Sensors最新文献

{"title":"An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors.","authors":"Atcharawan Rattanasak, Talit Jumphoo, Wongsathon Pathonsuwan, Kasidit Kokkhunthod, Khwanjit Orkweha, Khomdet Phapatanaburi, Pattama Tongdee, Porntip Nimkuntod, Monthippa Uthansakul, Peerapong Uthansakul","doi":"10.3390/s25051552","DOIUrl":"10.3390/s25051552","url":null,"abstract":"<p><p>Counting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilities. The device integrates accelerometer and gyroscope sensors with Internet of Things (IoT) technology to accurately differentiate between fetal and non-fetal movements. Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. The device utilized Particle Swarm Optimization (PSO) for feature selection and Extreme Gradient Boosting (XGB) with PSO hyper-tuning. It achieved a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%, reflecting commendable results. The IoT-enabled technology facilitated continuous monitoring with an average latency of 423.6 ms. It ensured complete data integrity and successful transmission, with the capability to operate continuously for up to 48 h on a single charge. The findings substantiate the efficacy of the proposed approach in detecting fetal movements, thereby demonstrating a practical and valuable technology for fetal movement detection applications.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRA-YOLOv8: A Network Enhancing Feature Extraction Ability for Photovoltaic Cell Defects.","authors":"Nannan Wang, Siqi Huang, Xiangpeng Liu, Zhining Wang, Yi Liu, Zhe Gao","doi":"10.3390/s25051542","DOIUrl":"10.3390/s25051542","url":null,"abstract":"<p><p>To address the challenges posed by complex backgrounds and the low occurrence in photovoltaic cell images captured by industrial sensors, we propose a novel defect detection method: MRA-YOLOv8. First, a multi-branch coordinate attention network (MBCANet) is introduced into the backbone. The coordinate attention network (CANet) is incorporated to mitigate the noise impact of background information on the detection task, and multiple branches are employed to enhance the model's feature extraction capability. Second, we integrate a multi-path feature extraction module, ResBlock, into the neck. This module provides finer-grained multi-scale features, improving feature extraction from complex backgrounds and enhancing the model's robustness. Finally, we implement alpha-minimum point distance-based IoU (AMPDIoU) to the head. This loss function enhances the accuracy and robustness of small object detection by integrating minimum point distance-based IoU (MPDIoU) and Alpha-IoU methods. The results demonstrate that MRA-YOLOv8 outperforms other mainstream methods in detection performance. On the photovoltaic electroluminescence anomaly detection (PVEL-AD) dataset, the proposed method achieves a <i>mAP</i>₅₀ of 91.7%, representing an improvement of 3.1% over YOLOv8 and 16.1% over detection transformer (DETR). On the SPDI dataset, our method achieves a <i>mAP</i>₅₀ of 69.3%, showing a 2.1% improvement over YOLOv8 and a 6.6% improvement over DETR. The proposed MRA-YOLOv8 also exhibits great deployment potential. It can be effectively integrated with drone-based inspection systems, allowing for efficient and accurate PV plant inspections. Moreover, to tackle the issue of data imbalance, we propose generating synthetic defect data via generative adversarial networks (GANs), which can supplement the limited defect samples and improve the model's generalization ability.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Iterative Deflectometry Method of Reconstruction of Separate Specular Surfaces.","authors":"Cheng Liu, Jianhua Liu, Yanming Xing, Xiaohui Ao, Hongda Shen, Chunguang Yang","doi":"10.3390/s25051549","DOIUrl":"10.3390/s25051549","url":null,"abstract":"<p><p>Phase measuring deflectometry (PMD) plays a more and more significant role in the measurement of specular surfaces. However, most of the deflectometric methods are only suitable for continuous specular surfaces, but not for the discontinuous surfaces. In this work, with the hardware of stereoscopic PMD, a mechanism is introduced so that a specular surface can be reconstructed iteratively with the pre-known coordinate of a reflecting point. Based on the mechanism and the excellent local properties of the B-spline surface, a reconstruction method suitable for both kinds of specular surfaces is proposed. Meanwhile, to resist the noise of the single point, this work mathematically analyzes the mechanism of the method. With the mathematical conclusion, the sparse point cloud solved using stereoscopic PMD is employed to scale the B-spline surfaces, improving the accuracy of reconstruction. Simulated and actual experiments are carried out, and the results show high accuracy and robustness of the PMD system and the reconstruction method.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the Level of Aflatoxin Infection in Pistachio Nuts by Applying Machine Learning Techniques to Hyperspectral Images.","authors":"Lizzie Williams, Pancham Shukla, Akbar Sheikh-Akbari, Sina Mahroughi, Iosif Mporas","doi":"10.3390/s25051548","DOIUrl":"10.3390/s25051548","url":null,"abstract":"<p><p>This paper investigates the use of machine learning techniques on hyperspectral images of pistachios to detect and classify different levels of aflatoxin contamination. Aflatoxins are toxic compounds produced by moulds, posing health risks to consumers. Current detection methods are invasive and contribute to food waste. This paper explores the feasibility of a non-invasive method using hyperspectral imaging and machine learning to classify aflatoxin levels accurately, potentially reducing waste and enhancing food safety. Hyperspectral imaging with machine learning has shown promise in food quality control. The paper evaluates models including Dimensionality Reduction with K-Means Clustering, Residual Networks (ResNets), Variational Autoencoders (VAEs), and Deep Convolutional Generative Adversarial Networks (DCGANs). Using a dataset from Leeds Beckett University with 300 hyperspectral images, covering three aflatoxin levels (<8 ppn, >160 ppn, and >300 ppn), key wavelengths were identified to indicate contamination presence. Dimensionality Reduction with K-Means achieved 84.38% accuracy, while a ResNet model using the 866.21 nm wavelength reached 96.67%. VAE and DCGAN models, though promising, were constrained by dataset size. The findings highlight the potential for machine learning-based hyperspectral imaging in pistachio quality control, and future research should focus on expanding datasets and refining models for industry application.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Activity Recognition Through Augmented WiFi CSI Signals by Lightweight Attention-GRU.","authors":"Hari Kang, Donghyun Kim, Kar-Ann Toh","doi":"10.3390/s25051547","DOIUrl":"10.3390/s25051547","url":null,"abstract":"<p><p>In this study, we investigate human activity recognition (HAR) using WiFi channel state information (CSI) signals, employing a single-layer gated recurrent unit (GRU) with an attention module. To overcome the limitations of existing state-of-the-art (SOTA) models, which, despite their good performance, have substantial model sizes, we propose a lightweight model that incorporates data augmentation and pruning techniques. Our primary goal is to maintain high performance while significantly reducing model complexity. The proposed method demonstrates promising results across four different datasets, in particular achieving an accuracy of about 98.92%, outperforming an SOTA model on the ARIL dataset while reducing the model size from 252.10 M to 0.0578 M parameters. Additionally, our method achieves a reduction in computational cost from 18.06 GFLOPs to 0.01 GFLOPs for the same dataset, making it highly suitable for practical HAR applications.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incoherent Region-Aware Occlusion Instance Synthesis for Grape Amodal Detection.","authors":"Yihan Wang, Shide Xiao, Xiangyin Meng","doi":"10.3390/s25051546","DOIUrl":"10.3390/s25051546","url":null,"abstract":"<p><p>Occlusion presents a significant challenge in grape phenotyping detection, where predicting occluded content (amodal detection) can greatly enhance detection accuracy. Recognizing that amodal detection performance is heavily influenced by the segmentation quality between occluder and occluded grape instances, we propose a grape instance segmentation model designed to precisely predict error-prone regions caused by mask size transformations during segmentation, with a particular focus on overlapping regions. To address the limitations of current occlusion synthesis methods in amodal detection, a novel overlapping cover strategy is introduced to replace the existing random cover strategy. This approach ensures that synthetic grape instances better align with real-world occlusion scenarios. Quantitative comparison experiments conducted on the grape amodal detection dataset demonstrate that the proposed grape instance segmentation model achieves superior amodal detection performance, with an IoU score of 0.7931. Additionally, the proposed overlapping cover strategy significantly outperforms the random cover strategy in amodal detection performance.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-Simulation of Interconnection Between Smart Power Grid and Smart Cities Platform via Massive Machine-Type Communication.","authors":"Luiz H N Rodrigues, Carlos F M Almeida, Nelson Kagan, Luiz H L Rosa, Milana L Dos Santos","doi":"10.3390/s25051517","DOIUrl":"10.3390/s25051517","url":null,"abstract":"<p><p>With the advent of Industry 5.0, the electrical sector has been endowed with intelligent devices that are propelling high penetration of distributed energy microgeneration, VPP, smart buildings, and smart plants and imposing new challenges on the sector. This new environment requires a smarter network, including transforming the simple electricity customer into a \"smart customer\" who values the quality of energy and its rational use. The SPG (smart power grid) is the perfect solution for meeting these needs. It is crucial to understand energy use to guarantee quality of service and meet data security requirements. The use of simulations to map the behavior of complex infrastructures is the best strategy because it overcomes the limitations of traditional analytical solutions. This article presents the ICT laboratory structure developed within the Department of Electrical Engineering of the Polytechnic School of the Universidade de São Paulo (USP). It is based on an architecture that utilizes LTE/EPC wireless technology (4G, 5G, and B5G) to enable machine-to-machine communication (mMTC) between SPG elements using edge computing (MEC) resources and those of smart city platforms. We evaluate this proposal through simulations using data from real and emulated equipment and co-simulations shared by SPG laboratories at POLI-USP. Finally, we present the preliminary results of integration of the power laboratory, network simulation (ns-3), and a smart city platform (InterSCity) for validation and testing of the architecture.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Apple Detection via Near-Field MIMO-SAR Imaging: A Multi-Scale and Context-Aware Approach.","authors":"Yuanping Shi, Yanheng Ma, Liang Geng","doi":"10.3390/s25051536","DOIUrl":"10.3390/s25051536","url":null,"abstract":"<p><p>Accurate fruit detection is of great importance for yield assessment, timely harvesting, and orchard management strategy optimization in precision agriculture. Traditional optical imaging methods are limited by lighting and meteorological conditions, making it difficult to obtain stable, high-quality data. Therefore, this study utilizes near-field millimeter-wave MIMO-SAR (Multiple Input Multiple Output Synthetic Aperture Radar) technology, which is capable of all-day and all-weather imaging, to perform high-precision detection of apple targets in orchards. This paper first constructs a near-field millimeter-wave MIMO-SAR imaging system and performs multi-angle imaging on real fruit tree samples, obtaining about 150 sets of SAR-optical paired data, covering approximately 2000 accurately annotated apple targets. Addressing challenges such as weak scattering, low texture contrast, and complex backgrounds in SAR images, we propose an innovative detection framework integrating Dynamic Spatial Pyramid Pooling (DSPP), Recursive Feature Fusion Network (RFN), and Context-Aware Feature Enhancement (CAFE) modules. DSPP employs a learnable adaptive mechanism to dynamically adjust multi-scale feature representations, enhancing sensitivity to apple targets of varying sizes and distributions; RFN uses a multi-round iterative feature fusion strategy to gradually refine semantic consistency and stability, improving the robustness of feature representation under weak texture and high noise scenarios; and the CAFE module, based on attention mechanisms, explicitly models global and local associations, fully utilizing the scene context in texture-poor SAR conditions to enhance the discriminability of apple targets. Experimental results show that the proposed method achieves significant improvements in average precision (AP), recall rate, and F1 score on the constructed near-field millimeter-wave SAR apple dataset compared to various classic and mainstream detectors. Ablation studies confirm the synergistic effect of DSPP, RFN, and CAFE. Qualitative analysis demonstrates that the detection framework proposed in this paper can still stably locate apple targets even under conditions of leaf occlusion, complex backgrounds, and weak scattering. This research provides a beneficial reference and technical basis for using SAR data in fruit detection and yield estimation in precision agriculture.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Energy Efficiency Maximization for Wirelessly-Powered UAV-Assisted Secure Sensor Network.","authors":"Fang Xu, Xinyu Zhang","doi":"10.3390/s25051534","DOIUrl":"10.3390/s25051534","url":null,"abstract":"<p><p>The rapid proliferation of Internet of Things (IoT) devices and applications has led to an increasing demand for energy-efficient and secure communication in wireless sensor networks. In this article, we firstly propose an intelligent approach to maximize the energy efficiency of the UAV in a secure sensor network with wireless power transfer (WPT). All sensors harvest energy via downlink signal and use it to transmit uplink information to the UAV. To ensure secure data transmission, the UAV needs to optimize the transmission parameters to decode received information under malicious interference from an attacker. Code Division Multiple Access (CDMA) is adopted to improve uplink communication robustness. To maximize the UAV's energy efficiency in data collection tasks, we formulate a constrained optimization problem that jointly optimizes charging power, charging duration, and data transmission duration. Applying Deep Deterministic Policy Gradient (DDPG) algorithm, we train an action policy to dynamically determine near-optimal transmission parameters in real time. Numerical results validate the superiority of proposed intelligent approach over exhaustive search and gradient ascent techniques. This work provides some important guidelines for the design of green secure wireless-powered sensor networks.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Response of Heart Rate Variability to Active Standing in Aortic Valve Disease: Insights from Recurrence Quantification Analysis.","authors":"Itayetzin Beurini Cruz-Vega, Nydia Ávila-Vanzzini, Gertrudis Hortensia González-Gómez, Rashidi Springall, Juan C Echeverría, Claudia Lerma","doi":"10.3390/s25051535","DOIUrl":"10.3390/s25051535","url":null,"abstract":"<p><strong>Introduction: </strong>Aortic valve disease (AVD) is an inflammatory, lipid infiltration and calcification disease that has been associated with changes in the conventional linear heart rate variability (HRV) indices showing a marked shift towards sympathetic predominance and a deterioration of the autonomic control.</p><p><strong>Objective: </strong>To explore the HRV dynamics in AVD patients through nonlinear methods by recurrence quantification analysis (RQA).</p><p><strong>Methods: </strong>In total, 127 subjects participated in a cross-sectional study categorized into three groups: healthy valve (HV), aortic valve sclerosis (AVSc), and aortic valve stenosis (AVS), as determined by echocardiographic assessment. HRV data were collected from five-minute ECG recordings at both a supine position and active standing. RQA indices were calculated using the Cross Recurrence Plot Toolbox.</p><p><strong>Results: </strong>In the supine position, patients with AVS exhibited larger determinism and trapping time than those with AVSc and HV. The analysis of these differences revealed that determinism and laminarity increased progressively from HV to AVS. In the same way, the magnitude of change (Δ) between positions decreased and presented the lowest values in AVS in most of the nonlinear indices.</p><p><strong>Conclusion: </strong>RQA indices of HRV in AVD patients indicate a rigidizing dynamic characterized by larger determinism and extended trapping times in fewer system states in relation to the severity of AVD. These findings establish a precedent for future perspective assessments for the implementation of these methods in medical software or devices.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}