IEEE Sensors Letters最新文献_第9页

Aluminum Doped Zinc Oxide and Silver Nanowire Composite Based Printed CO$_{2}$ Gas Sensor 基于铝掺杂氧化锌和银纳米线复合材料的印刷CO${2}$气体传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-19 DOI: 10.1109/LSENS.2025.3571193

Nikhila Patil;Neethu Thomas;Neha Sharma;Parasuraman Swaminathan;P. Sumathi

{"title":"Aluminum Doped Zinc Oxide and Silver Nanowire Composite Based Printed CO$_{2}$ Gas Sensor","authors":"Nikhila Patil;Neethu Thomas;Neha Sharma;Parasuraman Swaminathan;P. Sumathi","doi":"10.1109/LSENS.2025.3571193","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3571193","url":null,"abstract":"Carbon dioxide (CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>) is a significant greenhouse gas and an essential indicator of effective air circulation in enclosed spaces, requiring precise and continuous monitoring. Traditional chemiresistive CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> sensors have high operating temperatures that require external heating elements limiting their applicability in low-power portable electronics. This work demonstrates a miniaturized printed CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> gas sensor, based on aluminum-doped zinc oxide (AZO) and silver nanowire (Ag NW) nanocomposite ink, which operates efficiently at room temperature. The AZO-Ag NW nanocomposite ink is optimized for direct ink writing (DIW) to obtain a uniform printed pattern. The composite ink helps overcome the inherent high resistance of AZO nanostructures by taking advantage of Ag NW's high conductivity and surface reactivity. The sensor shows a quick response time of 19 s and a recovery time of 36 s for 400 ppm CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>. The sensor exhibits a response (R) of 32.5% with a limit of detection of 24.04 ppm, while operating at a low bias of 1 V. The integration of DIW, cost-effective ink formulation, and scalable fabrication is a significant advancement for real-time CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> monitoring at low power.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 6","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179174","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 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 : 2025-03-19 DOI: 10.1109/LSENS.2025.3549961

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 : 2025-03-19 DOI: 10.1109/LSENS.2025.3549957

引用次数: 0

A Non-intrusive, Non-obstructive, Versatile Venous Reservoir Blood Volume Sensor Based on Computer Vision for Clinical Cardiopulmonary Bypass 基于计算机视觉的临床体外循环非侵入性、非阻塞性、多用途静脉血容量传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-17 DOI: 10.1109/LSENS.2025.3551948

Ryan Kaddis;Chan-Jin Chung;Sean Murtha;Hao Jiang

引用次数: 0

Engineering a Point-of-Care Device for Multiplex Monitoring of Urinary Parameters 设计一种多点监测尿液参数的即时护理设备

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-17 DOI: 10.1109/LSENS.2025.3551929

Samiha Ahmed;Majed O. Althumayri;Weiming Xu;Azra Y. Tarman;Ramu Banavath;Jayesh Korgaonkar;Emily Wussow;Justin McMurray;Souvik Paul;Daniel Wollin;Gerard L. Coté;Hatice Ceylan Koydemir

{"title":"Engineering a Point-of-Care Device for Multiplex Monitoring of Urinary Parameters","authors":"Samiha Ahmed;Majed O. Althumayri;Weiming Xu;Azra Y. Tarman;Ramu Banavath;Jayesh Korgaonkar;Emily Wussow;Justin McMurray;Souvik Paul;Daniel Wollin;Gerard L. Coté;Hatice Ceylan Koydemir","doi":"10.1109/LSENS.2025.3551929","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3551929","url":null,"abstract":"Urinary tract infections (UTIs) are common and costly healthcare issues, particularly among catheterized patients. Current diagnostic methods, such as urine cultures and dipstick tests, suffer from delayed results and limited accuracy, highlighting the need for more reliable, real-time approaches. This letter presents a proof-of-concept point-of-care device for multiplex detection of key urine parameters—pH and nitrite levels—and urine volume in catheter bags used for intermittent or long-term urinary drainage. The system integrates commercial pH and nitrite probes with an ultrasonic liquid level sensor, all of which are managed by a control unit that processes and transmits data wirelessly via Bluetooth or near-field communication. The sensors were calibrated via standard solutions and tested for stability over four days, which revealed minimal drift. The system was further validated using human urine spiked with varying concentrations of nitrites and pH. The results demonstrated reliable sensor performance and accurate detection of urinary biomarkers in real time. While the system shows promise for the early detection of catheter-associated urinary tract infections, further validation is needed to confirm its validity over standard diagnostic methods. This device offers a feasible solution for real-time, noninvasive UTI monitoring at the point of care.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740328","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

Rotational Odometry Using Ultra Low Resolution Thermal Cameras 使用超低分辨率热像仪进行旋转里程测量

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-17 DOI: 10.1109/LSENS.2025.3552135

Ali Safa

引用次数: 0

3D-Printed Conductance-Based Force Sensors Using Single Traxels 3d打印基于电导的单轴力传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-16 DOI: 10.1109/LSENS.2025.3571198

Anders Frem Wolstrup;Thomas Schlaikjer Holst;Jon Spangenberg;Tiberiu Gabriel Zsurzsan

引用次数: 0

Former-HGR: Hand Gesture Recognition With Hybrid Feature-Aware Transformer 前hgr：手势识别与混合特征感知变压器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-15 DOI: 10.1109/LSENS.2025.3566022

Monu Verma;Garvit Gopalani;Saiyam Bharara;Santosh Kumar Vipparthi;Subrahmanyam Murala;Mohamed Abdel-Mottaleb

{"title":"Former-HGR: Hand Gesture Recognition With Hybrid Feature-Aware Transformer","authors":"Monu Verma;Garvit Gopalani;Saiyam Bharara;Santosh Kumar Vipparthi;Subrahmanyam Murala;Mohamed Abdel-Mottaleb","doi":"10.1109/LSENS.2025.3566022","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3566022","url":null,"abstract":"Hand gesture recognition (HGR) systems, using cameras and sensors, offer an intuitive method for human–machine interaction, sparking interest across various applications. However, these systems face challenges from environmental factors such as variations in illumination, complex backgrounds, diverse hand shapes, and similarities between different gesture classes. Achieving accurate gesture recognition under such conditions remains a complex task, necessitating robust solutions to ensure reliable performance. This letter proposes a novel approach named Former-HGR, a hybrid feature-aware transformer for HGR. Unlike traditional transformer-based HGR systems that heavily rely on computationally intensive self-attention mechanisms, Former-HGR enhances global feature perception by applying self-attention across channels through the integration of multidconv head transposed attention. In addition, Former-HGR improves feature extraction by incorporating multiscale features and effectively filters redundant information using a hybrid feature-aware network. Extensive experiments conducted on three datasets: NUSII, OUHANDS, and MUGD, demonstrate that Former-HGR outperforms recent benchmark HGR approaches, achieving accuracy improvements of up to 14% in person-independent validation schemes.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 6","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196723","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

Lightweight Electrocardiogram Signal Quality-Aware VT/VF Detector for Resource-Constrained Life-Threatening Monitoring Devices 用于资源受限危及生命监测设备的轻型心电图信号质量感知VT/VF检测器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-14 DOI: 10.1109/LSENS.2025.3570346

Nabasmita Phukan;M. Sabarimalai Manikandan;Ram Bilas Pachori;Niranjan Garg

{"title":"Lightweight Electrocardiogram Signal Quality-Aware VT/VF Detector for Resource-Constrained Life-Threatening Monitoring Devices","authors":"Nabasmita Phukan;M. Sabarimalai Manikandan;Ram Bilas Pachori;Niranjan Garg","doi":"10.1109/LSENS.2025.3570346","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3570346","url":null,"abstract":"Ventricular tachycardia (VT) and ventricular fibrillation (VF) are life-threatening arrhythmias, which lead to sudden cardiac arrest (SCA). The timely detection of VT and VF is vital, as automated external defibrillators rely on accurate VT/VF identification to deliver life-saving defibrillation and restore normal sinus rhythm during SCA. Continuous monitoring of electrocardiogram (ECG) signals plays a pivotal role in the early detection of VT/VF, potentially reducing mortality associated with SCA. However, the reliability of continuous ECG monitoring is often compromised by various noise sources, necessitating assessment of signal quality to ensure accurate VT/VF detection. This letter presents a real-time signal quality assessment (SQA)-based VT/VF detection method using zero-crossing rate. The SQA-based VT/VF detection method is tested on single and multilead datasets. The method is tested on real-time ECG signals collected from subjects with cardiac arrhythmias. Compared to zero-crossing rate-based VT/VF detection without SQA, the proposed SQA-based method reduced the false detection rate by up to 7.38% on a single-lead dataset and 59.22% on lead 1 of a multilead dataset. The method, implemented on the Arduino Due, consumed energy of 5.79 mJ and processing time of 13 ms, validating its real-time feasibility on resource-constrained wearable health monitoring devices.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 7","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472545","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

Human Activity Recognition Using WiFi Signal Features and Efficient Residual Packet Attention Network 基于WiFi信号特征和高效残包关注网络的人体活动识别

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-14 DOI: 10.1109/LSENS.2025.3551337

Senquan Yang;Junjie Yang;Chao Yang;Wei Yan;Pu Li

{"title":"Human Activity Recognition Using WiFi Signal Features and Efficient Residual Packet Attention Network","authors":"Senquan Yang;Junjie Yang;Chao Yang;Wei Yan;Pu Li","doi":"10.1109/LSENS.2025.3551337","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3551337","url":null,"abstract":"WiFi signal features, particularly channel state information (CSI), have gained considerable attention in human activity recognition (HAR) due to their nonintrusive and privacy–friendly nature. However, CSI packets are often nonstationary and exhibit fluctuations across various human activities. In this letter, we propose an end-to-end deep neural network (DNN) called efficient residual packet attention network (ERPANet) to tackle these challenges. In the proposed framework, we introduce the multilayer residual module composed of an attention residual (AR) operation and a downsampling attention residual (DAR) operation to effectively capture spatial-temporal features of CSI packets. In addition, a self-attention mechanism is embedded within AR and DAR to emphasize the importance of interrelationship among these multiscale CSI packet features. The proposed ERPANet aims to encode both channel information and long-range dependencies of CSI packet features. Extensive experiments show that ERPANet outperforms state-of-the-art methods, achieving average accuracies of 99.4% and 99.6% on the university of toronto human activity recognition (UT-HAR) and nanyang technological university human activity recognition (NTU-HAR) datasets, respectively.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777748","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