Sensors最新文献

{"title":"Restoring Anomalous Water Surface in DOM Product of UAV Remote Sensing Using Local Image Replacement.","authors":"Chunjie Wang, Ti Zhang, Liang Tao, Jiayuan Lin","doi":"10.3390/s25134225","DOIUrl":"10.3390/s25134225","url":null,"abstract":"<p><p>In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620243","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":"Entropy, Irreversibility, and Time-Series Deep Learning of Kinematic and Kinetic Data for Gait Classification in Children with Cerebral Palsy, Idiopathic Toe Walking, and Hereditary Spastic Paraplegia.","authors":"Alfonso de Gorostegui, Massimiliano Zanin, Juan-Andrés Martín-Gonzalo, Javier López-López, David Gómez-Andrés, Damien Kiernan, Estrella Rausell","doi":"10.3390/s25134235","DOIUrl":"10.3390/s25134235","url":null,"abstract":"<p><p>The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon's entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620165","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":"Identification of Chicken Bone Paste in Starch-Based Sausages Using Laser-Induced Breakdown Spectroscopy.","authors":"Haoyu Li, Li Shen, Xiang Han, Yu Liu, Yutong Wang","doi":"10.3390/s25134226","DOIUrl":"10.3390/s25134226","url":null,"abstract":"<p><p>This study aims to rapidly in situ identify starch sausage samples with the improper addition of chicken bone paste. Chicken bones play important roles in building materials, biomass fuels, and as food additives after enzymatic hydrolysis, but no current research indicates that chicken bones can be directly added to food for consumption. Especially in starch sausages, the addition of chicken bone paste is highly controversial due to potential risks of esophageal laceration and religious concerns. This paper first uses laser-induced breakdown spectroscopy (LIBS) to investigate the elemental differences between starch sausages and chicken bone paste. By preparing mixtures of starch sausages and chicken bone paste at different ratios, the relationships between the spectral peak intensities of elements, such as Ca, Ba, and Sr, and the proportion of chicken bone paste were determined. Through processing methods such as normalization with reference spectral lines, selection of the signal of the second laser pulse at the same position, and electron temperature correction, the determination coefficients (R²) of each element's spectral lines have significantly improved. Specifically, the R² values for Ca I, Ca II, Ba II, and Sr II have increased from 0.302, 0.694, 0.857, and 0.691 to 0.972, 0.952, 0.970, and 0.982, respectively. Finally, principal component analysis (PCA) was used to distinguish starch sausages, chicken bone paste, and their mixtures at different ratios, with further effective differentiation achieved through t-distributed stochastic neighbor embedding (t-SNE). The results show that LIBS technology can serve as an effective and rapid method for detecting elemental composition in food and distinguishing different food products, providing safety guarantees for food production and supervision.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620149","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":"Multiplexing and Demultiplexing of Aperture-Modulated OAM Beams.","authors":"Wanjun Wang, Liguo Wang, Lei Gong, Zhiqiang Yang, Ligong Yang, Yao Li, Zhensen Wu","doi":"10.3390/s25134229","DOIUrl":"10.3390/s25134229","url":null,"abstract":"<p><p>A multiplexing method for orbital angular momentum (OAM) beams was proposed. The aperture size as a new information carrier was provided, and it could be modulated by the external variable aperture. The field of the beams propagating through turbulence was derived and discretized with Gauss-Legendre quadrature formulas. Based on this, the demultiplexing method was improved, and the beam OAM states, amplitude, Gaussian spot radius and aperture radius were decoded. Moreover, the influence of turbulence on the multiplexing parameters was also analyzed, and the decoding precision of the aperture radius was higher than that of other parameters. The aperture radius was recommended as an extra carrier for multiplexing communication. This study provides a simple method to modulate the information carried by OAM beams, and it has promising applications in large capacity laser communication.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620161","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":"Accurate and Fast Numerical Estimation of Pattern Uncertainty for Mechanical Alignment Errors in High-Accuracy Spherical Near-Field Antenna Measurements.","authors":"Kyriakos Kaslis, Samel Arslanagic, Olav Breinbjerg","doi":"10.3390/s25134227","DOIUrl":"10.3390/s25134227","url":null,"abstract":"<p><p>Every experimental measurement is affected by random and/or systematic error sources, causing the measurand to have an associated uncertainty quantified in terms of a confidence interval and confidence level. For high-accuracy spherical near-field antenna measurements, there are approximately 20 error sources whose individual contributions to the measurand uncertainty must be estimated for each antenna under test; thus, this uncertainty estimation is a required task in each measurement project. The error sources associated with the mechanical alignment of the antenna under test are of particular importance, not only because the consequential pattern uncertainty differs significantly for different antennas under test, but also because the common practice of experimental uncertainty estimation is very time-consuming with separate uncertainty measurements, thus requiring the antenna under test as well as the measurement facility. We propose a numerical pattern uncertainty estimation for mechanical alignment errors based on a nominal full-sphere measurement without the need for separate uncertainty measurements. Thus, it does not occupy either the antenna under test or the measurement facilities. In addition, numerical uncertainty estimation enables the isolation of individual error sources and their contributions to pattern uncertainties.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620023","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":"Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors.","authors":"Jiri Konecny, Jaromir Konecny, Kamil Bancik, Miroslav Mikus, Jan Choutka, Jiri Koziorek, Ibrahim A Hameed, Algimantas Valinevicius, Darius Andriukaitis, Michal Prauzek","doi":"10.3390/s25134232","DOIUrl":"10.3390/s25134232","url":null,"abstract":"<p><p>Internet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising solution. For energy-harvesting technologies, accurate soil temperature profile data are needed. This study uses meteorological and soil temperature profile data collected in the Czech Republic to train machine learning models based on Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict the soil temperature profile. The results of the study indicate an error of 0.79 °C, which is approximately 10.9% lower than the temperature error reported in state-of-the-art studies. Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. This improvement significantly reduces the computational costs associated with the regression model, offering a more efficient approach to predicting soil temperature for the purpose of optimizing energy harvesting in IoT sensors.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620126","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":"ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification.","authors":"Chao-Hsiang Hsiao, Huan-Che Su, Yin-Tien Wang, Min-Jie Hsu, Chen-Chien Hsu","doi":"10.3390/s25134233","DOIUrl":"10.3390/s25134233","url":null,"abstract":"<p><p>Defect detection in mass production lines often involves small and imbalanced datasets, necessitating the use of few-shot learning methods. Traditional deep learning-based approaches typically rely on large datasets, limiting their applicability in real-world scenarios. This study explores few-shot learning models for detecting product defects using limited data, enhancing model generalization and stability. Unlike previous deep learning models that require extensive datasets, our approach effectively performs defect detection with minimal data. We propose a Siamese network that integrates Residual blocks, Squeeze and Excitation blocks, and Convolution Block Attention Modules (ResNet-SE-CBAM Siamese network) for feature extraction, optimized through triplet loss for embedding learning. The ResNet-SE-CBAM Siamese network incorporates two primary features: attention mechanisms and metric learning. The recently developed attention mechanisms enhance the convolutional neural network operations and significantly improve feature extraction performance. Meanwhile, metric learning allows for the addition or removal of feature classes without the need to retrain the model, improving its applicability in industrial production lines with limited defect samples. To further improve training efficiency with imbalanced datasets, we introduce a sample selection method based on the Structural Similarity Index Measure (SSIM). Additionally, a high defect rate training strategy is utilized to reduce the False Negative Rate (FNR) and ensure no missed defect detections. At the classification stage, a K-Nearest Neighbor (KNN) classifier is employed to mitigate overfitting risks and enhance stability in few-shot conditions. The experimental results demonstrate that with a good-to-defect ratio of 20:40, the proposed system achieves a classification accuracy of 94% and an FNR of 2%. Furthermore, when the number of defective samples increases to 80, the system achieves zero false negatives (FNR = 0%). The proposed metric learning approach outperforms traditional deep learning models, such as parametric-based YOLO series models in defect detection, achieving higher accuracy and lower miss rates, highlighting its potential for high-reliability industrial deployment.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620331","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":"Flexible Bioelectrodes-Integrated Miniaturized System for Unconstrained ECG Monitoring.","authors":"Yaoliang Zhan, Xue Wang, Jin Yang","doi":"10.3390/s25134213","DOIUrl":"10.3390/s25134213","url":null,"abstract":"<p><p>The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users' daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. Therefore, utilizing reusable and cost-effective flexible bioelectrodes (with a signal-to-noise ratio of 33 dB), a miniaturized wearable system (MWS) is proposed for unconstrained ECG monitoring, which holds a size of 65 × 52 × 12 mm³ and a weight of 69 g. Given these compelling features, this system enables reliable and high-quality ECG signal monitoring in individuals' daily activities, including sitting, walking, and cycling, with the acquired signals exhibiting distinguishable QRS characteristics. Furthermore, an exercise intensity classification model was developed based on ECG characteristics and a fully connected neural network (FCNN) algorithm, with an evaluation accuracy of 98%. These results exhibit the promising potential of the MWS in tracking individuals' physiological signals and assessing exercise intensity.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620183","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":"Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots.","authors":"Puneet Arya, Mandeep Singh, Mandeep Singh","doi":"10.3390/s25134210","DOIUrl":"10.3390/s25134210","url":null,"abstract":"<p><p>Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs' linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620343","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":"Detection and Tracking of Environmental Sensing System for Construction Machinery Autonomous Operation Application.","authors":"Junyi Chen, Qipeng Cai, Xinhai Hu, Qihuai Chen, Tianliang Lin, Haoling Ren","doi":"10.3390/s25134214","DOIUrl":"10.3390/s25134214","url":null,"abstract":"<p><p>There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620068","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}