IEEE Sensors Letters最新文献_第7页

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

FL-RTIS, a Novel Multimodal Sensor Using High-Speed Camera and Active 3-D Sonar for Insect Ensonification 基于高速摄像机和主动三维声纳的昆虫多模态传感器FL-RTIS

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-08 DOI: 10.1109/LSENS.2025.3527116

Jan Steckel;Pamela Rivera Parra;Arne Aerts;Dennis Laurijssen;Wouter Jansen;Walter Daems;Jesse Barber

引用次数: 0

A Fully Integrated CMOS 0.3 V 335 nW PWM-Based Light-to-Digital Converter for Optoelectronic Sensing Systems in Biomedical Applications

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-08 DOI: 10.1109/LSENS.2025.3527759

G. Di Patrizio Stanchieri;A. De Marcellis;M. Faccio;E. Palange;O. Aiello

{"title":"A Fully Integrated CMOS 0.3 V 335 nW PWM-Based Light-to-Digital Converter for Optoelectronic Sensing Systems in Biomedical Applications","authors":"G. Di Patrizio Stanchieri;A. De Marcellis;M. Faccio;E. Palange;O. Aiello","doi":"10.1109/LSENS.2025.3527759","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3527759","url":null,"abstract":"This letter reports on the design of a novel, fully integrated stand-alone light-to-digital converter (LDC) for optoelectronic sensors in wearable/implantable biomedical applications. The architecture designed in TSMC 180 nm standard Si CMOS technology integrates a Si photodiode (PD), a ring oscillator, two digital counters, and a voltage-to-pulsewidth modulation (PWM) stage as the basic block of the light-to-digital converter in a Si area of 0.018 mm2. The modulation stage, composed of ten transistors and a capacitor, provides a square waveform whose pulse width varies as a function of its input voltage provided by the Si photodiode operating in a photovoltaic mode that linearly depends on the light intensity impinging on its sensitive area. The value of the pulse width is digitalized by two digital counters driven by the ring oscillator. The complete system, powered at 0.3 V, has been fully characterized by postlayout simulations demonstrating an overall sensitivity of 0.062 LSB/lx, a power consumption of 335 nW, and a sample rate of 3 kS/s. A comparison with similar solutions in the literature shows that the proposed system achieves the best performance in power consumption, Si area, and supply voltage with a good sample rate value.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105620","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

Analysis of Dynamics of EEG Signals in Emotional Valence Using Super-Resolution Superlet Transform

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-07 DOI: 10.1109/LSENS.2025.3526907

Himanshu Kumar;Nagarajan Ganapathy;Ramakrishnan Swaminathan

{"title":"Analysis of Dynamics of EEG Signals in Emotional Valence Using Super-Resolution Superlet Transform","authors":"Himanshu Kumar;Nagarajan Ganapathy;Ramakrishnan Swaminathan","doi":"10.1109/LSENS.2025.3526907","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3526907","url":null,"abstract":"Electroencephalography (EEG)-based emotional state assessment is widely preferred due to its noninvasiveness and nonradiation approach. However, these signals are highly nonstationary and multicomponent, demonstrating large intrasubject variability. Extracting time and frequency information simultaneously from EEG addresses these challenges to effectively recognise the valence emotional states. Traditional time–frequency (TF) approaches optimise either temporal or frequency resolution, resulting in failure to identify fast transient oscillatory emotional events. In this letter, an attempt has been made to recognize emotional valence using super-resolution-based superlet transform (SLT). For this, the preprocessed EEG signals during emotion-evoking audio–visual stimuli from publicly available database is considered. The EEG signals are decomposed into theta, alpha, beta, and gamma frequency bands and are subjected to SLT. The TF skewness and kurtosis are extracted from the SLT. The statistical significance of features is evaluated, and the features are applied to three machine learning algorithms: random forest, Adaboost, and k-nearest neighbor. The results show that the SLT-based TF spectrum is able to provide variations of frequency components associated with emotional valence. Both the features exhibit statistically significant <inline-formula><tex-math>$(p < 0.05)$</tex-math></inline-formula> difference in the high-frequency gamma bands to characterize emotional valence. Among the classifiers, AdaBoost stands out as the most robust performer (F1 = 70.16%). Feature importance analysis highlights that SLT features from the fronto-central and parieto-occipital brain regions play a crucial role in valence detection. It appears that this method could be useful in analyzing various mental well-being conditions in clinical settings.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105515","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

Fast-Alignment of AR Headset From Local to Geodetic Coordinate Frame for Navigation and Mixed Reality Applications 导航和混合现实应用中AR头显从本地坐标系到大地坐标系的快速对准

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-07 DOI: 10.1109/LSENS.2025.3526597

Eudald Sangenis;Andrei M. Shkel

{"title":"Fast-Alignment of AR Headset From Local to Geodetic Coordinate Frame for Navigation and Mixed Reality Applications","authors":"Eudald Sangenis;Andrei M. Shkel","doi":"10.1109/LSENS.2025.3526597","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3526597","url":null,"abstract":"The integration of virtual reality (VR) and augmented reality (AR) technologies into location-based services and applications necessitates precise navigation within an absolute geodetic reference frame, utilizing latitude, longitude, and altitude (LLA) coordinates. Typically, VR/AR headsets establish a local Cartesian (XYZ) world coordinate (WC) frame with an arbitrary initial origin and orientation. For accurate geodetic navigation, it is essential to align these devices to the true North (TN). This letter introduces an AR-based method for achieving the initial alignment of the WC frame relative to the geodetic frame. We developed an AR user interface to visually guide users to a known target with LLA coordinates, indirectly aligning the system to TN. Using a Magic Leap 2 AR headset, we evaluated our approach against traditional magnetometer-based methods. Our experimental results demonstrated that our method reduces the mean angular error by a factor of 4× and the standard deviation (<inline-formula><tex-math>$sigma$</tex-math></inline-formula>) by 5× compared to traditional magnetometer methods. This improvement can eliminate the need for initial magnetometer calibration, offering a more efficient and robust solution for TN alignment in AR/VR applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993217","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