IEEE Sensors Letters最新文献_第9页

IEEE Sensors Letters Subject Categories for Article Numbering Information 用于物品编号信息的IEEE传感器字母主题分类

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 IEEE传感器通讯出版信息

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) 基于堆叠卷积受限玻尔兹曼机（SCRBM）的高级癫痫检测框架

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 M2S2：一种用于野外动物远程运动捕捉的多模态传感器系统

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 基于听觉融合特征和高效卷积注意网络的水声目标分类

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 4H-SiC光电晶体管的高响应性分析

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 基于高阶谱的无袖带血压估计集成学习方法

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

Design of Autoencoder Algorithm for Compression of Lightweight EEG Signals Based on 2-D Rhythm Feature Maps 基于二维节律特征映射的轻量脑电信号自编码器压缩算法设计

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-13 DOI: 10.1109/LSENS.2025.3541231

Peijun Ma;Cong Yao;Jiangyi Shi;Gongzhi Zhao;Mingyu Ma

{"title":"Design of Autoencoder Algorithm for Compression of Lightweight EEG Signals Based on 2-D Rhythm Feature Maps","authors":"Peijun Ma;Cong Yao;Jiangyi Shi;Gongzhi Zhao;Mingyu Ma","doi":"10.1109/LSENS.2025.3541231","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3541231","url":null,"abstract":"In recent years, with the development of brain science, the value of electroencephalography (EEG) data has become prominent. However, due to the characteristics of real-time transmission and large amounts of data, there is an urgent need for efficient and lightweight EEG compression algorithms. The existing EEG compression methods have many shortcomings, such as limited compression ratio (CR), poor reconstruction signal quality, and too large model scale, which cannot meet the developmental needs of portable wearable EEG detection devices. In this letter, a method of EEG signal compression based on 2-D rhythm feature maps is proposed. Through discrete wavelet transformation (DWT) extraction of the signal rhythm characteristics, the signal is compressed and reconstructed using encoding and reconstruction channels based on an autoencoder network. At the output end of the encoding channel, entropy coding is carried out to further compress the data volume. Through the discussion of several coding algorithms, JPEG2000 is selected as the local optimal coding algorithm. In addition, based on the idea of grouping convolution and void convolution kernel, a lightweight structure is designed to simplify the process of the proposed network and greatly reduce the number of model parameters. Experiments show that, compared with other similar algorithms, the percentage-root-mean-square distortion and mean squared error (MSE) of the proposed algorithm are 14.76% and 2.95%, respectively, at a relatively high CR (CR is about 16). And only 87.9-k parameters are used, which is more suitable for embedded scenarios and wearable devices.","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":"143564223","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

Fiber Bragg Grating-Based Sensor System for Strain and Angle Assessment in Passive Orthosis 基于光纤Bragg光栅的被动矫形器应变和角度传感器系统

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-13 DOI: 10.1109/LSENS.2025.3541344

João Coimbra;Pedro Lorenzutti;Arnaldo Leal-Junior

{"title":"Fiber Bragg Grating-Based Sensor System for Strain and Angle Assessment in Passive Orthosis","authors":"João Coimbra;Pedro Lorenzutti;Arnaldo Leal-Junior","doi":"10.1109/LSENS.2025.3541344","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3541344","url":null,"abstract":"In this letter, we present the development and application of a fiber Bragg grating (FBG) sensor system for the instrumentation of a lower limb passive orthosis for gait assistance. The sensors include FBG strain sensors attached to the orthosis structure for monitoring the strains in the structure during gait. In addition, another FBG is used as angle sensor positioned on the user lumbar for angle monitoring of the trunk in the frontal plane. The sensors were characterized as a function of strain and angle resulting in root-mean-squared errors of 6.15<inline-formula><tex-math>$;mu epsilon$</tex-math></inline-formula> and 0.30 ° for strain and angle, respectively. Then, in the application tests, the strain sensor demonstrate its feasibility by means of strain estimation within the range of 10–200<inline-formula><tex-math>$;mu epsilon$</tex-math></inline-formula> as well as the periodic strain pattern following the ground reaction force variation during the stance phase of the gait. Furthermore, the angle measurement during the wearable gait tests indicated a measurement range of −15 ° to 30 <inline-formula><tex-math>$^{circ }$</tex-math></inline-formula> with an estimated linear velocity of 4.0 km/h, which is the reference one used on the treadmill. Therefore, the proposed sensor system is an integrated solution for in-situ gait analysis, which can extent the functionalities not only of the passive orthosis, but also in active rehabilitation robots.","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":"143535526","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