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Sensors, Volume 25, Issue 24 (December-2 2025) – 23 articles

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11 pages, 1419 KB  
Article
Force and Temperature Characterization of a Novel Fiber Bragg Grating Overhead Line Sensor
by Grzegorz Fusiek and Pawel Niewczas
Sensors 2025, 25(24), 7425; https://doi.org/10.3390/s25247425 (registering DOI) - 6 Dec 2025
Abstract
This paper presents the characterization of a new optical sensor designed for monitoring overhead power lines (OHLs) by determining key mechanical parameters of electrical conductors. The device employs fiber Bragg gratings (FBGs) written into a metal-coated fiber and enclosed within a Kovar® [...] Read more.
This paper presents the characterization of a new optical sensor designed for monitoring overhead power lines (OHLs) by determining key mechanical parameters of electrical conductors. The device employs fiber Bragg gratings (FBGs) written into a metal-coated fiber and enclosed within a Kovar® capillary tube. Its epoxy-free design provides robust hermetic protection for the FBGs, enabling reliable performance with both conventional low-temperature and high-temperature low-sag (HTLS) conductors. The sensor configuration enables direct measurements of conductor strain and temperature, as well as indirect estimation of sag and related mechanical quantities such as tension and stress. Laboratory tests were carried out over a temperature range of 30 °C to 200 °C and for applied forces up to 2 kN. The experimentally determined sensitivities were about 0.4 nm/kN for force and 27 pm/°C for temperature. The device endured ten successive thermal cycles between 30 °C and 200 °C, maintaining its force sensitivity within 20% variation throughout the tests. These results confirm that the developed sensor can simultaneously track temperature and mechanical load across the investigated temperature range, demonstrating its potential for HTLS conductor monitoring in power transmission networks. Full article
(This article belongs to the Special Issue Optical Sensors for Industrial Applications)
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15 pages, 2680 KB  
Article
Study and Optimal Design of the Integrated 37° Unidirectional SV-EMAT for Rapid Rail Flaw Detection
by Wei Yuan
Sensors 2025, 25(24), 7424; https://doi.org/10.3390/s25247424 (registering DOI) - 6 Dec 2025
Abstract
The problem of poor coupling and wheel breakage is a critical issue in the rapid inspection of rails using contact piezoelectric ultrasonic technology for trolleys and vehicles. To overcome this shortcoming, a non-contact unidirectional Shear Vertical Wave EMAT (USV-EMAT) for rapid rail flaw [...] Read more.
The problem of poor coupling and wheel breakage is a critical issue in the rapid inspection of rails using contact piezoelectric ultrasonic technology for trolleys and vehicles. To overcome this shortcoming, a non-contact unidirectional Shear Vertical Wave EMAT (USV-EMAT) for rapid rail flaw detection with a larger emission angle is proposed and optimized. First, the core characteristics of the USV-EMAT and the Unidirectional Line-Focusing Shear Vertical Wave EMAT (ULSV-EMAT) are compared and analyzed, including emission angle, directivity, intensity, and detection scan distance. The results confirmed that the USV-EMAT is more suitable for rapid rail flaw detection. Secondly, the orthogonal experimental analysis method was used to optimize the structural parameters of the probe. This study systematically identified the key factors influencing the directivity and intensity of acoustic waves excited by the probe, as well as the detection blind zones. Finally, the structural parameters of the integrated 37° USV-EMAT probe were determined by comparing and analyzing the received signal characteristics of the transmit–receive racetrack coil and the self-transmitting–receiving meander coil. The results show that the optimized probe achieves a 14.3 dB SNR for detecting a 5 mm diameter, 50 mm deep transverse hole in the rail, and a 14.0 dB SNR for a 3 mm diameter, 25 mm long, 50 mm deep flat-bottomed hole. Additionally, this study reveals that as the burial depth of the transverse holes increases, the detection scan distance for such defects exhibits an “N”-shaped trend, with the minimum occurring at a depth of 90 mm. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Sustainable and Resilient Railway Infrastructures)
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45 pages, 3087 KB  
Review
A Comparative Overview of Technological Advances in Fall Detection Systems for Elderly People
by Omar Flor-Unda, Rafael Arcos-Reina, Cristina Estrella-Caicedo, Carlos Toapanta, Freddy Villao, Héctor Palacios-Cabrera, Susana Nunez-Nagy and Bernardo Alarcos
Sensors 2025, 25(24), 7423; https://doi.org/10.3390/s25247423 - 5 Dec 2025
Abstract
Population ageing is a growing global trend. It was estimated that by 2050, people over 60 years of age will represent 35% of the population in industrialised countries. This context demands strategies that incorporate technologies, such as fall detection systems, to facilitate remote [...] Read more.
Population ageing is a growing global trend. It was estimated that by 2050, people over 60 years of age will represent 35% of the population in industrialised countries. This context demands strategies that incorporate technologies, such as fall detection systems, to facilitate remote monitoring and the automatic activation of risk alarms, thus improving quality of life. This article presents a scoping review of the leading technological solutions developed over the last decade for detecting falls in older adults, describing their principles of operation, effectiveness, advantages, limitations, and future trends in their development. The review was conducted under the PRISMA® methodology, including articles indexed in SCOPUS, ScienceDirect, Web of Science, PubMed, IEEE Xplore and Taylor & Francis. There is a predominance in the use of inertial systems that use accelerometers and gyroscopes, valued for their low cost and wide availability. However, those approaches that combine image analysis with artificial intelligence and machine learning algorithms show superiority in terms of accuracy and robustness. Similarly, progress has been made in the development of multisensory solutions based on IoT technologies, capable of integrating information from various sources, which optimises decision-making in real time. Full article
(This article belongs to the Section Wearables)
37 pages, 1982 KB  
Article
A Quantum-Hybrid Framework for Urban Environmental Forecasting Integrating Advanced AI and Geospatial Simulation
by Janis Peksa, Andrii Perekrest, Kyrylo Vadurin and Dmytro Mamchur
Sensors 2025, 25(24), 7422; https://doi.org/10.3390/s25247422 - 5 Dec 2025
Abstract
The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to [...] Read more.
The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions. Full article
(This article belongs to the Section Environmental Sensing)
15 pages, 73866 KB  
Article
A Miniaturized Dual-Band Frequency Selective Surface with Enhanced Capacitance Loading for WLAN Applications
by Muhammad Idrees, Sai-Wai Wong, Abdul Majeed, Shu-Qing Zhang and Yejun He
Sensors 2025, 25(24), 7421; https://doi.org/10.3390/s25247421 - 5 Dec 2025
Abstract
This article presents a miniaturized dual-band frequency selective surface (FSS) based on capacitance-enhancing technique for RF shielding applications. The FSS incorporates two independent corner-modified square loop (CMSL) elements realized on a lossy dielectric, effectively suppressing the WiFi 2.45 GHz and WLAN 5.5 GHz [...] Read more.
This article presents a miniaturized dual-band frequency selective surface (FSS) based on capacitance-enhancing technique for RF shielding applications. The FSS incorporates two independent corner-modified square loop (CMSL) elements realized on a lossy dielectric, effectively suppressing the WiFi 2.45 GHz and WLAN 5.5 GHz bands simultaneously. The capacitance of FSS element is enhanced through corner truncation without using additional lumped elements. The symmetric geometry enables the FSS shield to manifest angularly stable and polarization-insensitive spectral responses under various oblique incident angles. Moreover, an equivalent circuit model (ECM) of the FSS structure is designed. A finite FSS prototype is fabricated and tested to verify the EM simulations. The measured results are in good agreement with the simulated responses. More importantly, the proposed design is scalable to other frequencies and is capable of selectively mitigating electromagnetic interference or confine the EM fields. Full article
(This article belongs to the Special Issue Antenna Technologies for Microwave and Millimeter-Wave Sensing)
18 pages, 5348 KB  
Article
Computational Simulation and Experimental Validation of Acoustic Reflectometry in Otitis Media
by Karl Nyberg, Manfred Lindmark, Mimmi Werner, Petter Holmlund, Thorbjörn Lundberg and Fredrik Öhberg
Sensors 2025, 25(24), 7420; https://doi.org/10.3390/s25247420 - 5 Dec 2025
Abstract
Otitis Media (OM) is a prevalent condition in children that can lead to hearing impairment and significant healthcare costs. Inaccuracy in primary care and equipment cost in developing countries are concerning issues in OM diagnostics. Acoustic Reflectometry (AR) offers a low-cost, non-invasive diagnostic [...] Read more.
Otitis Media (OM) is a prevalent condition in children that can lead to hearing impairment and significant healthcare costs. Inaccuracy in primary care and equipment cost in developing countries are concerning issues in OM diagnostics. Acoustic Reflectometry (AR) offers a low-cost, non-invasive diagnostic alternative, though it has fallen short on accuracy in previous studies. The primary aim of this study was to establish a computational simulation and an experimental model able to reproduce AR performed on human individuals to enable further research and accuracy improvement. The secondary aim was to perform a sensitivity analysis on AR instrument user error. Simulations and experiments were validated against measurements from human individuals with OM and normal ears, respectively. The results reveal that the simulation sufficiently reproduces human AR measurements and distinguishes an ear with OM from a healthy ear. The experiment delivered satisfying measurements on OM but underperformed in a normal ear scenario. The simulations and experiments overpredicted sound reflection in OM. The sensitivity study showed promising robustness of AR, concluding that computational simulation is a viable tool and complement to an experimental approach in research of AR. Future efforts should focus on paediatric models and partially filled middle ear simulations to promote clinical relevance. Full article
(This article belongs to the Section Biomedical Sensors)
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23 pages, 4184 KB  
Article
Robust Navigation in Multipath Environments Using GNSS/UWB/INS Integration with Anchor Position Estimation Toward eVTOL Operations
by Atsushi Osaka and Toshiaki Tsujii
Sensors 2025, 25(24), 7419; https://doi.org/10.3390/s25247419 - 5 Dec 2025
Abstract
Emerging technologies such as urban air mobility and autonomous vehicles increasingly rely on Global Navigation Satellite Systems (GNSS) for accurate positioning. However, GNSS alone suffers from severe degradation in complex environments, particularly due to multipath effects caused by reflections from surrounding structures. These [...] Read more.
Emerging technologies such as urban air mobility and autonomous vehicles increasingly rely on Global Navigation Satellite Systems (GNSS) for accurate positioning. However, GNSS alone suffers from severe degradation in complex environments, particularly due to multipath effects caused by reflections from surrounding structures. These effects distort pseudo-range measurements and, in combination with signal attenuation and blockage, lead to significant positioning errors. To address this challenge, this study proposes a loosely integrated navigation framework that combines GNSS, ultra-wideband (UWB), and inertial navigation system (INS) data. UWB enables high-precision ranging, and we further extend its application to estimate the locations of UWB anchors themselves. This approach alleviates a major technical limitation of UWB systems, which typically require anchor positions near buildings to be precisely surveyed beforehand. Field experiments were conducted in multipath-prone outdoor environments using a drone equipped with GNSS, UWB, and INS sensors. The results demonstrate that the proposed GNSS/UWB/INS integration reduces positioning errors by up to approximately 90% compared with GNSS/INS integration. Moreover, in areas surrounded by UWB anchors (UWB-Anchored Area), submeter-level positioning accuracy was achieved. These findings highlight the robustness of the proposed method against multipath interference and its potential to overcome anchor-dependency issues, thereby contributing to safe and reliable navigation solutions for future urban applications such as eVTOL operations. Full article
(This article belongs to the Section Navigation and Positioning)
21 pages, 5645 KB  
Article
A UAV Vision-Based Deformation Monitoring Method with 3D Scale Constraints
by Jianlin Liu, Jun Wu, Wujiao Dai, Deyong Pan, Min Zhou, Lei Xing and Zhiwu Yu
Sensors 2025, 25(24), 7418; https://doi.org/10.3390/s25247418 - 5 Dec 2025
Abstract
Aiming at the problem that low-quality images and low-precision control points lead to scale differences between the survey area model and the real model in UAV (Unmanned Aerial Vehicle) vision-based 3D deformation monitoring, which impairs the accuracy of deformation monitoring, this paper develops [...] Read more.
Aiming at the problem that low-quality images and low-precision control points lead to scale differences between the survey area model and the real model in UAV (Unmanned Aerial Vehicle) vision-based 3D deformation monitoring, which impairs the accuracy of deformation monitoring, this paper develops a spatial 3D scale for providing high-precision scale information and proposes a UAV vision-based deformation monitoring method with 3D scale constraints, thereby improving the deformation monitoring accuracy in large-scale survey areas. Experimental results show that compared with the monitoring method using only control points as constraints, the proposed method achieves accuracy (RMSE) improvement rates of 38.6% and 48.1% in the horizontal and elevation directions respectively during four phases of UAV operations, and the 3D deformation accuracy (RMSE) improvement rate remains at approximately 42.3% during seven phases of UAV operations. This verifies the effectiveness and reliability of the UAV vision-based deformation monitoring method with 3D scale constraints. Full article
(This article belongs to the Section Remote Sensors)
28 pages, 5110 KB  
Article
WISEST: Weighted Interpolation for Synthetic Enhancement Using SMOTE with Thresholds
by Ryotaro Matsui, Luis Guillen, Satoru Izumi and Takuo Suganuma
Sensors 2025, 25(24), 7417; https://doi.org/10.3390/s25247417 - 5 Dec 2025
Abstract
Imbalanced learning occurs when rare but critical events are missed because classifiers are trained primarily on majority-class samples. This paper introduces WISEST, a locality-aware weighted-interpolation algorithm that generates synthetic minority samples within a controlled threshold near class boundaries. Benchmarked on more than a [...] Read more.
Imbalanced learning occurs when rare but critical events are missed because classifiers are trained primarily on majority-class samples. This paper introduces WISEST, a locality-aware weighted-interpolation algorithm that generates synthetic minority samples within a controlled threshold near class boundaries. Benchmarked on more than a hundred real-world imbalanced datasets, such as KEEL, with different imbalance ratios, noise levels, geometries, and other security and IoT sets (IoT-23 and BoT–IoT), WISEST consistently improved minority detection in at least one of the metrics on about half of those datasets, achieving up to a 25% relative recall increase and up to an 18% increase in F1 compared to the original training and other approaches. However, in most cases, WISEST’s trade-off gains are in accuracy and precision depending on the dataset and classifier. These results indicate that WISEST is a practical and robust option when minority support and borderline structure permit safe synthesis, although no single sampler uniformly outperforms others across all datasets. Full article
(This article belongs to the Special Issue Advances in Security of Mobile and Wireless Communications)
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25 pages, 49211 KB  
Article
Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach
by Kenny Sau Kang Chu, Kuew Wai Chew, Yoong Choon Chang, Stella Morris, Yap Hoon and Chen Chen
Sensors 2025, 25(24), 7416; https://doi.org/10.3390/s25247416 - 5 Dec 2025
Abstract
Motor eccentricity faults, stemming from the misalignment of the rotor’s center and pivot point, lead to significant vibrations and noise, compromising motor reliability. This study emphasizes the need for an efficient diagnostic system to enable early detection and correction of these faults. Our [...] Read more.
Motor eccentricity faults, stemming from the misalignment of the rotor’s center and pivot point, lead to significant vibrations and noise, compromising motor reliability. This study emphasizes the need for an efficient diagnostic system to enable early detection and correction of these faults. Our research proposes a novel Eccentricity Fault Diagnosis Network (E-FDNet), designed for integration into a Motor Eccentricity Fault Diagnosis System (MEFDS), utilizing neural networks for detection. Evaluation tests reveal that a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is ideal as the internal neural network within the E-FDNet. Key contributions of this research include (1) E-FDNet that stabilizes transition predictions among SEF/DEF/MEF; (2) a steady-state characteristic normalization (SSCN) improving feature consistency under dynamic responses; (3) an integrated physics–FEM–experiment pipeline for controlled analysis and validation; (4) approximately 98.86% accuracy/F1 outperforming classical and deep baselines; and (5) a non-invasive, current-only sensing design suited for deployment. Full article
(This article belongs to the Special Issue Sensor Data-Driven Fault Diagnosis Techniques)
32 pages, 6954 KB  
Review
Decision Support Systems in Neurosurgery: Current Applications and Future Directions
by Mateusz Koryciński, Konrad A. Ciecierski and Ewa Niewiadomska-Szynkiewicz
Sensors 2025, 25(24), 7415; https://doi.org/10.3390/s25247415 - 5 Dec 2025
Abstract
Artificial intelligence (AI) is one of the fastest-developing research fields. Its methods are applied with great success to various problems across many industries. Healthcare is one of them, with AI applied to organizational problems, textual data analysis, and treatment decision support systems used [...] Read more.
Artificial intelligence (AI) is one of the fastest-developing research fields. Its methods are applied with great success to various problems across many industries. Healthcare is one of them, with AI applied to organizational problems, textual data analysis, and treatment decision support systems used for diagnosis and treatment. This paper reviews current AI methods in neurosurgery settings, discussing the potential use of decision support systems. As neurosurgery is highly technical and requires millimeter-precise guidance, AI systems can provide significant benefits in helping neurosurgeons navigate the surgical field. For example, AI-assisted neuronavigation during deep brain stimulation (DBS) surgeries shortens the length of the procedure and significantly reduces the risk of electrode misplacement, and the need for future costly re-operation. We do not limit our review to existing methods, as we also discuss possible future directions for such systems. When developing such methods, special emphasis must be put on precision, usability, security, and explainability. Full article
(This article belongs to the Section Biomedical Sensors)
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23 pages, 9897 KB  
Article
HyMambaNet: Efficient Remote Sensing Water Extraction Method Combining State Space Modeling and Multi-Scale Features
by Handan Liu, Guangyi Mu, Kai Li, Haowei Zhang, Yibo Sun, Hongqing Sun and Sijia Li
Sensors 2025, 25(24), 7414; https://doi.org/10.3390/s25247414 - 5 Dec 2025
Abstract
Accurate segmentation of water bodies from high-resolution remote sensing imagery is crucial for water resource management and ecological monitoring. However, small and morphologically complex water bodies remain difficult to detect due to scale variations, blurred boundaries, and heterogeneous backgrounds. This study aims to [...] Read more.
Accurate segmentation of water bodies from high-resolution remote sensing imagery is crucial for water resource management and ecological monitoring. However, small and morphologically complex water bodies remain difficult to detect due to scale variations, blurred boundaries, and heterogeneous backgrounds. This study aims to develop a robust and scalable deep learning framework for high-precision water body extraction across diverse hydrological and ecological scenarios. To address these challenges, we propose HyMambaNet, a hybrid deep learning model that integrates convolutional local feature extraction with the Mamba state space model for efficient global context modeling. The network further incorporates multi-scale and frequency-domain enhancement as well as optimized skip connections to improve boundary precision and segmentation robustness. Experimental results demonstrate that HyMambaNet significantly outperforms existing CNN and Transformer-based methods. On the LoveHY dataset, it achieves 74.82% IoU and 88.87% F1-score, exceeding UNet by 7.49% IoU and 7.12% F1. On the LoveDA dataset, it attains 81.30% IoU and 89.99% F1-score, surpassing advanced models such as Deeplabv3+, AttenUNet, and TransUNet. These findings confirm that HyMambaNet provides an efficient and generalizable solution for large-scale water resource monitoring and ecological applications based on remote sensing imagery. Full article
(This article belongs to the Section Environmental Sensing)
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25 pages, 1271 KB  
Article
Engaging Older Adults to Guide the Development of Passive Home Health Monitoring to Support Aging in Place
by Elinor Randi Schoenfeld, Tracy Trimboli, Kaylyn Schwartz, Givenchy Ayisi-Boahene, Patricia Bruckenthal, Erez Zadok, Shelley Horwitz and Fan Ye
Sensors 2025, 25(24), 7413; https://doi.org/10.3390/s25247413 - 5 Dec 2025
Abstract
By 2050, most adults aged 65 and older in the United States will want to age independently at home, a goal that will strain healthcare resources. Adults aged 50 and older (N = 112) were recruited for study participation between 2018 and 2022. [...] Read more.
By 2050, most adults aged 65 and older in the United States will want to age independently at home, a goal that will strain healthcare resources. Adults aged 50 and older (N = 112) were recruited for study participation between 2018 and 2022. They completed surveys and participated in discussion sessions to explore their needs and opinions regarding smart home sensors. Survey results indicated that older adults’ comfort with smart home sensors increased with their perceived need for monitoring when home alone (OR = 1.46; p = 0.012) or sick/recovering from an illness (OR = 2.21; p < 0.001). When sick compared to when healthy, individuals were 2.65 times more likely to prefer installing multiple sensors in the living room, 1.75 times more likely in the kitchen, 3.66 times more likely in the bedroom, and 3.41 times more likely in the bathroom (p < 0.05). Regarding data sharing, participants were most willing to share information with healthcare providers and family members on a regular basis (80 and 81%, respectively) and 71% on a regular basis or when sick/recovering. Comfort with data sharing with professional caregivers (OR = 1.67; p = 0.0017) and monitoring companies (OR = 1.34; p = 0.030) significantly increased when sick/recovering. Discussion sessions highlighted overwhelming concerns about personal security/privacy, loss of independence, and ethical issues in data collection. Participants emphasized the need for new systems to be flexible, cost-effective, user-friendly, and respectful of user autonomy, accommodating diverse life stages, comfort levels, home environments, income levels, and support structures. Insights are now informing sensor data collection in our model home. Study findings underscore the importance of involving potential users in technology development to create effective and acceptable solutions for aging in place. Full article
(This article belongs to the Special Issue Sensing and Signal Processing Technologies for Outpatient Monitoring and Rehabilitation)
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24 pages, 16899 KB  
Article
Adaptive Relay Free Space Networking for Autonomous Underwater Drone Swarms
by David Stack, Douglas Nuti and Mehdi Rahmati
Sensors 2025, 25(24), 7412; https://doi.org/10.3390/s25247412 - 5 Dec 2025
Abstract
Underwater wireless networking is an emerging field for exploration and monitoring, enabling real-time data transmission and communication with both static sensors and submersibles. Current approaches mostly focus on utilizing acoustic waves. The use of optics for this purpose has been known to have [...] Read more.
Underwater wireless networking is an emerging field for exploration and monitoring, enabling real-time data transmission and communication with both static sensors and submersibles. Current approaches mostly focus on utilizing acoustic waves. The use of optics for this purpose has been known to have several implementation challenges that have prevented it from being considered as a universal alternative. This study proposes that utilizing optics in an adaptive relay wireless network configuration can overcome its primary limitation of line-of-sight (LOS) propagation. In this paper, a network of strategically placed sensors is experimentally constructed with the ability to read and send modulated blue light, fit for extended submersion in water. This proposal represents a hypothetical aquatic drone swarm that is developed and programmed to follow adaptive relay logic. This network is able to demonstrate adaptation to obstructions in the LOS and maintain communication through configurations in which the sender and intended recipient would otherwise be unable to directly communicate. This finding allows the advantages of optical communications to be further explored for aquatic applications, primarily its higher potential data rate, which is inherently productive to a swarm. Full article
(This article belongs to the Special Issue Recent Challenges in Underwater Optical Communication and Detection)
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18 pages, 18184 KB  
Article
Photoacoustic Gas Sensing Using a Novel Fluidic Microphone Based on Thermal MEMS
by Akash Gupta, Anant Bhardwaj, Achim Bittner and Alfons Dehé
Sensors 2025, 25(24), 7411; https://doi.org/10.3390/s25247411 - 5 Dec 2025
Abstract
Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts [...] Read more.
Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts the development of truly miniaturised and broadband PAS systems. To address this limitation, we present a novel MEMS fluidic microphone (f-mic) that operates on a thermal sensing principle and is explicitly optimised for the infrasound regime. The sensor demonstrates a constant sensitivity of 32 μV/Pa for frequencies below 20 Hz. A detailed analytical model incorporating frequency-dependent effects is developed to identify and investigate the critical design parameters that influence system performance. The overall system exhibits a band-pass frequency response, enabling broadband operation. Based on these insights, a miniaturised photoacoustic cell is fabricated, ensuring efficient optical coupling and f-mic integration. Experimental validation using a CO2-targeted laser system demonstrates a linear response up to 5000 ppm, a sensitivity of 6 nV/ppm, and a theoretical detection limit of 300 ppb over 100 s, resulting in an NNEA of 6×10−6 W cm−1 Hz−0.5. These results establish the f-mic as a robust, scalable solution for non-resonant PAS, effectively overcoming a significant bottleneck in compact gas sensing technologies. Full article
(This article belongs to the Special Issue Advanced MEMS Resonators and Sensors: Materials, Designs and Applications)
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20 pages, 2816 KB  
Article
Real-Time Reconstruction of the Temperature Field of NSRT’s Back-Up Structure Based on Improved RIME-XGBoost
by Shi-Jiao Zhang, Qian Xu, Hui Wang, Fei Xue, Fei-Long He and Xiao-Man Cao
Sensors 2025, 25(24), 7410; https://doi.org/10.3390/s25247410 - 5 Dec 2025
Abstract
Obtaining an antenna’s back-up structure (BUS) temperature field is an essential prerequisite for analyzing its thermal deformation. Thermodynamic simulation can obtain the structure’s thermal distribution, but it has low computational accuracy. There is a problem with cumbersome wiring and difficult maintenance of the [...] Read more.
Obtaining an antenna’s back-up structure (BUS) temperature field is an essential prerequisite for analyzing its thermal deformation. Thermodynamic simulation can obtain the structure’s thermal distribution, but it has low computational accuracy. There is a problem with cumbersome wiring and difficult maintenance of the temperature measurement system. This study developed an improved RIME-XGBoost model to realize the temperature prediction of the BUS of the Nanshan 26-m Radio Telescope (NSRT). The proposed model successfully predicts the NSRT’s BUS temperature distribution based solely on environmental sensing (ambient temperature, angle of solar radiation, antenna’s orientation, etc.). The relative prediction accuracy between the predicted and actual BUS temperature is 97.15%, and the predictive error is less than 0.897 K (root mean square error, RMSE). This research result provides an alternative method for the real-time reconstruction of the structure’s thermal distribution in large-aperture radio telescopes. Full article
(This article belongs to the Section Environmental Sensing)
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16 pages, 2274 KB  
Article
Joint Function and Movement Variability During Daily Living Activities Performed Throughout the Home Setting: A Digital Twin Modeling Study
by Zhou Fang, Mohammad Yavari, Yiqun Chen, Davood Shojaei, Peter Vee Sin Lee, Abbas Rajabifard and David Ackland
Sensors 2025, 25(24), 7409; https://doi.org/10.3390/s25247409 - 5 Dec 2025
Abstract
Human mobility is commonly assessed in the laboratory environment, but accurate and robust joint motion measurement and task classification in the home setting are rarely undertaken. This study aimed to develop a digital twin model of a home to measure, visualize, and classify [...] Read more.
Human mobility is commonly assessed in the laboratory environment, but accurate and robust joint motion measurement and task classification in the home setting are rarely undertaken. This study aimed to develop a digital twin model of a home to measure, visualize, and classify joint motion during activities of daily living. A fully furnished single-bedroom apartment was digitally reconstructed using 3D photogrammetry. Ten healthy adults performed 19 activities of daily living over a 2 h period throughout the apartment. Each participant’s upper and lower limb joint motion was measured using inertial measurement units, and body spatial location was measured using an ultra-wide band sensor, registered to the digital home model. Supervised machine learning classified tasks with a mean 82.3% accuracy. Hair combing involved the highest range of shoulder elevation (124.2 ± 21.2°), while sit-to-stand exhibited both the largest hip flexion (75.7 ± 10.3°) and knee flexion (91.8 ± 8.6°). Joint motion varied from room to room, even for a given task. For example, subjects walked fastest in the living room (1.0 ± 0.2 m/s) and slowest in the bathroom (0.78 ± 0.10 m/s), while the mean maximum ankle dorsiflexion in the living room was significantly higher than that in the bathroom (mean difference: 4.9°, p = 0.002, Cohen’s d = 1.25). This study highlights the dependency of both upper and lower limb joint motion during activities of daily living on the internal home environment. The digital twin modeling framework reported may be useful in planning home-based rehabilitation, remote monitoring, and for interior design and ergonomics. Full article
(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)
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41 pages, 3086 KB  
Review
AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review
by Sumendra Thakur, Nurul I. Sarkar and Sira Yongchareon
Sensors 2025, 25(24), 7408; https://doi.org/10.3390/s25247408 - 5 Dec 2025
Abstract
Efficient routing remains the linchpin for achieving sustainable performance in Wireless Sensor Networks (WSNs) within the Internet of Things (IoT). However, traditional routing mechanisms increasingly struggle to cope with the growing complexity of network architectures, frequent changes in topology, and the dynamic behavior [...] Read more.
Efficient routing remains the linchpin for achieving sustainable performance in Wireless Sensor Networks (WSNs) within the Internet of Things (IoT). However, traditional routing mechanisms increasingly struggle to cope with the growing complexity of network architectures, frequent changes in topology, and the dynamic behavior of mobile nodes. These issues contribute to data congestion, uneven energy consumption, and potential communication breakdowns, underscoring the urgency for optimized routing strategies. In this paper, we present a comprehensive review of over 100 studies of spanning conventional and AI-enhanced energy-efficient routing techniques. It covers diverse approaches, including metaheuristics, machine learning, reinforcement learning, and AI-based cross-layer methods aimed at improving the performance of WSN-IoT systems. The key limitations of existing solutions are discussed along with performance metrics such as scalability, energy efficiency, throughput, and packet delivery. We also highlight various research challenges and provide research directions for future exploration. By synthesizing current trends and gaps, we provide researchers and practitioners with a structured foundation for advancing intelligent, energy-conscious routing in next-generation IoT-enabled WSNs. Full article
(This article belongs to the Special Issue IoT Sensor Systems: Design, Interfaces, Signals, Processing, and Applications)
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15 pages, 3348 KB  
Article
Efficient Dataset Creation for MEMS-Based Magnetic Sensor Systems in Intelligent Transportation Applications
by Michal Hodoň, Peter Šarafín, Lukáš Formanek and Andrea Kociánová
Sensors 2025, 25(24), 7407; https://doi.org/10.3390/s25247407 - 5 Dec 2025
Abstract
This article describes the innovative use of an advanced annotation tool designed specifically for creating datasets tailored to MEMS (Micro-Electro-Mechanical Systems) sensor systems for the intelligent transportation domain. By optimizing the data annotation process, this tool significantly enhances the efficiency and accuracy of [...] Read more.
This article describes the innovative use of an advanced annotation tool designed specifically for creating datasets tailored to MEMS (Micro-Electro-Mechanical Systems) sensor systems for the intelligent transportation domain. By optimizing the data annotation process, this tool significantly enhances the efficiency and accuracy of dataset development, which is critical for the optimal performance and reliability of MEMS-based applications. The tool was tested with a specialized sensor system based on magnetometers for traffic flow monitoring, demonstrating its practical applications and effectiveness in real-world scenarios. The proposed approach offered a clear improvement over manual labelling by reducing the time needed per event and increasing the number of events that could be processed, without compromising the consistency of the assigned labels. The discussion includes a detailed overview of the tool’s features, its integration into existing workflows, as well as the benefits it offers engineers and researchers in the field of sensor technology. Full article
(This article belongs to the Special Issue Sensors in Intelligent Transport Systems)
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14 pages, 2050 KB  
Article
Evaluation of the Reliability of Calibration Constants in Heat Flux Meters Using an ISO 5660-1 Cone Heater
by Woo-Geun Kim, Cheol-Hong Hwang and Sung Chan Kim
Sensors 2025, 25(24), 7406; https://doi.org/10.3390/s25247406 - 5 Dec 2025
Abstract
This study evaluates the reliability of calibration constants for heat flux meters (HFMs) using a secondary standard under controlled thermal conditions provided by an ISO 5660-1 cone heater. Six HFMs (three new and three previously used) were examined by comparing manufacturer-provided (MFG) and [...] Read more.
This study evaluates the reliability of calibration constants for heat flux meters (HFMs) using a secondary standard under controlled thermal conditions provided by an ISO 5660-1 cone heater. Six HFMs (three new and three previously used) were examined by comparing manufacturer-provided (MFG) and secondary-standard-based (SEC) calibration constants. The bias between the MFG and SEC calibration constants varied substantially depending on the manufacturer, calibration method, service history, and the coating condition of the sensing surface. When the Bland–Altman limits of agreement were defined with the new HFMs as the reference, an HFM from a different manufacturer or calibrated by a different method was found to fall outside these limits. Although the absolute accuracy of the secondary-standard HFM has not been independently validated, the approach using the cone heater is practical for field implementation in terms of equipment accessibility, cost, and operational simplicity. Recalibration against the same secondary-standard prior to use is recommended to ensure repeatability and reproducibility across experiments and institutions. Full article
(This article belongs to the Collection Instrument and Measurement)
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22 pages, 6114 KB  
Article
Remote Sensing Inversion of Full-Profile Topography Data for Coastal Wetlands Using Synergistic Multi-Platform Sensors from Space, Air, and Ground
by Jiabao Zhang, Jin Wang, Yu Dai, Yiyang Miao and Huan Li
Sensors 2025, 25(24), 7405; https://doi.org/10.3390/s25247405 - 5 Dec 2025
Abstract
This study proposes a “zonal inversion–fusion mosaicking” technical framework to address the challenge of acquiring continuous full-profile topography data in coastal wetland intertidal zones. The framework integrates and synergistically analyzes data from multi-platform sensors, including satellite, unmanned aerial vehicle (UAV), and ground-based instruments. [...] Read more.
This study proposes a “zonal inversion–fusion mosaicking” technical framework to address the challenge of acquiring continuous full-profile topography data in coastal wetland intertidal zones. The framework integrates and synergistically analyzes data from multi-platform sensors, including satellite, unmanned aerial vehicle (UAV), and ground-based instruments. Applied to the Min River Estuary wetland, this framework employs zone-specific optimization strategies: in the inundated zone, the topography was inverted using Landsat-9 OLI imagery and a Random Forest algorithm (R2 = 0.79, RMSE = 2.08 m); in the bare flat zone, a linear model was developed based on Sentinel-2 time-series imagery using the inundation frequency method, and it achieved an accuracy of R2 = 0.86 and RMSE = 0.34 m; and in the vegetated zone, high-precision topography was derived from UAV oblique photography with Kriging interpolation (RMSE = 0.10 m). The key innovation is the successful generation of a seamless full-profile digital elevation model (DEM) with an overall RMSE of 0.54 m through benchmark unification and precision-weighted fusion algorithms from the sensor data fusion perspective. This study demonstrates that the synergistic sensors framework effectively overcomes the limitations of single-sensor observations, providing a reliable and generalizable integrated solution for the full-profile topographic monitoring of tidal flats, which offers crucial support for coastal wetland research and management. Full article
(This article belongs to the Section Environmental Sensing)
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10 pages, 496 KB  
Article
Adaptive 3D Augmentation in StyleGAN2-ADA for High-Fidelity Lung Nodule Synthesis from Limited CT Volumes
by Oleksandr Fedoruk, Konrad Klimaszewski and Michał Kruk
Sensors 2025, 25(24), 7404; https://doi.org/10.3390/s25247404 - 5 Dec 2025
Abstract
Generative adversarial networks (GANs) typically require large datasets for effective training, which poses challenges for volumetric medical imaging tasks where data are scarce. This study addresses this limitation by extending adaptive discriminator augmentation (ADA) for three-dimensional (3D) StyleGAN2 to improve generative performance on [...] Read more.
Generative adversarial networks (GANs) typically require large datasets for effective training, which poses challenges for volumetric medical imaging tasks where data are scarce. This study addresses this limitation by extending adaptive discriminator augmentation (ADA) for three-dimensional (3D) StyleGAN2 to improve generative performance on limited volumetric data. The proposed 3D StyleGAN2-ADA redefines all 2D operations for volumetric processing and incorporates the full set of original augmentation techniques. Experiments are conducted on the NoduleMNIST3D dataset of lung CT scans containing 590 voxel-based samples across two classes. Two augmentation pipelines are evaluated—one using color-based transformations and another employing a comprehensive set of 3D augmentations including geometric, filtering, and corruption augmentations. Performance is compared against the same network and dataset without any augmentations at all by assessing generation quality with Kernel Inception Distance (KID) and 3D Structural Similarity Index Measure (SSIM). Results show that volumetric ADA substantially improves training stability and reduces the risk of a mode collapse, even under severe data constraints. A strong augmentation strategy improves the realism of generated 3D samples and better preserves anatomical structures relative to those without data augmentation. These findings demonstrate that adaptive 3D augmentations effectively enable high-quality synthetic medical image generation from extremely limited volumetric datasets. The source code and the weights of the networks are available in the GitHub repository. Full article
(This article belongs to the Section Biomedical Sensors)
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19 pages, 2855 KB  
Article
Printed Circuit Board Defect Detection Based on Lightweight Deep Learning Fusion Model
by Yuling Wang, Zhicheng Chen, Jie Wang, Peng Shang, Arcot Sowmya and Changming Sun
Sensors 2025, 25(24), 7403; https://doi.org/10.3390/s25247403 - 5 Dec 2025
Abstract
Printed circuit boards (PCBs) are ubiquitous and essential electronic components. Tiny targets and high precision are the focus of PCB defect detection. This paper proposes an improved model focusing on tiny defect detection and model compression to achieve better performance in PCB defect [...] Read more.
Printed circuit boards (PCBs) are ubiquitous and essential electronic components. Tiny targets and high precision are the focus of PCB defect detection. This paper proposes an improved model focusing on tiny defect detection and model compression to achieve better performance in PCB defect detection. The improved model has a compact structure based on MobileNet v3 Small-CA and an image-cutting layer. Moreover, it applies an improved multi-scale fusion step with a location weighted mechanism to enhance representation performance. The proposed model outperforms state-of-the-art detection algorithms such as Faster R-CNN, EfficientDet, SSD, and YOLO v7, based on experimental results on a public synthetic PCB dataset. The proposed tiny object detection model has better performances on speed and detection accuracy, thereby benefitting the manufacturing industries associated with PCBs. Full article
(This article belongs to the Section Industrial Sensors)
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