Sensors最新文献

{"title":"A Fault-Tolerant Localization Method for 5G/INS Based on Variational Bayesian Strong Tracking Fusion Filtering with Multilevel Fault Detection.","authors":"Zhongliang Deng, Ziyao Ma, Haiming Luo, Jilong Guo, Zidu Tian","doi":"10.3390/s25123753","DOIUrl":"https://doi.org/10.3390/s25123753","url":null,"abstract":"<p><p>In this paper, for the needs of high-precision and high-continuity localization in complex environments, a modeling method based on time-varying noise and outlier noise is proposed, and variational Bayesian strong tracking filtering is used for 5G/INS fusion localization. A hierarchical progressive fault detection mechanism is proposed to detect IMU rationality faults and consistency faults in 5G observation information. The main contributions are reflected in the following two aspects: first, by innovatively introducing Pearson VII-type distribution for noise modeling, dynamically adjusting the tail thickness characteristics of the probability density function through its shape parameter, and effectively capturing the distribution law of extreme values in the observation data. Afterward, this article combined the variational Bayesian strong tracking filtering algorithm to construct a robust state estimation framework, significantly improving the localization accuracy and continuity in non-Gaussian noise environments. Second, a hierarchical progressive fault detection mechanism is designed. A wavelet fault detection method based on a hierarchical voting mechanism is adopted for IMU data to extract the abrupt features of the observed data and quickly identify faults. In addition, a dual-channel consistency detection model with dynamic fault-tolerant management was constructed. Sudden and gradual faults were quickly detected through a dual-channel pre-check, and then, the fault source was identified through AIME. Based on the fault source detection results, corresponding compensation mechanisms were adopted to achieve robust continuous localization.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Parallel Plate Variable Capacitor-Based Wind Pressure Sensor: Closed-Form Solution and Numerical Design and Calibration.","authors":"Xiao-Ting He, Jun-Song Ran, Jing-Miao Yin, Jun-Yi Sun, Ying Guo","doi":"10.3390/s25123760","DOIUrl":"https://doi.org/10.3390/s25123760","url":null,"abstract":"<p><p>In this paper, a parallel plate variable capacitor-based wind pressure sensor is proposed, which uses a wind-driven peripherally fixed circular membrane as its pressure-sensitive element and a spring-reset parallel plate variable capacitor as its sensing element. The circular membrane is first driven by the wind, and then it pushes the spring-reset movable electrode plate of the parallel plate variable capacitor to move, resulting in a change in the capacitance of the capacitor. The wind pressure, i.e., the direct action force per unit area exerted by the wind on the circular membrane, is thus detected by measuring the capacitance change of the capacitor. The elastic contact problem between the circular membrane and the spring-reset movable electrode plate is analytically solved, and its closed-form solution is presented, where the usually adopted small rotation angle assumption of the membrane is given up. The analytical relationship between the input pressure and output capacitance of the capacitive wind pressure sensor proposed here is derived. The validity of the closed-form solution is proved, and how to use the closed-form solution and input/output analytical relationship for the numerical design and calibration of the capacitive wind pressure sensor proposed here is illustrated. Finally, the qualitative and quantitative effects of changing design parameters on the capacitance-pressure analytical relationship of the wind pressure measurement system are investigated comprehensively.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial Landmark-Driven Keypoint Feature Extraction for Robust Facial Expression Recognition.","authors":"Jaehyun So, Youngjoon Han","doi":"10.3390/s25123762","DOIUrl":"https://doi.org/10.3390/s25123762","url":null,"abstract":"<p><p>Facial expression recognition (FER) is a core technology that enables computers to understand and react to human emotions. In particular, the use of face alignment algorithms as a preprocessing step in image-based FER is important for accurately normalizing face images in terms of scale, rotation, and translation to improve FER accuracy. Recently, FER studies have been actively leveraging feature maps computed by face alignment networks to enhance FER performance. However, previous studies were limited in their ability to effectively apply information from specific facial regions that are important for FER, as they either only used facial landmarks during the preprocessing step or relied solely on the feature maps from the face alignment networks. In this paper, we propose the use of Keypoint Features extracted from feature maps at the coordinates of facial landmarks. To effectively utilize Keypoint Features, we further propose a Keypoint Feature regularization method using landmark perturbation for robustness, and an attention mechanism that emphasizes all Keypoint Features using representative Keypoint Features derived from a nasal base landmark, which carries information for the whole face, to improve performance. We performed experiments on the AffectNet, RAF-DB, and FERPlus datasets using a simply designed network to validate the effectiveness of the proposed method. As a result, the proposed method achieved a performance of 68.17% on AffectNet-7, 64.87% on AffectNet-8, 93.16% on RAF-DB, and 91.44% on FERPlus. Furthermore, the network pretrained on AffectNet-8 had improved performances of 94.04% on RAF-DB and 91.66% on FERPlus. These results demonstrate that the proposed Keypoint Features can achieve comparable results to those of the existing methods, highlighting their potential for enhancing FER performance through the effective utilization of key facial region features.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance.","authors":"Yicheng Li, Jinli Cui, Zhenyang Ma, Zhaobin Duan","doi":"10.3390/s25123763","DOIUrl":"https://doi.org/10.3390/s25123763","url":null,"abstract":"<p><p>The operational integrity of the BeiDou-3 Navigation Satellite System (BDS-3) has been significantly challenged by electromagnetic interference, particularly from Distance Measuring Equipment (DME) ground beacons to the newly implemented B2a signal, since its full operational deployment in 2020. This study developed a comprehensive interference evaluation model based on receiver signal processing principles to quantify the degradation of B2a signal reception performance under DME interference scenarios. Leveraging empirical data from the DME beacon network in the Chinese mainland, we systematically analyzed the interference effects through an effective carrier-to-noise ratio (C/N0), signal detection probability, carrier tracking accuracy, and demodulation bit error rate (BER). The results demonstrate that the effective C/N0 of the B2a signal degrades by up to 3.25 dB, the detection probability decreases by 33%, and the carrier tracking errors and BER increase by 2.57° and 5.1%, respectively, in worst-case interference scenarios. Furthermore, significant spatial correlation was observed between the interference hotspots and regions of high aircraft density. DME interference adversely affected the accuracy, availability, continuity, and integrity of the airborne BeiDou navigation system, thereby compromising civil aviation flight safety. These findings establish a scientific foundation for developing Minimum Operational Performance Standards for B2a signal receivers and for strategically optimizing DME beacon deployment throughout the Chinese mainland.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning.","authors":"Siwei Wei, Xiangyuan Xu, Dewen Liu, Chunzhi Wang, Lingyu Yan, Wangyu Wu","doi":"10.3390/s25123759","DOIUrl":"https://doi.org/10.3390/s25123759","url":null,"abstract":"<p><p>Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Autonomous Electric USV for Water Quality Detection.","authors":"Chiung-Hsing Chen, Yi-Jie Shang, Yi-Chen Wu, Yu-Chen Lin","doi":"10.3390/s25123747","DOIUrl":"https://doi.org/10.3390/s25123747","url":null,"abstract":"<p><p>With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), \"Life Below Water\".</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression-Based Docking System for Autonomous Mobile Robots Using a Monocular Camera and ArUco Markers.","authors":"Jun Seok Oh, Min Young Kim","doi":"10.3390/s25123742","DOIUrl":"https://doi.org/10.3390/s25123742","url":null,"abstract":"<p><p>This paper introduces a cost-effective autonomous charging docking system that utilizes a monocular camera and ArUco markers. Traditional monocular vision-based approaches, such as SolvePnP, are sensitive to viewing angles, lighting conditions, and camera calibration errors, limiting the accuracy of spatial estimation. To address these challenges, we propose a regression-based method that learns geometric features from variations in marker size and shape to estimate distance and orientation accurately. The proposed model is trained using ground-truth data collected from a LiDAR sensor, while real-time operation is performed using only monocular input. Experimental results show that the proposed system achieves a mean distance error of 1.18 cm and a mean orientation error of 3.11°, significantly outperforming SolvePnP, which exhibits errors of 58.54 cm and 6.64°, respectively. In real-world docking tests, the system achieves a final average docking position error of 2 cm and an orientation error of 3.07°, demonstrating that reliable and accurate performance can be attained using low-cost, vision-only hardware. This system offers a practical and scalable solution for industrial applications.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical Difference Representation-Based Transformer for Heterogeneous Change Detection.","authors":"Xinhui Cao, Minggang Dong, Xingping Liu, Jiaming Gong, Hanhong Zheng","doi":"10.3390/s25123740","DOIUrl":"https://doi.org/10.3390/s25123740","url":null,"abstract":"<p><p>Heterogeneous change detection refers to using image data from different sensors or modalities to detect change information in the same region by comparing images of the same region at different time periods. In recent years, methods based on deep learning and domain adaptation have become mainstream, which can effectively improve the accuracy and robustness of heterogeneous image change detection through feature alignment and multimodal data fusion. However, a lack of credible labels has stopped most current learning-based heterogeneous change detection methods from being put into application. To overcome this limitation, a weakly supervised heterogeneous change detection framework with a structure similarity-guided sample generating (S3G2) strategy is proposed, which employs differential structure similarity to acquire prior information for iteratively generating reliable pseudo-labels. Moreover, a Statistical Difference representation Transformer (SDFormer) is proposed to lower the influence of modality difference between bitemporal heterogeneous imagery and better extract relevant change information. Extensive experiments have been carried out to fully investigate the influences of inner manual parameters and compare them with state-of-the-art methods in several public heterogeneous change detection data sets. The experimental results indicate that the proposed methods have shown competitive performance.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AGEN: Adaptive Error Control-Driven Cross-View Geo-Localization Under Extreme Weather Conditions.","authors":"Mengmeng Xu, Hongxiang Lv, Hai Zhu, Enlai Dong, Fei Wu","doi":"10.3390/s25123749","DOIUrl":"https://doi.org/10.3390/s25123749","url":null,"abstract":"<p><p>Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., drone and satellite. Due to its GPS-free advantage, cross-view geo-localization is gaining increasing research interest, especially in drone-based localization and navigation applications. In order to guarantee system accuracy, existing methods mainly focused on image augmentation and denoising while still facing performance degradation when extreme weather conditions are considered. In this paper, we propose a robust end-to-end image retrieval framework, AGEN, serving for cross-view geo-localization under extreme weather conditions. Inspired by the strengths of the DINOv2 network, particularly its strong performance in global feature extraction, while acknowledging its limitations in capturing fine-grained details, we integrate the DINOv2 network with the Local Pattern Network (LPN) algorithm module to extract valuable classification features more efficiently. Additionally, to further enhance model robustness, we innovatively introduce an Adaptive Error Control (AEC) module based on fuzzy control to optimize the loss function dynamically. Specifically, by adjusting loss weights adaptively, the AEC module allows the model to better handle complex and challenging scenarios. Experimental results demonstrate that AGEN achieves a Recall@1 accuracy of 91.71% on the University160k-WX dataset under extreme weather conditions. Through extensive experiments on two well-known public datasets, i.e., University-1652 and SUES-200, AGEN achieves state-of-the-art Recall@1 accuracy in both drone-view target localization tasks and drone navigation tasks, outperforming existing models. In particular, on the University-1652 dataset, AGEN reaches 95.43% Recall@1 in the drone-view target localization task, showcasing its superior capability in handling challenging scenarios.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Miniaturized Design for a Terahertz Tri-Mirror CATR with High QZ Characteristics.","authors":"Zhi Li, Yuan Yao, Haiming Xin, Daocai Xiang","doi":"10.3390/s25123751","DOIUrl":"https://doi.org/10.3390/s25123751","url":null,"abstract":"<p><p>This paper proposes a miniaturized design for a terahertz tri-mirror compact antenna test range (CATR) system, composed of a square-aperture paraboloid primary mirror with a side length of 0.2 m and two shaped mirrors with circular apertures of 0.06 m and 0.07 m in diameter. The design first employs the cross-polarization cancelation method based on beam mode expansion to determine the geometric configuration of the system, thereby enabling the structure to exhibit low cross-polarization characteristics. Subsequently, the shaped mirrors, with beamforming and wave-front control capabilities, are synthesized using dynamic ray tracing based on geometric optics (GO) and the dual-paraboloid expansion method. Finally, the strong edge diffraction effects induced by the small-aperture primary mirror are suppressed by optimizing the desired quiet-zone (QZ) field width, adjusting the feed-edge taper, and incorporating rolled-edge structures on the primary mirror. Numerical simulation results indicate that within the 100-500 GHz frequency band, the system's cross-polarization level is below -40 dB, while the amplitude and phase ripples of the co-polarization in the QZ are, respectively, less than 1.6 dB and 10°, and the QZ usage ratio exceeds 70%. The designed CATR was manufactured and tested. The results show that at 183 GHz and 275 GHz, the measured co-polarization amplitude and phase ripples in the system's QZ are within 1.8 dB and 15°, respectively. While these values deviate slightly from simulations, they still meet the CATR evaluation criteria, which specify QZ co-polarization amplitude ripple < 2 dB and phase ripple < 20°. The overall physical structure sizes of the system are 0.61 m × 0.2 m × 0.66 m. The proposed miniaturized terahertz tri-mirror CATR design methodology not only enhances the QZ characteristics but also significantly reduces the spatial footprint of the entire system, demonstrating significant potential for practical engineering applications.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}