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Research on Multimodal Recognition Methods for Perimeter Security Based on the Fusion of DVS and Video Surveillance 基于分布式交换机与视频监控融合的周界安防多模态识别方法研究

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3582973

Wei Zhao;Shaodong Jiang;Yang Zhao;Faxiang Zhang

{"title":"Research on Multimodal Recognition Methods for Perimeter Security Based on the Fusion of DVS and Video Surveillance","authors":"Wei Zhao;Shaodong Jiang;Yang Zhao;Faxiang Zhang","doi":"10.1109/JSEN.2025.3582973","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3582973","url":null,"abstract":"Perimeter security systems widely employ distributed fiber-optic sensing technology and video surveillance as sensing means. However, significant limitations remain in practical applications. Distributed fiber-optic sensing technology is susceptible to interference from environmental noise coupling, resulting in a high false alarm rate. Meanwhile, video surveillance technology faces issues such as increased image noise and blurred target outlines in complex environments. These problems are compounded by the complexity of the background, which makes it difficult to accurately identify subtle behavioral differences. To address these challenges, this article proposes a multimodal fusion classification model, HMFusionNet, which leverages the complementary information from distributed vibration sensing (DVS) and video surveillance to improve classification accuracy. First, we introduce the CGANet module to extract features from 1-D fiber vibration signals and capture the periodic characteristics of the fiber time series. Second, we design the PoseMobiNet module to extract 2-D image features based on human keypoint data and RGB image information, addressing the complexities of the perimeter security background and the subtleties of behavioral differences among intruders. During the feature fusion stage, we propose a probabilistic weighting-based late fusion strategy to integrate decision-level information from both modalities. Finally, using a multimodal dataset constructed based on a real-world perimeter security scenario, the HMFusionNet achieves a detection accuracy of 97.7% with a recognition time of less than 0.1 s.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29213-29220"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Multi-Level Cell Spin-Orbit Torque Memory for Fast and Robust Memory Operations 用于快速和稳健存储操作的混合多级单元自旋轨道扭矩存储器

IF 2.1 4区工程技术

IEEE Transactions on Nanotechnology Pub Date : 2025-07-02 DOI: 10.1109/TNANO.2025.3585167

Kon-Woo Kwon;Yeongkyo Seo

引用次数: 0

High-Sensitivity Fiber Bragg Grating Pressure Sensor With a Hinged-Lever Structure 铰链杠杆结构的高灵敏度光纤光栅压力传感器

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583300

Qiang Liu;Shuhui Wei;Shenglong Gu;Jian Han;Chao Ma;Pengfei Lu;Jingwei Lv;Paul K. Chu;Chao Liu

{"title":"High-Sensitivity Fiber Bragg Grating Pressure Sensor With a Hinged-Lever Structure","authors":"Qiang Liu;Shuhui Wei;Shenglong Gu;Jian Han;Chao Ma;Pengfei Lu;Jingwei Lv;Paul K. Chu;Chao Liu","doi":"10.1109/JSEN.2025.3583300","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583300","url":null,"abstract":"This article presents a high-sensitivity fiber Bragg grating (FBG) pressure sensor with a metal diaphragm and hinge-lever structure designed for small-range pressure measurement. The sensor employs hinge groups and dual-lever structure to amplify the small strain induced by diaphragm deformation, thereby enhancing sensitivity. The sensor structure is analyzed and optimized by the finite element method. The sensor is fabricated and tested on a pressure calibration platform. The experimental data show that the pressure sensitivity of the sensor is 3.382 pm/kPa in the range of 0–1 MPa, and the correlation coefficient is 0.9999. Another FBG is employed to compensate for the influence of temperature with a sensitivity of 12.14 pm/°C in the range of <inline-formula> <tex-math>$20~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$70~^{circ }$ </tex-math></inline-formula>C and the correlation coefficient of 0.9998. In addition, the sensor is capable of maintaining stable pressure measurements within the temperature range of <inline-formula> <tex-math>$25~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$55~^{circ }$ </tex-math></inline-formula>C. The sensor with high sensitivity and stability is suitable for low-pressure, high-sensitivity detection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28314-28322"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual-Inertial State Estimation Based on Chebyshev Polynomial Optimization 基于切比雪夫多项式优化的视觉惯性状态估计

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583221

Hongyu Zhang;Maoran Zhu;Qi Cai;Yuanxin Wu

{"title":"Visual-Inertial State Estimation Based on Chebyshev Polynomial Optimization","authors":"Hongyu Zhang;Maoran Zhu;Qi Cai;Yuanxin Wu","doi":"10.1109/JSEN.2025.3583221","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583221","url":null,"abstract":"Visual-inertial navigation systems (VINS) are essential across various applications. Traditional optimization-based VINS mainly relies on the preintegration method for integrating inertial measurements. While this method avoids recalculating inertial integration during optimization by generating relative pose constraints, it compromises the quasi-Gaussian nature of the original measurements. Additionally, the constraints need to be updated through linearization after biases change. To address these problems, this article proposes a visual-inertial fusion method based on Chebyshev polynomial optimization. The proposed method directly incorporates the original inertial measurements into the objective function, thereby maintaining the quasi-Gaussian properties of the inertial measurements and the additive nature of the biases. Specifically, it represents the continuous navigation state using Chebyshev polynomials and has the unknown coefficients determined by minimizing weighted residuals of initial conditions, dynamics, and measurements. Simulations and experiments on public datasets demonstrate that the proposed method significantly improves batch optimization accuracy. It achieves approximately 40% improvement in velocity and about 50% improvement in position over the preintegration method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29618-29629"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DIDPT: Dense Interaction Deep Prompt RGBT Tracking dpt：密集交互深度提示右跟踪

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583417

Muyang Li;Xiwen Ren;Guangwen Luo;Haofei Zhang;Ruqian Hao;Juanxiu Liu;Lin Liu;Ping Zhang

{"title":"DIDPT: Dense Interaction Deep Prompt RGBT Tracking","authors":"Muyang Li;Xiwen Ren;Guangwen Luo;Haofei Zhang;Ruqian Hao;Juanxiu Liu;Lin Liu;Ping Zhang","doi":"10.1109/JSEN.2025.3583417","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583417","url":null,"abstract":"Existing RGB-infrared object tracking methods struggle with effectively fusing data from both modalities, further hindered by the magnitude disparity between them. While large-parameter models in RGB tracking demonstrate robustness on extensive datasets, their performance remains underutilized when incorporating infrared data. To address these challenges, this article proposes a deep prompt learning method based on dense interaction to enhance RGB-infrared fusion and leverage the strengths of large models in RGBT object tracking. We treat infrared information as a prompt for the tracker and freeze the pretrained parameters of the RGB backbone model. During the initial feature extraction phase of the backbone model, a dense infrared prompt interaction encoder is employed to integrate infrared information. Subsequently, we introduce learnable prompts in the Transformer module while freezing the parameters of the Transformer encoder layers, updating only the parameters of the learnable prompts and the fully connected operation layer to enhance the model’s capacity to learn information after expanding to an additional modality. This approach requires updating only 2.8% of the parameters in the model during training, thereby saving computational resources. Extensive experiments conducted on widely tested datasets RGBT234 and LasHeR demonstrate the effectiveness of the proposed method. Overall, our approach better integrates RGB and infrared images and introduces prompt learning to address the issue of magnitude imbalance in the data, providing a promising solution to the challenges in RGBT object tracking.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29310-29324"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Channel Information Exchange-Induced Spatiotemporal Graph Convolutional Network 信道信息交换诱导的时空图卷积网络

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3582952

Yuan Xu;Fan Qin;Yi Luo;Wei Ke;Qun-Xiong Zhu;Yan-Lin He;Yang Zhang;Ming-Qing Zhang

{"title":"Channel Information Exchange-Induced Spatiotemporal Graph Convolutional Network","authors":"Yuan Xu;Fan Qin;Yi Luo;Wei Ke;Qun-Xiong Zhu;Yan-Lin He;Yang Zhang;Ming-Qing Zhang","doi":"10.1109/JSEN.2025.3582952","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3582952","url":null,"abstract":"Accurately predicting traffic flow is a highly crucial task essential for providing functional services to urban road networks. Urban traffic is a complex and constantly evolving system, influenced not only by factors within individual regions but also by interactions among different regions within the entire city network. The majority of current traffic flow prediction methods rely on static geographic information, thus ignoring the cross-regional flow of traffic within cities. To tackle this issue, this article proposes a channel information exchange-induced spatiotemporal graph convolutional network (CIE-STGCN). This network constructs both a static adjacency matrix constructed from geographic information and a dynamic adjacency matrix based on adaptive parameter learning for nodes. The static and dynamic STGCNs operate on separate channels to extract features. Additionally, a channel information exchange module based on channel attention and gating mechanisms is designed to achieve global complementarity of static and dynamic features in traffic flow data. Validation using multiple real-world traffic flow datasets demonstrates the efficacy of the proposed model in reliably predicting traffic flow.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29262-29270"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HARP: 3-DoF High-Update-Rate Acoustic Rigid-Body Positioning HARP：三自由度高更新率声学刚体定位

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583052

Yuzhang Xi;Weimeng Cui;Guangyao Liu;Zhi Wang

{"title":"HARP: 3-DoF High-Update-Rate Acoustic Rigid-Body Positioning","authors":"Yuzhang Xi;Weimeng Cui;Guangyao Liu;Zhi Wang","doi":"10.1109/JSEN.2025.3583052","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583052","url":null,"abstract":"Acoustic indoor positioning has been widely studied due to its advantages of low cost, easy deployment, and high accuracy. However, the limitation of insufficient update rates remains a challenge for its application in mobile target localization and navigation. To tackle the obstacles, this article presents high-update-rate acoustic rigid-body positioning (HARP). HARP employs a combined frequency-division multiple access (FDMA) and code-division multiple access (CDMA) signal scheme, achieving stable signal detection at 50 Hz based on an optimal pairing initialization process and a constraint-enhanced cross correlation (CE-CC) detection algorithm. Furthermore, HARP is capable of achieving three-degree-of-freedom (3-DoF) estimation, including planar position and target heading, using only two anchors. HARP updates the initial position (IP) using three-point collinearity rigid-body localization (TPC-RBL) and converts time difference of arrival (TDOA) into time-of-arrival (TOA) observations via continuous distance increment (CDI), enabling localization without synchronization between anchors and sensors. Real-world experiments validate the system’s signal detection capability and localization accuracy. HARP achieves a 50-Hz update rate, significantly surpassing existing acoustic systems, while attaining an average localization accuracy of 38.8 mm and an angular accuracy of 6.42° within a room-scale area. This system provides a promising solution for low-cost indoor rigid-body localization (RBL) of mobile targets.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29238-29251"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Highly Uniform Field Three-Axis Coils Using Modified Grey-Wolf Optimizer for Atomic Magnetometers 采用改进灰狼优化器的原子磁强计高均匀场三轴线圈

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583159

Pengcheng Du;Pengfei Cheng;Feifan Yang;Shuaiwei Cui;Jin Li

{"title":"Highly Uniform Field Three-Axis Coils Using Modified Grey-Wolf Optimizer for Atomic Magnetometers","authors":"Pengcheng Du;Pengfei Cheng;Feifan Yang;Shuaiwei Cui;Jin Li","doi":"10.1109/JSEN.2025.3583159","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583159","url":null,"abstract":"Uniform magnetic field coils are an important part of high-precision atomic magnetometers. Such as, in vector atomic magnetometers (need three-axis uniform field coils). At present, the most widely used coils in atomic magnetometers are Helmholtz coils, which have insufficient magnetic field uniformity. With the miniaturization of high-precision atomic sensors, the conventional coils are inadequate to satisfy the magnetic field uniformity requirements when magnetometers reduce their overall volume. Modified-grey wolf optimizer/multiple objective grey wolf optimizer (MOD-GWO/MOGWO) modified is used for the first time in three-axis uniform field coil design in this article. Compared with the Helmholtz coil, the uniformity of the MOD-GWO/MOGWO coils is improved by one-three orders of magnitude along the axis [<inline-formula> <tex-math>$- 1{R}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$1{R}$ </tex-math></inline-formula>]. In the core region, MOD-GWO/MOGWO coils also improve the uniformity compared to Helmholtz coils and other conventional coils. The areas where the relative uniformity of the axial coils and radial coils is below 3%, obtained by actual measurement, account for approximately 100% and 82.44% along the axis [<inline-formula> <tex-math>$- 1{R}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$1{R}$ </tex-math></inline-formula>], respectively. The method this article proposed (MOD-GWO combined with Taylor expansion method) balances the difficulty of mathematical derivation and the algorithm time consumption, space consumption, and modifies the initialization mechanism and convergence mechanism of the original GWO/MOGWO.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28252-28264"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis and Experimental Validation of SSTDR for Simultaneous Distributed Diagnosis of Wire Networks 面向有线网络同时分布式诊断的SSTDR分析与实验验证

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583307

Mouad Addad;Ali Djebbari;Evan Benoit;Cynthia M. Furse

引用次数: 0

Robust Factor Graph Optimization for GNSS/INS Tightly Coupled Integration: A Flexible Strategy for Urban Navigation Resilience GNSS/INS紧密耦合集成鲁棒因子图优化：城市导航弹性的灵活策略

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583479

Kun Wang;Lei Wu;Yueyang Ben;Qian Li;Chen Lv;Liang Hou

{"title":"Robust Factor Graph Optimization for GNSS/INS Tightly Coupled Integration: A Flexible Strategy for Urban Navigation Resilience","authors":"Kun Wang;Lei Wu;Yueyang Ben;Qian Li;Chen Lv;Liang Hou","doi":"10.1109/JSEN.2025.3583479","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583479","url":null,"abstract":"Intelligent transportation systems, including autonomous vehicles, require precise and reliable navigation and positioning systems to ensure safety. However, Global Navigation Satellite System (GNSS) observations are degraded by signal interference, such as obstructions and reflections from high-rise buildings. Navigation methods struggle to effectively process GNSS outliers caused by multipath effects and non-line-of-sight (NLOS) reception in urban environments under changing scenarios. To address this issue, this study proposes a robust factor graph optimization (FGO) approach based on a flexible strategy. Initially, a quality evaluation method utilizing the Mahalanobis distance of pseudorange residual test statistics is developed, enabling smooth and dynamic adjustment of the measurement fusion weights for each satellite. Furthermore, by integrating both satellite quantity and quality data, a flexible strategy is proposed that fully accounts for the differences in measurement quality and the reference values of each satellite across different scenarios. This integration with the navigation environment improves the robustness of the tightly coupled navigation system. Three vehicle-mounted experiments in urban canyon environments were conducted, and the results indicate that, compared to conventional methods, the root-mean-square errors (RMSEs) of horizontal positioning were reduced by 13.24%, 26.06%, and 33.92%, respectively. The proposed method effectively mitigates the impact of GNSS outliers and enhances the reliability of GNSS-based navigation systems for applications such as autonomous vehicles, uncrewed aerial vehicle (UAV), and urban transportation systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29296-29309"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0