IEEE Sensors Letters最新文献

Enzymatic Glucose Sensing Using Glucose Oxidase-Integrated Conducting Polymer Extended Gate Field Effect Transistor 利用葡萄糖氧化酶集成的导电聚合物扩展门场效应晶体管进行酶促葡萄糖传感

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-06-22 DOI: 10.1109/LSENS.2026.3705902

Abhinay Dubey;Indu Kant Dwivedi;Shweta Tripathi

引用次数: 0

Sleep Posture Recognition for Dual-Occupancy Using Thermal Sensor Arrays 基于热传感器阵列的双人睡眠姿势识别

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-02 DOI: 10.1109/LSENS.2026.3709690

Selwan Abdussalam;Abdallah Naser;Ahmad Lotfi;Salisu Yahaya

引用次数: 0

A Dual-Top-Electrode ScAlN-Based Piezoelectric Micromachined Ultrasound Transducer With Series-Connected Electrode Topology for Enhanced Receiving Sensitivity 基于串联电极拓扑结构的双顶电极scaln压电微机械超声换能器提高了接收灵敏度

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-06 DOI: 10.1109/LSENS.2026.3710573

Zihan Lu;Hongxin Xu;Xuefei Yan;Huahuang Luo;Yuan Wang;Qingqing Ke

{"title":"A Dual-Top-Electrode ScAlN-Based Piezoelectric Micromachined Ultrasound Transducer With Series-Connected Electrode Topology for Enhanced Receiving Sensitivity","authors":"Zihan Lu;Hongxin Xu;Xuefei Yan;Huahuang Luo;Yuan Wang;Qingqing Ke","doi":"10.1109/LSENS.2026.3710573","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3710573","url":null,"abstract":"This letter presents a dual-top-electrode scandium aluminum nitride (ScAlN)-based piezoelectric micromachined ultrasonic transducer (PMUT) operating in the series-connected electrode (SCE) topology. In the transmitting mode, the SCE topology ideally halves the local voltage across each piezoelectric unit. This allows a higher external drive voltage for the same electric field constraint, potentially doubling the maximum displacement. For the receiving mode, the SCE topology yields an excellent receiving sensitivity of 5.71 mV/Pa, which is 1.58 times that of its conventional outer-floating electrode (OFE) topology. The enhancement of receiving sensitivity is attributed to the reduction in static capacitance by series connection. Due to the slightly reduced transmitted sound pressure and substantially enhanced receiving sensitivity, the pulse-echo signal captured in the SCE transmit–receive configuration exhibits a 51.8% improvement over the OFE-based scheme. The PMUT with SCE is believed to reveal significant potential for highly sensitive ranging applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"2504204-2504204"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628003","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

Label-Free Electrochemical Detection of Mucin in Urinary Extracellular Vesicles to Monitor High-Altitude Hypoxia Adaptation 尿细胞外囊泡粘蛋白无标记电化学检测监测高原缺氧适应

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-14 DOI: 10.1109/LSENS.2026.3713253

Nusrat Praween;Sunil Kumar;Palash Kumar Basu

{"title":"Label-Free Electrochemical Detection of Mucin in Urinary Extracellular Vesicles to Monitor High-Altitude Hypoxia Adaptation","authors":"Nusrat Praween;Sunil Kumar;Palash Kumar Basu","doi":"10.1109/LSENS.2026.3713253","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3713253","url":null,"abstract":"Severe hypoxic conditions cause systemic physiological stress, highlighting the need for noninvasive health monitoring. Urinary extracellular vesicles (uEVs) contain stable, hypoxia-responsive biomarkers like Mucin-1 (MUC1) and Mucin-5AC (MUC5AC). We introduce a compact, label-free electrochemical impedance spectroscopy biosensor with reagent-free electric field lysis method. A 30-s square wave (50 mV, 1 kHz) applied directly to urine quickly breaks down uEV lipid membranes, releasing intravesicular contents efficiently. Validated with enzyme-linked immunosorbent assay (ELISA), this physical on-chip lysis yields a 3.06-fold increase in biomarker recovery compared to untreated samples without denaturing chemicals. The sensor was tested with urine samples taken before and after a 10-day high-altitude (14 860 ft) hypoxic challenge. The dual-biomarker system detected significant, individual increases posthypoxia, ranging from 54.7% to 526.1% for EV-MUC5AC and 60.9% to 127.4% for EV-MUC1. By bypassing traditional isolation and extraction processes, this platform reduces workflow time by about 95%, offering a rapid, highly sensitive point-of-care method for real-time monitoring of physiological stress in extreme environments.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"4502804-4502804"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626394","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

Analysis and Closed-Loop Suppression of In-Plane Linear Acceleration Sensitivity for MEMS Quadruple Mass Gyroscope MEMS四倍质量陀螺仪平面内线性加速度灵敏度分析及闭环抑制

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-06-29 DOI: 10.1109/LSENS.2026.3708157

Jianjun Ma;Bin Zhou;Qi Wei;Rong Zhang

{"title":"Analysis and Closed-Loop Suppression of In-Plane Linear Acceleration Sensitivity for MEMS Quadruple Mass Gyroscope","authors":"Jianjun Ma;Bin Zhou;Qi Wei;Rong Zhang","doi":"10.1109/LSENS.2026.3708157","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3708157","url":null,"abstract":"This letter presents theoretical analysis, closed-loop suppression design, and experimental verification of in-plane linear acceleration sensitivity in a center-anchor-supported quadruple-mass gyroscope (CSQMG). Despite the inherent common-mode rejection of multimass structures, capacitive nonlinearity, and coupling electrostatic forces remain significant sources of g-sensitivity errors. A theoretical model considering capacitive nonlinearity in the sense electrodes and coupling electrostatic forces along the sense direction in the drive electrodes is developed for the CSQMG. Dedicated electrodes featuring a 45°-tilted comb design are implemented to suppress acceleration-induced displacement in a closed-loop feedback system. Experimental results demonstrate that the bias g-sensitivity is substantially reduced from 98.6°/h/g to 7.9°/h/g. Furthermore, scale factor g-sensitivity tests under centripetal acceleration show that the scale factor increment is suppressed from 0.27% to nearly zero. These results verify the effectiveness of the proposed closed-loop suppression technique for reducing MEMS gyroscope g-sensitivity, and further support the implementation of a novel single-drive six-axis integrated chip that combines a single-drive three-axis CSQMG and three-axis closed-loop accelerometers.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"2504004-2504004"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626872","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

Machine Learning-Based Virtual Luxmeters for Fault Compensation and Angular Sensing Expansion in Robotic Lighting Inspection 基于机器学习的虚拟亮度计在机器人照明检测中的故障补偿和角传感扩展

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-16 DOI: 10.1109/LSENS.2026.3713669

João Henrique Campos Soares;José Mario Nishihara de Albuquerque;Giovanni Braglia;André Eugênio Lazzaretti;André Schneider de Oliveira;Ronnier Frates Rohrich

{"title":"Machine Learning-Based Virtual Luxmeters for Fault Compensation and Angular Sensing Expansion in Robotic Lighting Inspection","authors":"João Henrique Campos Soares;José Mario Nishihara de Albuquerque;Giovanni Braglia;André Eugênio Lazzaretti;André Schneider de Oliveira;Ronnier Frates Rohrich","doi":"10.1109/LSENS.2026.3713669","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3713669","url":null,"abstract":"This letter proposes a machine learning-based virtual luxmeter framework for robotic lighting inspection systems, aiming to improve sensing robustness and expand angular measurement coverage without requiring additional hardware. Traditional robotic lighting inspection platforms rely exclusively on physical luxmeters, limiting angular resolution and making the system vulnerable to sensor failures or faulty readings. The proposed approach employs a multilayer perceptron model to learn the spatial correlations between multidirectional illuminance measurements and robot orientation, enabling both physical sensor reconstruction and virtual sensor generation. To support model training and validation, a controlled rotational acquisition methodology was developed using a seven-sensor robotic platform under fixed laboratory lighting conditions, generating ground-truth data for intermediate angular sensing. Experimental validation demonstrated high reconstruction accuracy for physical sensor replacement, achieving mean absolute errors below 1 lx, while virtual sensor inference achieved average errors of approximately 5 lx. Although this indoor Proof of Concept requires future retraining for complex outdoor deployment, the results validate the methodology’s capability to compensate faulty readings and generate reliable virtual estimations, increasing the sensing density and reliability of the framework.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"6007904-6007904"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627804","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

Radar-Based Sleep Posture Transition Sensing: An Efficient Deep Learning Hierarchical Temporal Architecture Utilizing Depthwise Convolution 基于雷达的睡眠姿势转换感知：一种利用深度卷积的高效深度学习分层时间结构

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-03 DOI: 10.1109/LSENS.2026.3710058

Sadman Rahman;Rishi Raj Sharma;Kazi Newaj Faisal

{"title":"Radar-Based Sleep Posture Transition Sensing: An Efficient Deep Learning Hierarchical Temporal Architecture Utilizing Depthwise Convolution","authors":"Sadman Rahman;Rishi Raj Sharma;Kazi Newaj Faisal","doi":"10.1109/LSENS.2026.3710058","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3710058","url":null,"abstract":"Sleep posture transitions (SPTs) are critical indicators of sleep quality, essential for preventing posture-related complications in clinical and assistive care. Conventional vision-based, infrared, and pressure-mat monitoring systems face limitations in privacy preservation and robustness. This letter presents a sensor-oriented radar signal processing method for SPT recognition using frequency-modulated continuous-wave radar, processing time-range (TR) maps through a hierarchical temporal architecture. The proposed single-branch network employs depthwise convolutions, multihead self-attention, generalized mean pooling, and bidirectional recurrent modeling to extract discriminative features from radar-signal-derived TR representations. Evaluated on a seven-class SPT dataset with 1407 TR samples from 20 participants, the method achieves 99.29% accuracy under fivefold cross-validation. A subject-independent leave-one-subject-out evaluation further confirms strong generalization, with performance of 99.22% <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.78% across subjects. Robustness analysis under Gaussian noise perturbation maintains 98.94% accuracy, indicating resilience to signal degradation and environmental variations. Comparative analysis with time-Doppler maps with 1400 samples achieves 95.71% accuracy, demonstrating the superiority of range-based displacement representations over velocity-based signatures. The compact 18.6 M parameter architecture with 2.10 giga floating point operations per second and 5.787 ms inference time enables real-time deployment in privacy-preserving healthcare monitoring systems while maintaining sensor-level signal fidelity.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"6007504-6007504"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628169","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 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-08-19 DOI: 10.1109/LSENS.2026.3719017

引用次数: 0

Multicondition Crack Detection and Surface Assessment for Underwater Metal Structures Using Adaptive Vision Models 基于自适应视觉模型的水下金属结构多条件裂纹检测与表面评估

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-06-29 DOI: 10.1109/LSENS.2026.3708183

Mohammad Arif Khan;Sourav Roy;Kaushik Bandopadhyay;Avishek Adhikary

{"title":"Multicondition Crack Detection and Surface Assessment for Underwater Metal Structures Using Adaptive Vision Models","authors":"Mohammad Arif Khan;Sourav Roy;Kaushik Bandopadhyay;Avishek Adhikary","doi":"10.1109/LSENS.2026.3708183","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3708183","url":null,"abstract":"Ensuring the integrity of underwater metal structures requires precise crack detection in regular basis. Identifying the surface condition is also crucial because rusted areas necessitate specific prerepair treatments in case of crack maintenance. An accurate image-based crack detection and surface classification (rust or nonrust) directly from the image data collected from the camera mounted on an under remotely operating vehicle can bring a paradignm shift in underwater maintenance. However, such detection accuracy is heavily affected by water types, turbidity, or the presence of algae in water. In this work, we present a dual-task learning framework incorporating DenseNet201 for surface classification and a you only look once (YOLOv8) models for crack detection, trained under various types of water: clean, turbid, or algae-affected. With customized YOLOv8 models by optimizing confidence threshold as per water types, we have achieved precision = 87.4% and mAP@50 = 84.0 for clean water. Furthermore, by proposing an adaptive scheme for camera positioning based on water turbidity, the proposed framework achieve an average mAP@50 82.7% for water turbidity ranging from 15 to 120 nephelometric turbidity units (clean to turbid). The DenseNet201 model on the other hand shows an accuracy of 99.38% accuracy in detecting rust surface in all water.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"5505004-5505004"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626410","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

AI-Generated Fingerprint Forgery Detection for Tiny Partial Fingerprint Sensors Using Multigenerative Model Framework With Feature Augmentation 基于特征增强的多生成模型框架的人工智能指纹伪造检测

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-14 DOI: 10.1109/LSENS.2026.3713275

Mao-Hsiu Hsu;Bo-Jie Zhang

引用次数: 0