreover, we identify research directions to further study and enhance caching mechanisms concerning new challenges in wireless networks. This extensive review offers useful findings on the design of sound caching strategies in the pursuit of enhancing next-generation wireless networks. Full article
(This article belongs to the Section Sensor Networks)
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12 pages, 989 KiB  
Article
Circularly Polarized Reconfigurable MIMO Antenna for WLAN Applications
by Tu Le-Tuan, Thai Dinh Nguyen, Nguyen Viet-Duc Tran, Hung Tran and Dat Nguyen-Tien
Sensors 2025, 25(4), 1257; https://doi.org/10.3390/s25041257 - 19 Feb 2025
Viewed by 238
Abstract
This paper presents a simple design of a two-element antenna with circularly polarized (CP) reconfigurability for multiple-input multiple-output wireless local-area network (WLAN) applications. A MIMO element consists of a reconfigurable feeding network, a CP source, and a 2 × 2 unit-cell metasurface (MS). [...] Read more.
This paper presents a simple design of a two-element antenna with circularly polarized (CP) reconfigurability for multiple-input multiple-output wireless local-area network (WLAN) applications. A MIMO element consists of a reconfigurable feeding network, a CP source, and a 2 × 2 unit-cell metasurface (MS). By controlling the ON/OFF state of PIN diodes, the proposed MIMO system can operate in either right-hand CP (RHCP) or left-hand CP (LHCP) for all ports, or either RHCP or LHCP for each port. For all operating modes, the proposed antenna exhibits good performance with a matching performance of less than –10 dB, an axial ratio of lower than 3 dB, as well as an inter-port isolation of better than 24 dB at 2.45 GHz. Additionally, the MIMO diversity performance is also satisfied by the proposed antenna. Compared to related works, the proposed antenna has advantages of high gain and compact size, as well as a simple switching mechanism with a small number of PIN diodes. Full article
(This article belongs to the Section Communications)
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22 pages, 4937 KiB  
Article
Method for Detecting Disorder of a Nonlinear Dynamic Plant
by Xuechun Wang and Vladimir Eliseev
Sensors 2025, 25(4), 1256; https://doi.org/10.3390/s25041256 - 19 Feb 2025
Viewed by 157
Abstract
This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder. The CCF-AE method does not use the reference model of the dynamic object, but only [...] Read more.
This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder. The CCF-AE method does not use the reference model of the dynamic object, but only considers real-time behavior changes, given by input and output time series. The proposed method was used to detect disorder in the process of a nonlinear pH neutralization reaction, and was compared with the cumulative sum control chart (CUSUM) and the exponentially weighted moving variance control chart (EWMV). The CCF-AE method demonstrates a better true detection rate and lower false alarm rate than CUSUM and EWMV. Also, CCF-AE has more advantages in detecting disorder of complex nonlinear processes. Full article
(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)
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29 pages, 1809 KiB  
Review
Technological Progress Toward Peanut Disease Management: A Review
by Muhammad Asif, Aleena Rayamajhi and Md Sultan Mahmud
Sensors 2025, 25(4), 1255; https://doi.org/10.3390/s25041255 - 19 Feb 2025
Viewed by 366
Abstract
Peanut (Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer [...] Read more.
Peanut (Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer technologies have enabled the development of precision or variable-rate sprayers, both ground-based and drone-based, that apply agrochemicals more accurately. Historically, crop disease scouting has been labor-intensive and costly. Recent innovations in computer vision, artificial intelligence (AI), and remote sensing have transformed disease identification and scouting, making the process more efficient and economical. Over the past decade, numerous studies have focused on developing technologies for peanut disease scouting and sprayer technology. The current research trend shows significant advancements in precision spraying technologies, facilitating smart spraying capabilities. These advancements include the use of various platforms, such as ground-based and unmanned aerial vehicle (UAV)-based systems, equipped with sensors like RGB (red–blue–green), multispectral, thermal, hyperspectral, light detection and ranging (LiDAR), and other innovative detection technologies, as highlighted in this review. However, despite the availability of some commercial precision sprayers, their effectiveness is limited in managing certain peanut diseases, such as white mold, because the disease affects the roots, and the chemicals often remain in the canopy, failing to reach the soil where treatment is needed. The review concludes that further advances are necessary to develop more precise sprayers that can meet the needs of large-scale farmers and significantly enhance production outcomes. Overall, this review paper aims to provide a review of smart spraying techniques, estimating the required agrochemicals and applying them precisely in peanut fields. Full article
(This article belongs to the Section Smart Agriculture)
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19 pages, 1229 KiB  
Article
Feasibility of Smartphone-Based Exercise Training Integrated with Functional Electrical Stimulation After Stroke (SETS): A Preliminary Study
by Rudri Purohit, Juan Pablo Appelgren-Gonzalez, Gonzalo Varas-Diaz, Shuaijie Wang, Matias Hosiasson, Felipe Covarrubias-Escudero and Tanvi Bhatt
Sensors 2025, 25(4), 1254; https://doi.org/10.3390/s25041254 - 19 Feb 2025
Viewed by 397
Abstract
One emerging method in home stroke rehabilitation is digital technology. However, existing approaches typically target one domain (e.g., upper limb). Moreover, existing interventions do not cater to older adults with stroke (OAwS), especially those with high motor impairment, who require adjunct therapeutic agents [...] Read more.
One emerging method in home stroke rehabilitation is digital technology. However, existing approaches typically target one domain (e.g., upper limb). Moreover, existing interventions do not cater to older adults with stroke (OAwS), especially those with high motor impairment, who require adjunct therapeutic agents to independently perform challenging exercises. We examined the feasibility of Smartphone-based Exercise Training after Stroke (SETS) with Functional Electrical Stimulation (FES). A total of 12 participants (67 ± 5 years) with stroke (onset > 6 months) exhibiting moderate-to-high motor impairment (Chedoke McMaster Leg ≤ 4/7) underwent 6 weeks of multicomponent (gait, functional strength, dynamic balance) training integrated with FES to paretic lower limb muscles. Primary measures included safety and adherence. Secondary measures included motivation, acceptability and attitude, usability, and clinical measures of gait and balance function like the 10-Meter Walk Test and Mini-BESTest. Participants reported no adverse events and moderate-to-high adherence (84.17 ± 11.24%) and improvement (up to 40%) in motivation, acceptability, and attitude and system usability. Participants also showed pre-post improvements in all measures of gait and balance function (p < 0.05). Integrating SETS and FES is feasible and yields short-term gains in gait and balance function among OAwS. Future studies could validate our findings by examining its efficacy with control groups to identify the differential effects of SETS and FES. Full article
(This article belongs to the Section Wearables)
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23 pages, 927 KiB  
Article
Microservice Workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints
by Wenzheng Li, Xiaoping Li, Long Chen and Mingjing Wang
Sensors 2025, 25(4), 1253; https://doi.org/10.3390/s25041253 - 19 Feb 2025
Viewed by 329
Abstract
With the continuous evolution of microservice architecture and containerization technology, the challenge of efficiently and reliably scheduling large-scale cloud services has become increasingly prominent. In this paper, we present a cost-optimized scheduling approach with resource configuration for microservice workflows in container environments, taking [...] Read more.
With the continuous evolution of microservice architecture and containerization technology, the challenge of efficiently and reliably scheduling large-scale cloud services has become increasingly prominent. In this paper, we present a cost-optimized scheduling approach with resource configuration for microservice workflows in container environments, taking into account deadline and reliability constraints. We introduce a graph deep learning model (DeepMCC) that automatically configures containers to meet various service quality (QoS) requirements. Additionally, we propose a reliability microservice workflow scheduling algorithm (RMWS), which incorporates heuristic leasing and deployment strategies to ensure reliability while reducing cloud resource leasing cost. Experiments on four scientific workflow datasets show that the proposed approach achieves an average cost reduction of 44.59% compared to existing reliability scheduling algorithms, with improvements of 26.63% in the worst case and 73.72% in the best case. Full article
(This article belongs to the Section Sensor Networks)
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19 pages, 6020 KiB  
Article
Numerical Simulation Study on the Impact of Blind Zones in Ground Penetrating Radar
by Wentian Wang, Wei Du, Siyuan Cheng and Jia Zhuo
Sensors 2025, 25(4), 1252; https://doi.org/10.3390/s25041252 - 18 Feb 2025
Viewed by 246
Abstract
Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition [...] Read more.
Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition algorithm is based on the assumption of far-field plane waves. Therefore, in the near-field area where the target is closer to the borehole, the electromagnetic waves reflected by the target cannot be regarded as plane waves but will have a certain curvature. The plane wave assumption is not valid in this area, so the azimuth recognition algorithm will have significant errors, forming blind zones for directional borehole radar detection. This article uses the finite-difference time-domain (FDTD) algorithm to numerically simulate how blind zones affect directional borehole radar systems, identify the impact patterns, and minimize them. After calculation and numerical simulation verification, it has been found that when the center frequency of the antenna is 1 GHz, within 2 m of the target from the borehole, there is a significant error in azimuth recognition, which can be defined as the near-field region. Similarly, through numerical simulation verification, the optimal antenna center frequency is between 600 MHz and 1100 MHz. Oil-based mud is superior to water-based mud. The optimal antenna center frequency decreases as the target distance increases. Full article
(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)
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18 pages, 3517 KiB  
Article
Heat on the Move: Contrasting Mobile and Fixed Insights into Temuco’s Urban Heat Islands
by Aner Martinez-Soto, Michelle Vera-Fonseca, Pablo Valenzuela-Toledo, Aliwen Melillan-Raguileo and Matthew Shupler
Sensors 2025, 25(4), 1251; https://doi.org/10.3390/s25041251 - 18 Feb 2025
Viewed by 243
Abstract
This study evaluates the combined use of mobile transects and fixed stations to analyze atmospheric urban heat islands (UHIs’a) in Temuco, Chile. Data were collected using 23 fixed stations and 3 mobile transects traversing predefined city routes, capturing temperature records at one-minute intervals. [...] Read more.
This study evaluates the combined use of mobile transects and fixed stations to analyze atmospheric urban heat islands (UHIs’a) in Temuco, Chile. Data were collected using 23 fixed stations and 3 mobile transects traversing predefined city routes, capturing temperature records at one-minute intervals. Results revealed moderate correlations between methodologies (coefficients: 0.55–0.62) and average temperature differences of 0.72 °C to 1.6 °C, confirming their compatibility for integrated use. UHI intensities ranged from weak (0.5 °C) to extremely strong (13 °C), with the highest urban temperature (33.1 °C) observed in Zone Z-3, contrasting with 25.4 °C at the rural Maquehue station. Simulations and isothermal maps identified four UHI zones, highlighting the influence of impervious surfaces, traffic density, and limited vegetation on temperature distribution. Fluctuation plots revealed rapid cooling in vegetated areas and high heat retention in dense urban zones. These findings validate the methodologies for spatial and temporal UHI analysis and provide actionable insights for urban planning. Targeted interventions, such as increasing vegetation in high-risk zones, are recommended to mitigate extreme heat and enhance thermal comfort in urban areas. Full article
(This article belongs to the Section Environmental Sensing)
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11 pages, 7711 KiB  
Article
Autocollimation-Based Roll Angle Sensor Using a Modified Right-Angle Prism for Large Range Measurements
by Yan Guo, Yu Zhang, Jiali Ji, Huige Di, Qing Yan, Li Wang and Dengxin Hua
Sensors 2025, 25(4), 1250; https://doi.org/10.3390/s25041250 - 18 Feb 2025
Viewed by 249
Abstract
An autocollimator is a popular angle measuring apparatus which lacks the capability to measure the roll angle. This paper proposes a novel roll angle sensor with a large measuring range that is based on the autocollimation principle. A modified right-angle prism (MRP) functions [...] Read more.
An autocollimator is a popular angle measuring apparatus which lacks the capability to measure the roll angle. This paper proposes a novel roll angle sensor with a large measuring range that is based on the autocollimation principle. A modified right-angle prism (MRP) functions as a reflector to admit a collimated beam and return two outgoing beams to the sensor head. The roll angle of the MRP can be attained by analyzing the moving tracks of the two light spots focused on a photodetector. The mathematical model is derived in detail, and the experimental results show that the measuring accuracy of the proposed sensor is ±13.85 arcsec over a range of 360°. These results verify the feasibility of the proposed sensor for roll angle measurements that require a large measuring range. Full article
(This article belongs to the Section Optical Sensors)
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20 pages, 8511 KiB  
Article
Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM
by Tianxiao Chen, Datao Xu, Zhifeng Zhou, Huiyu Zhou, Shirui Shao and Yaodong Gu
Sensors 2025, 25(4), 1249; https://doi.org/10.3390/s25041249 - 18 Feb 2025
Viewed by 276
Abstract
Traditional methods for collecting ground reaction forces (GRFs) mainly use lab force plates. Previous research broke this pattern by predicting GRFs with deep learning and data from IMUs like joint acceleration. Joint angle, as a geometric, is easier to collect than acceleration outdoors [...] Read more.
Traditional methods for collecting ground reaction forces (GRFs) mainly use lab force plates. Previous research broke this pattern by predicting GRFs with deep learning and data from IMUs like joint acceleration. Joint angle, as a geometric, is easier to collect than acceleration outdoors with cameras. LSTM is one of the deep learning models that have shown good performance in biomechanical studies. xLSTM, as an optimized version of LSTM, has not been used in biomechanical studies and no research has predicted GRFs during running solely using lower limb joint angles. This study collected lower-limb joint angle and vertical ground reaction force data at five speeds from 12 healthy male runners with Xsens sensors. Datasets including three joints and three planes were set as the inputs of four deep learning models for vertical-GRF prediction. CNN-xLSTM consistently performed best in the four deep learning models when different datasets were input (R2 = 0.909 ± 0.064, MAPE = 2.18 ± 0.09, rMSE = 0.061 ± 0.008), and the performance was at a relatively high level at the five speeds. The current findings may contribute to a new GRF measurement and provide a reference for future real-time motion detection and sport injury prediction. Full article
(This article belongs to the Special Issue Wearable Sensors and Machine Learning in the Biomechanics of Human Movement)
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25 pages, 3285 KiB  
Review
Sensor-Fusion Based Navigation for Autonomous Mobile Robot
by Vygantas Ušinskis, Michał Nowicki, Andrius Dzedzickis and Vytautas Bučinskas
Sensors 2025, 25(4), 1248; https://doi.org/10.3390/s25041248 - 18 Feb 2025
Viewed by 581
Abstract
Navigation systems are developing rapidly; nevertheless, tasks are becoming more complex, significantly increasing the number of challenges for robotic systems. Navigation can be separated into global and local navigation. While global navigation works according to predefined data about the environment, local navigation uses [...] Read more.
Navigation systems are developing rapidly; nevertheless, tasks are becoming more complex, significantly increasing the number of challenges for robotic systems. Navigation can be separated into global and local navigation. While global navigation works according to predefined data about the environment, local navigation uses sensory data to dynamically react and adjust the trajectory. Tasks are becoming more complex with the addition of dynamic obstacles, multiple robots, or, in some cases, inspection of places that are not physically reachable by humans. Cognitive tasks require not only detecting an object but also evaluating it without direct recognition. For this purpose, sensor fusion methods are employed. However, sensors of different physical nature sometimes cannot directly extract required information. As a result, AI methods are becoming increasingly popular for evaluating acquired information and for controlling and generating robot trajectories. In this work, a review of sensors for mobile robot localization is presented by comparing them and listing advantages and disadvantages of their combinations. Also, integration with path-planning methods is looked into. Moreover, sensor fusion methods are analyzed and evaluated. Furthermore, a concept for channel robot navigation, designed based on the research literature, is presented. Lastly, discussion and conclusions are drawn. Full article
(This article belongs to the Section Navigation and Positioning)
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