IEEE Sensors Journal最新文献_第4页

Validation of an MEMS-Based Pressure Sensor System for Atrial Fibrillation Detection From Wrist and Finger 基于mems的手腕和手指心房颤动压力传感器系统的验证

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-03 DOI: 10.1109/JSEN.2025.3574232

Yangyang Zhao;Olli Lahdenoja;Ismail Elnaggar;Tuija Vasankari;Samuli Jaakkola;Tuomas Kiviniemi;Juhani Airaksinen;Matti Kaisti;Tero Koivisto

{"title":"Validation of an MEMS-Based Pressure Sensor System for Atrial Fibrillation Detection From Wrist and Finger","authors":"Yangyang Zhao;Olli Lahdenoja;Ismail Elnaggar;Tuija Vasankari;Samuli Jaakkola;Tuomas Kiviniemi;Juhani Airaksinen;Matti Kaisti;Tero Koivisto","doi":"10.1109/JSEN.2025.3574232","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574232","url":null,"abstract":"To address the unmet need for a low-cost, low-power wearable solution for continuous cardiovascular health monitoring, we developed and validated an atrial fibrillation (AF) detection algorithm using clinical data collected with a microelectromechanical system (MEMS)-based pressure sensor. This sensor system, consisting of a circuit board, capacitive digitizer, and three MEMS elements, was specifically designed for early detection of AF—a common cardiac arrhythmia that requires frequent screening. The proposed algorithm extracts seven AF-related features, derived from autocorrelation analysis, interbeat interval (IBI) measurements, and differential IBI (dIBI) analysis, including a novel mean distance of points in the Poincaré plot (MDPP) feature. Clinical validation was conducted using data from 53 participants across three datasets: 13 healthy volunteers (wrist), 20 postcardiac surgery sinus rhythm (SR) patients (wrist), and 20 patients with AF (wrist and finger). Leave-one-out cross-validation showed that logistic regression achieved an area under the receiver operating characteristic curve (AUROC) of 93.0% using the full feature set. Performance remained stable across segment lengths ranging from 10 to 120 s, supporting the algorithm’s suitability for continuous monitoring. Consistent performance across seven different classifiers (average AUROC 92.1%) further demonstrated the clinical applicability and generalizability of the approach for wearable-based AF screening. To assess robustness against motion artifacts, we introduced five types of synthetic noise, with the algorithm maintaining strong AF detection performance under these conditions. Finally, a systematic evaluation of sensor waveform shape and signal strength across SR and AF at both the wrist and finger sites demonstrates the potential of the sensor system for wearable AF screening.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25615-25624"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Field-Circuit Coupling With Response Surface Model for the Optimization of Flexible Printed RF Coil in Unilateral NMR Logging Sensor 单侧核磁共振测井传感器柔性印刷射频线圈的场路耦合响应面模型优化

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-03 DOI: 10.1109/JSEN.2025.3574023

Xianneng Xu;Zheng Xu

{"title":"Field-Circuit Coupling With Response Surface Model for the Optimization of Flexible Printed RF Coil in Unilateral NMR Logging Sensor","authors":"Xianneng Xu;Zheng Xu","doi":"10.1109/JSEN.2025.3574023","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574023","url":null,"abstract":"The unilateral wireline nuclear magnetic resonance (NMR) logging sensor is an effective and promising tool for estimating petroleum reservoirs. The flexible printed coil (FPC) is a critical component for the sensor to get a high signal-to-noise ratio (SNR). Unfortunately, because of its intricate multiscale properties, it is difficult to calculate the magnetic field and equivalent circuit parameters of FPC in order to determine the SNR of the sensor. It is, therefore, impossible to achieve rapid optimization of the FPC to increase the SNR of the sensor. This study introduces a novel simulation approach that combines the 2-D field-circuit coupling method with the multiquadric radial basis function (MQ-RBF)-based response surface model. The purpose is to increase the SNR of the sensor by efficiently optimizing the FPC structure. The suggested 2-D field-circuit coupling method allows for quick calculation of FPC, while eliminating the need for sophisticated 3-D finite element simulation. Using the results from the 2-D field-circuit coupling method, the calculation efficiency for the SNR of the sensor with various FPC structures is further enhanced by employing the analytical MQ-RBF-based response surface model. This approach combines the response surface model for SNR prediction with the genetic algorithm (GA) for optimization, enabling the efficient identification of the optimal FPC structure with high SNR. The NMR signals of the sensor equipped with the new FPC and the old FPC were tested and compared using the Carr-Purcell–Meiboom-Gill (CPMG) sequence, with copper sulfate employed as the measurement sample. The experimental results demonstrate that the SNR of the sensor with the new FPC has improved by 32.2% compared to that with the old FPC.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24525-24534"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550452","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

CTISNeRF: Efficient 4-D Hyperspectral Scene Rendering and Generation With Computed Tomography Imaging Spectrometer CTISNeRF：高效的4-D高光谱场景渲染和生成与计算机断层成像光谱仪

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-03 DOI: 10.1109/JSEN.2025.3574423

Yifan Si;Sailing He

{"title":"CTISNeRF: Efficient 4-D Hyperspectral Scene Rendering and Generation With Computed Tomography Imaging Spectrometer","authors":"Yifan Si;Sailing He","doi":"10.1109/JSEN.2025.3574423","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574423","url":null,"abstract":"The hyperspectral data, renowned for its capacity to provide comprehensive spectral details, are widely applied in a range of low-level and high-level tasks in remote sensing and computer vision. In this article, we introduce an algorithm that, for the first time, leverages snapshot spectral imaging technology to generate 4-D hyperspectral-spatial data, named CTISNeRF. This advancement is made possible through the use of a computed tomography imaging spectrometer (CTIS), a cutting-edge sensor technology capable of capturing high-resolution spectral and spatial information in a single snapshot. In addition, a cutting-edge 360° panoramic hyperspectral dataset has been created and made publicly available. Our approach utilizes data from the CTIS sensor and a zeroth-order feature-sharing mechanism to adeptly learn spectral and spatial characteristics from diverse scenes. This enables the rendering of high-fidelity spectral cubes for novel views, significantly enhancing the quality and detail of hyperspectral imaging. Extensive experimental outcomes demonstrate that CTISNeRF not only markedly reduces the expenses associated with data collection but also achieves superior image quality. It reaches state-of-the-art standards in metrics, such as peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). Furthermore, CTISNeRF maintains a more stable generation capability even when the number of training samples is reduced, showcasing its robustness and efficiency. The associated dataset and our algorithm will be publicly accessible at the following repository: <uri>https://github.com/YifanSi/CTISNeRF</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24535-24547"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Supervised Triple Deep Learning Strategy for Fault Isolation and Tolerant Cruise Control in Connected Autonomous Vehicles 基于监督三重深度学习的互联自动驾驶汽车故障隔离与容错巡航控制策略

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3571253

Mehdi Mousavi;Mojtaba Kordestani;Milad Moradi;Ali Chaibakhsh;Mehrdad Saif

{"title":"A New Supervised Triple Deep Learning Strategy for Fault Isolation and Tolerant Cruise Control in Connected Autonomous Vehicles","authors":"Mehdi Mousavi;Mojtaba Kordestani;Milad Moradi;Ali Chaibakhsh;Mehrdad Saif","doi":"10.1109/JSEN.2025.3571253","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3571253","url":null,"abstract":"Fault-tolerant cruise control technologies are crucial for autonomous vehicles to ensure continuous operation and safety by effectively managing unexpected system failures. This article introduces a new supervised triple deep learning method for fault isolation and tolerant cruise control in connected autonomous vehicles (CAVs). First, the dynamic model of CAVs is captured using an autoencoder via measurable signals. Adaptive thresholds are then derived from the dynamic model to facilitate fault detection. Afterward, for fault isolation, a bidirectional long short-term memory (BiDLSTM) network is employed to classify the fault types. Later, three different supervised neural networks, including BiDLSTM, long short-term memory (LSTM), and fully connected networks, are trained to reconstruct the faulty signals. The outputs from these networks are integrated using a fusion method to generate robust reconstructed signals. Subsequently, these signals are forwarded into an adaptive neural network-based controller to mitigate the fault effects. The main contribution is that the proposed dynamic model with adaptive thresholding improves fault isolation with lower false alarm rates (FARs). In addition, the tolerant cruise control provided a robust control structure that can ensure connectivity and continuous operation. The comparative analysis with traditional methods shows that the proposed fault isolation and fault-tolerant method significantly improves efficiency and reliability. The proposed method leads to a missed alarm rate (MAR) of 3.7%, an FAR of 1.7%, and a correct detection ratio (CDR) of 97.0%. In addition, the fault classification process by using the BiDLSTM network achieves 98.1% accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25034-25046"},"PeriodicalIF":4.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550373","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

An Efficient Sparse Representation Method for Passive Radar 一种有效的无源雷达稀疏表示方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3573568

Quande Sun;Yuan Feng;Tao Shan;Juan Zhao;Xia Bai;Tianrun Wang;Zhi Wang

{"title":"An Efficient Sparse Representation Method for Passive Radar","authors":"Quande Sun;Yuan Feng;Tao Shan;Juan Zhao;Xia Bai;Tianrun Wang;Zhi Wang","doi":"10.1109/JSEN.2025.3573568","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573568","url":null,"abstract":"Passive radar (PR) commonly estimates target parameters by calculating the cross-ambiguity function (CAF), which is prone to generating a wider main lobe and higher sidelobes, leading to issues such as weak targets being masked and adjacent targets being difficult to distinguish. A parameter estimation method for PR based on sparse representation (SR) is proposed to address the above challenges. First, an SR model based on signal segmentation and Fourier transform is proposed to address the issue of excessively large dictionary matrix (DM) by using the fast Fourier transform (FFT). Then, an orthogonal matching pursuit (OMP) algorithm based on detection threshold (DT-OMP) is proposed to adaptively determine the number of atoms to be selected by a preset threshold. Furthermore, a model mismatch correction method for SR (MMC-SR) is proposed to achieve accurate estimation of target parameters in off-grid situations. Simulations and practical experiments have shown that the proposed method can effectively mitigate the influence of wider main lobe and higher sidelobes of CAF, thereby improving resolution and providing a refined estimation of target parameters, showcasing significant practical application value.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25288-25300"},"PeriodicalIF":4.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550208","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

ResNet-Based Single-Station Localization Method Based on Six-Component Seismometer 基于resnet的六分量地震仪单站定位方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3573281

Ziqi Zhou;Yanjun Chen;Pengxiang Zhao;Lanxin Zhu;Wenbo Wang;Xinyu Cao;Yan He;Fangshuo Shi;Huimin Huang;Zhengbin Li

{"title":"ResNet-Based Single-Station Localization Method Based on Six-Component Seismometer","authors":"Ziqi Zhou;Yanjun Chen;Pengxiang Zhao;Lanxin Zhu;Wenbo Wang;Xinyu Cao;Yan He;Fangshuo Shi;Huimin Huang;Zhengbin Li","doi":"10.1109/JSEN.2025.3573281","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573281","url":null,"abstract":"The adoption of intelligent mining systems improves operation safety and efficiency, requiring accurate localization of shearers in underground environments. However, traditional GPS-based localization methods are ineffective due to underground conditions. Alternative technologies often require receiver arrays or additional signal generators, which can suffer from unreliable connections and extensive hardware deployment. To address these challenges, we propose a novel single-station method that utilizes seismic signals generated by the shearer. This method simplifies the requirements for signal source by analyzing continuous oscillations produced during shearer operation and the hardware implementation using a single six-component (6-C) seismometer to record these oscillations as time-series data. Given the presence of ambient noise, we employ a residual network (ResNet) to extract relevant features from the seismic data. As a deep learning architecture, ResNet incorporates residual blocks to mitigate the vanishing gradient problem, enhancing feature extraction performance. The experimental results demonstrate the effectiveness of the proposed method. Using oscillation data collected during shearer operation to train the ResNet model, we achieve a prediction accuracy of 94.89%, outperforming traditional single-station methods such as cross correlation (CC) and 6-C multiple signal classification (MUSIC). This study demonstrates that our method does not require signal generators or extensive sensor deployments. By leveraging seismic data and deep learning techniques, significant enhancements in shearer localization can be achieved, contributing to the development of safer and more efficient mining operations.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25861-25871"},"PeriodicalIF":4.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550447","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 Multiclass Time-Series Signal Recognition Method Based on a Large Active Radar Jamming Database 基于大型有源雷达干扰数据库的多类时序信号识别方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3561367

Xiaoying Feng;Xiaoyu Zhang;Kunpeng He;Panlong Tan;Yutong Tang

{"title":"A Multiclass Time-Series Signal Recognition Method Based on a Large Active Radar Jamming Database","authors":"Xiaoying Feng;Xiaoyu Zhang;Kunpeng He;Panlong Tan;Yutong Tang","doi":"10.1109/JSEN.2025.3561367","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3561367","url":null,"abstract":"Active radar jamming recognition is a crucial technology in electronic countermeasures (ECMs). To tackle the challenge of intelligent recognition of complex radar jamming signals, we introduce the large-scale active radar jamming database (LARJD). This comprehensive database includes 19 distinct types of jamming signals and contains a total of 66500 time-series samples across five radar frequency bands, providing a robust dataset for radar jamming signal recognition. In parallel, we propose the multiclass time-series signal recognition network (MTSS-2DCNN), a deep learning architecture designed for classifying multiple types of time-series signals, including radar jamming signals. The MTSS-2DCNN architecture comprises three 2-D convolutional neural networks (2DCNNs), which extract features from both the time- and frequency-domain representations of the time-series data. By using a 2-D network structure to process 1-D signals, MTSS-2DCNN captures high-dimensional features from sequential signals while preserving the inherent characteristics of the temporal data. The model’s generalization capability is further enhanced through K-fold cross-validation and an adaptive learning rate adjustment strategy. Experimental results demonstrate that the proposed method achieves an impressive accuracy of over 99.67% on the LARJD, with significantly shorter training times compared to existing approaches. Moreover, by pretraining radar jamming signal recognition models, ECM applications can substantially improve the efficiency of intelligent recognition systems in both engineering and military contexts.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20051-20066"},"PeriodicalIF":4.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205905","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 Reconstruction of Monocular Human Video Based on KAN 基于KAN的单目人体视频快速重建

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3573354

Xiaolin Ma;Yifei Zha;Zehua Dong;Hailan Kuang;Xinhua Liu

{"title":"Fast Reconstruction of Monocular Human Video Based on KAN","authors":"Xiaolin Ma;Yifei Zha;Zehua Dong;Hailan Kuang;Xinhua Liu","doi":"10.1109/JSEN.2025.3573354","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573354","url":null,"abstract":"Creating 3-D digital people from monocular video provides many possibilities for a wide range of users and rich applications. In this article, we propose a fast, high-quality, and effective method for creating 3-D digital humans from monocular videos, achieving fast training (2.5 min) and real-time rendering. Specifically, we use 3-D Gaussian splatting (3DGS), based on the introduction of skinned multiperson linear model (SMPL) human structure prior, and an optimized Kolmogorov-Arnold network (KAN) neural network to build effective posture and linear blend skinning (LBS) weight estimation module to quickly and accurately learn the fine details of the 3-D human body. In addition, to achieve fast optimization in the densification and prune stages, we propose a two-stage optimization method. First, the local 3-D area that needs to be densified is extracted based on LightGlue, and then KL divergence combined with human body prior is further used to guide Gaussian splitting/cloning and merging operations. We conducted extensive experiments on the ZJU_MoCap dataset, and the peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS) metrics indicate that we effectively improved rendering quality while ensuring rendering speed.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24509-24516"},"PeriodicalIF":4.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550174","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

Longitudinal Guided Waves Attenuation in High-Strength Steel Wires Subject to Large-Area Corrosion Pits 大面积腐蚀坑作用下高强度钢丝纵导波衰减

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3573102

Sipeng Wan;Haijun Zhou;Yiqing Zou

{"title":"Longitudinal Guided Waves Attenuation in High-Strength Steel Wires Subject to Large-Area Corrosion Pits","authors":"Sipeng Wan;Haijun Zhou;Yiqing Zou","doi":"10.1109/JSEN.2025.3573102","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573102","url":null,"abstract":"When guided waves encounter defects, they generate echoes, enabling the determination of defect location and severity. However, detecting and characterizing large-area corrosion pits poses challenges as they often fails to produce distinct echoes. To address this issue, guided waves attenuation has been proposed as a potential solution, prompting the need to investigate the attenuation behavior of guided waves in the context of large-area corrosion pits. Corroded high-strength steel wire samples were obtained by accelerating corrosion using a neutral salt spray. Surface corrosion morphology was captured using a 3-D optical scanner, and the guided waves attenuation was measured across varying degrees of corrosion. An algorithm was developed to automate the identification of corrosion pits, followed by statistical analysis of pit parameters. The findings revealed four representative morphologies on corroded steel wires, with pit parameters and guided waves attenuation evolving in accordance with the mass loss ratio. The contributions of pit parameters to scattering attenuation are ranked in the order of pit volume, pit area, pit sharpness, and pit depth. Guided wave scattering attenuation is more sensitive to larger values of pit parameters, with only pit sharpness and pit depth exhibiting a critical threshold effect. Furthermore, statistical models were formulated to predict the probabilistic characteristics of corrosion morphology based on detection outcomes.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"23913-23925"},"PeriodicalIF":4.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557781","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

First-Photon Imaging Using a Data Preprocessing Method Based on Multiscale Image Segmentation 基于多尺度图像分割的第一光子成像数据预处理方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-02 DOI: 10.1109/JSEN.2025.3573438

Mingjie Sun;Yuchen Du;Jiaxin Wang;Xinyu Zhao;Labao Zhang

引用次数: 0