IEEE Sensors Journal最新文献_第8页

Enhancing Spatial Resolution and Signal Intensity of Laser Doppler Velocimetry for Flow Sensing Through Optical Path Optimization 通过光路优化提高激光多普勒流速测量的空间分辨率和信号强度

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-24 DOI: 10.1109/JSEN.2025.3552104

Lili Jiang;Xinyu Zhang;Baoyi Shan;Bingbing Li;Juan Su;Chi Wu

{"title":"Enhancing Spatial Resolution and Signal Intensity of Laser Doppler Velocimetry for Flow Sensing Through Optical Path Optimization","authors":"Lili Jiang;Xinyu Zhang;Baoyi Shan;Bingbing Li;Juan Su;Chi Wu","doi":"10.1109/JSEN.2025.3552104","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3552104","url":null,"abstract":"In response to the high spatial resolution and accuracy requirement for non-contact flow velocity sensors in complex flow field measurements, this article explores the impact of laser beam expansion in the laser emission path on the flow velocity sensing performance of the reference beam-type Laser Doppler Velocimeter (LDV), aiming to enhance the measurement capability of the LDV. Through simulation analysis and experimental verification, the study analyzes the effects of beam expansion on the measurement volume, scattered signal intensity, velocity signal intensity, and measurement accuracy of LDV. The results indicate that increasing the expansion factor from <inline-formula> <tex-math>$5times $ </tex-math></inline-formula> to <inline-formula> <tex-math>$9times $ </tex-math></inline-formula> leads to a 60.38% decrease in the LDV measurement volume, a 5.31-fold increase in scattered light intensity, and nearly a doubling of the velocity signal intensity measured by LDV. The reduction in measurement volume effectively improves the spatial resolution of the flow field measurement, and also improves the accuracy of velocity measurement. Additionally, the enhanced scattered light signal improves the LDV’s ability to sense velocity signals from small particles. Smaller particles exhibit better velocity tracking characteristics, making the flow velocity measurement results more accurate. These improvements are of great significance for precise sensing and analysis of complex flow fields and dynamic behaviors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"15013-15022"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Flexible Detection Blood Glucose Sensor Based on Copper-Doped Molybdenum Disulfide Composites 基于铜掺杂二硫化钼复合材料的柔性检测血糖传感器

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-24 DOI: 10.1109/JSEN.2025.3551726

Jingmin Ge;Shitai Wen;Zhaoyang Feng;Wei Peng;Pan Liu;Jingfang Ji;Guochen Qi

{"title":"A Flexible Detection Blood Glucose Sensor Based on Copper-Doped Molybdenum Disulfide Composites","authors":"Jingmin Ge;Shitai Wen;Zhaoyang Feng;Wei Peng;Pan Liu;Jingfang Ji;Guochen Qi","doi":"10.1109/JSEN.2025.3551726","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3551726","url":null,"abstract":"The development of enzyme-free flexible glucose sensors is essential for advancing diabetes management, improving blood glucose control, and reducing the burden of monitoring. Herein, a simple two-step method was used to synthesize Cu-MoS2 for glucose electrochemical sensing, which was then assembled onto flexible carbon cloth to form a flexible sensor (signed as Cu-MoS2). The Cu-MoS2 exhibits a sensitivity of <inline-formula> <tex-math>$364~mu $ </tex-math></inline-formula>A/(mM<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>cm2) over a wide linear range of <inline-formula> <tex-math>$1~mu $ </tex-math></inline-formula>M–20 mM. It is reliable, reproducible, and electrochemically stable, with high specificity for glucose. After bending and stretching the Cu-MoS2, its sensing performance was re-evaluated. The results showed that Cu-MoS2 retained excellent sensitivity, interference resistance, and stability, indicating its potential to meet the real-time blood glucose monitoring requirements of flexible sensors. Density generic function theory (DFT) calculations showed that the introduction of Cu into MoS2 can enhance the adsorption and activation of glucose molecules, which is conducive to the oxidative dehydrogenation steps of –CHO and –OH in glucose, thereby improving the glucose sensing performance. The preparation method for the flexible sensor proposed in this work provides valuable technical guidance and shows promising potential for the development of flexible blood glucose sensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"14629-14636"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global Discriminative Information Search and Focus for SAR Target Recognition SAR目标识别的全局判别信息搜索与聚焦

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-24 DOI: 10.1109/JSEN.2025.3552578

Chenxi Zhao;Daochang Wang;Siqian Zhang;Gangyao Kuang

{"title":"Global Discriminative Information Search and Focus for SAR Target Recognition","authors":"Chenxi Zhao;Daochang Wang;Siqian Zhang;Gangyao Kuang","doi":"10.1109/JSEN.2025.3552578","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3552578","url":null,"abstract":"Deep learning methods have been widely used in the field of synthetic aperture radar (SAR) target recognition. However, given the difficulty in obtaining high-quality SAR images, existing models tend to focus on non-target regions, leading to uncontrollable overfitting phenomena. To cope with such an inherent problem, a novel global discriminative information search and focus (GDI-SF) network is proposed. The proposed framework obtains a holistic and pure description of the target without increasing the extra model parameters and annotations. Specifically, to capture the global description of the target, we employ higherorder self-correlation (HSC) to enhance the interaction among features and elegantly aggregate global target-related information during the training period. In view of the special imaging mechanism and scattering characteristics of SAR images, the images contain complex interference information, which will be coupled with the target features during the feature global interaction and fail to be separated easily. Thus, we constrain the input data to converge to the target region and eliminate the influence of target-irrelevant information from the source input. Under the above losses constraint, purer global discriminative target features are captured to yield more robust and superior recognition results elegantly. Finally, we conduct experiments on the full aspect stationary targets-vehicle (FAST-Vehicle) dataset and SAR aircraft category (SAR-ACD) dataset to verify the superior performance of the proposed method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"15735-15749"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Rolling Bearing Fault Diagnosis Method Based on Complementary Fusion of Multidomain Features 基于多域特征互补融合的滚动轴承故障诊断方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-21 DOI: 10.1109/JSEN.2025.3551771

Pengli Jiang;Chen Shen;Jiesi Luo;Guijuan Lin;Shaohui Zhang

{"title":"A Rolling Bearing Fault Diagnosis Method Based on Complementary Fusion of Multidomain Features","authors":"Pengli Jiang;Chen Shen;Jiesi Luo;Guijuan Lin;Shaohui Zhang","doi":"10.1109/JSEN.2025.3551771","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3551771","url":null,"abstract":"Feature extraction is a critical step in fault diagnosis. In order to address the limitations of fault diagnosis methods based on single-domain feature extraction, which rely on the quality and quantity of data samples and suffer from insufficient information extraction and limited generalization capabilities, a fault diagnosis method for rolling bearings based on multidomain feature complementary fusion is proposed. First, recursive, time-domain, and frequency-domain features are extracted from the vibration signals, and the three domain features are fused to construct the original feature set. Considering that the fused feature set contains numerous irrelevant and redundant features, an improved distance evaluation (IDE) criterion is introduced to select relevant features from the original set, forming a sensitive feature subset. Finally, this sensitive feature subset is inputted into a classifier for fault diagnosis. This method is applied to rolling bearing datasets provided by Paderborn University in Germany and Jiangnan University. Fault diagnosis was performed on these datasets using common classifiers, such as support vector machine (SVM) and random forest (RF). The results indicate that multidomain fused features not only outperform single-domain features but also maintain robust diagnostic performance across different classifiers and datasets.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"15711-15722"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Convolution Compression Network for Coronary Artery Disease Detection Using Auscultation Signal 基于听诊信号的冠状动脉疾病快速卷积压缩网络

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-21 DOI: 10.1109/JSEN.2025.3551090

Chongbo Yin;Yan Shi;Yineng Zheng;Xingming Guo

{"title":"Fast Convolution Compression Network for Coronary Artery Disease Detection Using Auscultation Signal","authors":"Chongbo Yin;Yan Shi;Yineng Zheng;Xingming Guo","doi":"10.1109/JSEN.2025.3551090","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3551090","url":null,"abstract":"Heart sound auscultation combined with deep learning is a common method for coronary artery disease (CAD) detection. However, current studies predominantly focus on improving network accuracy while neglecting the lightweight structure, especially the runtime. To address the limitations, we propose a fast convolution compression network (FCCN) for automated CAD severity classification. Our experimental dataset comprises 150 clinical heart sound recordings with varying degrees of coronary stenosis, including 80 samples from severe CAD cases and 70 from nonsevere cases. The large tied one-shot aggregation convolution (LTOAC) module is proposed in FCCN, which utilizes shared convolutional filters and concise feature aggregation to improve feature utilization efficiency. FCCN integrates feature extraction and pattern recognition through an end-to-end framework without excessive speed latency and parameter costs. Experiment is performed on the dataset and demonstrates FCCN’s performance, achieving an accuracy of 85.82%, sensitivity of 85.3%, and specificity of 86.26% with 1.9 million parameters. The system balances model complexity with classification performance through parameter-efficient design. Our study based on clinical practice, provides an effective and fast method for CAD detection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"16213-16222"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subband STAP Based on the Sparse Frequency Waveform for Airborne Radar in Dense Unintentional Jamming Environment 基于稀疏频率波形的机载雷达密集非故意干扰子带STAP

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-21 DOI: 10.1109/JSEN.2025.3550325

Ming Hou;Wenchong Xie;Wei Chen;Yuanyi Xiong;Lingyan Dai

{"title":"Subband STAP Based on the Sparse Frequency Waveform for Airborne Radar in Dense Unintentional Jamming Environment","authors":"Ming Hou;Wenchong Xie;Wei Chen;Yuanyi Xiong;Lingyan Dai","doi":"10.1109/JSEN.2025.3550325","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3550325","url":null,"abstract":"Compared with ground-based radar, airborne radar has excellent low-altitude moving target detection performance. With the development of economics, ground radiation sources are widely distributed, and the low-frequency spectrum is extremely crowded. When airborne radar operates over land, dense unintentional jamming can have a severe impact on target detection. The effects are mainly reflected in the following three aspects: 1) it is easy for jamming to enter from the main lobe and the main-lobe jamming is formed, which is difficult to be suppressed by traditional anti-jamming methods; 2) since the jamming distribution is dense, the degrees of freedom (DOFs) of jamming exceed the spatial DOFs of the system; and 3) jamming and clutter exist simultaneously and strong clutter is difficult to be suppressed effectively. In this article, a subband space-time adaptive processing (STAP) method for airborne radar based on the sparse frequency waveform is proposed. First, the jamming frequency and azimuth angle are obtained in the passive mode, and accordingly, the sparse frequency waveform is designed to suppress main-lobe jamming. Then, the echo data are divided into subbands, and each subband is processed by subband sidelobe cancellation processing to achieve the suppression of dense sidelobe jamming. Finally, the strong clutter is suppressed by STAP. The simulation results demonstrate that the proposed method has excellent performance in suppressing dense unintentional jamming and clutter.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"15612-15624"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DFNet: A Dual LiDAR–Camera Fusion 3-D Object Detection Network Under Feature Degradation Condition 特征退化条件下双激光雷达-相机融合的三维目标检测网络

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-21 DOI: 10.1109/JSEN.2025.3551149

Tao Ye;Ruohan Liu;Chengzu Min;Yuliang Li;Xiaosong Li

{"title":"DFNet: A Dual LiDAR–Camera Fusion 3-D Object Detection Network Under Feature Degradation Condition","authors":"Tao Ye;Ruohan Liu;Chengzu Min;Yuliang Li;Xiaosong Li","doi":"10.1109/JSEN.2025.3551149","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3551149","url":null,"abstract":"LiDAR-camera fusion is widely used in 3-D perception tasks. In LiDAR and camera sensing tasks, the hierarchical feature abstraction capability possessed by the deep network is beneficial to capture the detailed information from point clouds and RGB images. However, it tends to filter some of the information to extract important features, where the problem of feature degradation due to loss of useful information is inevitable. The deterioration of LiDAR-camera fusion due to feature degradation, brought about by this factor, becomes a challenging problem. It reduces object recognition and leads to decreased detection accuracy. To address this problem, we propose a dual LiDAR-camera fusion network (DFNet) based on cross-modal compensation and feature enhancement. We design a multimodal feature extraction (MFE) module to complement the sparse features of the point cloud utilizing image features and focusing on the spatial information of the features. Then, we introduce a multiscale feature aggregation (MFA) module to generate bird’s-eye view (BEV) representations of the features, which generates feature proposals that are then input to the voxel-grid aggregation (VGA) module to obtain the grid-pooled features. Meanwhile, the VGA module receives the feature proposals extracted from the image backbone and projects the point cloud through voxels to obtain voxel-fused features. Finally, we aggregate the grid-pooled features and voxel-fused features to produce more informative fused features. The results on the KITTI dataset illustrate that DFNet outperforms most 3-D object detection methods, achieving the 3-D detection performance of 77.88% mAP, which indicates that our method is effective in dealing with feature degradation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"16223-16234"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design, Fabrication, and Characterization of Staggered Array Radial Coil RFEC Probe for Small Diameter Ferritic Steel Tube 小直径铁素体钢管交错阵列径向线圈RFEC探针的设计、制造与表征

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-21 DOI: 10.1109/JSEN.2025.3550944

T. Vijayachandrika;Arjun V.;S. Thirunavukkarasu;Anish Kumar

{"title":"Design, Fabrication, and Characterization of Staggered Array Radial Coil RFEC Probe for Small Diameter Ferritic Steel Tube","authors":"T. Vijayachandrika;Arjun V.;S. Thirunavukkarasu;Anish Kumar","doi":"10.1109/JSEN.2025.3550944","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3550944","url":null,"abstract":"This article deals with a first of its kind design and fabrication of an arrayed multicoil probe for detection and characterization of defects in steam generator (SG) (9 Cr-1 Mo) tube used in fast breeder reactor (FBR) by remote field eddy current (RFEC) testing. Preliminary studies on the choice of detector orientation and the optimization of the detector size, shape, and angular position are carried out using finite element (FE) model and verified experimentally. Equal sensitivity, good spatial resolution, absence of dead zone, and defect characterization are the main requirements of a good array probe. With a multicoil arrangement, the array probe exhibits high detection sensitivity and high speed for defect detection around the whole tube without a need for probe rotation. The detailed knowledge of the number of defects and their axial and circumferential positions provided by an array probe helps in enhanced defect characterization. Furthermore, a methodology to create uniform thresholding to separate the defect and defect free regions is also presented. Experimental results show that the developed probe provides high detectability and is capable of reconstructing smaller defects.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"15906-15913"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Neural Network With Local–Global Context Aggregation and Self-Distillation for Fish Counting in Deep-Sea Aquaculture 基于局部全局上下文聚合和自蒸馏的深度神经网络在深海水产养殖中的鱼类计数

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-21 DOI: 10.1109/JSEN.2025.3550322

Hanchi Liu;Xin Ma;Haoran Li

{"title":"Deep Neural Network With Local–Global Context Aggregation and Self-Distillation for Fish Counting in Deep-Sea Aquaculture","authors":"Hanchi Liu;Xin Ma;Haoran Li","doi":"10.1109/JSEN.2025.3550322","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3550322","url":null,"abstract":"Vision-based fish counting plays a vital role in monitoring breeding density, optimizing feeding strategies, and planning marketing schedules in deep-sea aquaculture. However, large-scale variations in fish and nonuniform background illumination in underwater images make it challenging to accurately count fish in deep-sea cages. Aiming to solve these issues, this study proposes a deep neural network (DNN) with local-global context aggregation and self-distillation called LGSDNet for fish counting and density estimation in deep-sea aquaculture. First, a local-global context aggregation module (LGCAM) is designed to aggregate the dense local multiscale context and global context in images, enabling the network to capture robust feature representations for fish with various scales under various background illumination conditions. Then, a self-distillation module (SDM) is designed to leverage information from the deep layers of the network to guide the learning of the shallow layers, enhancing the representation learning of the network without increasing the inference time. Extensive comparative experiments on the fish counting dataset collected from a deep-sea cage demonstrate the effectiveness of LGSDNet. It achieves a mean absolute error (MAE) of 5.68, a root-mean-squared error (RMSE) of 7.38, and a mean absolute percentage error (MAPE) of 3.27%, outperforming the Baseline with a reduction in the aforementioned metrics by 6.75, 7.92, and 3.42%, respectively. In addition, LGSDNet outperforms state-of-the-art fish and crowd counting methods on the dataset while having only 13.03 M parameters. Generalization experiments further demonstrate the adaptability of LGSDNet to diverse aquaculture environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 9","pages":"16411-16424"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Model for the Current-Voltage Characteristic of Membrane/Electrolyte Junctions 膜/电解质结的电流-电压特性模型

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-20 DOI: 10.1109/JSEN.2025.3550507

Leandro Julian Mele;Muhammad Ashraful Alam;Pierpaolo Palestri

引用次数: 0