IEEE Sensors Journal最新文献_第9页

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-31 DOI: 10.1109/JSEN.2024.3482915

引用次数: 0

Multiday Personal Identification and Authentication Using Electromyogram Signals and Bag-of-Words Classification Models

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-31 DOI: 10.1109/JSEN.2024.3485244

Irina Pavel;Iulian B. Ciocoiu

{"title":"Multiday Personal Identification and Authentication Using Electromyogram Signals and Bag-of-Words Classification Models","authors":"Irina Pavel;Iulian B. Ciocoiu","doi":"10.1109/JSEN.2024.3485244","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3485244","url":null,"abstract":"This article evaluates the performances of bag-of-words (BoWs) classification models on biometric applications using surface electromyographic (sEMG) signals generated by hand gestures. Extensive tests have been conducted on a publicly available multichannel multisession dataset collected from electrodes placed on the forearm and wrist of 43 persons while performing a set of 16 distinct gestures. FFT-based features extracted from six nonoverlapping frequency bands were combined with a BoW classifier and evaluated on authentication and identification tasks. A systematic ablation study considers the influence of the encoding strategy, the codebook dimension, and the length of the gesture-based password on the performances assessed in terms of the area under curve (AUC), equal error rate (EER), and cumulative match characteristics (CMCs). The definition of the training and test sets considered both within-day (WD) and cross-day scenarios. In the former case, average AUC and EER values indicate almost perfect operation for a password defined by three successive gestures, while CMC analysis showed Rank-5 performances above 99%. In the latter case, average AUC, EER, and Rank-5 CMC exhibited a small degradation of 1.1%, 3.1%, and 3.2%, respectively, showing significant robustness and improved performances against existing solutions.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42373-42383"},"PeriodicalIF":4.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driver Gaze Zone Estimation Based on Three-Channel Convolution-Optimized Vision Transformer With Transfer Learning

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-31 DOI: 10.1109/JSEN.2024.3486373

Zhao Li;Siyang Jiang;Rui Fu;Yingshi Guo;Chang Wang

{"title":"Driver Gaze Zone Estimation Based on Three-Channel Convolution-Optimized Vision Transformer With Transfer Learning","authors":"Zhao Li;Siyang Jiang;Rui Fu;Yingshi Guo;Chang Wang","doi":"10.1109/JSEN.2024.3486373","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3486373","url":null,"abstract":"Driver gaze zone estimation (DGZE) is essential for detecting the driver’s state and taking over rule-making in intelligent driving systems. However, convolutional neural network (CNN)-based multichannel models lack global feature extraction capability, with a large number of parameters and high computational complexity. Therefore, this article proposes a novel method that uses a three-channel convolution-optimized vision transformer (3C-CoViT) to estimate the driver’s gaze zone. The method replaces the linear projection in the pure ViT structure with convolutional projection, converts the input images of different channels into image sequences, and then adds a convolutional feed-forward network to extract the local features of the markers, enhance the correlation of adjacent tokens in spatial dimensions, and improve the performance and efficiency of the model. We then pretrained the model on the GazeCapture dataset based on transfer learning and then fine-tuned the model on the dataset built in the actual road experiment. To enhance the interpretability of the model, we presented a novel visualization method. Experimental results show that the proposed method can accurately identify driver gaze zones (98.04% average accuracy) and outperform state-of-the-art methods in terms of accuracy and reliability. Ablation studies proved the effectiveness of our proposed method over the pure ViT and the beneficial effects of transfer learning and three-channel information input.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42064-42078"},"PeriodicalIF":4.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844527","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

A Cross-Modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-Modulated Continuous Wave Radar

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3486154

Kai-Chun Liu;Sheng-Yu Peng;Yu Tsao;Che-Yu Liu;Zhu-An Chen;Zong Han Han;Wen-Chi Chen;Po-Quan Hsieh;You-Jin Li;Yu-Juei Hsu;Shun-Neng Hsu

{"title":"A Cross-Modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-Modulated Continuous Wave Radar","authors":"Kai-Chun Liu;Sheng-Yu Peng;Yu Tsao;Che-Yu Liu;Zhu-An Chen;Zong Han Han;Wen-Chi Chen;Po-Quan Hsieh;You-Jin Li;Yu-Juei Hsu;Shun-Neng Hsu","doi":"10.1109/JSEN.2024.3486154","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3486154","url":null,"abstract":"While traditional electrocardiogram (ECG) monitoring provides vital clinical information, its electrode-based setup restricts patient movement. To address this limitation, contactless ECG monitoring using frequency-modulated continuous-wave (FMCW) radar and deep learning has been developed. However, such approaches face challenges owing to the limited availability of training data and inherent discrepancies between radar and ECG signals. This article introduces a novel approach to transforming high-fidelity ECG signals from millimeter-wave (mmWave) radar signals reflecting cardiac mechanical activity. The proposed method uses a cascade framework with a cross-modal autoencoder trained using joint waveforms, spectrograms, and deep feature losses. This strategy enables the model to leverage a pretrained ECG-to-ECG autoencoder and a cardiac event (CE) predictor for learning general ECG representations while simultaneously capturing time- and frequency-domain features from limited data. We evaluated the effectiveness of the proposed autoencoder model in terms of signal quality and CE integrity using ablation studies on data from 20 healthy participants. The model achieved high transformation accuracy with a cross correlation of 0.914 and average timing errors below 31 ms for critical ECG features. These findings demonstrate the feasibility and effectiveness of the proposed FMCW radar-based contactless ECG monitoring method, particularly in overcoming the limitations imposed by small datasets and domain discrepancies.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41462-41473"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10739965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Remaining Useful Life Prognostic Framework Combining Sample Convolutional Interaction Network and Fractal Brownian Motion

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3485750

Shuai Lv;Shujie Liu;Hongkun Li;Siyuan Chen;Xuejun Liu

{"title":"A Novel Remaining Useful Life Prognostic Framework Combining Sample Convolutional Interaction Network and Fractal Brownian Motion","authors":"Shuai Lv;Shujie Liu;Hongkun Li;Siyuan Chen;Xuejun Liu","doi":"10.1109/JSEN.2024.3485750","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3485750","url":null,"abstract":"Power MOSFETs play a crucial role in power electronic systems, and accurately predicting their remaining useful life (RUL) is fundamentally important for enhancing the reliability, safety, and maintenance planning of such systems. To this end, this article develops an innovative prognostic framework for predicting the RUL of MOSFET devices. First, a power cycle accelerated aging experimental platform under constant shell temperature fluctuation is constructed to obtain the performance degradation parameters of MOSFETs. Second, a sample convolutional interaction network (SCINet) is applied to historical data, learning long-term degradation trends via multistep prediction. Subsequently, a nonlinear fractional Brownian motion (FBM) degradation model is constructed incorporating measurement uncertainties. A state-parameter joint estimation method is then developed by combining a state-space model (SSM) with Kalman filtering, particle filtering and maximum likelihood estimation (MLE). The proposed framework fuses both SCINet predictions and historical observations for self-adaptive updating of states and parameters. A Monte Carlo (MC) simulation scheme, combined with a degradation state recursive strategy, derives the RUL and probability distribution function. Validation of real MOSFET degradation data and performance comparisons against multiple advanced methods demonstrate the efficacy and superiority of this novel prognostic framework. This research meaningfully contributes to more accurate reliability evaluation and improved maintenance planning for MOSFET devices.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41378-41389"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859178","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

A Novel Dual-Branch Transformer With Gated Cross Attention for Remaining Useful Life Prediction of Bearings

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3485918

Jin Cui;J. C. Ji;Tianxiao Zhang;Licai Cao;Zixu Chen;Qing Ni

{"title":"A Novel Dual-Branch Transformer With Gated Cross Attention for Remaining Useful Life Prediction of Bearings","authors":"Jin Cui;J. C. Ji;Tianxiao Zhang;Licai Cao;Zixu Chen;Qing Ni","doi":"10.1109/JSEN.2024.3485918","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3485918","url":null,"abstract":"Features from different domains in vibration signals offer valuable insights for remaining useful life (RUL) prediction of bearings. While fusing these features can improve the prediction performance, traditional fusion methods lack effective information exchange across domains, limiting adaptive feature fusion. This limitation can lead to the information redundancy and hinder the accurate identification of bearing degradation states. To address these challenges, this study introduces a dual-branch Transformer with gated cross attention (DTGCA), designed to handle and integrate features from different domains for precise RUL prediction. Specifically, one branch processes 1-D time-series feature from the time and frequency domains, while the other branch uses a residual convolutional gated recurrent unit (res-ConvGRU) to handle 2-D time-frequency image features. The proposed gated cross-attention (GCA) mechanism enables adaptive information exchange between the branches, effectively fusing their information to provide a clearer representation of bearing degradation states. The proposed method is validated on the two real run-to-failure datasets. Comprehensive ablation experiments confirm the method’s underlying rationality, while the detailed comparative experiments with other approaches clearly demonstrate its superiority.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41410-41423"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859212","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

Analytical Solution for Positioning Based on Iridium NEXT SOPs TOA/FDOA

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3486099

Zhenbo Xu;Honglei Qin;Yansong Du

{"title":"Analytical Solution for Positioning Based on Iridium NEXT SOPs TOA/FDOA","authors":"Zhenbo Xu;Honglei Qin;Yansong Du","doi":"10.1109/JSEN.2024.3486099","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3486099","url":null,"abstract":"The low Earth orbit (LEO) satellite signals of opportunity (SOPs), with their strong antijamming capabilities, can fulfill the positioning requirements of users in global navigation satellite system (GNSS)-denied environments. Traditional LEO satellite SOPs positioning methods typically employ numerical techniques to solve nonlinear equations. However, such methods are sensitive to initial conditions, and under the circumstances of significant initial errors, the positioning results may converge slowly or even diverge. In this article, a two-step weighted least squares (TSWLSs) analytical solution method is proposed based on Iridium NEXT SOPs. This method utilizes Iridium NEXT satellite pseudorange and pseudorange-rate measurements, eliminating the need for prior knowledge about the receiver’s position and directly estimating the receiver’s position. Theoretical derivations and simulation results demonstrate that the proposed method, under the assumption of Gaussian measurement noise, achieves the Cramér-Rao lower bound (CRLB) based on the pseudorange/pseudorange-rate positioning model. A practical evaluation is conducted by comparing the traditional Iridium NEXT pseudorange-rate single-point positioning method with the proposed method. Experimental results indicate that the proposed method reduces convergence time by 78.7% and improves positioning accuracy by 34.1%, while also eliminating the need for initial position information.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41451-41461"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859116","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

Efficient Homomorphic Encryption for Multikey Compressed Sensing in Lightweight Cloud-Based Image Processing

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3485669

Yuning Qi;Jingguo Bi;Haipeng Peng;Lixiang Li

{"title":"Efficient Homomorphic Encryption for Multikey Compressed Sensing in Lightweight Cloud-Based Image Processing","authors":"Yuning Qi;Jingguo Bi;Haipeng Peng;Lixiang Li","doi":"10.1109/JSEN.2024.3485669","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3485669","url":null,"abstract":"With the rapid development of cloud storage and privacy computing technologies, users with limited resources are increasingly relying on cloud servers for the storage and computation of their image data. This approach ensures data security and convenient access. However, current image data security-sharing schemes that support privacy computing often face issues such as high bandwidth consumption and significant ciphertext expansion, limiting their applicability. To address these challenges, we propose an innovative multikey compressed sensing lightweight encryption scheme (MCSLE), based on compressed sensing (CS) technology. This scheme is the first to design a multikey conversion algorithm for CS. It allows each sampling end to independently compress the sampled images using different keys. The cloud platform then completes the key conversion and unification. It provides flexible compression sampling capabilities, a robust privacy protection mechanism, and comprehensive support for homomorphic computing. Furthermore, we have designed a specialized image reconstruction algorithm for this scheme. It has undergone in-depth testing in various practical application scenarios, including medical image analysis, fire monitoring, and handwritten text recognition. The experimental results demonstrate that, unlike existing schemes with several tens of times ciphertext expansion, MCSLE can support homomorphic computation at a compression rate of 0.5 while maintaining high-quality image reconstruction.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41365-41377"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859174","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

MLANet: A Robust Ship Segmentation Network Based on Multilevel Multiattention Feature Fusion for Complex Maritime Background Environments

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3485967

Zhongzheng Li;Hairong Dong;Liye Zhang;Xiaoyu Sun;Dong Kong

{"title":"MLANet: A Robust Ship Segmentation Network Based on Multilevel Multiattention Feature Fusion for Complex Maritime Background Environments","authors":"Zhongzheng Li;Hairong Dong;Liye Zhang;Xiaoyu Sun;Dong Kong","doi":"10.1109/JSEN.2024.3485967","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3485967","url":null,"abstract":"Synthetic aperture radar (SAR) is a powerful sensor for long-range, all-weather, and large-scale surveillance, making SAR-based ship semantic segmentation a research hotspot. However, accurate segmentation ships, especially small vessels, in near-port waters remain a challenge due to complex background interference in SAR images. Current methods often struggle to extract sufficient features for small ships, leading to high missed detection rates. Furthermore, the complex oceanic background increases false detection rates. To address these issues, we propose MLANet, a multilevel feature-enhanced, multiattention fusion network specifically designed for ship segmentation in SAR images. MLANet leverages the strengths of both convolutional neural network (CNN) and Transformer to perform efficient multiscale feature extraction. The feature enhancement module (FEM) refines global and local features, retaining critical information for small ships, while the attention fusion module reduces background interference. Additionally, a hybrid loss function emphasizes both the shape and boundary of vessels during segmentation. The experimental results show that MLANet achieves 94.83% mean pixel accuracy (mPA) and 90.66% mean intersection over union (mIoU) on the SAR ship detection dataset (SSDD), and 91.74% mPA and 87.72% mIoU on the high-resolution SAR image dataset (HRSID), demonstrating its strong competitiveness and effectiveness in challenging environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42404-42416"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844364","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

HLoc: Exploiting Height Difference for WiFi Indoor Localization With Single Commercial AP

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-10-30 DOI: 10.1109/JSEN.2024.3486008

Shuai Yang;Dongheng Zhang;Guanzhong Wang;Jinbo Chen;Zhi Lu;Qibin Sun;Yan Chen

{"title":"HLoc: Exploiting Height Difference for WiFi Indoor Localization With Single Commercial AP","authors":"Shuai Yang;Dongheng Zhang;Guanzhong Wang;Jinbo Chen;Zhi Lu;Qibin Sun;Yan Chen","doi":"10.1109/JSEN.2024.3486008","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3486008","url":null,"abstract":"WiFi indoor localization, as a fundamental task to many real-world applications, has attracted widespread attention from both academia and industry over the past decade. While existing works have already achieved decimeter-level accuracy, they either require multiple access points (APs) or rely on the assumption that the AP and the client are at the same height, and their performance will degrade dramatically when there exists a height difference. In this article, we explore the neglected elevation angle dimension and propose HLoc, the first elevation angle-based WiFi indoor localization system, which can achieve decimeter-level accuracy with a single commercial AP. Inspired by existing 4-D mmwave radar, HLoc transforms the horizontal distance between the client and the AP into an elevation angle estimation problem and efficiently resolves it through a modified sparse recovery algorithm. Moreover, existing commercial APs are usually equipped with nonuniform planar arrays to enhance communication performance, which brings us the opportunity to jointly estimate the azimuth and elevation angles. After obtaining the horizontal distance, the azimuth angle is used to determine the orientation, and thus a single AP can achieve localization. The impact of the client height error on system localization performance is also analyzed theoretically and experimentally. We evaluate HLoc under a variety of complex environments, and the experimental results show that HLoc can achieve median errors of 9.4° and 13.6° for azimuth and elevation angle estimation, respectively, and a 98-cm median localization error with only a single commercial AP. We believe that this additional height information can be combined with other existing systems and inspire researchers to further push indoor localization from laboratory to the wild.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41424-41436"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859111","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