IEEE Sensors Letters最新文献_第8页

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

Reviewers List 评论家列表

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-30 DOI: 10.1109/LSENS.2024.3521548

引用次数: 0

Analysis of Microwave Radiometry of Snow and Ice on an Outdoor Experimental Asphalt Surface 室外实验沥青路面冰雪微波辐射测量分析

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-30 DOI: 10.1109/LSENS.2024.3523905

Yasuhiro Tanaka;Kazutaka Tateyama

{"title":"Analysis of Microwave Radiometry of Snow and Ice on an Outdoor Experimental Asphalt Surface","authors":"Yasuhiro Tanaka;Kazutaka Tateyama","doi":"10.1109/LSENS.2024.3523905","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3523905","url":null,"abstract":"This letter presents the feasibility of a classification that divides six surfaces into more than four surfaces using radiometric values retrieved from brightness temperatures (TBs) observed at 6- and 36-GHz radiometers with vertical (V) and horizontal (H) polarizations on asphalt surface. The feasibility was investigated by using the Mahalanobis-distance-based approach of the canonical discriminant analysis. Combining 6 V (or 36 V) and 36 H emissivities, with the use of the surface temperature, showed the classification accuracy of 94%. In addition, combining the polarization ratio at 36 GHz TBs (PR36) and the cross-polarized gradient ratio between 36 H and 6 V TBs (XGPR36H06V), without the use of the surface temperature, showed the classification accuracy of 97%. Both the combination of 6 V and 36 H emissivities and the combination of PR36 and XGPR36H06V have the potential for dividing six surface conditions into five surface conditions. Results suggest that the combination of PR36 and XGPR36H06V potentially has a classification ability similar to that of 6 V and 36 H emissivities.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993290","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

Deep Learning-Based Multiswitch Open-Circuit Fault Diagnosis for Active Front-End Rectifiers Using Multisensor Signals 基于深度学习的前端有源整流器多开关开路故障多传感器诊断

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-30 DOI: 10.1109/LSENS.2024.3524033

Sourabh Ghosh;Ehtesham Hassan;Asheesh Kumar Singh;Sri Niwas Singh

{"title":"Deep Learning-Based Multiswitch Open-Circuit Fault Diagnosis for Active Front-End Rectifiers Using Multisensor Signals","authors":"Sourabh Ghosh;Ehtesham Hassan;Asheesh Kumar Singh;Sri Niwas Singh","doi":"10.1109/LSENS.2024.3524033","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3524033","url":null,"abstract":"Open-circuit switch faults (OCSFs) in power semiconductor switches are caused by wire bonding failures, gate driver malfunction, surge voltage/current, electromagnetic interference, and cosmic radiation. Under OCSFs, the signal characteristics are not excessively high, but prolonged OCSFs risk cascading system failures. This letter presents a comprehensive analysis of various deep neural network (DNN)-based architectures, such as long short-term memory (LSTM) and convolutional neural network (CNN), to diagnose multiclass OCSFs in three-phase active front-end rectifiers (TP-AFRs). A novel multisensor time-series sequence (MTSS) dataset is acquired at 500 Hz, comprising 624 observations from 19 sensor signals for single, double, and triple-switch OCSFs. The intertwining issue in the MTSS dataset is visualized using t-SNE, and the initial experiments with support vector machine (SVM) rendered the highest test accuracy of 93% against k-nearest neighbor, artificial neural network, and decision tree classifiers. Further, our investigations revealed that an architecture with two-layer CNN, one-layer LSTM, and one fully connected layer achieves a competitive testing accuracy of 95.03%, showing an improvement of 2.03% from the SVM classifier, and 7.03% from the one-layer LSTM network. These findings demonstrate the potential of this approach for enhancing reliability of TP-AFRs with the direct application of downsampled raw electrical signals.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976132","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

Auto-Fusing Covariance and Phase Locking Value With Brain-Inspired Spiking Neural Networks for EEG-Based Driver Reaction Time Prediction

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-27 DOI: 10.1109/LSENS.2024.3523443

Adarsh V Parekkattil;Vivek Singh;Tharun Kumar Reddy Bollu

引用次数: 0

Optimizing Activity Recognition Through Dominant Axis Identification in Inertial Sensors 基于优势轴辨识的惯性传感器活动识别优化

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-26 DOI: 10.1109/LSENS.2024.3523334

Rahul Mishra;Aishwarya Soni;Ayush Jain;Priyanka Lalwani;Raj Shah

{"title":"Optimizing Activity Recognition Through Dominant Axis Identification in Inertial Sensors","authors":"Rahul Mishra;Aishwarya Soni;Ayush Jain;Priyanka Lalwani;Raj Shah","doi":"10.1109/LSENS.2024.3523334","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3523334","url":null,"abstract":"Recent years have witnessed significant growth in sensors-based human locomotion activities recognition due to the availability of low-cost, low-power, and compact sensors and microcontroller units. While significant research has been conducted on human locomotion activity recognition using inertial sensors, most prior studies heavily rely on data from all axes of the sensors. However, the importance of dominant axes in reducing training and inference time has been largely overlooked in these investigations. This letter presents a novel approach, dominant axes-human activity recognition, which aims to identify the dominant axes of inertial sensors to effectively recognize human locomotion activities. The proposed approach effectively reduces both training and inference time while still achieving substantial accuracy. The approach begins with data collection through dedicated smartphone applications and sensory probes. Subsequently, the collected sensory data undergoes preprocessing and annotation for model training. Further, cross-validation is performed during the training phase to determine the dominant axes, leveraging information about the orientation within the dataset. Finally, this work conducts experiments on the collected dataset to assess the approach's efficacy in terms of accuracy and training time.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940715","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

EEG-BBNet: A Hybrid Framework for Brain Biometric Using Graph Connectivity EEG-BBNet：一个使用图连接的脑生物识别混合框架

IF 2.2

IEEE Sensors Letters Pub Date : 2024-12-26 DOI: 10.1109/LSENS.2024.3522981

Payongkit Lakhan;Nannapas Banluesombatkul;Natchaya Sricom;Phattarapong Sawangjai;Soravitt Sangnark;Tohru Yagi;Theerawit Wilaiprasitporn;Wanumaidah Saengmolee;Tulaya Limpiti

{"title":"EEG-BBNet: A Hybrid Framework for Brain Biometric Using Graph Connectivity","authors":"Payongkit Lakhan;Nannapas Banluesombatkul;Natchaya Sricom;Phattarapong Sawangjai;Soravitt Sangnark;Tohru Yagi;Theerawit Wilaiprasitporn;Wanumaidah Saengmolee;Tulaya Limpiti","doi":"10.1109/LSENS.2024.3522981","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3522981","url":null,"abstract":"Most EEG-based biometrics rely on either convolutional neural networks (CNNs) or graph convolutional neural networks (GCNNs) for personal authentication, potentially overlooking the limitations of each approach. To address this, we propose EEG-BBNet, a hybrid network that combines CNNs and GCNNs. EEG-BBNet leverages CNN's capability for automatic feature extraction and the GCNN's ability to learn connectivity patterns between EEG electrodes through graph representation. We evaluate its performance against solely CNN-based and graph-based models across three brain–computer interface tasks, focusing on daily motor and sensory activities. The results show that while EEG-BBNet with Rho index functional connectivity metric outperforms graph-based models, it initially lags behind CNN-based models. However, with additional fine-tuning, EEG-BBNet surpasses CNN-based models, achieving a correct recognition rate of approximately 90%. This improvement enables EEG-BBNet to adapt its learning in new sessions and to acquire different domain knowledge across various BCI tasks (e.g., motor imagery to steady-state visually evoked potentials), demonstrating promise for practical authentication.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993288","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