IEEE Sensors Letters最新文献_第2页

Wearable Multisensory Glove for Shape, Size, and Stiffness Recognition Based on Off-the-Shelf Components

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-05 DOI: 10.1109/LSENS.2025.3548264

Mohamad Yaacoub;Ali Ibrahim;Fatima Khansa;Leila Hammadi;Christian Gianoglio

引用次数: 0

FPGA-Based Real-Time Road Object Detection System Using mmWave Radar

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-03 DOI: 10.1109/LSENS.2025.3547008

Anand Mohan;Hemant Kumar Meena;Mohd Wajid;Abhishek Srivastava

{"title":"FPGA-Based Real-Time Road Object Detection System Using mmWave Radar","authors":"Anand Mohan;Hemant Kumar Meena;Mohd Wajid;Abhishek Srivastava","doi":"10.1109/LSENS.2025.3547008","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3547008","url":null,"abstract":"This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment's point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (VGG) 16 model. We then assessed four machine learning models for object detection and found that the support vector machine (SVM) model and logistic regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667612","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

Thin-Film Magneto-Optic Plasmonic Sensor With the Photonic Spin Hall Effect

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-03 DOI: 10.1109/LSENS.2025.3547745

Harshit Shukla;Alka Verma;Anuj K. Sharma;Yogendra Kumar Prajapati

引用次数: 0

IDMatchHAR: Semi-Supervised Learning for Sensor-Based Human Activity Recognition Using Pretraining

IF 2.2

IEEE Sensors Letters Pub Date : 2025-03-03 DOI: 10.1109/LSENS.2025.3546985

Koki Takenaka;Shunsuke Sakai;Tatsuhito Hasegawa

{"title":"IDMatchHAR: Semi-Supervised Learning for Sensor-Based Human Activity Recognition Using Pretraining","authors":"Koki Takenaka;Shunsuke Sakai;Tatsuhito Hasegawa","doi":"10.1109/LSENS.2025.3546985","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3546985","url":null,"abstract":"In sensor-based human activity recognition (HAR), the annotation cost for sensor data is higher compared to data, such as images. One can use semisupervised learning (semi-SL) to reduce annotation costs. This method lever-ages unlabeled datasets by assigning pseudolabels. How- ever, these methods have the issue of confirmation bias, where performance degrades due to incorrect pseudolabels. Some approaches have attempted to solve this problem by performing multistage pretraining with labeled and unlabeled data, but these methods require significant computational resources. We propose a framework called IDMatchHAR, which performs semi-SL with a single-stage pretraining process on small-scale datasets. We use instance discrimination (ID) during pretraining to learn robust feature representations applied to the subsequent semi-SL task. We verify the effectiveness of the proposed framework using various convolutional neural networks (CNNs), such as VGG and residual network (ResNet), as well as Transformers, on HASC, WISDM, and Pamap2. Our proposed framework significantly reduces the computational cost of pretraining while demonstrating performance comparable to or exceeding that of existing semi-SL methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645167","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 Charge Balanced Stimulators With a Twin Compensation Loop for Evoked Neural Potential Sensing

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-28 DOI: 10.1109/LSENS.2025.3546239

Longbin Zhu;Wenjie Wang;Risheng Su;Zhijun Zhou;Keping Wang

{"title":"A Charge Balanced Stimulators With a Twin Compensation Loop for Evoked Neural Potential Sensing","authors":"Longbin Zhu;Wenjie Wang;Risheng Su;Zhijun Zhou;Keping Wang","doi":"10.1109/LSENS.2025.3546239","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3546239","url":null,"abstract":"Neurological functional electrical stimulation (NFES) provides a well-controlled and high degree of reconfigurability amount for producing the desired stimulation effect. For evaluating the effect of stimulation, the evoked potentials can be sensed. However, in practice, the unbalanced charge accumulation leads to residual charges and toxic electrochemical reactions at the electrode-tissue sensing interface, which causes electrode corrosion and tissue damage. This letter presents a twin compensation loop (TCL) charge-balancing (CB) topology for the NFES. The TCL-CB consists of two compensation loops, including a positive compensation loop (PCL) and a negative compensation loop (NCL). The PCL and NCL continuously detect and compensate for the accumulated negative and positive charges at the output of the Wilson current mirror (WSC), respectively. The circuits are targeted at integrated circuit (IC) realization, designed and fabricated in a 0.18-<italic>μ</i>m complementary metal oxide semiconductor (CMOS) technology. The power consumption of the WSC with TCL-CB is circa 79.2 μW and occupies a die area of 0.047 mm<sup>2</sup> (0.265 μm × 0.18 μm). Benefiting from TCL-CB, the charge mismatch at the output of WSC is reduced to less than 1%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645302","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

Optimal Load Type for Passive Magnetic Energy Harvesters 无源磁能采集器的最佳负载类型

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-27 DOI: 10.1109/LSENS.2025.3546246

Alon Kuperman

引用次数: 0

Localization of RFID Tags Through Real-Time Angle-of-Arrival Estimation

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-26 DOI: 10.1109/LSENS.2025.3546092

Antonello Florio;Gianfranco Avitabile;Giuseppe Coviello

引用次数: 0

A Lightweight Causal Sound Separation Model for Real-Time Hearing Aid Applications

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-26 DOI: 10.1109/LSENS.2025.3546132

Harsh Mishra;Mahendra K. Shukla;Priyanshu;Som Dengre;Yashveer Singh;Om Jee Pandey

引用次数: 0

Smoothing Objective Function for 3-D Electrical Resistivity Inversion by CNNs Regularizer

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-24 DOI: 10.1109/LSENS.2025.3544577

Peng Jiang;Shengjie Qiao;Yonghao Pang;Yongheng Zhang;Zhengyu Liu

{"title":"Smoothing Objective Function for 3-D Electrical Resistivity Inversion by CNNs Regularizer","authors":"Peng Jiang;Shengjie Qiao;Yonghao Pang;Yongheng Zhang;Zhengyu Liu","doi":"10.1109/LSENS.2025.3544577","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3544577","url":null,"abstract":"In tunnel geological forecasting, the electrical resistivity inversion method is extensively employed due to its high sensitivity to water-bearing bodies. Traditional inversion methods, such as least squares, simplify nonlinear problems into linear ones. However, they often converge to local minima, making it challenging to identify the global optimal solution, and their inversion results are highly dependent on the choice of the initial model. To address these challenges, we propose integrating convolutional neural networks (CNNs) into the conventional iterative inversion framework. Instead of directly optimizing the initial resistivity model, our approach focuses on updating the network parameters, with the resistivity model subsequently generated by the CNN. This enables the CNN structure to regularize the resistivity model, resulting in a smoother objective function. Consequently, our method exhibits greater robustness to variations in the initial model, leading to improved inversion results. Our numerical simulations and practical applications in engineering projects demonstrate that, compared to traditional inversion methods, the proposed approach is less sensitive to the initial model and achieves superior inversion outcomes, thereby validating our hypothesis.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611907","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 of a 4H-SiC Lateral PMOSFET Temperature Sensor Between 14 K–482 K

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-24 DOI: 10.1109/LSENS.2025.3544712

Nicola Rinaldi;Mathias Rommel;Alexander May;Rosalba Liguori;Alfredo Rubino;Gian Domenico Licciardo;Luigi Di Benedetto

{"title":"Analysis of a 4H-SiC Lateral PMOSFET Temperature Sensor Between 14 K–482 K","authors":"Nicola Rinaldi;Mathias Rommel;Alexander May;Rosalba Liguori;Alfredo Rubino;Gian Domenico Licciardo;Luigi Di Benedetto","doi":"10.1109/LSENS.2025.3544712","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3544712","url":null,"abstract":"A temperature sensor based on a diode-connected 4H-Silicon Carbide p-type metal oxide semiconductor field effect transistor is characterized in the temperature range between 14 K and 482 K and its performance has been analyzed. The study shows that the sensor characteristics are mainly affected by the threshold voltage, but an unusual reduction of the device current is shown for temperatures lower than 76 K, which limits the linearity of the sensor response. Indeed, two operating temperature ranges could be defined. The first one is in the range <inline-formula><tex-math>$ text{76 K}leq T leq text{175 K}$</tex-math></inline-formula>, showing a linearity of 0.991 in terms of coefficient of determination and a sensitivity of <inline-formula><tex-math>$ text{6.24 mV/K}$</tex-math></inline-formula> at a device current of 3.34 nA, instead the second range is for temperatures higher than 175 K with a linearity of 0.986 and a sensitivity of <inline-formula><tex-math>$ text{66.37 mV/K}$</tex-math></inline-formula> for a current of 263 nA. Moreover, the device is fully compatible with 4H-SiC CMOS technology making possible its use in integrated circuits.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553316","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