IEEE Sensors Letters最新文献_第3页

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

Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-24 DOI: 10.1109/LSENS.2025.3544656

Dima Bykhovsky;Zikang Chen;Yiwei Huang;Xiaoying Zheng;Tom Trigano

引用次数: 0

High-Performance VA-MoS$_{2}$/GO Heterostructure-Based Sensor for In-Situ Soil Moisture Sensing

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-24 DOI: 10.1109/LSENS.2025.3545267

Prajjwal Shukla;Rahul Gond;Brajesh Rawat

{"title":"High-Performance VA-MoS$_{2}$/GO Heterostructure-Based Sensor for In-Situ Soil Moisture Sensing","authors":"Prajjwal Shukla;Rahul Gond;Brajesh Rawat","doi":"10.1109/LSENS.2025.3545267","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3545267","url":null,"abstract":"This letter presents a vertically aligned (VA)-MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>/graphene oxide (GO) heterostructure-based resistive sensor designed for high-performance soil moisture monitoring. The sensing film is fabricated using chemical vapor deposition to grow VA-MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> nanoflakes, followed by a spin coating of a colloidal GO solution. The sensor achieves a response of approximately 8.4% at 20% relative humidity (RH) and 41.18% at 80% RH, which demonstrates a broad detection range with high sensitivity. It also exhibits excellent stability, repeatability, and fast response and recovery times. In soil moisture measurement experiments, the sensor shows % responses of around 64.3, 139.8, and 160.67 for black soil with moisture contents of 5.9%, 16.2%, and 19.3%, respectively. With its wide sensing range, linear response, reliable performance, and cost-effective and scalable fabrication process, the VA-MoS<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>/GO heterostructure holds great promise for next-generation soil moisture monitoring applications.","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":"143667610","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 Vehicle Signatures: Comparison of Up-to-Date Automotive Radar Sensors With Different Characteristics

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-21 DOI: 10.1109/LSENS.2025.3544457

Philip Aust;Florian Hau;Jürgen Dickmann;Matthias A. Hein

{"title":"Radar Vehicle Signatures: Comparison of Up-to-Date Automotive Radar Sensors With Different Characteristics","authors":"Philip Aust;Florian Hau;Jürgen Dickmann;Matthias A. Hein","doi":"10.1109/LSENS.2025.3544457","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3544457","url":null,"abstract":"The increasing angular resolution of modern automotive radar sensors enables a more detailed and more accurate perception of the environment. This has implications for the measured target detections of extended objects, such as vehicles, which cause complex backscatter signatures. Simulation-based approaches strive to enable efficient testing concepts, but require high-fidelity sensor models. To assess the impact of different sensors on the virtual replication of sensor data, it is important to examine the variations between measurements from various sensors. In this letter, radar detections of a passenger vehicle using three radars with different characteristics are presented. Similarities between the spatial distributions of the detections are revealed and differences in the number of target detections, and the accuracy of their localization within the bounding box of the vehicle are identified. Furthermore, the spatial fluctuations of point clouds between succeeding measurement cycles are investigated. The results suggest that existing data-driven modeling approaches can be applied to different sensors as well, but that particular attention must be paid to the distinct spatial spread of the detections and to the fluctuations of point clouds.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 3","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570846","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

LG-STSGCN: Long-Term Gated Pedestrian Trajectory Prediction Based on Spatial–Temporal Synchronous Graph Convolutional Network

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-21 DOI: 10.1109/LSENS.2025.3541437

Yang Chen;Juwei Guo;Ke Wang;Dongfang Yang;Xu Yan;Lihong Qiu

{"title":"LG-STSGCN: Long-Term Gated Pedestrian Trajectory Prediction Based on Spatial–Temporal Synchronous Graph Convolutional Network","authors":"Yang Chen;Juwei Guo;Ke Wang;Dongfang Yang;Xu Yan;Lihong Qiu","doi":"10.1109/LSENS.2025.3541437","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3541437","url":null,"abstract":"Pedestrian trajectory prediction is fundamental research in many practical applications, such as video surveillance, autonomous vehicles, and robotic systems. However, the existing methods do not capture the spatial–temporal correlation of pedestrians well and simultaneously, as well as do not learn the temporal global interaction features of pedestrians effectively. To address these issues, we propose a long-term gated pedestrian trajectory prediction model based on spatial–temporal synchronous graph convolutional network. The proposed method consists of three components. First, we construct a localized spatial–temporal graph to characterize the temporal information, spatial information and spatial–temporal correlation information among pedestrians in the pedestrian trajectory prediction fully. Then, we introduce a gated mechanism into the temporal convolutional network, in parallel with the gated spatial–temporal synchronous graph convolutional network, in order to improve the model's ability to capture the global correlation of spatial–temporal data. Finally, we add random noise and use a diversity loss function to train and predict trajectories. We conduct experiments on ETH and UCY datasets and the proposed method is proved to outperform previous approaches.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667225","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

Lightweight Wearable Headband With Flexible Hybrid Electronics for Head-Kinematic Monitoring and Mild Traumatic Brain Injury Risk Detection

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-20 DOI: 10.1109/LSENS.2025.3544119

Jeneel Kachhadiya;Jaden Romero;Shuting Kou;Yang Wan;Haneesh Kesari;Ron Szalkowski;Joseph Andrews

{"title":"Lightweight Wearable Headband With Flexible Hybrid Electronics for Head-Kinematic Monitoring and Mild Traumatic Brain Injury Risk Detection","authors":"Jeneel Kachhadiya;Jaden Romero;Shuting Kou;Yang Wan;Haneesh Kesari;Ron Szalkowski;Joseph Andrews","doi":"10.1109/LSENS.2025.3544119","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3544119","url":null,"abstract":"Mild traumatic brain injuries are a significant health risk in sports and military environments, often caused by high-impact forces. This letter presents a flexible hybrid headband system for real-time monitoring of head kinematics during impacts of varying magnitude and direction. It integrates eight triaxial accelerometers in a near-regular tetrahedral configuration and employs an acceleration-only algorithm to measure linear accelerations without gyroscopes. Firmware uses a parallel queue system for efficient real-time data collection at 1600-Hz bandwidth. Testing on a Hybrid III head form via the dummy for rotational evaluation of wearable system evaluated five impact magnitudes and directions (front, rear, and left). CORrelation and Analysis (CORA) validated system accuracy, with average CORA scores of 0.840 (rear), 0.883 (front), and 0.832 (left). Some individual impacts achieved scores up to 0.98. Repeatability tests showed minimal variation, confirming consistent performance. These results demonstrate the system's potential for real-time, reliable head-kinematic monitoring in military helmets and high-impact sports.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740329","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

IF 2.2

IEEE Sensors Letters Pub Date : 2025-02-18 DOI: 10.1109/LSENS.2025.3541477