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Sensors, Volume 25, Issue 3 (February-1 2025) – 333 articles

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16 pages, 3786 KiB  
Article
Dynamic 3D Measurement Based on Camera-Pixel Mismatch Correction and Hilbert Transform
by Xingfan Chen, Qican Zhang and Yajun Wang
Sensors 2025, 25(3), 924; https://doi.org/10.3390/s25030924 (registering DOI) - 3 Feb 2025
Abstract
In three-dimensional (3D) measurement, the motion of objects inevitably introduces errors, posing significant challenges to high-precision 3D reconstruction. Most existing algorithms for compensating motion-induced phase errors are tailored for object motion along the camera’s principal axis (Z direction), limiting their applicability in real-world [...] Read more.
In three-dimensional (3D) measurement, the motion of objects inevitably introduces errors, posing significant challenges to high-precision 3D reconstruction. Most existing algorithms for compensating motion-induced phase errors are tailored for object motion along the camera’s principal axis (Z direction), limiting their applicability in real-world scenarios where objects often experience complex combined motions in the X/Y and Z directions. To address these challenges, we propose a universal motion error compensation algorithm that effectively corrects both pixel mismatch and phase-shift errors, ensuring accurate 3D measurements under dynamic conditions. The method involves two key steps: first, pixel mismatch errors in the camera subsystem are corrected using adjacent coarse 3D point cloud data, aligning the captured data with the actual spatial geometry. Subsequently, motion-induced phase errors, observed as sinusoidal waveforms with a frequency twice that of the projection fringe pattern, are eliminated by applying the Hilbert transform to shift the fringes by π/2. Unlike conventional approaches that address these errors separately, our method provides a systematic solution by simultaneously compensating for camera-pixel mismatch and phase-shift errors within the 3D coordinate space. This integrated approach enhances the reliability and precision of 3D reconstruction, particularly in scenarios with dynamic and multidirectional object motions. The algorithm has been experimentally validated, demonstrating its robustness and broad applicability in fields such as industrial inspection, biomedical imaging, and real-time robotics. By addressing longstanding challenges in dynamic 3D measurement, our method represents a significant advancement in achieving high-accuracy reconstructions under complex motion environments. Full article
(This article belongs to the Special Issue 3D Reconstruction with RGB-D Cameras and Multi-sensors)
20 pages, 8259 KiB  
Article
Comparative Study of Selected Order-Picking Methods: Efficiency, Ergonomics, and Adaptation Rate of New Employees
by Marcin Łopuszyński, Kamil Janusz and Dawid Karwat
Sensors 2025, 25(3), 923; https://doi.org/10.3390/s25030923 (registering DOI) - 3 Feb 2025
Abstract
The human manual order-picking process in the warehouse is still the leading method despite increasing automation. This manual process is supported by indicating and receipt systems to reduce the order-picking time and the number of errors. Many studies in the literature compare the [...] Read more.
The human manual order-picking process in the warehouse is still the leading method despite increasing automation. This manual process is supported by indicating and receipt systems to reduce the order-picking time and the number of errors. Many studies in the literature compare the Pick-by-Light system with the Pick-by-Paper and other systems, and it is more challenging to study the Pick-by-Point system. This paper presents the results of laboratory comparative studies of the most straightforward Pick-by-Paper system with Pick-by-Light and Pick-by-Point systems supported by receipt systems. In the case of the Pick-by-Light system, the receipt system is a button on the module that the picker presses to confirm the pick-up of an item. In the case of Pick-by-Point, the receipt system is a wrist scanner that the picker uses to confirm the pick-up of an item. A total of 71 people participated in the study. Participants completed five orders with five items per system. Comparisons were made of the time it took to pick the orders with the support of these systems, the number of errors made, ergonomics, and the speed of adaptation for new employees without experience. A person with nine years of experience in the picking process took part in the study, whose order-picking times were compared with those of the others. In the study, the Pick-by-Light system proved to be the fastest regarding order picking and the adaptation of new employees. On the other hand, the Pick-by-Point system was the most error-proof. Full article
(This article belongs to the Section Intelligent Sensors)
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13 pages, 842 KiB  
Article
A Deep Learning Model for Detecting the Arrival Time of Weak Underwater Signals in Fluvial Acoustic Tomography Systems
by Weicong Zheng, Xiaojian Yu, Xuming Peng, Chen Yang, Shu Wang, Hanyin Chen, Zhenxuan Bu, Yu Zhang, Yili Zhang and Lingli Lin
Sensors 2025, 25(3), 922; https://doi.org/10.3390/s25030922 (registering DOI) - 3 Feb 2025
Abstract
The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation [...] Read more.
The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation within a certain time as the signal arrival time point, but this method is difficult to be effectively applied to the complex underwater environment, especially in the case of extremely low SNR. To solve this problem, a two-channel deep learning model (DCA-Net) is proposed to detect the arrival time of acoustic chromatographic signals. Firstly, an interactive module is designed to transmit the auxiliary information from the cross-correlation subnetwork to the original signal subnet to improve the feature information extraction capability of the network. In addition, an attention module is designed to enable the network to selectively focus on the important features of the received acoustic signals. Under the background of white Gaussian noise and real river environment noise, we use the received signals of the acoustic tomography system collected in the field to synthesize low SNR data of −10, −15, and −20 different decibels as datasets. The experimental results show that the proposed network model is superior to the traditional matching filtering method and some other deep neural networks in three low SNR datasets. Full article
(This article belongs to the Special Issue Sensors Technologies for Measurements and Signal Processing)
28 pages, 3695 KiB  
Article
Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents
by Anton Saevskiy, Natalia Suntsova, Peter Kosenko, Md Noor Alam and Andrey Kostin
Sensors 2025, 25(3), 921; https://doi.org/10.3390/s25030921 (registering DOI) - 3 Feb 2025
Abstract
Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state [...] Read more.
Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state scoring using single-channel electroencephalogram (EEG) recordings from rats and mice. The algorithm employs artifact processing, multi-band frequency analysis, and Gaussian mixture model (GMM)-based clustering to classify wakefulness, non-rapid, and rapid eye movement sleep (NREM and REM sleep, respectively). Combining narrow and broad frequency bands across the delta, theta, and sigma ranges, it uses a majority voting system to enhance accuracy, with tailored preprocessing and voting criteria improving REM detection. Validation on datasets from 10 rats and 10 mice under standard conditions showed sleep–wake state detection accuracies of 92% and 93%, respectively, closely matching manual scoring and comparable to existing methods. REM sleep detection accuracies of 89% (mice) and 91% (rats) align with previously reported (85–90%). Processing a full day of EEG data within several minutes, the algorithm is advantageous for large-scale and longitudinal studies. Its open-source design, flexibility, and scalability make it a robust, efficient tool for automated rodent sleep scoring, advancing research in standard experimental conditions, including aging and sleep deprivation. Full article
(This article belongs to the Special Issue Advances in Sensing Technologies for Sleep Monitoring)
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16 pages, 4896 KiB  
Communication
Fiber Fabry–Perot Sensor Based on Ion-Imprinted Sodium Alginate/Graphene Oxide Hydrogel for Copper Ion Detection Using Vernier Effect
by Ning Wang, Shiqi Liu, Liang Xu, Longjiao Wang, Ming He, Chuanjie Lei and Linyufan Xiao
Sensors 2025, 25(3), 920; https://doi.org/10.3390/s25030920 (registering DOI) - 3 Feb 2025
Abstract
This work proposes an optical fiber copper ion sensor, which is fabricated by an ion-imprinted sodium alginate/graphene oxide (SA/GO) hydrogel and single-mode fiber (SMF). This sensing Fabry–Perot Interferometer (FPI) achieves −1.98 nm/(mg/L) sensitivity with 0.998 linearity. To achieve higher sensitivity, we add a [...] Read more.
This work proposes an optical fiber copper ion sensor, which is fabricated by an ion-imprinted sodium alginate/graphene oxide (SA/GO) hydrogel and single-mode fiber (SMF). This sensing Fabry–Perot Interferometer (FPI) achieves −1.98 nm/(mg/L) sensitivity with 0.998 linearity. To achieve higher sensitivity, we add a reference FPI to create a Vernier effect. We achieve 19.58 nm/mg/L sensitivity and 0.989 linearity at a concentration range of 0 mg/L–1.4 mg/L. It was 9.9 times higher than that of a single-sensing FPI. The experimental results also demonstrate that when the FSR values of two FPIs are closer, the higher response sensitivity is achieved. The sensor also has good measurement repeatability and dynamic response. In addition, the experimental results of response selectivity show that its response sensitivity to copper ions is significantly higher than other six types of ions, including iron ions, lead ions, magnesium ions, manganese ion, zinc ions, chromium ions. The copper ion is also mixed with six types of ions to deeply investigate the response selectivity. Good response selectivity and cross-responding are demonstrated by experimental results. Full article
(This article belongs to the Section Optical Sensors)
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35 pages, 4640 KiB  
Article
Prediction of Member Forces of Steel Tubes on the Basis of a Sensor System with the Use of AI
by Haiyu Li and Heungjin Chung
Sensors 2025, 25(3), 919; https://doi.org/10.3390/s25030919 (registering DOI) - 3 Feb 2025
Abstract
The rapid development of AI (artificial intelligence), sensor technology, high-speed Internet, and cloud computing has demonstrated the potential of data-driven approaches in structural health monitoring (SHM) within the field of structural engineering. Algorithms based on machine learning (ML) models are capable of discerning [...] Read more.
The rapid development of AI (artificial intelligence), sensor technology, high-speed Internet, and cloud computing has demonstrated the potential of data-driven approaches in structural health monitoring (SHM) within the field of structural engineering. Algorithms based on machine learning (ML) models are capable of discerning intricate structural behavioral patterns from real-time data gathered by sensors, thereby offering solutions to engineering quandaries in structural mechanics and SHM. This study presents an innovative approach based on AI and a fiber-reinforced polymer (FRP) double-helix sensor system for the prediction of forces acting on steel tube members in offshore wind turbine support systems; this enables structural health monitoring of the support system. The steel tube as the transitional member and the FRP double helix-sensor system were initially modeled in three dimensions using ABAQUS finite element software. Subsequently, the data obtained from the finite element analysis (FEA) were inputted into a fully connected neural network (FCNN) model, with the objective of establishing a nonlinear mapping relationship between the inputs (strain) and the outputs (reaction force). In the FCNN model, the impact of the number of input variables on the model’s predictive performance is examined through cross-comparison of different combinations and positions of the six sets of input variables. And based on an evaluation of engineering costs and the number of strain sensors, a series of potential combinations of variables are identified for further optimization. Furthermore, the potential variable combinations were optimized using a convolutional neural network (CNN) model, resulting in optimal input variable combinations that achieved the accuracy level of more input variable combinations with fewer sensors. This not only improves the prediction performance of the model but also effectively controls the engineering cost. The model performance was evaluated using several metrics, including R2, MSE, MAE, and SMAPE. The results demonstrated that the CNN model exhibited notable advantages in terms of fitting accuracy and computational efficiency when confronted with a limited data set. To provide further support for practical applications, an interactive graphical user interface (GUI)-based sensor-coupled mechanical prediction system for steel tubes was developed. This system enables engineers to predict the member forces of steel tubes in real time, thereby enhancing the efficiency and accuracy of SHM for offshore wind turbine support systems. Full article
(This article belongs to the Section Sensors Development)
23 pages, 3895 KiB  
Article
RGANet: A Human Activity Recognition Model for Extracting Temporal and Spatial Features from WiFi Channel State Information
by Jianyuan Hu, Fei Ge, Xinyu Cao and Zhimin Yang
Sensors 2025, 25(3), 918; https://doi.org/10.3390/s25030918 (registering DOI) - 3 Feb 2025
Abstract
With the rapid advancement of communication technologies, wireless networks have not only transformed people’s lifestyles but also spurred the development of numerous emerging applications and services. Against this backdrop, research on Wi-Fi-based human activity recognition (HAR) has become a hot topic in both [...] Read more.
With the rapid advancement of communication technologies, wireless networks have not only transformed people’s lifestyles but also spurred the development of numerous emerging applications and services. Against this backdrop, research on Wi-Fi-based human activity recognition (HAR) has become a hot topic in both academia and industry. Channel State Information (CSI) contains rich spatiotemporal information. However, existing deep learning methods for human activity recognition (HAR) typically focus on either temporal or spatial features. While some approaches do combine both types of features, they often emphasize temporal sequences and underutilize spatial information. In contrast, this paper proposes an enhanced approach by modifying residual networks (ResNet) instead of using simple CNN. This modification allows for effective spatial feature extraction while preserving temporal information. The extracted spatial features are then fed into a modifying GRU model for temporal sequence learning. Our model achieves an accuracy of 99.4% on the UT_HAR dataset and 99.24% on the NTU-FI HAR dataset. Compared to other existing models, RGANet shows improvements of 1.21% on the UT_HAR dataset and 0.38% on the NTU-FI HAR dataset. Full article
(This article belongs to the Section Communications)
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10 pages, 7042 KiB  
Communication
A Large Voltage Responsivity Pyroelectric Sensor Based on Hot-Pressed Lead Zirconate Titanate Ceramic
by Yanhao Guo, Shaobo Guo, Chunhua Yao, Zhiwei Pan and Genshui Wang
Sensors 2025, 25(3), 917; https://doi.org/10.3390/s25030917 (registering DOI) - 3 Feb 2025
Abstract
In this article, hot-pressed PZT ceramics were used as a sensitive element material and made into a pyroelectric chip. Three current mode sensors were fabricated using a pyroelectric chip of different thicknesses (80 μm, 40 μm, and 30 μm). The voltage responsivity of [...] Read more.
In this article, hot-pressed PZT ceramics were used as a sensitive element material and made into a pyroelectric chip. Three current mode sensors were fabricated using a pyroelectric chip of different thicknesses (80 μm, 40 μm, and 30 μm). The voltage responsivity of sensors reached the order of magnitude of 105. The size effect resulting from varying the thickness was studied. The results indicate that as the thickness decreases, the performance significantly increases. When the modulation frequency is 10 Hz, the specific detectivity of the sensor with a 30 μm PZT ceramic pyroelectric chip reaches 5.3 × 108 cm·Hz1/2/W. Full article
(This article belongs to the Section Electronic Sensors)
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25 pages, 2748 KiB  
Article
CAREUP: An Integrated Care Platform with Intrinsic Capacity Monitoring and Prediction Capabilities
by Marcin Kolakowski, Andrea Lupica, Seif Ben Bader, Vitomir Djaja-Josko, Jerzy Kolakowski, Jacek Cichocki, Jaouhar Ayadi, Luca Gilardi, Angelo Consoli, Irina Georgiana Mocanu, Oana Cramariuc, Lionello Ferrazzini, Eva Reithner, Magdalena Velciu, Barbara Borgogni, Sofia Rivaira, Sara Leonzi, Giacomo Cucchieri and Vera Stara
Sensors 2025, 25(3), 916; https://doi.org/10.3390/s25030916 (registering DOI) - 3 Feb 2025
Abstract
This paper describes CAREUP, a novel older adult healthy aging support platform based on Intrinsic Capacity (IC) monitoring. Besides standard functionalities like storing health measurement data or providing users with personalized recommendations, the platform includes novel intrinsic capacity assessment and prediction algorithms. Older [...] Read more.
This paper describes CAREUP, a novel older adult healthy aging support platform based on Intrinsic Capacity (IC) monitoring. Besides standard functionalities like storing health measurement data or providing users with personalized recommendations, the platform includes novel intrinsic capacity assessment and prediction algorithms. Older adults’ performance is continuously monitored in all five IC domains—locomotion, psychology, cognition, vitality, and sensory capacity—based on measurement results and answers to questionnaires gathered using the platform’s mobile applications. The users are also presented with a machine learning-based prediction of how their intrinsic capacity might change over the following years. The platform’s operation was successfully tested with the participation of older adults and their caregivers in three countries: Austria, Italy, and Romania. Full article
(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)
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14 pages, 1422 KiB  
Article
Design of Bilayer Crescent Chiral Metasurfaces for Enhanced Chiroptical Response
by Semere A. Asefa, Myeongsu Seong and Dasol Lee
Sensors 2025, 25(3), 915; https://doi.org/10.3390/s25030915 (registering DOI) - 3 Feb 2025
Abstract
Chiral metasurfaces exploit structural asymmetry to control circular polarized light, presenting new possibilities for the design of optical devices, specifically in the dynamic control of light and enhanced optical sensing fields. This study employed theoretical and computational methods to examine the chiroptical properties [...] Read more.
Chiral metasurfaces exploit structural asymmetry to control circular polarized light, presenting new possibilities for the design of optical devices, specifically in the dynamic control of light and enhanced optical sensing fields. This study employed theoretical and computational methods to examine the chiroptical properties of a bilayer crescent chiral metasurface, demonstrating the effect of the angle of rotation on the chiroptical response. We particularly investigated the changes in transmittance, electric field distribution, and circular dichroism (CD) across various rotation angles. The crescent chiral metasurface demonstrated the maximum CD and showed significant control over the CD and electric field distribution across different rotation angles in the near-infrared region. The highest CD value was observed at a 23° rotation angle, where the chiroptical response reached its maximum. In addition, the left circular polarized light showed a stronger intensity of the electric field along the crescent metasurface edge relative to the right circular polarized light, underscoring the significant difference in the intensity and field localization. It was also shown that the configuration with a 2 by 2-unit cell, compared with a single-unit cell, exhibited significantly enhanced CD, thus underlining the importance of the unit cell arrangement in optimizing the chiroptical properties of metasurfaces for advanced photonic applications. These results prove that the 2 by 2 bilayer crescent chiral metasurface can be tailored to a fine degree for specific applications such as improved biosensing, enhanced optical communications, and precise polarization control by optimizing the configuration. The insight presented by this theoretical and computational study will contribute to the broad understanding of chiroptical phenomena as well as pave the way for potential applications in developing advanced optical devices with tuned chiroptical interactions. Full article
(This article belongs to the Section Physical Sensors)
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23 pages, 2148 KiB  
Article
Enhancing Microservice Security Through Vulnerability-Driven Trust in the Service Mesh Architecture
by Rami Alboqmi and Rose F. Gamble
Sensors 2025, 25(3), 914; https://doi.org/10.3390/s25030914 (registering DOI) - 3 Feb 2025
Abstract
Cloud-native computing enhances the deployment of microservice architecture (MSA) applications by improving scalability and resilience, particularly in Beyond 5G (B5G) environments such as Sixth-Generation (6G) networks. This is achieved through the ability to replace traditional hardware dependencies with software-defined solutions. While service meshes [...] Read more.
Cloud-native computing enhances the deployment of microservice architecture (MSA) applications by improving scalability and resilience, particularly in Beyond 5G (B5G) environments such as Sixth-Generation (6G) networks. This is achieved through the ability to replace traditional hardware dependencies with software-defined solutions. While service meshes enable secure communication for deployed MSAs, they struggle to identify vulnerabilities inherent to microservices. The reliance on third-party libraries and modules, essential for MSAs, introduces significant supply chain security risks. Implementing a zero-trust approach for MSAs requires robust mechanisms to continuously verify and monitor the software supply chain of deployed microservices. However, existing service mesh solutions lack runtime trust evaluation capabilities for continuous vulnerability assessment of third-party libraries and modules. This paper introduces a mechanism for continuous runtime trust evaluation of microservices, integrating vulnerability assessments within a service mesh to enhance the deployed MSA application. The proposed approach dynamically assigns trust scores to deployed microservices, rewarding secure practices such as timely vulnerability patching. It also enables the sharing of assessment results, enhancing mitigation strategies across the deployed MSA application. The mechanism is evaluated using the Train Ticket MSA, a complex open-source benchmark MSA application deployed with Docker containers, orchestrated using Kubernetes, and integrated with the Istio service mesh. Results demonstrate that the enhanced service mesh effectively supports dynamic trust evaluation based on the vulnerability posture of deployed microservices, significantly improving MSA security and paving the way for future self-adaptive solutions. Full article
(This article belongs to the Special Issue Zero Trust Security: Techniques, Architectures, Models, and Applications in 6G)
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15 pages, 5709 KiB  
Article
Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network
by Weixiong Jiang, Guanhui Zhao, Zhan Gao, Yuanhang Wang and Jun Wu
Sensors 2025, 25(3), 913; https://doi.org/10.3390/s25030913 (registering DOI) - 3 Feb 2025
Abstract
Wind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein, a compound fault diagnosis method based on modified signal quality [...] Read more.
Wind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein, a compound fault diagnosis method based on modified signal quality coefficient (MSQC) and versatile residual shrinkage network (VRSN) is proposed to resolve these issues. In detail, the MSQC is designed to remove the noise components irrelevant to wind turbine operation status, and it has the ability to balance the denoised effect and signal fidelity. The VRSN is constructed for compound fault diagnosis, and it consists of two heterogeneous residual shrinkage networks. The former is designed to count the number of faults, and the latter is adopted to identify the single or compound fault pattern. Finally, a self-built wind turbine gearbox compound fault test rig is adopted to verify the proposed method’s effectiveness. The results demonstrate that the proposed method is competitive in terms of compound fault diagnosis accuracy. 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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20 pages, 6545 KiB  
Article
RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion
by Gang Liu, Yingzheng Huang, Shuguang Yan and Enxiang Hou
Sensors 2025, 25(3), 912; https://doi.org/10.3390/s25030912 (registering DOI) - 3 Feb 2025
Viewed by 74
Abstract
The paper proposes a model based on receptive field enhancement and cross-scale fusion (RFCS-YOLO). It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. First, an efficient feature extraction module (EFEM) is created. It reconfigures the [...] Read more.
The paper proposes a model based on receptive field enhancement and cross-scale fusion (RFCS-YOLO). It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. First, an efficient feature extraction module (EFEM) is created. It reconfigures the backbone network. This helps to make the receptive field better and improves its ability to extract features of targets at different scales. Next, a cross-scale fusion module (CSF) is introduced. It uses the receptive field coordinate attention mechanism (RFCA) to fuse information from different scales well. It also filters out noise and background information that might interfere. Also, a new Focaler-Minimum Point Distance Intersection over Union (F-MPDIoU) loss function is proposed. It makes the model converge faster and deals with issues of leakage and false detection. Experiments were conducted on the expanded Vehicle Detection in Adverse Weather Nature dataset (DWAN). The results show significant improvements compared to the conventional You Only Look Once v7 (YOLOv7) model. The mean Average Precision ([email protected]), precision, and recall are enhanced by 4.2%, 8.3%, and 1.4%, respectively. The mean Average Precision is 86.5%. The frame rate is 68 frames per second (FPS), which meets the requirements for real-time detection. A generalization experiment was conducted using the autonomous driving dataset SODA10M. The [email protected] achieved 56.7%, which is a 3.6% improvement over the original model. This result demonstrates the good generalization ability of the proposed method. Full article
(This article belongs to the Section Remote Sensors)
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23 pages, 743 KiB  
Article
FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation
by Abdul Wahab Mamond, Majid Kundroo, Seong-eun Yoo, Seonghoon Kim and Taehong Kim
Sensors 2025, 25(3), 911; https://doi.org/10.3390/s25030911 (registering DOI) - 3 Feb 2025
Viewed by 118
Abstract
The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable [...] Read more.
The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. Managing agents and fostering collaboration in a multi-agent reinforcement learning scenario remains a challenging task. This paper introduces a cooperative multi-agent federated reinforcement learning algorithm named FLDQN to address the challenge of agent cooperation by solving travel time minimization challenges in dynamic multi-agent reinforcement learning (MARL) scenarios. FLDQN leverages federated learning to facilitate collaboration and knowledge sharing among intelligent agents, optimizing vehicle routing and reducing congestion in dynamic traffic environments. Using the SUMO simulator, multiple agents equipped with deep Q-learning models interact with their local environments, share model updates via a federated server, and collectively enhance their policies using unique local observations while benefiting from the collective experiences of other agents. Experimental evaluations demonstrate that FLDQN achieves a significant average reduction of over 34.6% in travel time compared to non-cooperative methods while simultaneously lowering the computational overhead through distributed learning. FLDQN underscores the vital impact of agent cooperation and provides an innovative solution for enabling agent cooperation in a multi-agent environment. Full article
(This article belongs to the Section Intelligent Sensors)
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17 pages, 7649 KiB  
Article
Design and Tracking Control Experimental Study of a Hybrid Reluctance-Actuated Fast Steering Mirror with an Integrated Sensing Unit
by Jian Zhou, Yudong Fan, Liang Li, Feng Zhang, Bo Feng and Minglong Xu
Sensors 2025, 25(3), 910; https://doi.org/10.3390/s25030910 (registering DOI) - 3 Feb 2025
Viewed by 123
Abstract
This study proposes the design of a hybrid reluctance-actuated fast steering mirror (HRAFSM) using Maxwell’s electromagnetic normal stress principle. Strain gauges were attached to the flexible supports as sensors for measuring the rotation angles. According to Maxwell’s stress tensor theory and the theory [...] Read more.
This study proposes the design of a hybrid reluctance-actuated fast steering mirror (HRAFSM) using Maxwell’s electromagnetic normal stress principle. Strain gauges were attached to the flexible supports as sensors for measuring the rotation angles. According to Maxwell’s stress tensor theory and the theory of vibration mechanics, we obtained the dynamic equation of the HRAFSM in the uniaxial direction to investigate the relationship between the input current and the output angle of the entire system. Further, we propose a control algorithm combining proportional-integral-derivative (PID) and adaptive inverse control (AIC) to achieve high-precision control. We established an experimental system for testing and validation of the control method. The experimental results showed that the designed HRAFSM can achieve the expected rotation angle of ±1.5 mrad, and revealed a linear relationship between the rotation angle of the two axes and their corresponding strain voltages. The effectiveness of the designed controller was verified, and the amplitude tracking errors of the x- and y-axes were 0.1% and 0.14%, respectively. Full article
(This article belongs to the Special Issue Spacecraft Vibration Suppression and Measurement Sensor Technology)
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16 pages, 11619 KiB  
Article
Adaptive Grasp Pose Optimization for Robotic Arms Using Low-Cost Depth Sensors in Complex Environments
by Aiguo Chen, Xuanfeng Li, Kerui Cen and Chitin Hon
Sensors 2025, 25(3), 909; https://doi.org/10.3390/s25030909 (registering DOI) - 3 Feb 2025
Viewed by 30
Abstract
This paper presents an efficient grasp pose estimation algorithm for robotic arm systems with a two-finger parallel gripper and a consumer-grade depth camera. Unlike traditional deep learning methods, which suffer from high data dependency and inefficiency with low-precision point clouds, the proposed approach [...] Read more.
This paper presents an efficient grasp pose estimation algorithm for robotic arm systems with a two-finger parallel gripper and a consumer-grade depth camera. Unlike traditional deep learning methods, which suffer from high data dependency and inefficiency with low-precision point clouds, the proposed approach uses ellipsoidal modeling to overcome these issues. The algorithm segments the target and then applies a three-stage optimization to refine the grasping path. Initial estimation fits an ellipsoid to determine principal axes, followed by nonlinear optimization for a six-degree-of-freedom grasp pose. Validation through simulations and experiments showed a target grasp success rate (TGSR) of over 83% under low noise, with only a 4.9% drop under high noise—representing a 68.0% and a 42.4% improvement over GPD and PointNetGPD, respectively. In real-world tests, success rates ranged from 95 to 100%, and the computational efficiency was improved by 56.3% compared to deep learning methods, proving its practicality for real-time applications. These results demonstrate stable and reliable grasping performance, even in noisy environments and with low-cost sensors. Full article
(This article belongs to the Section Sensors and Robotics)
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22 pages, 684 KiB  
Systematic Review
Relationship Between Signals from Cerebral near Infrared Spectroscopy Sensor Technology and Objectively Measured Cerebral Blood Volume: A Systematic Scoping Review
by Noah Silvaggio, Kevin Y. Stein, Amanjyot Singh Sainbhi, Nuray Vakitbilir, Tobias Bergmann, Abrar Islam, Rakibul Hasan, Mansoor Hayat and Frederick A. Zeiler
Sensors 2025, 25(3), 908; https://doi.org/10.3390/s25030908 (registering DOI) - 3 Feb 2025
Viewed by 145
Abstract
Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared [...] Read more.
Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared spectroscopy (NIRS), a non-invasive sensor, can be used to estimate CBV. However, this association remains vastly unexplored. As such, this scoping review aimed to examine the literature on the relationship between cerebral NIRS signals and CBV. A search of six databases was conducted conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to assess the following search question: What are the associations between various NIRS cerebral signals and CBV? The database search yielded 3350 unique results. Seven of these articles were included in this review based on the inclusion and exclusion criteria. An additional study was identified and included while examining the articles’ reference sections. Overall, the literature for this systematic scoping review shows extreme variation in the association between cerebral NIRS signals and CBV, with few sources objectively documenting a true statistical association between the two. This review highlights the current critical knowledge gap and emphasizes the need for further research in the area. Full article
(This article belongs to the Section Biomedical Sensors)
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28 pages, 3412 KiB  
Article
Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks
by Fahad R. Albogamy
Sensors 2025, 25(3), 907; https://doi.org/10.3390/s25030907 (registering DOI) - 3 Feb 2025
Viewed by 161
Abstract
The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning framework for HAR, leveraging a hybrid Long Short-Term [...] Read more.
The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning framework for HAR, leveraging a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to enhance feature extraction and classification in decentralized environments. Utilizing three public datasets—UCI-HAR, HARTH, and HAR7+—which contain diverse sensor data collected from free-living activities, the proposed system is designed to address the inherent privacy risks associated with centralized data processing by deploying Federated Averaging for local model training. To optimize recognition accuracy, the author introduces a dual-feature extraction mechanism, combining convolutional blocks for capturing local patterns and a hybrid LSTM-GRU structure to detect complex temporal dependencies. Furthermore, the author integrates an attention mechanism to focus on significant global relationships within the data. The proposed system is evaluated on the three public datasets—UCI-HAR, HARTH, and HAR7+—achieving superior performance compared to recent works in terms of F1-score and recognition accuracy. The results demonstrate that the proposed approach not only provides high classification accuracy but also ensures privacy preservation, making it a scalable and reliable solution for real-world HAR applications in decentralized and privacy-conscious environments. This work showcases the potential of federated learning in transforming human activity recognition, combining advanced feature extraction methodologies and privacy-respecting frameworks to deliver robust, real-time activity classification. Full article
(This article belongs to the Section Internet of Things)
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38 pages, 2147 KiB  
Article
Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach
by Diana-Andreea Căuniac, Andreea-Alexandra Cîrnaru, Simona-Vasilica Oprea and Adela Bâra
Sensors 2025, 25(3), 906; https://doi.org/10.3390/s25030906 (registering DOI) - 2 Feb 2025
Viewed by 445
Abstract
As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection [...] Read more.
As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around data systems and IoT technologies, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the application of technologies, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on machine learning and IoT applications. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on smart technologies and systems. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on digital transformation. Topic 6 focuses on technical aspects such as modeling, system performance and prediction algorithms. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
16 pages, 4586 KiB  
Article
Ultra-Sensitive Gas Sensor Based on CDs@ZnO
by Shuo Xiao, Zheng Jiao and Xuechun Yang
Sensors 2025, 25(3), 905; https://doi.org/10.3390/s25030905 (registering DOI) - 2 Feb 2025
Viewed by 358
Abstract
Ethylene glycol (EG) is a colorless and odorless organic compound, which is an important industrial raw material but harmful to the environment and human health. Thus, it is necessary to develop high-performance sensing materials to monitor EG gas. Herein, sea urchin-shaped ZnO was [...] Read more.
Ethylene glycol (EG) is a colorless and odorless organic compound, which is an important industrial raw material but harmful to the environment and human health. Thus, it is necessary to develop high-performance sensing materials to monitor EG gas. Herein, sea urchin-shaped ZnO was successfully synthesized by a hydrothermal method. Subsequently, a series of carbon dot (CD)-modified ZnO nanocomposites were successfully prepared using a simple mechanical grinding method. The prepared CDs@ZnO-1 sensor exhibits an excellent response to EG gas, with a response value of 1356.89 to 100 ppm EG at the optimal operating temperature (220 °C). After five cycles of detection, the sensor can still maintain a stable response. The enhanced sensing performance of EG can be attributed to rich oxygen vacancies that are generated on the surface of CDs@ZnO, and the heterojunction formed between p-type CDs and n-type ZnO. This study provides inspiration for the development of high-response semiconductor metal oxide sensors. Full article
(This article belongs to the Special Issue New Sensors Based on Inorganic Material)
22 pages, 10690 KiB  
Article
Tilting Pad Thrust Bearing Fault Diagnosis Based on Acoustic Emission Signal and Modified Multi-Feature Fusion Convolutional Neural Network
by Meijiao Mao, Zhiwen Jiang, Zhifei Tan, Wenqiang Xiao and Guangchao Du
Sensors 2025, 25(3), 904; https://doi.org/10.3390/s25030904 (registering DOI) - 2 Feb 2025
Viewed by 359
Abstract
Tilting pad thrust bearings are widely utilized in large rotating machinery such as steam turbines and hydraulic turbines. Defects in their shaft tiles directly impact lubrication characteristics, thereby influencing the overall safety performance of the entire unit. To address this issue, this paper [...] Read more.
Tilting pad thrust bearings are widely utilized in large rotating machinery such as steam turbines and hydraulic turbines. Defects in their shaft tiles directly impact lubrication characteristics, thereby influencing the overall safety performance of the entire unit. To address this issue, this paper presents a fault diagnosis method for tilting pad thrust bearings using a modified multi-feature fused convolutional neural network (MMFCNN). Initially, an experimental bench for diagnosing faults in tilting pad thrust bearings was developed to collect multi-channel acoustic emission (AE) signals from both normal and faulty pads. Subsequently, the squeeze-and-excitation (SE) module was employed to reallocate the weights of each channel and fuse the features of multi-channel signals. Learning was then conducted on the signal fused with multiple features using the inverse-add module and spanning convolution. Next, a comparative analysis was carried out among the CNN1D, ResNet, and DFCNN models, and the MMFCNN model proposed in this study. The results show that under consistent operating conditions, the MMFCNN model achieves an average fault diagnosis accuracy of 99.58% when utilizing AE signal data from tilting pad thrust bearings in four states as inputs. Furthermore, when different operational conditions are introduced, the MMFCNN model also outperforms other models in terms of accuracy. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
20 pages, 1155 KiB  
Article
An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
by Chuanyu Wu, Yuanfa Ji and Xiyan Sun
Sensors 2025, 25(3), 903; https://doi.org/10.3390/s25030903 (registering DOI) - 2 Feb 2025
Viewed by 330
Abstract
Spoofing detection is critical for GNSS security. To address the issues of low detection rates and insufficient coverage in traditional methods, this study proposes an eye diagram detection method based on the multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). Unlike conventional correlation [...] Read more.
Spoofing detection is critical for GNSS security. To address the issues of low detection rates and insufficient coverage in traditional methods, this study proposes an eye diagram detection method based on the multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). Unlike conventional correlation peak distortion detection techniques, the proposed method uses the MSC-MMP algorithm to perform multiscale edge extraction from the eye diagram generated from the receiver's correlation values. It then calculates the image threshold using minimum misjudgment probability to ensure the accuracy of the eye diagram's edges. This enables the accurate detection of subtle changes in the eye diagram, leading to the better identification of spoofing signals. The results show that the MSC-MMP outperforms traditional edge extraction algorithms by over 0.072 in terms of the optimal dataset scale F score (ODS-F). Compared to signal quality monitoring (SQM) and Carrier-to-Noise Ratio methods, the EDDM-MSC-MMP method increases spoofing detection coverage by over 60%, achieving the highest detection rate in the TEXBAT dataset. Overall, the EDDM-MSC-MMP method improves the reliability and coverage of spoofing detection, providing an effective solution for GNSS spoofing detection. Full article
(This article belongs to the Section Navigation and Positioning)
42 pages, 1595 KiB  
Article
Hierarchical Resource Management for Mega-LEO Satellite Constellation
by Liang Gou, Dongming Bian, Yulei Nie, Gengxin Zhang, Hongwei Zhou, Yulin Shi and Lei Zhang
Sensors 2025, 25(3), 902; https://doi.org/10.3390/s25030902 (registering DOI) - 2 Feb 2025
Viewed by 133
Abstract
The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global [...] Read more.
The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS. Full article
(This article belongs to the Section Remote Sensors)
10 pages, 2740 KiB  
Communication
Yttrium Doping of Perovskite Oxide La2Ti2O7 Nanosheets for Enhanced Proton Conduction and Gas Sensing Under HighHumidity Levels
by Jian Wang, Caicai Sun, Jusheng Bao, Zhiwei Yang, Jian Zhang and Xiao Huang
Sensors 2025, 25(3), 901; https://doi.org/10.3390/s25030901 (registering DOI) - 2 Feb 2025
Viewed by 296
Abstract
Water molecules from the environment or human breath are one of the main factors affecting the accuracy, efficiency, and long-term stability of electronic gas sensors. In this contribution, yttrium (Y)-doped La2Ti2O7 (LTO) nanosheets were synthesized by a hydrothermal [...] Read more.
Water molecules from the environment or human breath are one of the main factors affecting the accuracy, efficiency, and long-term stability of electronic gas sensors. In this contribution, yttrium (Y)-doped La2Ti2O7 (LTO) nanosheets were synthesized by a hydrothermal reaction, demonstrating improved proton conductivity compared to their non-doped counterparts. The response of Y-doped LTO with the optimal doping concentration to 100 ppm NO2 at 43% relative humidity (RH) was −21%, which is four times higher than that of bare La2Ti2O7. As the humidity level increased to 75%, the response of Y-doped LTO further increased to −64%. Unlike the gas doping effect observed in previous studies of semiconducting metal oxides, the sensing mechanism of Y-doped LTO nanosheets is based on the enhanced dissociation of H2O in the presence of target NO2 molecules, leading to the generation of more protons for ion conduction. This also resulted in a greater resistance drop and thus a larger sensing response at elevated humidity levels. Our work demonstrates that proton-conductive oxide materials are promising gas-sensing materials under humid conditions. Full article
(This article belongs to the Special Issue Advanced Sensors in Atomic Level)
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22 pages, 7345 KiB  
Article
Analysis of Structural Design Variations in MEMS Capacitive Microphones
by Tzu-Huan Peng, Huei-Ju Hsu and Jin H. Huang
Sensors 2025, 25(3), 900; https://doi.org/10.3390/s25030900 (registering DOI) - 2 Feb 2025
Viewed by 153
Abstract
Different microstructures significantly affect the acoustic performance of MEMS capacitive microphones, particularly in key specifications of interest. This paper presents several microstructures, including rib-reinforced backplates, suspended diaphragms, and outer vent holes. Three MEMS microphone designs were implemented to analyze the impact of these [...] Read more.
Different microstructures significantly affect the acoustic performance of MEMS capacitive microphones, particularly in key specifications of interest. This paper presents several microstructures, including rib-reinforced backplates, suspended diaphragms, and outer vent holes. Three MEMS microphone designs were implemented to analyze the impact of these microstructures. Equivalent circuit models corresponding to each design were constructed to simulate specifications such as sensitivity, signal-to-noise ratio (SNR), and low corner frequency (LCF), which were validated through experimental measurements. Finite Element Analysis (FEA) was also employed to calculate the acoustic damping of certain microstructures, the mechanical lumped parameters of the diaphragm, and the pre-deformation of the MEMS structure. A compressed air test (CAT) was conducted to evaluate the mechanical reliability of microphone samples. The results of simulations and measurements indicate that rib-reinforced backplates effectively improve microphone reliability, increasing the pass rate in CAT. Compared to fully clamped diaphragms, suspended diaphragms exhibit higher mechanical compliance, which enhances SNR performance but reduces AOP. Outer vent holes can achieve similar functionality to diaphragm vent holes, but their impact on improving AOP requires further design and testing. Full article
(This article belongs to the Collection Next Generation MEMS: Design, Development, and Application)
10 pages, 2903 KiB  
Communication
Evaluation of Average Quantum Efficiency of Industrial Digital Camera
by Zhuochen Deng, Lingfeng Chen, Xuemeng Wei and Xusheng Zhang
Sensors 2025, 25(3), 899; https://doi.org/10.3390/s25030899 (registering DOI) - 2 Feb 2025
Viewed by 194
Abstract
Quantum efficiency (QE) is a critical metric for assessing the performance of industrial digital cameras. The current EMVA1288 standard relies on monochromatic light for QE measurements. Comprehensive QE tests across the visible spectrum often involve elaborate setups and extensive data acquisition. Additionally, such [...] Read more.
Quantum efficiency (QE) is a critical metric for assessing the performance of industrial digital cameras. The current EMVA1288 standard relies on monochromatic light for QE measurements. Comprehensive QE tests across the visible spectrum often involve elaborate setups and extensive data acquisition. Additionally, such tests may not fully capture camera performance under broadband illumination, which is frequently encountered in industrial applications. This study introduces the concept of average quantum efficiency (AQE) using white light sources and proposes a novel testing method. Systematic experiments and data analyses were performed on two industrial digital cameras under white light sources with different spectral distributions. The results suggest that AQE testing offers a practical and efficient means to evaluate camera performance under broadband illumination, complementing existing monochromatic QE measurement methods. Full article
(This article belongs to the Special Issue Optical Sensors for Industrial Applications)
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27 pages, 621 KiB  
Article
Long-Term Energy Consumption Minimization Based on UAV Joint Content Fetching and Trajectory Design
by Elhadj Moustapha Diallo, Rong Chai, Abuzar B. M. Adam, Gezahegn Abdissa Bayessa, Chengchao Liang and Qianbin Chen
Sensors 2025, 25(3), 898; https://doi.org/10.3390/s25030898 (registering DOI) - 2 Feb 2025
Viewed by 179
Abstract
Caching the contents of unmanned aerial vehicles (UAVs) could significantly improve the content fetching performance of request users (RUs). In this paper, we study UAV trajectory design, content fetching, power allocation, and content placement problems in multi-UAV-aided networks, where multiple UAVs can transmit [...] Read more.
Caching the contents of unmanned aerial vehicles (UAVs) could significantly improve the content fetching performance of request users (RUs). In this paper, we study UAV trajectory design, content fetching, power allocation, and content placement problems in multi-UAV-aided networks, where multiple UAVs can transmit contents to the assigned RUs. To minimize the energy consumption of the system, we develop a constrained optimization problem that simultaneously designs UAV trajectory, power allocation, content fetching, and content placement. Since the original minimization problem is a mixed-integer nonlinear programming (MINLP) problem that is difficult to solve, the optimization problem was first transformed into a semi-Markov decision process (SMDP). Next, we developed a new technique to solve the joint optimization problem: option-based hierarchical deep reinforcement learning (OHDRL). We define UAV trajectory planning and power allocation as the low-level action space and content placement and content fetching as the high-level option space. Stochastic optimization can be handled using this strategy, where the agent makes high-level option selections, and the action is carried out at a low level based on the chosen option’s policy. When comparing the proposed approach to the current technique, the numerical results show that it can produce more consistent learning performance and reduced energy consumption. Full article
(This article belongs to the Section Communications)
16 pages, 564 KiB  
Article
Efficient Elliptic-Curve-Cryptography-Based Anonymous Authentication for Internet of Things: Tailored Protocols for Periodic and Remote Control Traffic Patterns
by Shunfang Hu, Yuanyuan Zhang, Yanru Guo, Yanru Chen and Liangyin Chen
Sensors 2025, 25(3), 897; https://doi.org/10.3390/s25030897 (registering DOI) - 2 Feb 2025
Viewed by 235
Abstract
IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes [...] Read more.
IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes using one-time pseudonyms face resynchronization issues after desynchronization attacks, and the high computational overhead of bilinear pairing and public key encryption limits its applicability. Existing schemes also lack essential security features, causing issues such as vulnerability to ephemeral secret leakage attacks and key compromise impersonation. To address these issues, we propose two novel AKA schemes, PUAKA and RCAKA, designed for different IoT traffic patterns. PUAKA improves end device anonymity in the periodic update pattern by updating one-time pseudonyms with authenticated session keys. RCAKA, for the remote control pattern, ensures anonymity while reducing communication and computation costs using shared signatures and temporary random numbers. A key contribution of RCAKA is its ability to resynchronize end devices with incomplete data in the periodic update pattern, supporting continued authentication. Both protocols’ security is proven under the Real-or-Random model. The performance comparison results show that the proposed protocols exceed existing solutions in security features and communication costs while reducing computational overhead by 32% to 50%. Full article
(This article belongs to the Special Issue IoT Network Security (Second Edition))
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24 pages, 10651 KiB  
Article
CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement
by Mingming Cao, Jie Wan and Xiang Gu
Sensors 2025, 25(3), 896; https://doi.org/10.3390/s25030896 (registering DOI) - 1 Feb 2025
Viewed by 440
Abstract
Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development of artificial intelligence (AI) and Internet of Things (IoT), sensor-based HAR has gained increasing attention and presents great advantages to existing work. [...] Read more.
Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development of artificial intelligence (AI) and Internet of Things (IoT), sensor-based HAR has gained increasing attention and presents great advantages to existing work. Relying solely on existing labeled data may not adequately address the challenge of ensuring the model’s generalization ability to new data. The ’CLEAR’ method is designed to improve the accuracy of multimodal human activity recognition. This approach employs data augmentation, multimodal feature fusion, and contrastive learning techniques. These strategies are utilized to refine and extract highly discriminative features from various data sources, thereby significantly enhancing the model’s capacity to identify and classify diverse human activities accurately. CLEAR achieves high generalization performance on unknown datasets using only training data. Furthermore, CLEAR can be directly applied to various target domains without retraining or fine-tuning. Specifically, CLEAR consists of two parts. First, it employs data augmentation techniques in both the time and frequency domains to enrich the training data. Second, it optimizes feature extraction using attention-based multimodal fusion techniques and employs supervised contrastive learning to improve feature discriminability. We achieved accuracy rates of 81.09%, 90.45%, and 82.75% on three public datasets USC-HAD, DSADS, and PAMAP2, respectively. Additionally, when the training data are reduced from 100% to 20%, the model’s accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities. Additionally, when the training data are reduced from 100% to 20%, the model’s accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities. Full article
(This article belongs to the Section Physical Sensors)
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28 pages, 6954 KiB  
Article
An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series
by Colin O. Quinn, Ronald H. Brown, George F. Corliss and Richard J. Povinelli
Sensors 2025, 25(3), 895; https://doi.org/10.3390/s25030895 (registering DOI) - 1 Feb 2025
Viewed by 353
Abstract
Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, [...] Read more.
Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm’s key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4–4.3% WMAPE improvement for billing cycle data and a 4.6–10.4% improvement for residential data over existing methods. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Sensor Systems)
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