IEEE Sensors Letters最新文献_第4页

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

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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

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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

Design of Autoencoder Algorithm for Compression of Lightweight EEG Signals Based on 2-D Rhythm Feature Maps

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

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

Hybrid Sensor System for Integrated Structural Motion Measurement

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-10 DOI: 10.1109/LSENS.2025.3540358

Jörg F. Wagner;Sebastian Wille;Carl C. Rheinländer;Michael Kohl;Benedikt Györfi

{"title":"Hybrid Sensor System for Integrated Structural Motion Measurement","authors":"Jörg F. Wagner;Sebastian Wille;Carl C. Rheinländer;Michael Kohl;Benedikt Györfi","doi":"10.1109/LSENS.2025.3540358","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3540358","url":null,"abstract":"Over the last three decades, gyroscopes have become increasingly miniaturized and have enabled the design of very small, light, and cost-effective inertial measurement units (IMUs). This allows new applications of inertial sensors. The motion measurement or motion capture of structures which, in contrast to classic inertial navigation, are no longer single rigid bodies but can show a variable shape is such a new application. However, this approach is only realizable if such a “flexible” structure is equipped with several spatially distributed and closely networked IMUs. At the same time, this measurement approach, similar to classic inertial navigation, requires the use of additional, complementary aiding sensors, which must also be distributed across the structure. Such systems or networks, which consist of inertial and structural sensors in parallel, are novel and not state of the art. This letter, therefore, presents a modular, experimental sensor system of this type designed as a demonstrator for basic research purposes, describes the specific challenges in designing the system, and reports on initial practical experiences in using the system on a test rig for the motion measurement of a flexible beam.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512936","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