IEEE Sensors Letters最新文献_第7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 : 2025-01-15 DOI: 10.1109/LSENS.2025.3527776

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

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

Medical Sensor Data Security: A DNN Framework for SOP Prediction in Two-Way Relay NOMA Systems

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-14 DOI: 10.1109/LSENS.2025.3528978

Astitva Kamble;Harsh Dalwadi;Mahendra K. Shukla;Om Jee Pandey;Vishal Krishna Singh

引用次数: 0

Wearable Sensing in Low-Field (0.55 T) MRI Environment

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-13 DOI: 10.1109/LSENS.2025.3528305

Felix Muñoz;Krishna S. Nayak;Yasser Khan

引用次数: 0

High-Speed, Low-Power Bootstrapped Class-B Driver Amplifier for LCoS Applications

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-13 DOI: 10.1109/LSENS.2025.3528395

Yingqi Feng;Yuwei Jiang;Chenghe Yang;Hui Wang;Li Tian;Yongxin Zhu;Qiliang Li;Zunkai Huang

引用次数: 0

Electrochemical Sensor With Dynamic Self-Calibration for Acetaminophen Detection in Water

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-10 DOI: 10.1109/LSENS.2025.3528342

Bryan E. Alvarez-Serna;Daniel A. Arcos-Santiago;Jorge A. Uc-Martín;Roberto G. Ramírez-Chavarría

引用次数: 0

Room-Temperature-Operated Fe2O3/PANI-Based Flexible and Eco-Friendly Ammonia Sensor With Sub-ppm Detectability 室温工作Fe2O3/ pani基柔性环保亚ppm检测氨传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-08 DOI: 10.1109/LSENS.2025.3527229

Ajay Beniwal;Rahul Gond;Xenofon Karagiorgis;Brajesh Rawat;Chong Li

{"title":"Room-Temperature-Operated Fe2O3/PANI-Based Flexible and Eco-Friendly Ammonia Sensor With Sub-ppm Detectability","authors":"Ajay Beniwal;Rahul Gond;Xenofon Karagiorgis;Brajesh Rawat;Chong Li","doi":"10.1109/LSENS.2025.3527229","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527229","url":null,"abstract":"In this letter, a room temperature (RT) (∼27 °C) operated ferric oxide/polyaniline (Fe2O3/PANI) composite-based flexible ammonia sensor with substantial sensing performance is reported. Initially, interdigitated electrodes were screen printed (using graphene-carbon-based ink) on a bio-degradable paper substrate. Further, PANI nanofibers were electrospun on printed IDEs, followed by drop casting a layer of Fe2O3. X-ray diffraction and Fourier transform infrared spectroscopy studies were performed to confirm the composite formation, followed by scanning electron microscopy analysis to examine the sensing surface morphology. The ammonia sensing performance was examined within the range of 0.5 ppm (i.e., 500 ppb) to 50 ppm, with a 1.99% response achieved even at 0.5 ppm. The response/recovery times were noted as 950/250 s toward 0.5 ppm of ammonia. In addition, selectivity toward interference gases including carbon dioxide, nitrogen dioxide, carbon monoxide, and sulfur dioxide was also investigated. The proposed sensing mechanism of the composite material toward ammonia gas detection is also presented.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993287","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

Graph Regularized AutoFuse: Robust Sensor Fusion With Noisy Labels

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-08 DOI: 10.1109/LSENS.2025.3527058

Saurabh Sahu;Kriti Kumar;Angshul Majumdar;A Anil Kumar;M Girish Chandra

{"title":"Graph Regularized AutoFuse: Robust Sensor Fusion With Noisy Labels","authors":"Saurabh Sahu;Kriti Kumar;Angshul Majumdar;A Anil Kumar;M Girish Chandra","doi":"10.1109/LSENS.2025.3527058","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527058","url":null,"abstract":"Manufacturing defects, wear, and operational conditions pose a huge risk for single-sensor-based sensing systems. The evolution of sensor technology and computing has led to the emergence of multisensor fusion systems, offering robust and improved performance. However, the effectiveness of the existing multisensor fusion methods is heavily reliant on the availability of labeled data. This challenge intensifies when known labels are corrupted by noise, which is quite common in practical scenarios. To address these issues, this letter introduces the graph regularized autoencoder-based multisensor fusion framework (<italic>GrAutoFuse). <italic>GrAutoFuse utilizes autoencoders to learn representations from individual sensors and combines them for robust classification within a semi-supervised learning framework. Unlike other semi-supervised methods, this approach can identify noisy labels, perform label estimation and correction through label propagation on a graph that captures correlations between different sensors. Here, we present a joint optimization formulation for learning sensor-specific representations, fused representations, and a classifier by estimating missing and correcting noisy labels. This results in a robust fusion model for classification. Experimental results on two datasets from different domains illustrate the generalizability and superior performance of GrAutoFuse compared to state-of-the-art methods, showcasing its effectiveness in handling missing and noisy labels.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105534","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