IEEE Access最新文献_第9页

A Novel Lo-Ra-Based Wireless Communication Module for Industrial Data Acquisition-Telemetry 一种基于lo - ra的工业数据采集遥测无线通信模块

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-19 DOI: 10.1109/ACCESS.2025.3581373

Ranu Wijaya;Ahmad Radhy;Safira F. Mujiyanti

{"title":"A Novel Lo-Ra-Based Wireless Communication Module for Industrial Data Acquisition-Telemetry","authors":"Ranu Wijaya;Ahmad Radhy;Safira F. Mujiyanti","doi":"10.1109/ACCESS.2025.3581373","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3581373","url":null,"abstract":"The development of communication technology must be implemented to improve the efficiency of industrial systems. One promising approach is the use of Ultra High Frequency (UHF) modulation for long-distance communication between sensors and industrial controllers. This study aims to design and implement a UHF communication module that can be integrated with conventional temperature transmitters and Siemens industrial controllers to enable effective remote measurement and monitoring. The implementation of the UHF communication module considers critical aspects such as reliability, data throughput, and latency for industrial applications. Testing and evaluation were conducted to assess the module’s performance, including measurement variations and distance testing between the systems. In addition to field trials, the module was evaluated in a controlled environment at the Instrumentation Laboratory of Institut Teknologi Sepuluh Nopember (ITS), where it transmitted RTD (Resistance Temperature Detector) temperature data. Key findings indicate that the system achieved reliable wireless communication up to 150 meters, with a bit error rate (BER) of <inline-formula> <tex-math>$1.2times 10 ^{-3}$ </tex-math></inline-formula>, signal-to-noise ratio (SNR) above 14 dB, and packet loss below 2% across the tested range. These results confirm the reliability and accuracy of the module for industrial telemetry applications. With specially designed UHF transmitter and receiver modules, the system aims to enhance measurement performance, reduce downtime, and support prompt decision-making in complex industrial operations. The proposed solution offers a cost-effective and scalable communication platform suitable for diverse industrial environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"108043-108050"},"PeriodicalIF":3.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11044339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Loudness Growth Prediction From EEG Signals Using Autoencoder and Multi-Target Regression 基于自编码器和多目标回归的脑电信号响度增长自动预测

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3580833

D. Rama Harshita;Nitya Tiwari;Himanshu Padole;K. S. Nataraj

{"title":"Automated Loudness Growth Prediction From EEG Signals Using Autoencoder and Multi-Target Regression","authors":"D. Rama Harshita;Nitya Tiwari;Himanshu Padole;K. S. Nataraj","doi":"10.1109/ACCESS.2025.3580833","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3580833","url":null,"abstract":"Accurately assessing loudness perception is crucial for optimizing hearing aid fittings, especially for individuals who are unable to perform subjective tests. This study presents an automated method for estimating frequency-specific loudness growth curves using tone-burst auditory brainstem responses (ABRs), which are subsets of EEG (electroencephalography) signals. Unlike traditional methods that rely on manually engineered features, the proposed method uses a convolutional autoencoder to learn latent representations of ABR signals, reducing dimensionality while preserving critical auditory information. The extracted features are mapped to psychoacoustic loudness growth estimates using a multi-target regression model based on a convolutional neural network. An ablation study was conducted to analyze the impact of different autoencoder configurations on feature extraction performance. The results demonstrate strong predictive consistency, with high Pearson correlation coefficients (PCC <inline-formula> <tex-math>$geq 0.9$ </tex-math></inline-formula>) and low mean square errors (MSE <inline-formula> <tex-math>$leq 0.0011$ </tex-math></inline-formula>) across different stimulus frequencies and subjects.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"106561-106572"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Adaptive Framework for Collective Anomaly Detection in Key Performance Indicators From Mobile Networks 移动网络关键性能指标集体异常检测的自适应框架

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3581120

Madalena Cilínio;Thaína Saraiva;Marco Sousa;Pedro Vieira;António Rodrigues

{"title":"An Adaptive Framework for Collective Anomaly Detection in Key Performance Indicators From Mobile Networks","authors":"Madalena Cilínio;Thaína Saraiva;Marco Sousa;Pedro Vieira;António Rodrigues","doi":"10.1109/ACCESS.2025.3581120","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3581120","url":null,"abstract":"Anomaly detection is a critical component of Self-Organizing Networks (SON), enhancing network efficiency and resilience. This paper proposes a novel framework for detecting collective anomalies in univariate Key Performance Indicators (KPIs) in mobile networks. By leveraging data mining and Machine Learning (ML) techniques, the framework enables timely anomaly detection without requiring expert-labeled data. The proposed framework starts by clustering time series from 11 distinct KPIs into four groups. Then, representative KPIs from each cluster are selected to evaluate the anomaly detection performance using two algorithms: the Smart Trouble Ticket Management (STTM) and STUMPY. The STTM algorithm is applied to KPIs with low variability, such as Call Setup Success Rate and Service Drop Rate, showing high accuracy in anomaly detection. For KPIs with higher variability, such as User Downlink (DL) Average Throughput and DL Resource Block Utilization Rate, the STUMPY algorithm is employed, yielding similarly accurate results. The framework, composed of the STTM and the STUMPY algorithms, demonstrates effective anomaly detection, achieving high precision (0.94), recall (0.86), and an F1-score of 0.90, with minimal False Positives (FP). These results underline the framework’s reliability across different types of KPIs, providing a robust solution for anomaly detection in mobile network monitoring, outperforming benchmark algorithms in all key metrics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"105828-105849"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Measurement Correction of Antenna Radiation Responses Obtained in Non-Anechoic Environments 非消声环境下天线辐射响应的多测量校正

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3580757

Vorya Waladi;Adrian Bekasiewicz;Qingsha S. Cheng

引用次数: 0

3DDGD: 3D Deepfake Generation and Detection Using 3D Face Meshes 3DDGD：使用3D人脸网格的3D深度伪造生成和检测

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3580950

Hichem Felouat;Huy H. Nguyen;Junichi Yamagishi;Isao Echizen

{"title":"3DDGD: 3D Deepfake Generation and Detection Using 3D Face Meshes","authors":"Hichem Felouat;Huy H. Nguyen;Junichi Yamagishi;Isao Echizen","doi":"10.1109/ACCESS.2025.3580950","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3580950","url":null,"abstract":"3D face technology is revolutionizing various fields by providing superior security and realism compared with 2D methods. In biometric authentication, 3D facial features serve as unique, hard-to-forge identifiers, improving accuracy in facial recognition for border control and criminal identification. Additionally, 3D avatars enhance virtual interactions. In this study, we aimed to strengthen 3D facial biometric systems against deepfakes. Key contributions include proving the superior protection of 3D faces over 2D ones, creating a dataset of real and fake 3D faces, and developing advanced models for accurate 3D deepfake detection. We evaluated our models for generalization to other datasets and stability when changing training data. Our experiments used the mesh multi-layer perceptron model for deepfake detection along with self-attention mechanisms and the newly introduced TabTransformer model. Results indicate that 3D face meshes greatly improve security by distinguishing real faces from deepfakes. Future work will focus on enhancing detection tools and integrating geometric features with facial textures for more accurate 3D deepfake detection. The dataset and models are publicly available on GitHub, excluding licensed elements: <uri>https://github.com/hichemfelouat/3DDGD</uri>","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"107429-107441"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual Fencing for Safety-Critical Cyber-Physical Systems: Computer-Vision Enabled Digital Twins 安全关键网络物理系统的虚拟防护：计算机视觉支持的数字孪生

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3581041

Ali Aghazadeh Ardebili;Marco Zappatore;Antonella Longo;Antonio Ficarella

{"title":"Virtual Fencing for Safety-Critical Cyber-Physical Systems: Computer-Vision Enabled Digital Twins","authors":"Ali Aghazadeh Ardebili;Marco Zappatore;Antonella Longo;Antonio Ficarella","doi":"10.1109/ACCESS.2025.3581041","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3581041","url":null,"abstract":"Early warning zones (EWZs) are pivotal for future crowd management in smart cities, leveraging computer vision to transform dynamic environments into controllable cyber-physical systems. This approach aims to restrict unsafe or threatening movements by creating EWZs that enhance the resilience of critical infrastructures and ensure citizen safety. While conventional virtual fencing uses GPS-based solutions for outdoor zone-monitoring or surveillance of densely populated areas, as well as fixed cameras for indoor environments (e.g., museums), this article explores the use of computer vision and Uncrewed Aerial Vehicles (UAVs) to create risk-ranked EWZs. These zones can safeguard critical infrastructure and densely populated areas by using UAVs (where fixed cameras are not installed), thus promising to enhance crowd management and safety in smart cities. In this study, the EWZs are categorized by risk levels, with the proximity to hazardous areas determining the severity from low to high. This tiered structure allows for appropriate and timely responses to potential threats, thereby ensuring a robust early warning mechanism. A physical testbed was constructed to monitor human movement as a reflection of behavior within this cyber-physical-social environment. Experiments simulating virtual fence (V-fence) crossings demonstrated the system’s effectiveness in providing early warnings. The results also showed that the system successfully tracked multiple persons through a lightweight framework that can be deployed at the edge, ensuring real-time surveillance and response.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"108212-108234"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image-Based Accelerated Prediction of Thermal Properties of Package Substrates Using Combined Deep-Learning and an Enhanced Thermal Network Model 结合深度学习和增强热网络模型的基于图像的封装基板热特性加速预测

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3580727

Jeong-Hyeon Park;Jaechoon Kim;Sukwon Jang;Sungho Mun;Eun-Ho Lee

{"title":"Image-Based Accelerated Prediction of Thermal Properties of Package Substrates Using Combined Deep-Learning and an Enhanced Thermal Network Model","authors":"Jeong-Hyeon Park;Jaechoon Kim;Sukwon Jang;Sungho Mun;Eun-Ho Lee","doi":"10.1109/ACCESS.2025.3580727","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3580727","url":null,"abstract":"As the need for processing large amounts of data increases, power consumption and the complexity of semiconductor package patterns also rise, making thermal management crucial. Traditional analytical models suffer from accuracy issues when analyzing thermal behaviors of complex patterns in commercial packages. To enable accurate and fast prediction of thermal behavior during the design stage in practical industry applications, this study proposes an image-based accelerated prediction method for the thermal properties of complex patterns in package substrates by using combined deep-learning and an enhanced thermal network model. The proposed method divides the layer-wise image data of package substrates into subdomains to define unit cells, and applies a thermal network with a new structure. The specified thermal networks are then matched with unit cell images and used for deep learning, thus automating the process for quick thermal property assessment. The proposed method is applied to commercialized package substrate designs and validated through experiments and finite element-based models, demonstrating high accuracy with R-squared values over 0.99 and reduction in prediction time exceeding 90%.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"107926-107935"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","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 Wideband Dual-Polarized Reconfigurable Reflectarray Using Polarization Rotation Technique 基于偏振旋转技术的宽带双偏振可重构反射阵

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3581161

Jalaledin Tayebpour;Raafat R. Mansour

{"title":"A Wideband Dual-Polarized Reconfigurable Reflectarray Using Polarization Rotation Technique","authors":"Jalaledin Tayebpour;Raafat R. Mansour","doi":"10.1109/ACCESS.2025.3581161","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3581161","url":null,"abstract":"This paper presents a wideband dual-polarized 1-bit reconfigurable reflectarray antenna (RRA) at Ku-band, featuring a novel multi-layer unit cell with two substrates and three metallic layers. By utilizing the out-of-phase characteristic of the electric field in the dominant mode of the patch and polarization conversion, the design achieves two distinct phase states with accurate resolution over a wide bandwidth. Two PIN diodes serve as switches, enabling 180° phase difference between states while converting an x-polarized incident wave to a y-polarized reflected wave (and vice versa). A single control signal simultaneously operates both diodes, significantly simplifying the control system. Simulations confirm a 180° ± 2° phase difference from 10 GHz to 15 GHz. To explore beam steering capability, a <inline-formula> <tex-math>$10 times 10$ </tex-math></inline-formula> reconfigurable reflectarray prototype was fabricated and tested, with each unit cell wired to a DC biasing board and controlled via an SPDT manual switch. Measured results demonstrate beam steering up to 50° in both planes for both polarizations, with a 1-dB gain bandwidth of 13.7% (12.2 GHz-14 GHz), closely matching simulations. The combination of wide bandwidth, dual-polarization capability, and simplified fabrication makes this design highly suitable for beam-steering applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"106906-106915"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Ultra-High-Speed Spots Using RTK-GNSS Sensor Fusion for UAV-to-UAV mmWave/THz Communications 利用RTK-GNSS传感器融合预测无人机对无人机毫米波/太赫兹通信的超高速点

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3580781

Phuc Duc Nguyen;Ryosuke Isogai;Keitarou Kondou;Yozo Shoji

{"title":"Prediction of Ultra-High-Speed Spots Using RTK-GNSS Sensor Fusion for UAV-to-UAV mmWave/THz Communications","authors":"Phuc Duc Nguyen;Ryosuke Isogai;Keitarou Kondou;Yozo Shoji","doi":"10.1109/ACCESS.2025.3580781","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3580781","url":null,"abstract":"mmWave/THz communications depend on highly focused and directive narrow beams. Accurate prediction of the beam’s spatial location and attitude in space, as well as the time it takes to reach its ultra-high-speed coverage area, referred to as the ultra-spot, is crucial for uncrewed aerial vehicles (UAVs) to adjust their flight direction and approach velocity. This adjustment increases the likelihood of successful communication between UAVs. This paper introduces a novel approach for detecting these ultra-spots using real-time kinematic (RTK)-GNSS and inertial measurement unit (IMU) sensor fusion positioning powered by an extended Kalman filter (RTK-GNSS-EKF). To achieve this, we implemented a mechanism that exchanges six degrees of freedom (6-DOF) information of positions among UAVs via a 920MHz wireless communication link. Additionally, we propose an algorithm that accurately estimates the time and distance from the in-flight UAV to the ultra-spot. For the first time, this work investigates the real-world 6-DOF fluctuations in position, velocity, and attitude experienced by an in-flight UAV due to wind, and analyzes the impact of these fluctuations on the ultra-spot prediction issue. Additionally, we analyze scenarios where the ultra-spot alters its attitude by actively changing the antenna angle, assessing the consequent effects on the volume of data transmitted and received at the ultra-spot. We demonstrate the effectiveness of the proposed method by simulation and verification with actual UAV-to-UAV and UAV-to-ground-station field experiments. Experimental results indicate an average ultra-spot detection accuracy of 172ms in time and 32.7cm in distance, with measurements taken 1s before the UAV’s actual approach to the ultra-spot. These findings confirm the feasibility of the proposed method for detecting mobile ultra-spots in UAV-to-UAV mmWave communication.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"106942-106957"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLF3D: A Coarse-Labeling Framework to Facilitate 3D Object Detection in Point Clouds CLF3D：一种促进点云中3D目标检测的粗标记框架

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-06-18 DOI: 10.1109/ACCESS.2025.3580824

Nuo Cheng;Chuanyu Luo;Han Li;Sikun Ma;Shengguang Lei;Pu Li

{"title":"CLF3D: A Coarse-Labeling Framework to Facilitate 3D Object Detection in Point Clouds","authors":"Nuo Cheng;Chuanyu Luo;Han Li;Sikun Ma;Shengguang Lei;Pu Li","doi":"10.1109/ACCESS.2025.3580824","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3580824","url":null,"abstract":"Tremendous scenarios have to be considered for autonomous driving, leading to extremely large amount of point cloud data which need to be labeled for model training. Manually labeling such data is labor-intensive and highly expensive. In this paper, we propose CLF3D, a simple and effective coarse-labeling framework designed to improve existing automated labeling methods by fully leveraging scene-specific information in the unlabeled data to significantly enhance detection accuracy. Specifically, CLF3D first utilizes a pre-trained model to generate initial pseudo-labels, which are subsequently refined using a two-stage filtering strategy in combination with an instance bank built from high-quality annotated instances. These refined pseudo-labels are then used to fine-tune the model, progressively improving its detection performance on unlabeled data. Through iterative refinement of pseudo-labels, the model parameters, and the instance bank, CLF3D continuously improves label quality and accuracy. Experimental results demonstrate that the proposed method improves the detection accuracy by up to 14% compared to the originally pre-trained model across datasets of various sizes. This means our approach can reduce 14% of the manual workload for labeling point cloud data in comparison to the existing methods.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"105753-105765"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0