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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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21 pages, 12628 KB  
Article
Convection Parameters from Remote Sensing Observations over the Southern Great Plains
by Kylie Hoffman and Belay Demoz
Sensors 2025, 25(13), 4163; https://doi.org/10.3390/s25134163 - 4 Jul 2025
Cited by 3 | Viewed by 1069
Abstract
Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN), commonly used measures of the instability and inhibition within a vertical column of the atmosphere, serve as a proxy for estimating convection potential and updraft strength for an air parcel. In operational forecasting, CAPE [...] Read more.
Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN), commonly used measures of the instability and inhibition within a vertical column of the atmosphere, serve as a proxy for estimating convection potential and updraft strength for an air parcel. In operational forecasting, CAPE and CIN are typically derived from radiosonde thermodynamic profiles, launched only twice daily, and supplemented by model-simulated equivalent values. This study uses remote sensing observations to derive CAPE and CIN from continuous data, expanding upon previous research by evaluating the performance of both passive and active profiling systems’ CAPE/CIN against in situ radiosonde CAPE/CIN. CAPE and CIN values are calculated from Atmospheric Emitted Radiance Interferometer (AERI), Microwave Radiometer (MWR), Raman LiDAR, and Differential Absorption LiDAR (DIAL) systems. Among passive sensors, results show significantly greater accuracy in CAPE and CIN from AERI than MWR. Incorporating water vapor profiles from active LiDAR systems further improves CAPE values when compared to radiosonde data, although the impact on CIN is less significant. Beyond the direct capability of calculating CAPE, this approach enables evaluation of the various relationships between the water vapor mixing ratio, CAPE, cloud development, and moisture transport. Full article
(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)
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25 pages, 2820 KB  
Article
Fault Detection of Cyber-Physical Systems Using a Transfer Learning Method Based on Pre-Trained Transformers
by Pooya Sajjadi, Fateme Dinmohammadi and Mahmood Shafiee
Sensors 2025, 25(13), 4164; https://doi.org/10.3390/s25134164 - 4 Jul 2025
Cited by 5 | Viewed by 1792
Abstract
As industries become increasingly dependent on cyber-physical systems (CPSs), failures within these systems can cause significant operational disruptions, underscoring the critical need for effective Prognostics and Health Management (PHM). The large volume of data generated by CPSs has made deep learning (DL) methods [...] Read more.
As industries become increasingly dependent on cyber-physical systems (CPSs), failures within these systems can cause significant operational disruptions, underscoring the critical need for effective Prognostics and Health Management (PHM). The large volume of data generated by CPSs has made deep learning (DL) methods an attractive solution; however, imbalanced datasets and the limited availability of fault-labeled data continue to hinder their effective deployment in real-world applications. To address these challenges, this paper proposes a transfer learning approach using a pre-trained transformer architecture to enhance fault detection performance in CPSs. A streamlined transformer model is first pre-trained on a large-scale source dataset and then fine-tuned end-to-end on a smaller dataset with a differing data distribution. This approach enables the transfer of diagnostic knowledge from controlled laboratory environments to real-world operational settings, effectively addressing the domain shift challenge commonly encountered in industrial CPSs. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on publicly available datasets generated from a laboratory-scale replica of a modern industrial water purification facility. The results show that the model achieves an average F1-score of 93.38% under K-fold cross-validation, outperforming baseline models such as CNN and LSTM architectures, and demonstrating the practicality of applying transformer-based transfer learning in industrial settings with limited fault data. To enhance transparency and better understand the model’s decision process, SHAP is applied for explainable AI (XAI). Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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16 pages, 3101 KB  
Article
Enhanced High-Resolution and Long-Range FMCW LiDAR with Directly Modulated Semiconductor Lasers
by Luís C. P. Pinto and Maria C. R. Medeiros
Sensors 2025, 25(13), 4131; https://doi.org/10.3390/s25134131 - 2 Jul 2025
Cited by 2 | Viewed by 3736
Abstract
Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. [...] Read more.
Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article
(This article belongs to the Special Issue Feature Papers in Optical Sensors 2025)
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18 pages, 2887 KB  
Article
Polymer-Based Chemicapacitive Hybrid Sensor Array for Improved Selectivity in e-Nose Systems
by Pavithra Munirathinam, Mohd Farhan Arshi, Haleh Nazemi, Gian Carlo Antony Raj and Arezoo Emadi
Sensors 2025, 25(13), 4130; https://doi.org/10.3390/s25134130 - 2 Jul 2025
Viewed by 3577
Abstract
Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses [...] Read more.
Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article
(This article belongs to the Special Issue Advanced Sensors for Gas Monitoring)
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21 pages, 2109 KB  
Article
Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method
by Behnam Seyedi and Octavian Postolache
Sensors 2025, 25(13), 4098; https://doi.org/10.3390/s25134098 - 30 Jun 2025
Cited by 3 | Viewed by 997
Abstract
The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) [...] Read more.
The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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27 pages, 2935 KB  
Article
A Pilot Study on Emotional Equivalence Between VR and Real Spaces Using EEG and Heart Rate Variability
by Takato Kobayashi, Narumon Jadram, Shukuka Ninomiya, Kazuhiro Suzuki and Midori Sugaya
Sensors 2025, 25(13), 4097; https://doi.org/10.3390/s25134097 - 30 Jun 2025
Cited by 1 | Viewed by 3894
Abstract
In recent years, the application of virtual reality (VR) for spatial evaluation has gained traction in the fields of architecture and interior design. However, for VR to serve as a viable substitute for real-world environments, it is essential that experiences within VR elicit [...] Read more.
In recent years, the application of virtual reality (VR) for spatial evaluation has gained traction in the fields of architecture and interior design. However, for VR to serve as a viable substitute for real-world environments, it is essential that experiences within VR elicit emotional responses comparable to those evoked by actual spaces. Despite this prerequisite, there remains a paucity of studies that objectively compare and evaluate the emotional responses elicited by VR and real-world environments. Consequently, it is not yet fully understood whether VR can reliably replicate the emotional experiences induced by physical spaces. This study aims to investigate the influence of presentation modality on emotional responses by comparing a VR space and a real-world space with identical designs. The comparison was conducted using both subjective evaluations (Semantic Differential method) and physiological indices (electroencephalography and heart rate variability). The results indicated that the real-world environment was associated with impressions of comfort and preference, whereas the VR environment evoked impressions characterized by heightened arousal. Additionally, elevated beta wave activity and increased beta/alpha ratios were observed in the VR condition, suggesting a state of high arousal, as further supported by positioning on the Emotion Map. Moreover, analysis of pNN50 revealed a transient increase in parasympathetic nervous activity during the VR experience. This study is positioned as a pilot investigation to explore physiological and emotional differences between VR and real spaces. Full article
(This article belongs to the Special Issue Neuroarchitecture: Innovations in the Human-Environment Relationship Through Sensors and Sensing Technologies)
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18 pages, 7709 KB  
Article
Orientation Controllable RCS Enhancement Electromagnetic Surface to Improve the Road Barriers Detectability for Autonomous Driving Radar
by Yanbin Chen, Tong Wang, Qi Liu, Haochen Wang and Cheng Jin
Sensors 2025, 25(13), 4048; https://doi.org/10.3390/s25134048 - 29 Jun 2025
Cited by 1 | Viewed by 1002
Abstract
An orientation controllable radar cross section (RCS) enhancement surface is presented in this paper, which can be used to improve the road pile detectability of on-board microwave radar for autonomous driving system. In addition, the RCS enhancement orientation can be controlled in a [...] Read more.
An orientation controllable radar cross section (RCS) enhancement surface is presented in this paper, which can be used to improve the road pile detectability of on-board microwave radar for autonomous driving system. In addition, the RCS enhancement orientation can be controlled in a specified direction without interfering with other microwave systems. We first designed a modified one-dimensional VanAtta array with adjustable phase for retrodirective backtracking the incoming electromagnetic waves, which can achieve wide-angle RCS enhancement. Then, we arranged the one-dimensional VanAtta array in another dimension forming a two-dimensional array, enabling adjustable orientation RCS enhancement due to the controllable phase of the reflected electromagnetic waves. We designed, manufactured, and tested a 4 × 8 array to validate the theory and assess the design’s feasibility. Finally, six orientation controllable VanAtta arrays were mounted on the outside surface of a cylinder road barrier, and measurements demonstrated that RCS enhancement of over 10 dB have been achieved compared to the same pile with perfect electric conductor surface. Full article
(This article belongs to the Section Radar Sensors)
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28 pages, 1634 KB  
Review
AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring
by Venkatraman Manikandan and Suresh Neethirajan
Sensors 2025, 25(13), 4058; https://doi.org/10.3390/s25134058 - 29 Jun 2025
Cited by 4 | Viewed by 5105
Abstract
Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures [...] Read more.
Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. Through rigorous bibliometric co-occurrence mapping and thematic clustering analysis, this review exposes persistent methodological bottlenecks: dataset standardization deficits, evaluation protocol inconsistencies, and algorithmic interpretability limitations. Critical knowledge gaps emerge in cross-species domain generalization and contextual acoustic adaptation, demanding urgent research prioritization. The findings underscore explainable AI integration as essential for establishing stakeholder trust and regulatory compliance in automated welfare monitoring systems. This synthesis positions acoustic AI as a cornerstone technology enabling ethical, transparent, and scientifically robust precision livestock farming, bridging computational innovation with biological relevance for sustainable poultry production systems. Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2025)
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25 pages, 5526 KB  
Article
Implementation of Integrated Smart Construction Monitoring System Based on Point Cloud Data and IoT Technique
by Ju-Yong Kim, Suhyun Kang, Jungmin Cho, Seungjin Jeong, Sanghee Kim, Youngje Sung, Byoungkil Lee and Gwang-Hee Kim
Sensors 2025, 25(13), 3997; https://doi.org/10.3390/s25133997 - 26 Jun 2025
Cited by 4 | Viewed by 4617
Abstract
This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations [...] Read more.
This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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27 pages, 10314 KB  
Article
Immersive Teleoperation via Collaborative Device-Agnostic Interfaces for Smart Haptics: A Study on Operational Efficiency and Cognitive Overflow for Industrial Assistive Applications
by Fernando Hernandez-Gobertti, Ivan D. Kudyk, Raul Lozano, Giang T. Nguyen and David Gomez-Barquero
Sensors 2025, 25(13), 3993; https://doi.org/10.3390/s25133993 - 26 Jun 2025
Cited by 1 | Viewed by 4224
Abstract
This study presents a novel investigation into immersive teleoperation systems using collaborative, device-agnostic interfaces for advancing smart haptics in industrial assistive applications. The research focuses on evaluating the quality of experience (QoE) of users interacting with a teleoperation system comprising a local robotic [...] Read more.
This study presents a novel investigation into immersive teleoperation systems using collaborative, device-agnostic interfaces for advancing smart haptics in industrial assistive applications. The research focuses on evaluating the quality of experience (QoE) of users interacting with a teleoperation system comprising a local robotic arm, a robot gripper, and heterogeneous remote tracking and haptic feedback devices. By employing a modular device-agnostic framework, the system supports flexible configurations, including one-user-one-equipment (1U-1E), one-user-multiple-equipment (1U-ME), and multiple-users-multiple-equipment (MU-ME) scenarios. The experimental set-up involves participants manipulating predefined objects and placing them into designated baskets by following specified 3D trajectories. Performance is measured using objective QoE metrics, including temporal efficiency (time required to complete the task) and spatial accuracy (trajectory similarity to the predefined path). In addition, subjective QoE metrics are assessed through detailed surveys, capturing user perceptions of presence, engagement, control, sensory integration, and cognitive load. To ensure flexibility and scalability, the system integrates various haptic configurations, including (1) a Touch kinaesthetic device for precision tracking and grounded haptic feedback, (2) a DualSense tactile joystick as both a tracker and mobile haptic device, (3) a bHaptics DK2 vibrotactile glove with a camera tracker, and (4) a SenseGlove Nova force-feedback glove with VIVE trackers. The modular approach enables comparative analysis of how different device configurations influence user performance and experience. The results indicate that the objective QoE metrics varied significantly across device configurations, with the Touch and SenseGlove Nova set-ups providing the highest trajectory similarity and temporal efficiency. Subjective assessments revealed a strong correlation between presence and sensory integration, with users reporting higher engagement and control in scenarios utilizing force feedback mechanisms. Cognitive load varied across the set-ups, with more complex configurations (e.g., 1U-ME) requiring longer adaptation periods. This study contributes to the field by demonstrating the feasibility of a device-agnostic teleoperation framework for immersive industrial applications. It underscores the critical interplay between objective task performance and subjective user experience, providing actionable insights into the design of next-generation teleoperation systems. Full article
(This article belongs to the Special Issue Recent Development of Flexible Tactile Sensors and Their Applications)
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18 pages, 2462 KB  
Article
Autonomous Drilling and the Idea of Next-Generation Deep Mineral Exploration
by George Nikolakopoulos, Anton Koval, Matteo Fumagalli, Martyna Konieczna-Fuławka, Laura Santas Moreu, Victor Vigara-Puche, Kashish Verma, Bob de Waard and René Deutsch
Sensors 2025, 25(13), 3953; https://doi.org/10.3390/s25133953 - 25 Jun 2025
Cited by 2 | Viewed by 3204
Abstract
Remote drilling technologies play a crucial role in automating both underground and open-pit hard rock mining operations. These technologies enhance efficiency and, most importantly, improve safety in the mining sector. Autonomous drilling rigs can navigate to pre-determined positions and utilize the appropriate parameters [...] Read more.
Remote drilling technologies play a crucial role in automating both underground and open-pit hard rock mining operations. These technologies enhance efficiency and, most importantly, improve safety in the mining sector. Autonomous drilling rigs can navigate to pre-determined positions and utilize the appropriate parameters to drill boreholes effectively. This article explores various aspects of automation, including the integration of advanced data collection methods that monitor the drilling parameters and facilitate the creation of 3D models of rock hardness. The shift toward machine automation involves transitioning from human-operated machines to systems powered by artificial intelligence, which are capable of making real-time decisions. Navigating underground environments presents unique challenges, as traditional RF-based localization systems often fail in these settings. New solutions, such as constant localization and mapping techniques like SLAM (simultaneous localization and mapping), provide innovative methods for navigating mines, particularly in uncharted territories. The development of robotic exploration rigs equipped with modules that can operate autonomously in hazardous areas has the potential to revolutionize mineral exploration in underground mines. This article also discusses solutions aimed at validating and improving existing methods by optimizing drilling strategies to ensure accuracy, enhance efficiency, and ensure safety. These topics are explored in the context of the Horizon Europe-funded PERSEPHONE project, which seeks to deliver fully autonomous, sensor-integrated robotic systems for deep mineral exploration in challenging underground environments. Full article
(This article belongs to the Section Sensors and Robotics)
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16 pages, 2985 KB  
Article
Fault Identification Model Using Convolutional Neural Networks with Transformer Architecture
by Yongxin Fan, Yiming Dang and Yangming Guo
Sensors 2025, 25(13), 3897; https://doi.org/10.3390/s25133897 - 23 Jun 2025
Cited by 3 | Viewed by 2039
Abstract
With the advancement of industrial manufacturing and the shift toward high automation, machines have increasingly taken over many production tasks, greatly improving efficiency and reducing human labor. However, this also introduces new challenges, particularly the inability of machines to autonomously detect and diagnose [...] Read more.
With the advancement of industrial manufacturing and the shift toward high automation, machines have increasingly taken over many production tasks, greatly improving efficiency and reducing human labor. However, this also introduces new challenges, particularly the inability of machines to autonomously detect and diagnose faults. Such undetected issues may cause unexpected breakdowns, interrupting critical operations, leading to economic losses and potential safety hazards. To address this, the present study proposes a novel hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) for feature extraction with Transformer architecture for temporal modeling. The model is validated using NASA’s CMAPSS dataset, a widely used benchmark that includes multi-sensor data and Remaining Useful Life (RUL) labels from aeroengines. By learning from time-series sensor data, the framework achieves accurate RUL predictions and early fault detection. Experimental results show that the model attains over 97% accuracy under both single and multiple operating conditions, highlighting its robustness and adaptability. These findings suggest the framework’s potential in developing intelligent maintenance systems and contribute to the field of Prognostics and Health Management (PHM), enabling more reliable, efficient, and self-monitoring industrial systems. Full article
(This article belongs to the Special Issue Communications and Networking Based on Artificial Intelligence)
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21 pages, 3901 KB  
Article
Research on CTSA-DeepLabV3+ Urban Green Space Classification Model Based on GF-2 Images
by Ruotong Li, Jian Zhao and Yanguo Fan
Sensors 2025, 25(13), 3862; https://doi.org/10.3390/s25133862 - 21 Jun 2025
Cited by 2 | Viewed by 1464
Abstract
As an important part of urban ecosystems, urban green spaces play a key role in ecological environmental protection and urban spatial structure optimization. However, due to the complex morphology and high degree of fragmentation of urban green spaces, it is still challenging to [...] Read more.
As an important part of urban ecosystems, urban green spaces play a key role in ecological environmental protection and urban spatial structure optimization. However, due to the complex morphology and high degree of fragmentation of urban green spaces, it is still challenging to effectively distinguish urban green space types from high spatial resolution images. To solve the problem, a Contextual Transformer and Squeeze Aggregated Excitation Enhanced DeepLabV3+ (CTSA-DeepLabV3+) model was proposed for urban green space classification based on Gaofen-2 (GF-2) satellite images. A Contextual Transformer (CoT) module was added to the decoder part of the model to enhance the global context modeling capability, and the SENetv2 attention mechanism was employed to improve its key feature capture ability. The experimental results showed that the overall classification accuracy of the CTSA-DeepLabV3+ model is 96.21%, and the average intersection ratio, precision, recall, and F1-score reach 89.22%, 92.56%, 90.12%, and 91.23%, respectively, which is better than DeepLabV3+, Fully Convolutional Networks (FCNs), U-Net (UNet), the Pyramid Scene Parseing Network (PSPNet), UperNet-Swin Transformer, and other mainstream models. The model exhibits higher accuracy and provides efficient references for the intelligent interpretation of urban green space with high-resolution remote sensing images. Full article
(This article belongs to the Section Remote Sensors)
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21 pages, 21726 KB  
Article
Evaluation of Positioning Accuracy Using Smartphone RGB and LiDAR Sensors with the viDoc RTK Rover
by Sara Zollini and Laura Marconi
Sensors 2025, 25(13), 3867; https://doi.org/10.3390/s25133867 - 21 Jun 2025
Cited by 3 | Viewed by 5582
Abstract
Modern surveying is increasingly focused on fast data acquisition and processing using lightweight, low-cost equipment, particularly for the continuous monitoring of structures and infrastructures. This study investigates the use of LiDAR and RGB sensors embedded in Apple and Android smartphones, paired with an [...] Read more.
Modern surveying is increasingly focused on fast data acquisition and processing using lightweight, low-cost equipment, particularly for the continuous monitoring of structures and infrastructures. This study investigates the use of LiDAR and RGB sensors embedded in Apple and Android smartphones, paired with an innovative device, the viDoc RTK Rover, for centimeter-level surveying. Three case studies were selected, each characterized by different materials, functional uses, and environmental contexts. The methodology centers on evaluating final accuracy during both the data acquisition and processing phases. Coordinates of target points were obtained directly via the viDoc device and indirectly through dense point clouds. Validation was conducted using a geodetic GNSS receiver. Results demonstrate that, in most cases, the system achieves accuracy comparable to traditional surveying methods. The findings confirm that these emerging tools offer a reliable and efficient solution for rapid 3D surveys with centimeter-level accuracy. Full article
(This article belongs to the Section Navigation and Positioning)
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28 pages, 3303 KB  
Review
Structural Fault Detection and Diagnosis for Combine Harvesters: A Critical Review
by Haiyang Wang, Liyun Lao, Honglei Zhang, Zhong Tang, Pengfei Qian and Qi He
Sensors 2025, 25(13), 3851; https://doi.org/10.3390/s25133851 - 20 Jun 2025
Cited by 5 | Viewed by 2067
Abstract
Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing [...] Read more.
Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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18 pages, 1032 KB  
Article
AI for Sustainable Recycling: Efficient Model Optimization for Waste Classification Systems
by Oriol Chacón-Albero, Mario Campos-Mocholí, Cédric Marco-Detchart, Vicente Julian, Jaime Andrés Rincon and Vicent Botti
Sensors 2025, 25(12), 3807; https://doi.org/10.3390/s25123807 - 18 Jun 2025
Cited by 4 | Viewed by 3879
Abstract
The increasing volume of global waste presents a critical environmental and societal challenge, demanding innovative solutions to support sustainable practices such as recycling. Advances in Computer Vision (CV) have enabled automated waste recognition systems that guide users in correctly sorting their waste, with [...] Read more.
The increasing volume of global waste presents a critical environmental and societal challenge, demanding innovative solutions to support sustainable practices such as recycling. Advances in Computer Vision (CV) have enabled automated waste recognition systems that guide users in correctly sorting their waste, with state-of-the-art architectures achieving high accuracy. More recently, attention has shifted toward lightweight and efficient models suitable for mobile and edge deployment. These systems process data from integrated camera sensors in Internet of Things (IoT) devices, operating in real time to classify waste at the point of disposal, whether embedded in smart bins, mobile applications, or assistive tools for household use. In this work, we extend our previous research by improving both dataset diversity and model efficiency. We introduce an expanded dataset that includes an organic waste class and more heterogeneous images, and evaluate a range of quantized CNN models to reduce inference time and resource usage. Additionally, we explore ensemble strategies using aggregation functions to boost classification performance, and validate selected models on real embedded hardware and under simulated lighting variations. Our results support the development of robust, real-time recycling assistants for resource-constrained devices. We also propose architectural deployment scenarios for smart containers, and cloud-assisted solutions. By improving waste sorting accuracy, these systems can help reduce landfill use, support citizen engagement through real-time feedback, increase material recovery, support data-informed environmental decision making, and ease operational challenges for recycling facilities caused by misclassified materials. Ultimately, this contributes to circular economy objectives and advances the broader field of environmental intelligence. Full article
(This article belongs to the Special Issue Internet of Things (IoT) and Cloud Computing Technologies for Smart Cities and Rural Areas: Sensing and Information Management Technologies)
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12 pages, 4292 KB  
Article
Machine Learning-Based Identification of Plastic Types Using Handheld Spectrometers
by Hedde van Hoorn, Fahimeh Pourmohammadi, Arie-Willem de Leeuw, Amey Vasulkar, Jerry de Vos and Steven van den Berg
Sensors 2025, 25(12), 3777; https://doi.org/10.3390/s25123777 - 17 Jun 2025
Cited by 5 | Viewed by 2787
Abstract
Plastic waste and pollution is growing rapidly worldwide and most plastics end up in landfill or are incinerated because high-quality recycling is not possible. Plastic-type identification with a low-cost, handheld spectral approach could help in parts of the world where high-end spectral imaging [...] Read more.
Plastic waste and pollution is growing rapidly worldwide and most plastics end up in landfill or are incinerated because high-quality recycling is not possible. Plastic-type identification with a low-cost, handheld spectral approach could help in parts of the world where high-end spectral imaging systems on conveyor belts cannot be implemented. Here, we investigate how two fundamentally different handheld infrared spectral devices can identify plastic types by benchmarking the same analysis against a high-resolution bench-top spectral approach. We used the handheld Plastic Scanner, which measures a discrete infrared spectrum using LED illumination at different wavelengths, and the SpectraPod, which has an integrated photonics chip which has varying responsivity in different channels in the near-infrared. We employ machine learning using SVM, XGBoost, Random Forest and Gaussian Naïve Bayes models on a full dataset of plastic samples of PET, HDPE, PVC, LDPE, PP and PS, with samples of varying shape, color and opacity, as measured with three different experimental approaches. The high-resolution spectral approach can obtain an accuracy (mean ± standard deviation) of (0.97 ± 0.01), whereas we obtain (0.93 ± 0.01) for the SpectraPod and (0.70 ± 0.03) for the Plastic Scanner. Differences of reflectance at subsequent wavelengths prove to be the most important features in the plastic-type classification model when using high-resolution spectroscopy, which is not possible with the other two devices. Lower accuracy for the handheld devices is caused by their limitations, as the spectral range of both devices is limited—up to 1600 nm for the SpectraPod, while the Plastic Scanner has limited sensitivity to reflectance at wavelengths of 1100 and 1350 nm, where certain plastic types show characteristic absorbance bands. We suggest that combining selective sensitivity channels (as in the SpectraPod) and illuminating the sample with varying LEDs (as with the Plastic Scanner) could increase the accuracy in plastic-type identification with a handheld device. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning: 2nd Edition)
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16 pages, 1747 KB  
Article
Augmented and Virtual Reality for Improving Safety in Railway Infrastructure Monitoring and Maintenance
by Marina Ricci, Nicola Mosca and Maria Di Summa
Sensors 2025, 25(12), 3772; https://doi.org/10.3390/s25123772 - 17 Jun 2025
Cited by 2 | Viewed by 1645
Abstract
The highly demanding safety standards adopted in the railway context imply that cutting-edge technologies must limit accidents. This paper presents the human-centered outcomes of the VRAIL project, an industrial research project aiming to use enabling technologies and develop methodologies for operators directly involved [...] Read more.
The highly demanding safety standards adopted in the railway context imply that cutting-edge technologies must limit accidents. This paper presents the human-centered outcomes of the VRAIL project, an industrial research project aiming to use enabling technologies and develop methodologies for operators directly involved in infrastructure management in the railway field. Developing integrated monitoring systems and applications that exploit Augmented Reality (AR) and Virtual Reality (VR) becomes crucial to support the awareness of planning and maintenance operators required to comply with high-quality standards. This paper addresses the abovementioned issue by proposing the development of two different prototype applications in both AR and VR for railway infrastructure data management. These environments will provide the planning operator with a complete platform to explore, use to plan maintenance interventions, and gather detailed reports to improve the overall safety of the railway line effectively. Full article
(This article belongs to the Special Issue Sensing Technologies and Applied Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering)
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18 pages, 6134 KB  
Article
Large- and Small-Scale Beam-Steering Phased Array Antennas Using Variable Phase BLC for Millimeter-Wave Applications
by Fayyadh H. Ahmed and Salam K. Khamas
Sensors 2025, 25(12), 3714; https://doi.org/10.3390/s25123714 - 13 Jun 2025
Cited by 2 | Viewed by 2135
Abstract
This paper presents a novel switchable branch-line coupler (BLC) designed to achieve variable phase shifts while maintaining a constant output power. The proposed design incorporates low stepwise phase shifters with incremental phase shifts of 10° to 20°, covering phase ranges from −3° to [...] Read more.
This paper presents a novel switchable branch-line coupler (BLC) designed to achieve variable phase shifts while maintaining a constant output power. The proposed design incorporates low stepwise phase shifters with incremental phase shifts of 10° to 20°, covering phase ranges from −3° to 150°. The initial structure is based on a 3 dB branch-line coupler with arm electrical lengths of 3λg/2. A novel delay line structure is integrated within the BLC arms, consisting of a λg/4 section bridged by a tapered stripline to accommodate a PIN diode switch, thereby altering the current path direction. Additionally, two interdigital capacitors (IDCs), uniquely mounted on a crescent-shaped extension, are implemented alongside the tapered line to elongate the current path when the PIN diode is in the OFF state. By controlling the PIN diode states, the delay time is differentially adjusted, resulting in variable differential phase shifts at the output ports. To validate the functionality, the proposed BLC was integrated with a two-element antenna array to demonstrate differential beam steering. The measurement results confirm that the phased array antenna can switch its main beam between −27° and 25° in the elevation plane, achieving an average realized gain of approximately 7 dBi. The BLC was designed and simulated using CST Microwave Studio and was fabricated on an RO4003C Roger substrate (εr = 3.55, 0.406 mm). The proposed design is well-suited for future Butler matrix-based beamforming networks in antenna array systems, particularly for 5G wireless applications. Full article
(This article belongs to the Special Issue Antenna Technologies for Microwave and Millimeter-Wave Sensing)
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23 pages, 1475 KB  
Article
Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies
by Danilo Pietro Pau, Simone Tognocchi and Marco Marcon
Sensors 2025, 25(12), 3679; https://doi.org/10.3390/s25123679 - 12 Jun 2025
Cited by 1 | Viewed by 4351
Abstract
This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, [...] Read more.
This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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21 pages, 8691 KB  
Article
Hybrid Supervised and Reinforcement Learning for Motion-Sickness-Aware Path Tracking in Autonomous Vehicles
by Yukang Lv, Yi Chen, Ziguo Chen, Yuze Fan, Yongchao Tao, Rui Zhao and Fei Gao
Sensors 2025, 25(12), 3695; https://doi.org/10.3390/s25123695 - 12 Jun 2025
Cited by 2 | Viewed by 1485
Abstract
Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges [...] Read more.
Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges in balancing tracking accuracy with computational overhead, and more critically, lack consideration for Motion Sickness (MS) mitigation. However, as AD applications divert occupants’ attention to non-driving activities at varying degrees, MS in self-driving vehicles has been significantly exacerbated. This study presents a novel framework, the Hybrid Supervised–Reinforcement Learning (HSRL), designed to reduce passenger discomfort while achieving high-precision tracking performance with computational efficiency. The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). Simultaneously, the RL architecture integrates a passenger MS mechanism into a multi-objective reward function. This design enhances model robustness and control performance, achieving both high-precision tracking and passenger comfort optimization. Simulation experiments demonstrate that the HSRL significantly outperforms Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC), achieving improved tracking accuracy and significantly reducing passengers’ cumulative Motion Sickness Dose Value (MSDV) across several test scenarios. Full article
(This article belongs to the Section Vehicular Sensing)
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34 pages, 5724 KB  
Article
Wearable Fall Detection System with Real-Time Localization and Notification Capabilities
by Chin-Kun Tseng, Shi-Jia Huang and Lih-Jen Kau
Sensors 2025, 25(12), 3632; https://doi.org/10.3390/s25123632 - 10 Jun 2025
Cited by 6 | Viewed by 10797
Abstract
Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely [...] Read more.
Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities—running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting—while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system’s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities. Full article
(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)
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42 pages, 473 KB  
Review
Non-Destructive Testing and Evaluation of Hybrid and Advanced Structures: A Comprehensive Review of Methods, Applications, and Emerging Trends
by Farima Abdollahi-Mamoudan, Clemente Ibarra-Castanedo and Xavier P. V. Maldague
Sensors 2025, 25(12), 3635; https://doi.org/10.3390/s25123635 - 10 Jun 2025
Cited by 13 | Viewed by 7827
Abstract
Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, [...] Read more.
Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, fiber–metal laminates, sandwich composites, and functionally graded materials, traditional NDT techniques face growing limitations in sensitivity, adaptability, and diagnostic reliability. This comprehensive review presents a multi-dimensional classification of NDT/NDE methods, structured by physical principles, functional objectives, and application domains. Special attention is given to hybrid and multi-material systems, which exhibit anisotropic behavior, interfacial complexity, and heterogeneous defect mechanisms that challenge conventional inspection. Alongside established techniques like ultrasonic testing, radiography, infrared thermography, and acoustic emission, the review explores emerging modalities such as capacitive sensing, electromechanical impedance, and AI-enhanced platforms that are driving the future of intelligent diagnostics. By synthesizing insights from the recent literature, the paper evaluates comparative performance metrics (e.g., sensitivity, resolution, adaptability); highlights integration strategies for embedded monitoring and multimodal sensing systems; and addresses challenges related to environmental sensitivity, data interpretation, and standardization. The transformative role of NDE 4.0 in enabling automated, real-time, and predictive structural assessment is also discussed. This review serves as a valuable reference for researchers and practitioners developing next-generation NDT/NDE solutions for hybrid and high-performance structures. Full article
(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies—Second Edition)
20 pages, 3609 KB  
Article
Mesoporous Bi2S3/Bi2O3 Heterostructure-Based Sensors for Sub-ppm NO2 Detection at Room Temperature
by Wei Liu, Jiashuo Chen, Ding Gu, Shupeng Sun, Xinlei Li and Xiaogan Li
Sensors 2025, 25(12), 3612; https://doi.org/10.3390/s25123612 - 9 Jun 2025
Viewed by 1381
Abstract
Novel Bi2S3/Bi2O3 hybrid materials with unique mesoporous structures were successfully synthesized via a facile in situ elevated-temperature thermal oxidation method using the Bi2S3 as a precursor in air. The as-prepared Bi2S [...] Read more.
Novel Bi2S3/Bi2O3 hybrid materials with unique mesoporous structures were successfully synthesized via a facile in situ elevated-temperature thermal oxidation method using the Bi2S3 as a precursor in air. The as-prepared Bi2S3/Bi2O3 heterostructure-based sensor exhibits an excellent performance for detecting sub-ppm concentrations of NO2 at room temperature (RT). In the presence of 8 ppm NO2, the sensor registers a response of approximately 7.85, reflecting a 3.5-fold increase compared to the pristine Bi2S3-based sensor. The response time is 71 s, while the recovery time is 238 s, which are reduced by 32.4% and 24.2%, respectively, compared to the pristine Bi2S3-based sensor. The Bi2S3/Bi2O3 heterostructure-based sensor achieves an impressively low detection limit of 0.1 ppm for NO2, and the sensor has been demonstrated to possess superior signal repeatability, gas selectivity, and long-term stability. The optimal preparation conditions of the hybrid materials were explored, and the formation of mesoporous structure was analyzed. The obviously improved gas sensitivity of the Bi2S3/Bi2O3 heterostructure-based sensor can be assigned to the combined influence of electronic sensitization and its distinctive morphological structure. The potential gas-sensitive mechanisms were revealed by employing density functional theory (DFT). It was found that the formation of heterostructures could enhance the adsorption energies and increase the amount of electron transfer between NO2 molecules and the hybrid materials. Furthermore, the electron redistribution driven by orbital hybridization between O and Bi atoms improves the capacity of NO2 molecules to capture additional electrons from the Bi2S3/Bi2O3 heterostructures. The content of this work supplies an innovative design strategy for constructing NO2 sensor with high performance and low energy consumption at RT. Full article
(This article belongs to the Section Chemical Sensors)
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34 pages, 4041 KB  
Review
Sensor Technologies for Non-Invasive Blood Glucose Monitoring
by Jiale Shi, Raúl Fernández-García and Ignacio Gil
Sensors 2025, 25(12), 3591; https://doi.org/10.3390/s25123591 - 7 Jun 2025
Cited by 5 | Viewed by 13579
Abstract
Diabetes poses a significant global health challenge, underscoring the urgent need for accurate and continuous glucose monitoring technologies. This review provides a comprehensive analysis of both invasive and non-invasive sensor technologies, with a particular focus on antenna-sensors and their working principle. Key aspects, [...] Read more.
Diabetes poses a significant global health challenge, underscoring the urgent need for accurate and continuous glucose monitoring technologies. This review provides a comprehensive analysis of both invasive and non-invasive sensor technologies, with a particular focus on antenna-sensors and their working principle. Key aspects, including the selection of substrates and conductive materials, fabrication techniques, and recent advancements in rigid and flexible antenna-sensor designs, are critically evaluated. Notably, textile antenna-sensors are gaining increasing attention due to their potential for seamless integration into daily clothing. Furthermore, the influence of the human body on antenna-sensor performance is examined, emphasizing the importance of human phantom simulation and fabrication for precise modeling and validation. Finally, this review highlights the current technical challenges in the development of flexible antenna-sensors and discusses their transformative potential in enabling next-generation, non-invasive, and patient-centric glucose monitoring solutions. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)
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26 pages, 4371 KB  
Article
A Robust Rotation-Equivariant Feature Extraction Framework for Ground Texture-Based Visual Localization
by Yuezhen Cai, Linyuan Xia, Ting On Chan, Junxia Li and Qianxia Li
Sensors 2025, 25(12), 3585; https://doi.org/10.3390/s25123585 - 6 Jun 2025
Cited by 1 | Viewed by 1405
Abstract
Ground texture-based localization leverages environment-invariant, planar-constrained features to enhance pose estimation robustness, thus offering inherent advantages for seamless localization. However, traditional feature extraction methods struggle with reliable performance under large-scale rotations and texture sparsity in the case of ground texture-based localization. This study [...] Read more.
Ground texture-based localization leverages environment-invariant, planar-constrained features to enhance pose estimation robustness, thus offering inherent advantages for seamless localization. However, traditional feature extraction methods struggle with reliable performance under large-scale rotations and texture sparsity in the case of ground texture-based localization. This study addresses these challenges through a learning-based feature extraction framework—Ground Texture Rotation-Equivariant Keypoints and Descriptors (GT-REKD). The GT-REKD framework employs group-equivariant convolutions over the cyclic rotation group, augmented with directional attention and orientation-encoding heads, to produce dense keypoints and descriptors that are exactly invariant to 0–360° in-plane rotations. The experimental results for ground texture localization show that GT-REKD achieves 96.14% matching in pure rotation tests, 94.08% in incremental localization, and relocalization errors of 5.55° and 4.41 px (≈0.1 cm), consistently outperforming baseline methods under extreme rotations and sparse textures, highlighting its applicability to visual localization and simultaneous localization and mapping (SLAM) tasks. Full article
(This article belongs to the Section Navigation and Positioning)
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32 pages, 1912 KB  
Review
The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies
by Tymoteusz Miller, Grzegorz Mikiciuk, Irmina Durlik, Małgorzata Mikiciuk, Adrianna Łobodzińska and Marek Śnieg
Sensors 2025, 25(12), 3583; https://doi.org/10.3390/s25123583 - 6 Jun 2025
Cited by 63 | Viewed by 22986
Abstract
The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in [...] Read more.
The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in smart sensing technologies for arable crops and grasslands. We analyzed the peer-reviewed literature published between 2020 and 2024, focusing on the adoption of IoT-based sensor networks and AI-driven analytics across various agricultural applications. The findings reveal a significant increase in research output, particularly in the use of optical, acoustic, electromagnetic, and soil sensors, alongside machine learning models such as SVMs, CNNs, and random forests for optimizing irrigation, fertilization, and pest management strategies. However, this review also identifies critical challenges, including high infrastructure costs, limited interoperability, connectivity constraints in rural areas, and ethical concerns regarding transparency and data privacy. To address these barriers, recent innovations have emphasized the potential of Edge AI for local inference, blockchain systems for decentralized data governance, and autonomous platforms for field-level automation. Moreover, policy interventions are needed to ensure fair data ownership, cybersecurity, and equitable access to smart farming tools, especially in developing regions. This review is the first to systematically examine AI-integrated sensing technologies with an exclusive focus on arable crops and grasslands, offering an in-depth synthesis of both technological progress and real-world implementation gaps. Full article
(This article belongs to the Special Issue Smart Sensing Systems for Arable Crop and Grassland Management)
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27 pages, 4448 KB  
Article
Remaining Useful Life Prediction for Rolling Bearings Based on TCN–Transformer Networks Using Vibration Signals
by Xiaochao Jin, Yaping Ji, Shiteng Li, Kailang Lv, Jianzheng Xu, Haonan Jiang and Shengnan Fu
Sensors 2025, 25(11), 3571; https://doi.org/10.3390/s25113571 - 5 Jun 2025
Cited by 3 | Viewed by 4160
Abstract
Remaining useful life (RUL) prediction plays a core role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Developing reliable deep learning-based models based on multi-sensor monitoring data is fundamental for accurately [...] Read more.
Remaining useful life (RUL) prediction plays a core role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Developing reliable deep learning-based models based on multi-sensor monitoring data is fundamental for accurately predicting vibration trends during bearing operation and is crucial for bearing fault diagnosis and RUL prediction. In this work, a method for constructing a health index based on vibration signal is developed to describe the performance features of rolling bearings, which mainly includes feature extraction, sensitive feature index selection, dimensionality reduction, and normalization methods. In addition, a new RUL prediction method, TCN–Transformer, is developed which can efficiently learn and integrate local and global features of vibration signals, addressing the long time series prediction problem in RUL prediction. The TCN extracts local features, while the Transformer learns global features, both of which are seamlessly integrated through a specially designed feature fusion attention module. Both the health indicator (HI) constructed from extracted time domain and frequency domain feature parameters and the RUL prediction method were rigorously validated using the IEEE PHM 2012 Data Challenge dataset for rolling bearing prognostics. By employing the proposed HI construction method, the average comprehensive bearing performance index, used to evaluate RUL prediction accuracy, is improved by 8.69% across the entire dataset compared to the original feature-based composite index. The proposed RUL prediction model can more accurately predict the RUL of rolling bearings under different conditions, reducing the RMSE and MAE by 14.62% and 9.26%, respectively, and improving the SCORE by 13.04%. These results underscore the efficacy and superiority of our approach in RUL prediction of rotating machinery across varying conditions. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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22 pages, 3223 KB  
Article
An EMG-Based GRU Model for Estimating Foot Pressure to Support Active Ankle Orthosis Development
by Praveen Nuwantha Gunaratne and Hiroki Tamura
Sensors 2025, 25(11), 3558; https://doi.org/10.3390/s25113558 - 5 Jun 2025
Cited by 4 | Viewed by 2370
Abstract
As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely [...] Read more.
As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics. Full article
(This article belongs to the Special Issue Sensor-Based Human Activity Recognition)
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19 pages, 3834 KB  
Article
A Sensitive and Selective Sensor Based on Orthorhombic Copper Molybdate Decorated on Reduced Graphene Oxide for the Detection of Promethazine Hydrochloride
by Venkatachalam Vinothkumar, Yellatur Chandra Sekhar, Shen-Ming Chen, Natesan Manjula and Tae Hyun Kim
Sensors 2025, 25(11), 3569; https://doi.org/10.3390/s25113569 - 5 Jun 2025
Cited by 5 | Viewed by 1715
Abstract
Promethazine hydrochloride (PMH) is a first-generation antipsychotic drug created from phenothiazine derivatives that is widely employed to treat psychiatric disorders in human healthcare systems. However, an overdose or long-term intake of PMH can lead to severe health issues in humans. Hence, establishing a [...] Read more.
Promethazine hydrochloride (PMH) is a first-generation antipsychotic drug created from phenothiazine derivatives that is widely employed to treat psychiatric disorders in human healthcare systems. However, an overdose or long-term intake of PMH can lead to severe health issues in humans. Hence, establishing a sensitive, accurate, and efficient detection approach to detect PMH in human samples is imperative. In this study, we designed orthorhombic copper molybdate microspheres decorated on reduced graphene oxide (Cu3Mo2O9/RGO) composite via the effective one-pot hydrothermal method. The structural and morphological features of the designed hybrid were studied using various spectroscopic methods. Subsequently, the electrochemical activity of the composite-modified screen-printed carbon electrode (Cu3Mo2O9/RGO/SPCE) was assessed by employing voltammetric methods for PMH sensing. Owing to the uniform composition and structural benefits, the combination of Cu3Mo2O9 and RGO has not only improved electrochemical properties but also enhanced the electron transport between PMH and Cu3Mo2O9/RGO. As a result, the Cu3Mo2O9/RGO/SPCE exhibited a broad linear range of 0.4–420.8 µM with a low limit of detection (LoD) of 0.015 µM, highlighting excellent electrocatalytic performance to PMH. It also demonstrated good cyclic stability, reproducibility, and selectivity in the presence of chlorpromazine and biological and metal compounds. Furthermore, the Cu3Mo2O9/RGO/SPCE sensor displayed satisfactory recoveries for real-time monitoring of PMH in human urine and serum samples. This study delivers a promising electrochemical sensor for the efficient analysis of antipsychotic drug molecules. Full article
(This article belongs to the Special Issue Electrochemical Sensors and Biosensors: Materials, Methods and Applications)
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21 pages, 3470 KB  
Article
Lignin-Based Nanostructured Sensor for Selective Detection of Volatile Amines at Trace Levels
by Paolo Papa, Giuseppina Luciani, Rossella Grappa, Virginia Venezia, Ettore Guerriero, Simone Serrecchia, Fabrizio De Cesare, Emiliano Zampetti, Anna Rita Taddei and Antonella Macagnano
Sensors 2025, 25(11), 3536; https://doi.org/10.3390/s25113536 - 4 Jun 2025
Cited by 1 | Viewed by 1615
Abstract
A nanostructured sensing platform was developed by integrating gold-decorated lignin nanoparticles (AuLNPs) into electrospun polylactic acid (PLA) fibre mats. The composite material combines the high surface-to-volume ratio of PLA nanofibres with the chemical functionality of lignin—a polyphenolic biopolymer rich in hydroxyl and aromatic [...] Read more.
A nanostructured sensing platform was developed by integrating gold-decorated lignin nanoparticles (AuLNPs) into electrospun polylactic acid (PLA) fibre mats. The composite material combines the high surface-to-volume ratio of PLA nanofibres with the chemical functionality of lignin—a polyphenolic biopolymer rich in hydroxyl and aromatic groups—enabling selective interactions with volatile amines through hydrogen bonding and Van der Waals forces. The embedded gold nanoparticles (AuNPs) further enhance the sensor’s electrical conductivity and provide catalytic sites for improved analyte interaction. The sensor exhibited selective adsorption of amine vapours, showing particularly strong affinity for dimethylamine (DMA), with a limit of detection (LOD) of approximately 440 ppb. Relative humidity (RH) was found to significantly influence sensor performance by facilitating amine protonation, thus promoting interaction with the sensing surface. The developed sensor demonstrated excellent selectivity, sensitivity and reproducibility, highlighting its potential for real-time detection of amines in environmental monitoring, industrial safety and healthcare diagnostics. Full article
(This article belongs to the Special Issue Gas Sensors: Progress, Perspectives and Challenges)
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22 pages, 5559 KB  
Article
Smart Structural Monitoring: Real-Time Bridge Response Using Digital Twins and Inverse Analysis
by Sanduni Jayasinghe, Zhiyan Sun, Amir Sidiq, Mojtaba Mahmoodian, Farham Shahrivar and Sujeeva Setunge
Sensors 2025, 25(11), 3513; https://doi.org/10.3390/s25113513 - 2 Jun 2025
Cited by 5 | Viewed by 3167
Abstract
Continuous monitoring is significant to ensure the safe operation of infrastructure systems despite the high costs of traditional methods. The current study presents the development of a real-time digital twin of a laboratory-scaled bridge that can assist in the infrastructure monitoring process. Initially, [...] Read more.
Continuous monitoring is significant to ensure the safe operation of infrastructure systems despite the high costs of traditional methods. The current study presents the development of a real-time digital twin of a laboratory-scaled bridge that can assist in the infrastructure monitoring process. Initially, the bridge model was instrumented with strain gauges, and a script was developed to conduct an inverse structural analysis and subsequently, run a finite element analysis to visualize the overall structural response. Three main loading scenarios were tested, and observations highlighted that the digital twin model emulated the actual structural behavior with a high accuracy. Also, the magnitude and the location of the applied loads on the real structure were correctly identified and a linear elastic behavior was identified in the digital model as expected from the actual structure. Further, the rates of change in the strain values and deflections were also evaluated while discussing the significance of digital twin development. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)
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24 pages, 1613 KB  
Article
Partial Discharge-Based Cable Vulnerability Ranking with Fuzzy and FAHP Models: Application in a Danish Distribution Network
by Mohammad Reza Shadi, Hamid Mirshekali and Hamid Reza Shaker
Sensors 2025, 25(11), 3454; https://doi.org/10.3390/s25113454 - 30 May 2025
Cited by 3 | Viewed by 1256
Abstract
Aging underground cables pose a threatening issue in distribution systems. Replacing all cables at once is economically unfeasible, making it crucial to prioritize replacements. Traditionally, age-based strategies have been used, but they are likely to fail to depict the real condition of cables. [...] Read more.
Aging underground cables pose a threatening issue in distribution systems. Replacing all cables at once is economically unfeasible, making it crucial to prioritize replacements. Traditionally, age-based strategies have been used, but they are likely to fail to depict the real condition of cables. Insulation faults are influenced by electrical, mechanical, thermal, and chemical stresses, and partial discharges (PDs) often serve as early indicators and accelerators of insulation aging. The trends in PD activity provide valuable information about insulation condition, although they do not directly reveal the cable’s real age. Due to the absence of an established ranking methodology for such condition-based data, this paper proposes a fuzzy logic and fuzzy analytic hierarchy process (FAHP)-based cable vulnerability ranking framework that effectively manages uncertainty and expert-based conditions. The proposed framework requires only basic and readily accessible data inputs, specifically cable age, which utilities commonly maintain, and PD measurements, such as peak values and event counts, which can be acquired through cost-effective, noninvasive sensing methods. To systematically evaluate the method’s performance and robustness, particularly given the inherent uncertainties in cable age and PD characteristics, this study employs Monte Carlo simulations coupled with a Spearman correlation analysis. The effectiveness of the developed framework is demonstrated using real operational cable data from a Danish distribution network, meteorological information from the Danish Meteorological Institute (DMI), and synthetically generated PD data. The results confirm that the FAHP-based ranking approach delivers robust and consistent outcomes under uncertainty, thereby supporting utilities in making more informed and economical maintenance decisions. Full article
(This article belongs to the Section Sensor Networks)
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23 pages, 1867 KB  
Article
An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network
by Jiadi Qi, Xiaoke Lu and Jinping Sun
Sensors 2025, 25(11), 3465; https://doi.org/10.3390/s25113465 - 30 May 2025
Cited by 1 | Viewed by 1142
Abstract
Accurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or multi-target [...] Read more.
Accurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or multi-target intersections. This study proposes an intelligent association method that includes a multi-dimensional track data preprocessing algorithm and the characteristic-aware attention long short-term memory (CA-LSTM) network. The algorithm can segment and temporally align track segments containing multi-dimensional characteristics. The CA-LSTM model is built to perform track segment association and has two basic parts. One part focuses on the target characteristic dimension and utilizes the separation and importance evaluation of physical characteristics to make association decisions. The other part focuses on the time dimension, matching the application scenarios of short, medium and long tracks by obtaining the temporal characteristics of different time spans. The method is verified on a multi-source track association dataset. Experimental results show that association accuracy rate is 85.19% for short-range track segments and 96.97% for long-range track segments. Compared with the typical traditional method LSTM, this method has a 9.89% improvement in accuracy on short tracks. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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31 pages, 2799 KB  
Article
A Cluster Head Selection Algorithm for Extending Last Node Lifetime in Wireless Sensor Networks
by Marcin Lewandowski and Bartłomiej Płaczek
Sensors 2025, 25(11), 3466; https://doi.org/10.3390/s25113466 - 30 May 2025
Cited by 5 | Viewed by 2247
Abstract
This paper introduces a new cluster head selection algorithm for wireless sensor networks (WSNs) to maximize the time until the last sensor node depletes its energy. The algorithm is based on a formal analysis in which network lifetime is modeled as a function [...] Read more.
This paper introduces a new cluster head selection algorithm for wireless sensor networks (WSNs) to maximize the time until the last sensor node depletes its energy. The algorithm is based on a formal analysis in which network lifetime is modeled as a function of node energy consumption. In contrast to existing energy-balancing strategies, this analytical foundation leads to a distinctive selection rule that prioritizes the node with the highest transmission probability and the lowest initial energy as the initial cluster head. The algorithm employs distributed per-cluster computation, enabling scalability without increasing complexity relative to network size. Unlike traditional approaches that rotate cluster heads based on time or equal energy use, our method adapts to heterogeneous energy consumption patterns and enforces a cluster head rotation order that maximizes the lifetime of the final active node. To validate the effectiveness of the proposed approach, we implement it on a real-world LoRaWAN-based sensor network prototype. Experimental results demonstrate that our method significantly extends the lifetime of the last active node compared to representative state-of-the-art algorithms. This research provides a practical and robust solution for energy-efficient WSN operation in real deployment scenarios by considering realistic and application-driven communication behavior along with hardware-level energy consumption. Full article
(This article belongs to the Special Issue Architectures, Protocols and Algorithms of Sensor Networks—Second Edition)
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20 pages, 19291 KB  
Article
New Model for Weather Stations Integrated to Intelligent Meteorological Forecasts in Brasilia
by Thomas Alexandre da Silva, Andre L. M. Serrano, Erick R. C. Figueiredo, Geraldo P. Rocha Filho, Fábio L. L. de Mendonça, Rodolfo I. Meneguette and Vinícius P. Gonçalves
Sensors 2025, 25(11), 3432; https://doi.org/10.3390/s25113432 - 29 May 2025
Cited by 1 | Viewed by 2931
Abstract
This paper presents a new model for low-cost solar-powered Automatic Weather Stations based on the ESP-32 microcontroller, modern sensors, and intelligent forecasts for Brasilia. The proposed system relies on compact, multifunctional sensors and features an open-source firmware project and open-circuit board design. It [...] Read more.
This paper presents a new model for low-cost solar-powered Automatic Weather Stations based on the ESP-32 microcontroller, modern sensors, and intelligent forecasts for Brasilia. The proposed system relies on compact, multifunctional sensors and features an open-source firmware project and open-circuit board design. It includes a BME688, AS7331, VEML7700, AS3935 for thermo-hygro-barometry (plus air quality), ultraviolet irradiance, luximetry, and fulminology, besides having a rainfall gauge and an anemometer. Powered by photovoltaic panels and batteries, it operates uninterruptedly under variable weather conditions, with data collected being sent via WiFi to a Web API that adapts the MZDN-HF (Meteorological Zone Delimited Neural Network–Hourly Forecaster) model compilation for Brasilia to produce accurate 24 h multivariate forecasts, which were evaluated through MAE, RMSE, and R2 metrics. Installed at the University of Brasilia, it demonstrates robust hardware performance and strong correlation with INMET’s A001 data, suitable for climate monitoring, precision agriculture, and environmental research. Full article
(This article belongs to the Special Issue Ubiquitous Sensors in Environmental Monitor, Precision Agriculture, and Food Safety)
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30 pages, 1745 KB  
Review
The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence
by Pratyusha Muddaloor, Bhavana Baraskar, Hriday Shah, Keerthy Gopalakrishnan, Divyanshi Sood, Prem C. Pasupuleti, Akshay Singh, Dipankar Mitra, Sumedh S. Hoskote, Vivek N. Iyer, Scott A. Helgeson and Shivaram P. Arunachalam
Sensors 2025, 25(11), 3424; https://doi.org/10.3390/s25113424 - 29 May 2025
Cited by 2 | Viewed by 8283
Abstract
The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is [...] Read more.
The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution. Full article
(This article belongs to the Special Issue AI/ML in RF and Microwave Sensors for Medicine and Biomedical Applications)
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36 pages, 2706 KB  
Article
Towards Intelligent Assessment in Personalized Physiotherapy with Computer Vision
by Victor García and Olga C. Santos
Sensors 2025, 25(11), 3436; https://doi.org/10.3390/s25113436 - 29 May 2025
Cited by 2 | Viewed by 2655
Abstract
Effective physiotherapy requires accurate and personalized assessments of patient mobility, yet traditional methods can be time-consuming and subjective. This study explores the potential of open-source computer vision algorithms, specifically YOLO Pose, to support automated, vision-based analysis in physiotherapy settings using information collected from [...] Read more.
Effective physiotherapy requires accurate and personalized assessments of patient mobility, yet traditional methods can be time-consuming and subjective. This study explores the potential of open-source computer vision algorithms, specifically YOLO Pose, to support automated, vision-based analysis in physiotherapy settings using information collected from optical sensors such as cameras. By extracting skeletal data from video input, the system enables objective evaluation of patient movements and rehabilitation progress. The visual information is then analyzed to propose a semantic framework that facilitates a structured interpretation of clinical parameters. Preliminary results indicate that YOLO Pose provides reliable pose estimation, offering a solid foundation for future enhancements, such as the integration of natural language processing (NLP) to improve patient interaction through empathetic, AI-driven support. Full article
(This article belongs to the Special Issue Deep Learning Applications for Pose Estimation and Human Action Recognition—2nd Edition)
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22 pages, 640 KB  
Review
A Review of Optical-Based Three-Dimensional Reconstruction and Multi-Source Fusion for Plant Phenotyping
by Songhang Li, Zepu Cui, Jiahang Yang and Bin Wang
Sensors 2025, 25(11), 3401; https://doi.org/10.3390/s25113401 - 28 May 2025
Cited by 7 | Viewed by 3751
Abstract
In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural [...] Read more.
In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article
(This article belongs to the Section Smart Agriculture)
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32 pages, 3638 KB  
Article
Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing
by Suchuan Xing, Yihan Wang and Wenhe Liu
Sensors 2025, 25(11), 3396; https://doi.org/10.3390/s25113396 - 28 May 2025
Cited by 4 | Viewed by 3064
Abstract
Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep [...] Read more.
Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. Our approach employs contrastive learning to create unified representations of diverse service metrics and incorporates causal inference mechanisms to trace fault propagation paths. We evaluated our framework using a semi-supervised learning approach that leveraged both labeled anomalies and abundant normal data, achieving 95.4% detection accuracy, 93.8% F1-score, and 87.6% precision in fault component localization. The system reduced the average troubleshooting time by 43% and false localization rates by 31% compared to state-of-the-art methods, while maintaining a computational efficiency suitable for real-time monitoring. These results demonstrate the effectiveness of our approach in identifying and precisely localizing anomalies in complex microservice environments through intelligent sensing of system metrics, enabling proactive maintenance strategies that minimize service disruptions. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)
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19 pages, 13655 KB  
Article
Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning
by Ruizhi Liu, Zhenhang Qin, Xinghui Song, Lei Yang, Yue Lin and Hongtao Xu
Sensors 2025, 25(11), 3377; https://doi.org/10.3390/s25113377 - 27 May 2025
Cited by 1 | Viewed by 3010
Abstract
Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost [...] Read more.
Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost suppression method that integrates multi-target tracking with point cloud deep learning. Our approach consists of four key steps: (1) point cloud pre-segmentation, (2) inter-frame trajectory tracking, (3) trajectory feature aggregation, and (4) feature broadcasting, effectively combining spatiotemporal information with point-level features. Experiments on an indoor dataset demonstrate its superior performance compared to existing methods, achieving 93.5% accuracy and 98.2% AUROC. Ablation studies demonstrate the importance of each component, particularly the complementary benefits of pre-segmentation and trajectory processing. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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33 pages, 5679 KB  
Article
Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network
by Yonggang Wang, Wenpeng Li, Haoran Chen, Yuanchu Ma, Bingbing Yu and Yadong Yu
Sensors 2025, 25(11), 3378; https://doi.org/10.3390/s25113378 - 27 May 2025
Cited by 7 | Viewed by 1537
Abstract
The output of photovoltaic (PV) power generation systems remains uncertain primarily due to the uncontrollable nature of weather conditions, which may introduce disturbances to the power grid upon integrating PV systems. Accurate short-term PV power forecasting is an essential approach for ensuring the [...] Read more.
The output of photovoltaic (PV) power generation systems remains uncertain primarily due to the uncontrollable nature of weather conditions, which may introduce disturbances to the power grid upon integrating PV systems. Accurate short-term PV power forecasting is an essential approach for ensuring the stability of the power system. The paper proposes a short-term PV power forecasting model based on improved zebra optimization algorithm (IZOA)-stochastic configuration network (SCN). First, the historical PV data are divided into three weather patterns, effectively reducing the uncertainty of PV power. Second, a prediction model based on SCN is developed. To enhance the forecasting model’s accuracy even further, the IZOA is introduced to optimize the key parameters of the SCN. Finally, IZOA-SCN is employed for short-term PV power through various weather patterns. Experiment results show that the proposed method significantly improves the prediction accuracy in contrast to other comparison models. Full article
(This article belongs to the Section Sensor Networks)
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19 pages, 14298 KB  
Article
BETAV: A Unified BEV-Transformer and Bézier Optimization Framework for Jointly Optimized End-to-End Autonomous Driving
by Rui Zhao, Ziguo Chen, Yuze Fan, Fei Gao and Yuzhuo Men
Sensors 2025, 25(11), 3336; https://doi.org/10.3390/s25113336 - 26 May 2025
Cited by 1 | Viewed by 3223
Abstract
End-to-end autonomous driving demands precise perception, robust motion planning, and efficient trajectory generation to navigate complex and dynamic environments. This paper proposes BETAV, a novel framework that addresses the persistent challenges of low 3D perception accuracy and suboptimal trajectory smoothness in autonomous driving [...] Read more.
End-to-end autonomous driving demands precise perception, robust motion planning, and efficient trajectory generation to navigate complex and dynamic environments. This paper proposes BETAV, a novel framework that addresses the persistent challenges of low 3D perception accuracy and suboptimal trajectory smoothness in autonomous driving systems through unified BEV-Transformer encoding and Bézier-optimized planning. By leveraging Vision Transformers (ViTs), our approach encodes multi-view camera data into a Bird’s Eye View (BEV) representation using a transformer architecture, capturing both spatial and temporal features to enhance scene understanding comprehensively. For motion planning, a Bézier curve-based planning decoder is proposed, offering a compact, continuous, and parameterized trajectory representation that inherently ensures motion smoothness, kinematic feasibility, and computational efficiency. Additionally, this paper introduces a set of constraints tailored to address vehicle kinematics, obstacle avoidance, and directional alignment, further enhancing trajectory accuracy and safety. Experimental evaluations on Nuscences benchmark datasets and simulations demonstrate that our framework achieves state-of-the-art performance in trajectory prediction and planning tasks, exhibiting superior robustness and generalization across diverse and challenging Bench2Drive driving scenarios. Full article
(This article belongs to the Section Vehicular Sensing)
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21 pages, 12229 KB  
Article
A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan
by Wei-Fang Sun, Sheng-Yan Pan, Yao-Hung Liu, Hao Kuo-Chen, Chin-Shang Ku, Che-Min Lin and Ching-Chou Fu
Sensors 2025, 25(11), 3353; https://doi.org/10.3390/s25113353 - 26 May 2025
Cited by 4 | Viewed by 4886
Abstract
A timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system integrates continuous data from high-quality [...] Read more.
A timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system integrates continuous data from high-quality seismic networks via SeedLink with deep learning models and automated processing workflows. This approach enables the generation of an earthquake catalog with higher resolution and efficiency than the standard catalog announced by the Central Weather Administration, Taiwan. The RT-MEMS is designed to capture both background seismicity and earthquake sequences. The system employs the SeisBlue deep learning model, trained with a local dataset, to process continuous waveform data and pick P- and S-wave arrivals. Earthquake events are then associated and located using a modified version of PhasePAPY. Three stable RT-MEMS have been established in Taiwan: one for monitoring background seismicity along a creeping fault segment and two for monitoring mainshock–aftershock sequences. The system can provide timely information on changes in seismic activity following major earthquakes and generate long-term catalogs. The refined catalogs from RT-MEMS contribute to a more detailed understanding of seismotectonic structures and serve as valuable datasets for subsequent research. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Seismic Detection and Monitoring)
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24 pages, 3748 KB  
Article
Adaptive Resource Optimization for LoRa-Enabled LEO Satellite IoT System in High-Dynamic Environments
by Chen Zhang, Haoyou Peng, Yonghua Ji, Tao Hong and Gengxin Zhang
Sensors 2025, 25(11), 3318; https://doi.org/10.3390/s25113318 - 25 May 2025
Cited by 2 | Viewed by 2345
Abstract
The integration of Low-Earth Orbit (LEO) satellites with Long Range Radio (LoRa)-based Internet of Things (IoT) systems for extensive wide-area coverage has gained traction in academia and industry, challenging traditional terrestrial resource optimization designed for semi-static single-base-station environments. This paper addresses LEO’s high [...] Read more.
The integration of Low-Earth Orbit (LEO) satellites with Long Range Radio (LoRa)-based Internet of Things (IoT) systems for extensive wide-area coverage has gained traction in academia and industry, challenging traditional terrestrial resource optimization designed for semi-static single-base-station environments. This paper addresses LEO’s high dynamics and satellite-ground channel variability by introducing a beacon-triggered framework for LoRa-LEO IoT systems as a foundation for resource optimization. Then, in order to decouple the intertwined objectives of optimizing energy efficiency and maximizing the data extraction rate, an adaptive spreading factor (SF) allocation algorithm is proposed to mitigate collisions and resource waste, followed by a practical dynamic power control mechanism optimizing LoRa device power usage. Simulations validate that the proposed adaptive resource optimization outperforms conventional methods in dynamic, resource-constrained LEO environments, offering a robust solution for satellite IoT applications. In terms of energy efficiency and data extraction rate, the algorithm proposed in this paper outperforms other comparative algorithms. When the number of users reaches 3000, the energy efficiency is improved by at least 119%, and the data extraction rate is increased by at least 48%. Full article
(This article belongs to the Section Internet of Things)
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27 pages, 6433 KB  
Article
Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks
by Yang Liu, Lanting Guo, Xiaoyu Hu and Mengjie Zhou
Sensors 2025, 25(11), 3320; https://doi.org/10.3390/s25113320 - 25 May 2025
Cited by 4 | Viewed by 2113
Abstract
This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. Our approach transforms traditional material discovery paradigms by enabling end-to-end [...] Read more.
This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. Our approach transforms traditional material discovery paradigms by enabling end-to-end design from desired performance metrics to material composition. We developed a GAN-driven inverse design architecture specifically optimized for food packaging applications, integrating sensor-derived data on critical constraints such as biodegradability and barrier properties directly into the generative process. This integration occurs at three levels: (1) sensor-measured properties define conditioning targets for the GAN, (2) sensor data train the property prediction network, and (3) sensor-based characterization validates generated materials. An enhanced EquiformerV2 graph neural network was employed to accurately predict the formation energy, stability, and sensor-measurable properties of candidate materials. The model achieved a mean absolute error of 12 meV/atom for formation energy on the OMat24 test set (25% improvement over baseline models), while predictions of sensor-measured functional properties reached R2 values of 0.84–0.89 through the integration of experimental measurements and physics-based proxy models. The framework successfully generated over 100 theoretically viable candidate materials, with 20% exhibiting superior barrier properties and controlled degradation characteristics. Our computational approach demonstrated a 20–100× acceleration in screening efficiency compared to traditional DFT calculations while maintaining high accuracy. This work presents a significant advancement in computational materials discovery for sustainable packaging applications, offering a promising pathway to address the urgent global challenges of food waste and plastic pollution. Full article
(This article belongs to the Special Issue New Sensors Based on Inorganic Material)
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25 pages, 9541 KB  
Review
Review on Multispectral Photoacoustic Imaging Using Stimulated Raman Scattering Light Sources
by Yuon Song, Sang Min Park, Yongjae Jeong, Jeesu Kim and Hwidon Lee
Sensors 2025, 25(11), 3325; https://doi.org/10.3390/s25113325 - 25 May 2025
Cited by 3 | Viewed by 3680
Abstract
Photoacoustic imaging is an advanced biomedical imaging technique that has been widely developed and applied in diverse biomedical studies. By generating optical-absorption-based signals with ultrasound resolution, it enables in vivo visualization of molecular functional information in biological tissues. Extensive research has been conducted [...] Read more.
Photoacoustic imaging is an advanced biomedical imaging technique that has been widely developed and applied in diverse biomedical studies. By generating optical-absorption-based signals with ultrasound resolution, it enables in vivo visualization of molecular functional information in biological tissues. Extensive research has been conducted to develop the multispectral light sources required for functional photoacoustic imaging. Among the various approaches, multispectral light sources generated using stimulated Raman scattering have shown considerable promise, particularly in photoacoustic microscopy, where achieving multispectral illumination remains challenging. This review summarizes photoacoustic imaging systems that employ stimulated Raman scattering for multispectral light sources and delves into their configurations and applications in the functional analyses of biological tissues. In addition, the review discusses the future directions of multispectral light sources by comparing different technologies based on key factors such as wavelength tunability, repetition rate, and power, which critically affect the accuracy and quality of multispectral photoacoustic imaging. Full article
(This article belongs to the Special Issue Feature Review Papers in Optical Sensors)
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12 pages, 361 KB  
Article
Analysis of Electrodermal Signal Features as Indicators of Cognitive and Emotional Reactions—Comparison of the Effectiveness of Selected Statistical Measures
by Marcin Jukiewicz and Joanna Marcinkowska
Sensors 2025, 25(11), 3300; https://doi.org/10.3390/s25113300 - 24 May 2025
Cited by 2 | Viewed by 4796
Abstract
This study investigates which statistical measures of electrodermal activity (EDA) signal features most effectively differentiate between responses to stimuli and resting states in participants performing tasks with varying cognitive and emotional reactions. The study involved 30 healthy participants. Collected EDA data were statistically [...] Read more.
This study investigates which statistical measures of electrodermal activity (EDA) signal features most effectively differentiate between responses to stimuli and resting states in participants performing tasks with varying cognitive and emotional reactions. The study involved 30 healthy participants. Collected EDA data were statistically analyzed, comparing the effectiveness of twelve statistical signal measures in detecting stimulus-induced changes. The aim of this study is to answer the following research question: Which statistical features of the electrodermal activity signal most effectively indicate changes induced by cognitive and emotional reactions, and are there such significant similarities (high correlations) among these features that some of them can be considered redundant? The results indicated that amplitude-related measures—mean, median, maximum, and minimum—were most effective. It was also found that some signal features were highly correlated, suggesting the possibility of simplifying the analysis by choosing just one measure from each correlated pair. The results indicate that stronger emotional stimuli lead to more pronounced changes in EDA than stimuli with a low emotional load. These findings may contribute to the standardization of EDA analysis in future research on cognitive and emotional reaction engagement. Full article
(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)
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46 pages, 2208 KB  
Review
A Survey on Free-Space Optical Communication with RF Backup: Models, Simulations, Experience, Machine Learning, Challenges and Future Directions
by Sabai Phuchortham and Hakilo Sabit
Sensors 2025, 25(11), 3310; https://doi.org/10.3390/s25113310 - 24 May 2025
Cited by 8 | Viewed by 6229
Abstract
As sensor technology integrates into modern life, diverse sensing devices have become essential for collecting critical data that enables human–machine interfaces such as autonomous vehicles and healthcare monitoring systems. However, the growing number of sensor devices places significant demands on network capacity, which [...] Read more.
As sensor technology integrates into modern life, diverse sensing devices have become essential for collecting critical data that enables human–machine interfaces such as autonomous vehicles and healthcare monitoring systems. However, the growing number of sensor devices places significant demands on network capacity, which is constrained by the limitations of radio frequency (RF) technology. RF-based communication faces challenges such as bandwidth congestion and interference in densely populated areas. To overcome these challenges, a combination of RF with free-space optical (FSO) communication is presented. FSO is a laser-based wireless solution that offers high data rates and secure communication, similar to fiber optics but without the need for physical cables. However, FSO is highly susceptible to atmospheric turbulence and conditions such as fog and smoke, which can degrade performance. By combining the strengths of both RF and FSO, a hybrid FSO/RF system can enhance network reliability, ensuring seamless communication in dynamic urban environments. This review examines hybrid FSO/RF systems, covering both theoretical models and real-world applications. Three categories of hybrid systems, namely hard switching, soft switching, and relay-based mechanisms, are proposed, with graphical models provided to improve understanding. In addition, multi-platform applications, including autonomous, unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, are presented. Finally, the paper identifies key challenges and outlines future research directions for hybrid communication networks. Full article
(This article belongs to the Special Issue Sensing Technologies and Optical Communication)
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Article
Color QR Codes for Smartphone-Based Analysis of Free Chlorine in Drinking Water
by María González-Gómez, Ismael Benito-Altamirano, Hanna Lizarzaburu-Aguilar, David Martínez-Carpena, Joan Daniel Prades and Cristian Fàbrega
Sensors 2025, 25(11), 3251; https://doi.org/10.3390/s25113251 - 22 May 2025
Viewed by 2027
Abstract
Free chlorine (FC) plays a crucial role in ensuring the safety of drinking water by effectively inactivating pathogenic microorganisms. However, traditional methods for measuring FC levels often require specialized equipment and laboratory settings, limiting their accessibility and practicality for on-site or point-of-use monitoring. [...] Read more.
Free chlorine (FC) plays a crucial role in ensuring the safety of drinking water by effectively inactivating pathogenic microorganisms. However, traditional methods for measuring FC levels often require specialized equipment and laboratory settings, limiting their accessibility and practicality for on-site or point-of-use monitoring. QR Codes are powerful machine-readable patterns that are used worldwide to encode information (i.e., URLs or IDs), but their computer vision features allow QR Codes to act as carriers of other features for several applications. Often, this capability is used for aesthetics, e.g., embedding a logo in the QR Code. In this work, we propose using our technique to build back-compatible Color QR Codes, which can embed dozens of colorimetric references, to assist in the color correction to readout sensors. Specifically, we target two well-known products in the HORECA (hotel/restaurant/café) sector that qualitatively measure chlorine levels in samples of water. The two targeted methods were a BTB strip and a DPD powder. First, the BTB strip was a pH-based indicator distributed by Sensafe®, which uses the well-known bromothymol blue as a base-reactive indicator; second, the DPD powder was a colorimetric test distributed by Hach®, which employs diethyl-p-phenylenediamine (DPD) to produce a pink coloration in the presence of free chlorine. Custom Color QR Codes were created for both color palettes and exposed to several illumination conditions, captured with three different mobile devices and tested over different water samples. Results indicate that both methods could be correctly digitized in real-world conditions with our technology, rendering a 88.10% accuracy for the BTB strip measurement, and 84.62% for the DPD powder one. Full article
(This article belongs to the Special Issue Colorimetric Sensors: Methods and Applications (2nd Edition))
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