IEEE Sensors Letters最新文献_第6页

LG-STSGCN: Long-Term Gated Pedestrian Trajectory Prediction Based on Spatial–Temporal Synchronous Graph Convolutional Network

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-21 DOI: 10.1109/LSENS.2025.3541437

Yang Chen;Juwei Guo;Ke Wang;Dongfang Yang;Xu Yan;Lihong Qiu

{"title":"LG-STSGCN: Long-Term Gated Pedestrian Trajectory Prediction Based on Spatial–Temporal Synchronous Graph Convolutional Network","authors":"Yang Chen;Juwei Guo;Ke Wang;Dongfang Yang;Xu Yan;Lihong Qiu","doi":"10.1109/LSENS.2025.3541437","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3541437","url":null,"abstract":"Pedestrian trajectory prediction is fundamental research in many practical applications, such as video surveillance, autonomous vehicles, and robotic systems. However, the existing methods do not capture the spatial–temporal correlation of pedestrians well and simultaneously, as well as do not learn the temporal global interaction features of pedestrians effectively. To address these issues, we propose a long-term gated pedestrian trajectory prediction model based on spatial–temporal synchronous graph convolutional network. The proposed method consists of three components. First, we construct a localized spatial–temporal graph to characterize the temporal information, spatial information and spatial–temporal correlation information among pedestrians in the pedestrian trajectory prediction fully. Then, we introduce a gated mechanism into the temporal convolutional network, in parallel with the gated spatial–temporal synchronous graph convolutional network, in order to improve the model's ability to capture the global correlation of spatial–temporal data. Finally, we add random noise and use a diversity loss function to train and predict trajectories. We conduct experiments on ETH and UCY datasets and the proposed method is proved to outperform previous approaches.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Wearable Headband With Flexible Hybrid Electronics for Head-Kinematic Monitoring and Mild Traumatic Brain Injury Risk Detection

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-20 DOI: 10.1109/LSENS.2025.3544119

Jeneel Kachhadiya;Jaden Romero;Shuting Kou;Yang Wan;Haneesh Kesari;Ron Szalkowski;Joseph Andrews

{"title":"Lightweight Wearable Headband With Flexible Hybrid Electronics for Head-Kinematic Monitoring and Mild Traumatic Brain Injury Risk Detection","authors":"Jeneel Kachhadiya;Jaden Romero;Shuting Kou;Yang Wan;Haneesh Kesari;Ron Szalkowski;Joseph Andrews","doi":"10.1109/LSENS.2025.3544119","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3544119","url":null,"abstract":"Mild traumatic brain injuries are a significant health risk in sports and military environments, often caused by high-impact forces. This letter presents a flexible hybrid headband system for real-time monitoring of head kinematics during impacts of varying magnitude and direction. It integrates eight triaxial accelerometers in a near-regular tetrahedral configuration and employs an acceleration-only algorithm to measure linear accelerations without gyroscopes. Firmware uses a parallel queue system for efficient real-time data collection at 1600-Hz bandwidth. Testing on a Hybrid III head form via the dummy for rotational evaluation of wearable system evaluated five impact magnitudes and directions (front, rear, and left). CORrelation and Analysis (CORA) validated system accuracy, with average CORA scores of 0.840 (rear), 0.883 (front), and 0.832 (left). Some individual impacts achieved scores up to 0.98. Repeatability tests showed minimal variation, confirming consistent performance. These results demonstrate the system's potential for real-time, reliable head-kinematic monitoring in military helmets and high-impact sports.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Sensors Letters Subject Categories for Article Numbering Information

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-18 DOI: 10.1109/LSENS.2025.3541477

引用次数: 0

Applications for the EM-Based Classifier in Radar Sensor Network 基于电磁的分类器在雷达传感器网络中的应用

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-18 DOI: 10.1109/LSENS.2025.3540732

Linjie Yan;Mohammed Jahangir;Michail Antoniou;Chengpeng Hao;Carmine Clemente;Danilo Orlando

引用次数: 0

IEEE Sensors Letters Publication Information

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-18 DOI: 10.1109/LSENS.2025.3541413

引用次数: 0

Advanced Seizure Detection Framework Using Stacked Convolutional Restricted Boltzmann Machine (SCRBM)

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-17 DOI: 10.1109/LSENS.2025.3543141

Vaddi Venkata Narayana;Prakash Kodali

引用次数: 0

M2S2: A Multimodal Sensor System for Remote Animal Motion Capture in the Wild

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-14 DOI: 10.1109/LSENS.2025.3542233

Azraa Vally;Gerald Maswoswere;Nicholas Bowden;Stephen Paine;Paul Amayo;Andrew Markham;Amir Patel

引用次数: 0

Underwater Acoustic Target Classification Using Auditory Fusion Features and Efficient Convolutional Attention Network

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-13 DOI: 10.1109/LSENS.2025.3541593

Junjie Yang;Zhenyu Zhang;Wei Li;Xiang Wang;Senquan Yang;Chao Yang

{"title":"Underwater Acoustic Target Classification Using Auditory Fusion Features and Efficient Convolutional Attention Network","authors":"Junjie Yang;Zhenyu Zhang;Wei Li;Xiang Wang;Senquan Yang;Chao Yang","doi":"10.1109/LSENS.2025.3541593","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3541593","url":null,"abstract":"Underwater acoustic target classification (UATC) aims to identify the type of unknown acoustic sources using passive sonar in oceanic remote sensing scenarios. However, the variability of underwater acoustic environment and the presence of complex background noise pose significant challenges to enhancing the accuracy of UATC. To address these challenges, we develop an innovative deep neural network algorithm integrated by multiscale feature extractor and efficient channel attention mechanism. The proposed algorithm leverages tailored representations and deep learning to enhance adaptability and reliability of UATC performance. We employ auditory cofeatures, such as Mel-frequency cepstral coefficients and Gammatone frequency cepstral coefficients, combined with their first-order and second-order differentials, to capture the dynamic variations and contextual information of underwater acoustic signals in time-frequency domain. In addition, we integrate multiscale convolution with an efficient channel attention mechanism to share and exploit the interrelationship of auditory cofeatures. Experimental validations using ShipsEar and Deepship datasets, along with various noise types, have demonstrated the effectiveness of our algorithm in comparison to state-of-the-art methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High Responsivity Analysis of 4H-SiC Phototransistor

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-13 DOI: 10.1109/LSENS.2025.3536037

Danyang Huang;Xiaolong Zhao;Shuwen Guo;Xianghe Fu;Peiwen Cui;Sien Ye;Zixia Yu;Yongning He

引用次数: 0

Higher Order Spectra-Based Ensemble Learning Approach for Cuffless Blood Pressure Estimation

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-13 DOI: 10.1109/LSENS.2025.3541397

Vinit Kumar;Kishor Sarawadekar;Priya Ranjan Muduli

{"title":"Higher Order Spectra-Based Ensemble Learning Approach for Cuffless Blood Pressure Estimation","authors":"Vinit Kumar;Kishor Sarawadekar;Priya Ranjan Muduli","doi":"10.1109/LSENS.2025.3541397","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3541397","url":null,"abstract":"Cuffless blood pressure (BP) estimation has emerged as an alternative and effective technique to mitigate the limitations of conventional sphygmomanometers for prolonged BP monitoring. Cuffless BP can be estimated from cardiovascular measurements, including photoplethysmogram and electrocardiogram signals. Several machine learning-based BP estimation methods are available in the literature. However, the effectiveness of higher order spectral features, such as bispectrum, bicoherence, and trispectrum, for BP estimation has never been explored. This letter proposes efficient ensemble learning-based approaches for cuffless BP estimation utilizing the higher order spectrum of the cardio signals. The extracted higher order spectral features are incorporated in ensemble learning-based extra trees and categorical boosting models. These methods incorporate multiple weak learners to produce the desired estimates. The novel features capture the nonlinear interactions and phase coupling between different frequency components. The proposed techniques are validated using various international standards for cuffless BP estimation tasks. The experimental results demonstrate that the proposed methods outperform state-of-the-art techniques. Furthermore, the proposed machine learning models are executed on the Xilinx PYNQ-Z2 board to verify the hardware compatibility.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0