发布求助
文献互助 智能选刊 最新文献

IEEE Sensors Letters最新文献

筛选
英文 中文
Blind Extraction-Based Multichannel Speech Enhancement in Noisy and Reverberation Environments
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-24 DOI: 10.1109/LSENS.2025.3533642
Yuan Xie;Tao Zou;Weijun Sun;Shengli Xie
{"title":"Blind Extraction-Based Multichannel Speech Enhancement in Noisy and Reverberation Environments","authors":"Yuan Xie;Tao Zou;Weijun Sun;Shengli Xie","doi":"10.1109/LSENS.2025.3533642","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3533642","url":null,"abstract":"Speech enhancement has important applications in sensor, hearing aids, robotics, and video conferencing. However, the speech enhancement performance is severely deteriorated by additional background noise and high reverberations. To solve the problem of speech enhancement in noisy and acoustically reverberant scenarios, this letter proposes a multichannel speech enhancement algorithm based on blind extraction to achieve speech denoising and dereverberation. First, a new model for speech enhancement is constructed by assuming the reverberations generated by later reflections as additional and unrelated noise components. Subsequently, a blind signal extraction approach is designed to extract the direct sound and early reflected sounds, achieving dereverberation and denoising. Experimental results confirm that the proposed algorithm achieves better speech enhancement in noisy and acoustic reverberation scenarios and that the effect of dereverberation and noise reduction is superior to that of popular speech enhancement algorithms, especially in high reverberation environments.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521455","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
An Efficient Linearizing Demodulator Interface for LVDT
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-23 DOI: 10.1109/LSENS.2025.3533036
Bhavesh Raj Singh Nehra;Devika S Kumar;Anoop Chandrika Sreekantan
{"title":"An Efficient Linearizing Demodulator Interface for LVDT","authors":"Bhavesh Raj Singh Nehra;Devika S Kumar;Anoop Chandrika Sreekantan","doi":"10.1109/LSENS.2025.3533036","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3533036","url":null,"abstract":"In this letter, we introduce an efficient linearizing demodulator circuit for linear variable differential transformers (LVDTs). A key innovation of this circuit lies in the integration of linearization and demodulation functionalities within simple electronics, while effectively addressing the limitations of traditional LVDT interfaces. It uses a simple saw-tooth excitation for LVDT and incorporates an enhanced inverse synthesis circuit as the core signal conditioner for processing the LVDT's secondary outputs. Further, this approach offers other unique features, including first, compensation for transients, rise-time, and other common LVDT errors, second, low component count, third, non-requirement of precision oscillator and demodulators, and fourth, a broad range. The efficacy of the proposed method was evaluated through simulation and experimental studies, showing a remarkable improvement in linearity (over six times) across a 100 mm span. The developed system achieved a nonlinearity error of 0.9%, a signal-to-noise ratio of 52 dB, and a repeatability error of 0.014%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379557","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
Condition Sensing of Overhead Line Insulator Strings Using an Advanced CNN-Based Classifier Based on Infrared Thermography
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-21 DOI: 10.1109/LSENS.2025.3532290
R. K. Mandal;S. Dalai;Chandan Jana;R. Barua;Subhajit Maur;B. Chatterjee;Susanta Ray;Sovan Dalai
{"title":"Condition Sensing of Overhead Line Insulator Strings Using an Advanced CNN-Based Classifier Based on Infrared Thermography","authors":"R. K. Mandal;S. Dalai;Chandan Jana;R. Barua;Subhajit Maur;B. Chatterjee;Susanta Ray;Sovan Dalai","doi":"10.1109/LSENS.2025.3532290","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3532290","url":null,"abstract":"This letter proposes an advanced convolutional neural network (CNN)-based classifier for detecting the contamination level of in-service insulator strings. The goal is to enhance condition monitoring of insulators and ensure safe and reliable power system operation under adverse weather conditions and polluted environments. All possible partial and full contamination cases of a string of three disc insulators have been considered. Infrared thermography images taken from a safe distance have been cropped to consider the effective portions before being fed into a convolution-based deep neural network. The classifier has been trained with a total of 1248 thermal images across 12 contamination classes achieving an accuracy level of 99.04%. The proposed classifier has also been compared with two other benchmark CNN models.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455096","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
RespTrack-Net: Respiration Parameters Tracking From PPG Signal Using Deep Learning Model
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-21 DOI: 10.1109/LSENS.2025.3532445
Amit Bhongade;Prathosh AP;Tapan Kumar Gandhi
{"title":"RespTrack-Net: Respiration Parameters Tracking From PPG Signal Using Deep Learning Model","authors":"Amit Bhongade;Prathosh AP;Tapan Kumar Gandhi","doi":"10.1109/LSENS.2025.3532445","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3532445","url":null,"abstract":"Photoplethysmography (PPG) signals are widely used for nonintrusive health monitoring, but existing methods often struggle with noise susceptibility and computational complexity, limiting their practical utility. This research introduces two key innovations: the wearable low-cost PPG acquisition device (WeLOVE) and the RespTrack-Net model. The WeLOVE device is designed to provide high-quality PPG signal acquisition at low cost, addressing the accessibility challenges of current systems. The RespTrack-Net model introduces a novel architecture tailored for extracting respiration rate (RR) and cardiovascular parameters with enhanced robustness to noise and motion artifacts. The proposed approach was validated using two datasets: an experimental database (eight subjects) collected in this study and the publicly available CapnoBase database (42 subjects). RespTrack-Net achieved mean absolute errors of 1.58 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.30 and 3.16 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 3.36 for RR estimation on these datasets, respectively, outperforming State-of-the-Art methods. These contributions demonstrate the system's novelty and potential for reliable, real-time health monitoring in diverse settings. Future research will explore the use of the proposed device for sleep apnea detection, offering a cost-effective and comfortable alternative to current polysomnography (PSG) methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361075","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
Tweelie: Tactile Wheel-Shaped Sensor for Force Reconstruction and Localization Over Curved Spherical Surface
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-20 DOI: 10.1109/LSENS.2025.3531939
Thijs Van Hauwermeiren;Anatolii Sianov;Annelies Coene;Guillaume Crevecoeur
{"title":"Tweelie: Tactile Wheel-Shaped Sensor for Force Reconstruction and Localization Over Curved Spherical Surface","authors":"Thijs Van Hauwermeiren;Anatolii Sianov;Annelies Coene;Guillaume Crevecoeur","doi":"10.1109/LSENS.2025.3531939","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3531939","url":null,"abstract":"This letter introduces Tweelie: a tactile wheel-shaped sensor with soft elastomer skin based on barometric pressure transducers. Tweelie enables high impact force reconstruction and localization over a curved spherical surface. Multiple contacts occurring simultaneously can be detected and inferred over a 6457 <inline-formula><tex-math>$text{mm}^{2}$</tex-math></inline-formula> surface. Based on the spatial distribution of 48 micro-electromechanical system (MEMS) sensors along a cylindrical surface, a graph is constructed to infer the contact state. The 3-D force localization is done by mapping the pressure readings onto an appropriate pressure distribution based on the shape of the Tweelie sensor; the 3-D force is obtained by integrating this distribution. Results show a localization error of 2<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula> and regression error of less than 1 N for a single contact on rigid surface, enabling direct force localization and reconstruction for locomotion and other tactile applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379558","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 Publication Information IEEE传感器通讯出版信息
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-15 DOI: 10.1109/LSENS.2025.3527772
{"title":"IEEE Sensors Letters Publication Information","authors":"","doi":"10.1109/LSENS.2025.3527772","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527772","url":null,"abstract":"","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"C2-C2"},"PeriodicalIF":2.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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 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 : 2025-01-15 DOI: 10.1109/LSENS.2025.3527776
{"title":"IEEE Sensors Letters Subject Categories for Article Numbering Information","authors":"","doi":"10.1109/LSENS.2025.3527776","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527776","url":null,"abstract":"","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"3-3"},"PeriodicalIF":2.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Electric Field-Induced Exosome Lysis and Quantification of TSG101-Derived Protein via Electrochemical Sensing
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-15 DOI: 10.1109/LSENS.2024.3522106
Nusrat Praween;Pammi Guru Krishna Thej;Palash Kumar Basu
{"title":"Electric Field-Induced Exosome Lysis and Quantification of TSG101-Derived Protein via Electrochemical Sensing","authors":"Nusrat Praween;Pammi Guru Krishna Thej;Palash Kumar Basu","doi":"10.1109/LSENS.2024.3522106","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3522106","url":null,"abstract":"Exosomes that contain TSG101 biomarkers are synthesized by both healthy and malignant cells and have the potential to accurately diagnose a wide range of diseases, including cancer. For exosomal protein quantification, exosomes must be isolated from serum and then used for protein extraction. Ultracentrifugation is the most common way to isolate. Although detergents are commonly employed to extract the encapsulated exosomal proteins, they may compromise their protein integrity. The present work involves two detailed studies: the lysing of exosomes immobilized on the Au screen printed electrode (SPE) and the development of a nonfaradaic electrochemical sensor by utilizing SPE to quantity TSG101 protein. To lyse exosomes attached to the SPE surface, we applied different amplitudes of square signals to the SPE to disrupt the exosomes and facilitate the release of their contents. The lysate solution was utilized for electrochemical impedance spectroscopy (EIS) by faradic and nonfaradic techniques. Results of both types of EIS were similar, showing that nonfaradaic sensing could be an effective alternative. Hence, we employed nonfaradaic EIS to quantify the TSG101 protein released by electric lysis and validated the result with ELISA. We achieved a linear response, specifically at concentrations ranging from 0.125 to 8 ng/mL, with a detection limit of 0.10 ng/mL for human serum. Cross-reactivity analysis demonstrated selectivity to TSG101 with minimal interaction with nonspecific biomolecules.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105621","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
An Automatic Feature Extraction Method for Gas Sensors Based on Color-Enhanced Phase Space
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-15 DOI: 10.1109/LSENS.2025.3529584
Guangfen Wei;Xuerong Wang;Aixiang He;Wei Zhang;Baichuan Wang
{"title":"An Automatic Feature Extraction Method for Gas Sensors Based on Color-Enhanced Phase Space","authors":"Guangfen Wei;Xuerong Wang;Aixiang He;Wei Zhang;Baichuan Wang","doi":"10.1109/LSENS.2025.3529584","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3529584","url":null,"abstract":"Aiming to improve the effectiveness and the identity of features extracted from gas sensor responses, a novel automatic feature extraction method is proposed and studied. A simple color-enhanced phase-space approach is proposed to convert the dynamic gas sensor signals into images, which emphasizes the internal features of phase space. A lightweight neural network, i.e., MobileNetV2, is adopted to automatically extract the features and classify the odors. The method has been embedded into a lab system to classify the freshness of yellow peaches, and the final freshness classification accuracy reaches 98.58%, which is more than 20% improvement of average classification accuracy than the traditional time domain or frequency domain feature extraction and recognition methods. Compared to the original phase space, more than 10% improvement in average classification accuracy has also been obtained.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706819","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
Phase-Based Approaches for Rapid Construction of Magnetic Fields in NV Magnetometry
IF 2.2
IEEE Sensors Letters Pub Date : 2025-01-14 DOI: 10.1109/LSENS.2025.3529780
Prabhat Anand;Ankit Khandelwal;Achanna Anil Kumar;M Girish Chandra;Pavan K Reddy;Anuj Bathla;Dasika Shishir;Kasturi Saha
{"title":"Phase-Based Approaches for Rapid Construction of Magnetic Fields in NV Magnetometry","authors":"Prabhat Anand;Ankit Khandelwal;Achanna Anil Kumar;M Girish Chandra;Pavan K Reddy;Anuj Bathla;Dasika Shishir;Kasturi Saha","doi":"10.1109/LSENS.2025.3529780","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3529780","url":null,"abstract":"With the second quantum revolution underway, quantum-enhanced sensors are moving from laboratory demonstrations to field deployments, providing enhanced and even new capabilities. Signal processing and operational software are becoming integral parts of these emerging sensing systems to reap the benefits of this progress. This letter looks into widefield nitrogen vacancy (NV) center-based magnetometry and focuses on estimating the magnetic field from the optically detected magnetic resonances (ODMR) signal, a crucial output for various applications. Mapping the shifts of ODMR signals to phase estimation, a computationally efficient approaches are proposed. Involving Fourier transform (FT) and filtering as preprocessing steps, the suggested approaches involve linear curve fit or complex frequency estimation based on well known super-resolution technique estimation of signal parameters via rotational invariant techniques (ESPRIT). The existing methods in the quantum sensing literature take different routes based on Lorentzian fitting for determining magnetic field maps. To showcase the functionality and effectiveness of the suggested techniques, relevant results, based on experimental data are provided, which shows a significant reduction in computational time with the proposed method over existing methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379544","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信