IEEE Sensors Letters最新文献_第10页

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</i>). <italic>GrAutoFuse</i> 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 mm<sup>2</sup>. 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

Multistream CNN-BiLSTM Framework for Enhanced Human Activity Recognition Leveraging Physiological Signal 利用生理信号增强人体活动识别的多流CNN-BiLSTM框架

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-06 DOI: 10.1109/LSENS.2025.3526446

Abisek Dahal;Soumen Moulik

引用次数: 0

MoS$_{2}$ MEMS-FET Nn Force Sensor With Suspended Body FET and Piezoresistive-Based Hybrid Transduction

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-06 DOI: 10.1109/LSENS.2025.3526361

Mayank Kohli;Joel Zacharias;V. Seena

{"title":"MoS$_{2}$ MEMS-FET Nn Force Sensor With Suspended Body FET and Piezoresistive-Based Hybrid Transduction","authors":"Mayank Kohli;Joel Zacharias;V. Seena","doi":"10.1109/LSENS.2025.3526361","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3526361","url":null,"abstract":"In this letter, we present a comprehensive study on the design, simulation, and modeling of nano-Newton (nN) force sensor using 2-D molybdnem disulphide (MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>)-based suspended body dual-gate field-effect transistor (2D SB-DG-FET) with integrated piezoresistor. The sensor uses the hybrid transduction scheme involving suspended body (SB) FET and piezoresistive load resistors in common source amplifier (CSA) configuration. The sensor consist of a MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>-based FET integrated on a suspended microelectromechanical systems (MEMS) structure with MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> piezoresistors acting as a load. The choice of MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> allows the use of same functional material as both FET channel and piezoresitive load. During force sensing, MEMS structure ensures the constant gate capacitance change leading to an output current change of the SB-DG-FET. Simultaneously the applied force also causes resistance change in the piezoresistors. COMSOL Multiphysics 6.0 and CoventorWare MP 10.3 have been used for the design and simulation of the MEMS structure. The design and simulation of the 2D SB-DG-FET and its application in CSA configuration with piezoresistive load have been carried out in COMSOL using a lookup table. The CSA exhibits the linear response with output sensitivity 1.15 <inline-formula><tex-math>$upmu text{V}/text{nN}$</tex-math></inline-formula> and maximum detection range upto 2 <inline-formula><tex-math>$upmu text{N}$</tex-math></inline-formula>. This letter demonstrates the advantage of this hybrid transduction scheme due to the response of SB-FET and piezoresistor in CSA circuit.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403779","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

Employing Nondestructive Approach of Spectral Imaging to Detect Artificially Degreened Lemon 采用无损光谱成像方法检测人工脱脂柠檬

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-02 DOI: 10.1109/LSENS.2025.3525485

Anish Prabhu;Aparajita Naik;Sakshi Raut;Narayan Vetrekar;Raghavendra Ramachandra;R. S. Gad

引用次数: 0

Tomographic Inversion of Urban Area via Tikhonov Regularization and Bayesian Information Criterion 基于吉洪诺夫正则化和贝叶斯信息准则的城市区域层析反演

IF 2.2

IEEE Sensors Letters Pub Date : 2025-01-02 DOI: 10.1109/LSENS.2024.3525127

Hui Bi;Weihao Xu;Shuang Jin;Jingjing Zhang

{"title":"Tomographic Inversion of Urban Area via Tikhonov Regularization and Bayesian Information Criterion","authors":"Hui Bi;Weihao Xu;Shuang Jin;Jingjing Zhang","doi":"10.1109/LSENS.2024.3525127","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3525127","url":null,"abstract":"As an extension of synthetic aperture radar (SAR), SAR tomography (TomoSAR) technology can reduce the overlapping in 2-D SAR image and separate multiscatterer along the elevation direction, thereby achieving the high-precision 3-D reconstruction of the surveillance area. However, in practical spaceborne TomoSAR application, the quality of 3-D imaging is restricted by the limited number of baselines and their uneven distribution. Therefore, it is necessary to find advanced signal processing technology to achieve the target 3-D recovery when the amount of data is limited. In this letter, a novel Tikhonov regularization and Bayesian information criterion (BIC)-based nonparametric iterative adaptive approach (IAA), named RIAA-BIC, is proposed and introduced to the spaceborne data processing. Compared with conventional spectral estimation, compressed sensing-based, and IAA algorithms, the proposed method incorporates the Tikhonov regularization term to avoid the problem of solving nonlinear ill-posed equation in the elevation inversion. Furthermore, the BIC model selection tool can eliminate the false or weak scatterers, thereby improving the 3-D reconstruction accuracy of the surveillance area. Experimental results based on TerraSAR-X dataset verify the proposed method.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993373","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

A System-Level Demonstration of Low-Frequency Magnetoelectric Power Transfer System

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-31 DOI: 10.1109/LSENS.2024.3524317

Dibyajyoti Mukherjee;Dhiman Mallick

{"title":"A System-Level Demonstration of Low-Frequency Magnetoelectric Power Transfer System","authors":"Dibyajyoti Mukherjee;Dhiman Mallick","doi":"10.1109/LSENS.2024.3524317","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3524317","url":null,"abstract":"This letter presents a complete system-level demonstration of a low-frequency magnetoelectric (ME) wireless power transfer (WPT) system for low-voltage applications. The proposed WPT system incorporates a trilayered ME transducer featuring polyvinylidene fluoride as the piezoelectric layer and Metglas as the magnetostrictive layer. The dimension of the ME device has been micromachined into a dimension of 3.5 × 5 mm <inline-formula><tex-math>$^{2}$</tex-math></inline-formula> to operate it at <inline-formula><tex-math>$approx$</tex-math></inline-formula> 50 kHz. The ME device generates an output voltage of 0.4 V at a 0.4 Oe magnetic field. The corresponding power across an optimum load of 8 k<inline-formula><tex-math>$Omega$</tex-math></inline-formula> is 6.65 <inline-formula><tex-math>$upmu$</tex-math></inline-formula>W. The alignment orientation study of the ME device confirms that its radiation characteristics are similar to those of the loop antenna. The maximum voltage degradation in the azimuth and elevation planes is 5<inline-formula><tex-math>${%}$</tex-math></inline-formula> and 15<inline-formula><tex-math>${%}$</tex-math></inline-formula>, respectively. Moreover, a power management circuit (PMC) is designed to extract maximum power from the ME device and generate a regulated DC voltage. The PMC consumes an area of 6.5 × 5.5 cm<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> and is capable of producing 2.5 V from an input voltage ranging from 0.7 to 5 V, with the peak efficiency of 85<inline-formula><tex-math>${%}$</tex-math></inline-formula>.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105532","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