Sensors

876 KiB

Open AccessArticle

Long-Distance FBG Sensor Networks Multiplexed in Asymmetric Tree Topology

by Keiji Kuroda

Sensors 2025, 25(13), 4158; https://doi.org/10.3390/s25134158 (registering DOI) - 3 Jul 2025

This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that are multiplexed in an asymmetric tree topology. At each stage in the topology, FBGs are connected at one output port of a 50:50 coupler with fibers of different lengths. This [...] Read more.

This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that are multiplexed in an asymmetric tree topology. At each stage in the topology, FBGs are connected at one output port of a 50:50 coupler with fibers of different lengths. This asymmetric structure allows the simultaneous interrogation of long-distance and parallel sensor networks to be realized. Time- and wavelength-division multiplexing techniques are used to multiplex the FBGs. Using the hesterodyne detection technique, high-sensitivity detection of reflection signals that have been weakened by losses induced by a round-trip transmission through the couplers and long-distance propagation is performed. Quasi-distributed FBGs are interrogated simultaneously, over distances ranging from 15 m to 80 km. Full article

(This article belongs to the Special Issue Advances and Innovations in Optical Fiber Sensors)

1581 KiB

Open AccessArticle

A Novel Time–Frequency Similarity Method for P-Wave First-Motion Polarity Detection

by Yanji Yao, Xin Xu, Jing Wang, Lintao Liu and Zifei Ma

Sensors 2025, 25(13), 4157; https://doi.org/10.3390/s25134157 (registering DOI) - 3 Jul 2025

Abstract

P-wave first-motion polarity is a critical parameter for determining earthquake focal mechanisms. Extracting relative P-wave arrival times and polarity information using waveform cross-correlation techniques can enhance the accuracy of earthquake location and focal mechanism inversion. However, seismic noise severely hampers the reliable detection [...] Read more.

P-wave first-motion polarity is a critical parameter for determining earthquake focal mechanisms. Extracting relative P-wave arrival times and polarity information using waveform cross-correlation techniques can enhance the accuracy of earthquake location and focal mechanism inversion. However, seismic noise severely hampers the reliable detection of P-wave onsets and their first-motion polarities. To address this issue, we propose a noise-resistant polarity detection method based on the normal time–frequency transform (NTFT), termed the time–frequency similarity coefficient (TFSC). The TFSC method computes relative delays and similarity coefficients by calculating the real part of the NTFT coefficients between two seismic signals. We validated the proposed approach using both synthetic and real earthquake data. Without any filtering or preprocessing, the TFSC method demonstrated significantly greater robustness and reliability compared to the conventional time-domain normalized cross-correlation (NCC) method. These results indicate that the TFSC method has strong potential for practical application and provides a novel perspective for accurate detection of P-wave first-motion polarity in noisy seismic environments. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals—Second Edition)

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3927 KiB

Open AccessArticle

DFT Exploration of a Pd-Doped InSe Monolayer as a Novel Gas Sensing Candidate upon SF₆ Decomposition: SO₂, SOF_2, and SO₂F₂

by Xu Yang, Hao Cui, Zhongchao Liu and Yun Liu

Sensors 2025, 25(13), 4156; https://doi.org/10.3390/s25134156 (registering DOI) - 3 Jul 2025

Abstract

Monitoring SF₆ decomposition gases has emerged as a vital diagnostic technique for evaluating insulation conditions and identifying faults in SF₆-based electrical equipment. This study comprehensively explores the adsorption properties and sensing capabilities of a Pd-doped InSe (Pd-InSe) monolayer for SF [...] Read more.

Monitoring SF₆ decomposition gases has emerged as a vital diagnostic technique for evaluating insulation conditions and identifying faults in SF₆-based electrical equipment. This study comprehensively explores the adsorption properties and sensing capabilities of a Pd-doped InSe (Pd-InSe) monolayer for SF₆ decomposition gases, including SO₂, SOF₂, and SO₂F₂, through density functional theory calculations. The Pd-InSe monolayer is constructed by substituting one Se atom with a Pd atom in the pristine InSe structure. Then, the Pd doping effect on the InSe monolayer and the adsorption behaviors of the Pd-InSe monolayer for three gases are thoroughly examined. The adsorption configurations, charge density differences, and electron localization functions are scrutinized to elucidate the gas adsorption mechanisms of the Pd-InSe monolayer; and the band structures, along with the density of states, are analyzed to gain insights into the resistive gas sensing mechanisms for detecting these three gases. Finally, the temperature-dependent recovery characteristics are evaluated to assess the reusability of the monolayer. These findings not only underscore the potential of the Pd-InSe monolayer for sensing SF₆ decomposition gases but also open new avenues for the development of next-generation 2D materials in gas sensing applications within the field of electrical engineering. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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6218 KiB

Open AccessArticle

The Design and Data Analysis of an Underwater Seismic Wave System

by Dawei Xiao, Qin Zhu, Jingzhuo Zhang, Taotao Xie and Qing Ji

Sensors 2025, 25(13), 4155; https://doi.org/10.3390/s25134155 - 3 Jul 2025

Abstract

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage [...] Read more.

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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1730 KiB

Open AccessArticle

Optimizing Mental Stress Detection via Heart Rate Variability Feature Selection

by Mohsen Behradfar, Shotabdi Roy and Joseph Nuamah

Sensors 2025, 25(13), 4154; https://doi.org/10.3390/s25134154 - 3 Jul 2025

Abstract

The increasing prevalence of stress-related disorders necessitates accurate and efficient detection methods for timely intervention. This study explored the potential of heart rate variability as a biomarker for detecting mental stress using a publicly available dataset. A total of 93 heart rate variability [...] Read more.

The increasing prevalence of stress-related disorders necessitates accurate and efficient detection methods for timely intervention. This study explored the potential of heart rate variability as a biomarker for detecting mental stress using a publicly available dataset. A total of 93 heart rate variability features extracted from electrocardiogram signals were analyzed to differentiate stress from non-stress conditions. Our methodology involved data preprocessing, feature computation, and three feature selection strategies—filter-based, wrapper, and embedded—to identify the most relevant heart rate variability features. By leveraging Recursive Feature Elimination combined with Nested Leave-One-Subject-Out Cross-Validation, we achieved a peak F1 score of 0.76. The results demonstrate that two heart rate variability features—the median absolute deviation of the RR intervals (the time elapsed between consecutive R-waves on an electrocardiogram), which is normalized by the median, and the normalized low frequency power—consistently distinguished the stress states across multiple classifiers. To assess the robustness and generalizability of our best-performing model, we evaluated it on a completely unseen dataset, which resulted in an average F1 score of 0.63. These findings emphasize the value of targeted feature selection in optimizing stress detection models, particularly when handling high-dimensional datasets with potentially redundant features. This study contributes to the development of efficient stress monitoring systems, paving the way for improved mental health assessment and intervention. Full article

(This article belongs to the Section Intelligent Sensors)

1754 KiB

Open AccessArticle

Construction and Validation of a Digital Twin-Driven Virtual-Reality Fusion Control Platform for Industrial Robots

by Wenxuan Chang, Wenlei Sun, Pinghui Chen and Huangshuai Xu

Sensors 2025, 25(13), 4153; https://doi.org/10.3390/s25134153 - 3 Jul 2025

Abstract

Traditional industrial robot programming methods often pose high usage thresholds due to their inherent complexity and lack of standardization. Manufacturers typically employ proprietary programming languages or user interfaces, resulting in steep learning curves and limited interoperability. Moreover, conventional systems generally lack capabilities for [...] Read more.

Traditional industrial robot programming methods often pose high usage thresholds due to their inherent complexity and lack of standardization. Manufacturers typically employ proprietary programming languages or user interfaces, resulting in steep learning curves and limited interoperability. Moreover, conventional systems generally lack capabilities for remote control and real-time status monitoring. In this study, a novel approach is proposed by integrating digital twin technology with traditional robot control methodologies to establish a virtual–real mapping architecture. A high-precision and efficient digital twin-based control platform for industrial robots is developed using the Unity3D (2022.3.53f1c1) engine, offering enhanced visualization, interaction, and system adaptability. The high-precision twin environment is constructed from the three dimensions of the physical layer, digital layer, and information fusion layer. The system adopts the socket communication mechanism based on TCP/IP protocol to realize the real-time acquisition of robot state information and the synchronous issuance of control commands, and constructs the virtual–real bidirectional mapping mechanism. The Unity3D platform is integrated to develop a visual human–computer interaction interface, and the user-oriented graphical interface and modular command system effectively reduce the threshold of robot use. A spatially curved part welding experiment is carried out to verify the adaptability and control accuracy of the system in complex trajectory tracking and flexible welding tasks, and the experimental results show that the system has high accuracy as well as good interactivity and stability. Full article

(This article belongs to the Section Sensors and Robotics)

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Open AccessArticle

MRC-DETR: A High-Precision Detection Model for Electrical Equipment Protection in Power Operations

by Shenwang Li, Yuyang Zhou, Minjie Wang, Li Liu and Thomas Wu

Sensors 2025, 25(13), 4152; https://doi.org/10.3390/s25134152 - 3 Jul 2025

Abstract

Ensuring that electrical workers use personal protective equipment (PPE) correctly is critical to electrical safety, but existing detection methods face significant limitations when applied in the electrical industry. This paper introduces MRC-DETR (Multi-Scale Re-calibration Detection Transformer), a novel framework for detecting Power Engineering [...] Read more.

Ensuring that electrical workers use personal protective equipment (PPE) correctly is critical to electrical safety, but existing detection methods face significant limitations when applied in the electrical industry. This paper introduces MRC-DETR (Multi-Scale Re-calibration Detection Transformer), a novel framework for detecting Power Engineering Personal Protective Equipment (PEPPE) in complex electrical operating environments. Our method introduces two technical innovations: a Multi-Scale Enhanced Boundary Attention (MEBA) module, which significantly improves the detection of small and occluded targets through optimized feature representation, and a knowledge distillation strategy that enables efficient deployment on edge devices. We further contribute a dedicated PEPPE dataset to address the lack of domain-specific training data. Experimental results demonstrate superior performance compared to existing methods, particularly in challenging power industry scenarios. Full article

(This article belongs to the Section Industrial Sensors)

6136 KiB

Open AccessArticle

A ROS-Based Online System for 3D Gaussian Splatting Optimization: Flexible Frontend Integration and Real-Time Refinement

by Li’an Wang, Jian Xu, Xuan An, Yujie Ji, Yuxuan Wu and Zhaoyuan Ma

Sensors 2025, 25(13), 4151; https://doi.org/10.3390/s25134151 - 3 Jul 2025

Abstract

The 3D Gaussian splatting technique demonstrates significant efficiency advantages in real-time scene reconstruction. However, when its initialization process relies on traditional SfM methods (such as COLMAP), there are obvious bottlenecks, such as high computational resource consumption, as well as the decoupling problem between [...] Read more.

The 3D Gaussian splatting technique demonstrates significant efficiency advantages in real-time scene reconstruction. However, when its initialization process relies on traditional SfM methods (such as COLMAP), there are obvious bottlenecks, such as high computational resource consumption, as well as the decoupling problem between camera pose optimization and map construction. This paper proposes an online 3DGS optimization system based on ROS. Through the design of a loose-coupling architecture, it realizes real-time data interaction between the frontend SfM/SLAM module and backend 3DGS optimization. Using ROS as a middleware, this system can access the keyframe poses and point-cloud data generated by any frontend algorithms (such as ORB-SLAM, COLMAP, etc.). With the help of a dynamic sliding-window strategy and a rendering-quality loss function that combines L1 and SSIM, it achieves online optimization of the 3DGS map. The experimental data shows that compared with the traditional COLMAP-3DGS process, this system reduces the initialization time by 90% and achieves an average PSNR improvement of 1.9 dB on the TUM-RGBD, Tanks and Temples, and KITTI datasets. Full article

(This article belongs to the Special Issue Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems)

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1978 KiB

Open AccessArticle

Prediction of Magnetic Fields in Single-Phase Transformers Under Excitation Inrush Based on Machine Learning

by Qingjun Peng, Hantao Du, Zezhong Zheng, Haowei Zhu and Yuhang Fang

Sensors 2025, 25(13), 4150; https://doi.org/10.3390/s25134150 - 3 Jul 2025

Abstract

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an [...] Read more.

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer’s magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction. Full article

(This article belongs to the Section Physical Sensors)

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6019 KiB

Open AccessArticle

Deploying a Wireless Sensor Network to Track Pesticide Pollution in Kiu Wetland Wells: A Field Study

by Titus Mutunga, Sinan Sinanovic, Funmilayo B. Offiong and Colin Harrison

Sensors 2025, 25(13), 4149; https://doi.org/10.3390/s25134149 - 3 Jul 2025

Abstract

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and [...] Read more.

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table. Full article

(This article belongs to the Collection Sensing Technology in Smart Agriculture)

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2185 KiB

Open AccessArticle

Interplay Among Muscle Oxygen Saturation, Activation, and Power on a Swim-Bench

by Vittorio Coloretti, Claudio Quagliarotti, Giorgio Gatta, Maria Francesca Piacentini, Matteo Cortesi and Silvia Fantozzi

Sensors 2025, 25(13), 4148; https://doi.org/10.3390/s25134148 - 3 Jul 2025

Abstract

Muscle activity during exercise is typically assessed using oximeters, to evaluate local oxygen saturation (SmO₂), or surface electromyography (sEMG), to analyze electrical activation. Despite the importance of combining these analyses, no study has evaluated both of them during specific swimming exercises [...] Read more.

Muscle activity during exercise is typically assessed using oximeters, to evaluate local oxygen saturation (SmO₂), or surface electromyography (sEMG), to analyze electrical activation. Despite the importance of combining these analyses, no study has evaluated both of them during specific swimming exercises in combination with mechanical power output. This study aimed to assess muscle activity during an incremental test on a swim-bench utilizing oximeters and sEMG. Nine male swimmers performed a five-steps test: PRE (3 min at rest), STEP 1, 2, and 3 (swimming at a frequency of 25, 30, and 40 cycle/min for a duration of 2, 2, and 1 min, respectively), and POST (5 min at rest). Each swimmer wore two oximeters and sEMG, one for each triceps brachii. Stroke frequency and arm mechanical power (from ~13 to ~52 watts) estimated by the swim-bench were different among all steps, while no differences between arms were found. SmO₂ (from ~70% to ~60%) and sEMG signals (from ~20 to ~65% in signal amplitude) showed a significant increase among all steps. In both arms, a large/very large correlation was found between mechanical power and SmO₂ (r < −0.634), mechanical power and sEMG onset/amplitude (r > 0.581), and SmO₂ and sEMG amplitude (r > 0.508). No correlations were found between the slope of the sEMG spectral indexes and the slope of SmO₂; only sEMG detected electrical manifestation of muscle fatigue through the steps (p < 0.05). Increased muscle activity, assessed by both oximeters and sEMG, was found at mechanical power increases, revealing both devices can detect effort variation during exercise. However, only sEMG seems to detect peripheral manifestations of fatigue in dynamic conditions. Full article

(This article belongs to the Section Wearables)

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9083 KiB

Open AccessArticle

An Efficient Fine-Grained Recognition Method Enhanced by Res2Net Based on Dynamic Sparse Attention

by Qifeng Niu, Hui Wang and Feng Xu

Sensors 2025, 25(13), 4147; https://doi.org/10.3390/s25134147 - 3 Jul 2025

Abstract

Fine-grained recognition tasks face significant challenges in differentiating subtle, class-specific details against cluttered backgrounds. This paper presents an efficient architecture built upon the Res2Net backbone, significantly enhanced by a dynamic Sparse Attention mechanism. The core approach leverages the inherent multi-scale representation power of [...] Read more.

Fine-grained recognition tasks face significant challenges in differentiating subtle, class-specific details against cluttered backgrounds. This paper presents an efficient architecture built upon the Res2Net backbone, significantly enhanced by a dynamic Sparse Attention mechanism. The core approach leverages the inherent multi-scale representation power of Res2Net to capture discriminative patterns across different granularities. Crucially, the integrated Sparse Attention module operates dynamically, selectively amplifying the most informative features while attenuating irrelevant background noise and redundant details. This combined strategy substantially improves the model’s ability to focus on pivotal regions critical for accurate classification. Furthermore, strategic architectural optimizations are applied throughout to minimize computational complexity, resulting in a model that demands significantly fewer parameters and exhibits faster inference times. Extensive evaluations on benchmark datasets demonstrate the effectiveness of the proposed method. It achieves a modest but consistent accuracy gain over strong baselines (approximately 2%) while simultaneously reducing model size by around 30% and inference latency by about 20%, proving highly effective for practical fine-grained recognition applications requiring both high accuracy and operational efficiency. Full article

(This article belongs to the Special Issue Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions)

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3434 KiB

Open AccessReview

Multisource Heterogeneous Sensor Processing Meets Distribution Networks: Brief Review and Potential Directions

by Junliang Wang and Ying Zhang

Sensors 2025, 25(13), 4146; https://doi.org/10.3390/s25134146 - 3 Jul 2025

Abstract

The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents [...] Read more.

The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents dual implications: large-scale datasets offer an enhanced foundation for reliability assessment and dispatch planning in DNs; the dramatic escalation in data volume imposes demands on the computational precision and response speed of traditional evaluation approaches. The identification of critical influencing factors under extreme operating conditions, coupled with dynamic assessment and prediction of DN reliability through MSH data approaches, has emerged as a pressing challenge to address. Through a brief analysis of existing technologies and algorithms, this article reviews the technological development of MSH data analysis in DNs. By integrating the stability advantages of conventional approaches in practice with the computational adaptability of artificial intelligence, this article focuses on discussing key approaches for MSH data processing and assessment. Based on the characteristics of DN data, e.g., diverse sources, heterogeneous structures, and complex correlations, this article proposes several practical future directions. It is expected to provide insights for practitioners in power systems and sensor data processing that offer technical inspirations for intelligent, reliable, and stable next-generation DN construction. Full article

(This article belongs to the Special Issue Recent Progress on Sensors in Power Industry: System, Signal Processing, and Data Analysis)

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4471 KiB

Open AccessArticle

Comb-Tipped Coupled Cantilever Sensor for Enhanced Real-Time Detection of E. coli Bacteria

by Syed Ali Raza Bukhari, Elham Alaei, Zongchao Jia and Yongjun Lai

Sensors 2025, 25(13), 4145; https://doi.org/10.3390/s25134145 - 3 Jul 2025

Abstract

The detection of particulate matter, particularly pathogenic bacteria, is essential in environmental monitoring, food safety, and clinical diagnostics. Among the various sensing techniques used, cantilever-based sensors offer a promising platform for label-free, real-time detection due to their high sensitivity. Here, we present a [...] Read more.

The detection of particulate matter, particularly pathogenic bacteria, is essential in environmental monitoring, food safety, and clinical diagnostics. Among the various sensing techniques used, cantilever-based sensors offer a promising platform for label-free, real-time detection due to their high sensitivity. Here, we present a coupled cantilever sensor incorporating interdigitated comb-shaped structures to enhance dielectrophoretic (DEP) capture of Escherichia coli in liquid samples. During operation, one cantilever is externally actuated and the other oscillates passively through fluid-mediated coupling. The sensor was experimentally evaluated across a broad concentration range from 10 to 10⁵ cells/mL and the resonant frequency shifts were recorded for both beams. The results showed a strong linear frequency shift across all tested concentrations, without saturation. This demonstrates the sensor’s ability to detect both trace and high bacterial loads without needing recalibration. High frequency shifts of 4863 Hz were recorded for 10⁵ cells/mL and 225 Hz for the lowest concentration of 10 cells/mL, giving a limit of detection of 10 cells/mL. The sensor also showed a higher signal to noise ratio of 265.7 compared to previously reported designs. These findings showed that the enhanced sensor design enables sensitive, linear, and reliable bioparticle detection across a wide range, making it suitable for diverse applications. Full article

(This article belongs to the Section Biosensors)

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6621 KiB

Open AccessArticle

Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study

by Yanqiong Ding, Baojiang Han, Hua Jiang, Hao Hu, Lei Xue, Jiasen Weng, Zhili Tang and Yuzhang Liu

Sensors 2025, 25(13), 4144; https://doi.org/10.3390/s25134144 - 3 Jul 2025

Abstract

Manhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex environment [...] Read more.

Manhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex environment of manhole covers. To address these challenges, an intelligent management model based on the improved YOLOv8 model is proposed for three types of urban high-frequency defects: “breakage, loss and shift”. We design a lightweight dual-stream feature extraction network and use EfficientNetV2 as the backbone. By introducing the fused MBConv structure, the computational complexity is significantly reduced, while the efficiency of feature extraction is improved. An innovative foreground attention module is introduced to adaptively enhance the features of manhole cover defects, improving the model’s ability to identify defects of various scales. In addition, an optimized feature fusion architecture is constructed by integrating NAS-FPN modules. This structure utilizes bidirectional feature transfer and automatic structure search, significantly enhancing the expressiveness of multi-scale features. A combined loss function design using GIoU loss, dynamically weighted BCE loss, and Distribution Focal Loss (DFL) is adopted to address the issues of sample imbalance and inter-class differences. The experimental results show that the model achieved excellent performance in multiple indicators of manhole cover defect recognition, especially in classification accuracy, recall rate, and F1-score, with an overall recognition accuracy of 98.6%. The application of the improved model in the new smart management system for urban manhole covers can significantly improve management efficiency. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)

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5269 KiB

Open AccessArticle

Borohydride Synthesis of Silver Nanoparticles for SERS Platforms: Indirect Glucose Detection and Analysis Using Gradient Boosting

by Viktoriia Bakal, Olga Gusliakova, Anastasia Kartashova, Mariia Saveleva, Polina Demina, Ilya Kozhevnikov, Evgenii Ryabov, Daniil Bratashov and Ekaterina Prikhozhdenko

Sensors 2025, 25(13), 4143; https://doi.org/10.3390/s25134143 - 3 Jul 2025

Abstract

In recent years, non-invasive methods for the analysis of biological fluids have attracted growing interest. In this study, we propose a straightforward approach to fabricating silver nanoparticle (AgNP)-coated non-woven polyacrylonitrile substrates for surface-enhanced Raman scattering (SERS). AgNPs were synthesized directly on the substrate [...] Read more.

In recent years, non-invasive methods for the analysis of biological fluids have attracted growing interest. In this study, we propose a straightforward approach to fabricating silver nanoparticle (AgNP)-coated non-woven polyacrylonitrile substrates for surface-enhanced Raman scattering (SERS). AgNPs were synthesized directly on the substrate using borohydride reduction, ensuring uniform distribution. The optimized SERS substrates exhibited a high enhancement factor (EF) of up to 10⁵ for the detection of 4-mercaptobenzoic acid (4-MBA). To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). These results highlight the potential of AgNP-modified substrates for reliable and reusable biochemical sensing applications. Full article

(This article belongs to the Section Biosensors)

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24 pages, 9035 KiB

Open AccessArticle

MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization

by Yue Zheng, Ang Li, Zihan Chen, Yapeng Wang, Xu Yang and Sio-Kei Im

Sensors 2025, 25(13), 4142; https://doi.org/10.3390/s25134142 - 2 Jul 2025

Abstract

The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate [...] Read more.

The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate 3D spaces remain significant challenges. This study proposes a novel framework (MPN-RRT*) that integrates Motion Planning Networks (MPNet) with RRT* to enhance UAV navigation in 3D urban maps. A key innovation lies in reducing computational complexity through dimensionality reduction, where 3D urban terrains are sliced into 2D maze representations while preserving critical obstacle information. Transfer learning is applied to adapt a pre-trained MPNet model to the simplified maps, enabling intelligent sampling that guides RRT* toward promising regions and reduces redundant exploration. Extensive MATLAB simulations validate the framework’s efficacy across two distinct 3D environments: a sparse 200 × 200 × 200 map and a dense 800 × 800 × 200 map with no-fly zones. Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). In complex scenarios, the hybrid method maintains superior performance, reducing flight time by 14.2% and path length by 13.9% compared to RRT*. These results demonstrate that the integration of deep learning with sampling-based planning significantly enhances computational efficiency, path optimality, and smoothness, addressing critical limitations in UAV navigation for urban applications. The study underscores the potential of data-driven approaches to augment classical algorithms, providing a scalable solution for real-time autonomous systems operating in high-dimensional dynamic environments. Full article

(This article belongs to the Special Issue Recent Advances in UAV Communications and Networks)

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25 pages, 6368 KiB

Open AccessArticle

Development of a Thermal Infrared Network for Volcanic and Environmental Monitoring: Hardware Design and Data Analysis Software Code

by Fabio Sansivero, Giuseppe Vilardo and Ciro Buonocunto

Sensors 2025, 25(13), 4141; https://doi.org/10.3390/s25134141 - 2 Jul 2025

Abstract

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work [...] Read more.

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

(This article belongs to the Special Issue Recent Advances in Infrared Thermography and Sensing Technologies)

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29 pages, 3101 KiB

Open AccessArticle

Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS

by Ural Mutlu and Yasin Kabalci

Sensors 2025, 25(13), 4140; https://doi.org/10.3390/s25134140 - 2 Jul 2025

Abstract

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation [...] Read more.

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

(This article belongs to the Special Issue Communication, Sensing and Localization in 6G Systems)

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