IEEE Access最新文献_第9页

HERCULES: Haptically-Enabled Remotely Controlled Ultrasound Examination System 赫拉克勒斯：触觉遥控超声检查系统

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565231

Parham M. Kebria;Saeid Nahavandi;Numan Kutaiba;Niki Koutrouza;Natalie Yang;Hamed Asadi;Glenn Guest

{"title":"HERCULES: Haptically-Enabled Remotely Controlled Ultrasound Examination System","authors":"Parham M. Kebria;Saeid Nahavandi;Numan Kutaiba;Niki Koutrouza;Natalie Yang;Hamed Asadi;Glenn Guest","doi":"10.1109/ACCESS.2025.3565231","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565231","url":null,"abstract":"The main purpose of the study is to investigate and demonstrate the feasibility and practicality of using a haptically-enabled remotely controlled ultrasound examination system (HERCULES) to perform point-of-care ultrasound. Robotic ultrasound is an emerging and important technology. This technology can help in performing ultrasound imaging in potentially contagious patients while minimizing risks of infections for sonographers (persons who perform ultrasound). This study assesses whether the robotic ultrasound system can reduce the musculoskeletal injuries sonographers endure. We developed a haptically-enabled robotic ultrasound system, which provides sonographers with a sense of touch throughout the scan. The system has haptic capabilities in which the sonographer can feel the contact force remotely and would be able to apply pressure appropriately to safeguard the patient. The system is equipped with various force thresholds. The sonographer can view the patient as well as the transducer’s position and orientation. More than 500 robotic images were captured, and a supplementary evaluation by expert radiologists was conducted to provide initial insights into image quality. In total, 56 subjects, 31 female and 25 male, aged from 21 to 55 years, participated in the clinical trials. An assessment is also carried out on the stimulation of the sonographer’s muscles during conventional vs. robotic scanning. As a result, the sonographer experienced substantial relief in back and neck muscles, right abductor pollicis brevis and right C4 paraspinal, by 88.12%, 89.19%, 93.57%, 82.0%, 72.83%, and 75.1% reduction from manual to teleoperated scenario, respectively. Subjects also reported a much more comfortable experience during robotic ultrasound scans.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"75585-75598"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925111","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

Myoelectric Stimulation Silent Subwoofer Which Presents the Deep Bass-Induced Body-Sensory Acoustic Sensation 肌电刺激静音低音炮表现出低音诱发的身体感觉声学感觉

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565283

Keiichi Zempo;Ryo Kashiwabara;Naoto Wakatsuki;Koichi Mizutani

引用次数: 0

Quantitative Method for Ultrasonic Testing of Lead Seal Defects in High Voltage Cable Accessories 高压电缆附件铅封缺陷的超声定量检测方法

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565290

Zheng Hai;Cai Qiushen;Zheng Jishi;Zhen Zhiming;Zou Wei;Chen Jianping

{"title":"Quantitative Method for Ultrasonic Testing of Lead Seal Defects in High Voltage Cable Accessories","authors":"Zheng Hai;Cai Qiushen;Zheng Jishi;Zhen Zhiming;Zou Wei;Chen Jianping","doi":"10.1109/ACCESS.2025.3565290","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565290","url":null,"abstract":"Phased array ultrasound technology has demonstrated its capability in detecting lead seal defects within high-voltage cable terminals. However, conventional ultrasound quantitative methods often fall short in accurately measuring the dimensions of these defects. This paper introduces a novel method for the detection and quantification of lead seal defects in high-voltage cable terminals. By focusing on the longitudinal wave fan scan images of these defects and integrating threshold segmentation with corrosion algorithms, the method provides real-time information on defect characteristics, including cross-sectional area and height. The findings reveal significant improvements over the traditional −6dB method: a 5% reduction in distance error, a 10% enhancement in defect size accuracy, and an overall accuracy rate exceeding 85%. This research holds substantial reference value for the engineering application of lead sealing defect detection in high-voltage cables, contributing to the advancement of lead sealing technology and ensuring the reliability and safety of power grid operations.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76047-76057"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979861","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918548","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

Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition 低分辨率人脸识别的神经进化卷积神经网络设计

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565304

Jhon I. Pilataxi;Juan P. Perez;Claudio A. Perez;Kevin W. Bowyer

{"title":"Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition","authors":"Jhon I. Pilataxi;Juan P. Perez;Claudio A. Perez;Kevin W. Bowyer","doi":"10.1109/ACCESS.2025.3565304","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565304","url":null,"abstract":"Face recognition (FR) is one of the most widely used biometric methods for identity authentication. Although most of the recently proposed methods demonstrate remarkable performance on high-quality datasets, such as LFW, their effectiveness is limited when assessed on low-resolution images. To address this challenge, knowledge distillation and super-resolution techniques have been applied, primarily using the ResNet architecture. However, the performance of deep learning approaches depends in part on the architecture used. In this study, we use neuroevolution with a genetic algorithm (GA) to design Convolutional Neural Networks (CNNs) automatically for low-resolution (LR) FR. To reduce the search time, a binary classifier is used to identify which generated architectures should be trained and which should not. The selected architectures are then trained and evaluated using QUMUL-TinyFace (training partition), a native LR dataset, to obtain their fitness, while the remaining architectures are assessed using a performance predictor model to estimate their fitness, bypassing the training stage. The classifier and performance predictor are trained using the CNN architectures evaluated from previous generations, with the architecture encoding used as a feature vector. The proposed method was assessed on both the QMUL-TinyFace (for face identification) and QMUL-SurvFace (for face verification) datasets, achieving a rank-1 recognition rate of 74.7% and a mean verification accuracy rate of 85.1%, respectively, outperforming results from previously published methods.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"75911-75923"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918624","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

DASL: An Index for Enhancing Tail Latency, Microarchitecture Friendliness, and Restructuring Overhead DASL：一个增强尾部延迟、微架构友好性和重构开销的索引

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565528

Hojin Shin;Gunhee Choi;Bryan S. Kim;Seehwan Yoo;Jongmoo Choi

{"title":"DASL: An Index for Enhancing Tail Latency, Microarchitecture Friendliness, and Restructuring Overhead","authors":"Hojin Shin;Gunhee Choi;Bryan S. Kim;Seehwan Yoo;Jongmoo Choi","doi":"10.1109/ACCESS.2025.3565528","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565528","url":null,"abstract":"The skip list is a popular in-memory index in modern database systems. It maintains multiple levels of lists, which makes it efficient in traversing sorted data. In addition, it is flexible in inserting and deleting data, while avoiding the restructuring overhead of tree-based structures. However, there are considerable challenges in the conventional skip list design. First, the linked list structure has a drawback in utilizing microarchitecture features such as cache, pipeline, and SIMD (Single Instruction Multiple Data) capability. Second, the skip list randomly selects the level of a new node. That is, the skip list runs based on probability rather than data distribution, which can lead to suboptimal lookup performance. Unlike balanced tree structures, the worst-case lookup performance of a skip list remains O(n). This paper proposes a new data structure called DASL (Deterministic Arrayed Skip List). It follows the algorithm of the skip list, but seamlessly integrates the array and devises a new deterministic raise operation in order to obtain flexibility, microarchitecture-friendliness, and reduced tail latency. In specific, a node in DASL consists of an array structure with multiple elements instead of a single element, taking advantage of the array within a list structure. Additionally, the raise operation is conducted deterministically instead of probabilistically, allowing data to be more balanced in multiple lists. Furthermore, we devise two optimization techniques, utilization-based adaptive intra-node search and uneven split operation. Experimental results with various synthetic and real-world workloads demonstrate that DASL outperforms other state-of-the-art in-memory indexes, including skip list, B+tree, and ART.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78303-78319"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918685","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

Decoding Phishing Evasion: Analyzing Attacker Strategies to Circumvent Detection Systems 破解网络钓鱼规避：分析攻击者规避检测系统的策略

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565619

A. Ghafoor;M. A. Shah;M. A. Al-Naeem;C. Maple

{"title":"Decoding Phishing Evasion: Analyzing Attacker Strategies to Circumvent Detection Systems","authors":"A. Ghafoor;M. A. Shah;M. A. Al-Naeem;C. Maple","doi":"10.1109/ACCESS.2025.3565619","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565619","url":null,"abstract":"Phishing remains a critical security threat, involving the creation of fraudulent websites to capture sensitive information. Despite existing detection systems, sophisticated attackers have developed advanced evasion techniques that undermine these defenses. This paper highlights the significant challenge of these novel methods, focusing on how attackers manage to prolong the operational lifespan of phishing sites. Our research investigates how attackers circumvent traditional security layers by employing a combination of target filtering mechanisms, bot detection evasion, blacklisting avoidance, and honeypots. Our experimental findings indicate that these evasion strategies can achieve an effectiveness rate of 80% to 85% in extending the viability of phishing sites. We have empirically demonstrated the exposure of current systems to these attacks, revealing specific vulnerabilities and exploitation points. These results underscore the urgent need for enhanced detection frameworks that address the layered and adaptive nature of modern phishing tactics. Our work highlights a critical gap in current security measures and poses a challenge to solution providers: there is a pressing need for novel mitigations to safeguard users against these sophisticated phishing threats.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78513-78526"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918780","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

Enhancing Student Management Through Hybrid Machine Learning and Rough Set Models: A Framework for Positive Learning Environments

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565585

Ateeq Ur Rehman Butt;Hamid Ali;Muhammad Asif;Hessa Alfraihi;Mohamad Khairi Ishak;Khalid Ammar

{"title":"Enhancing Student Management Through Hybrid Machine Learning and Rough Set Models: A Framework for Positive Learning Environments","authors":"Ateeq Ur Rehman Butt;Hamid Ali;Muhammad Asif;Hessa Alfraihi;Mohamad Khairi Ishak;Khalid Ammar","doi":"10.1109/ACCESS.2025.3565585","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565585","url":null,"abstract":"Effective student management is crucial for fostering productive learning environments. This study presents a hybrid framework integrating machine learning (ML) techniques with rough set theory to enhance student management by identifying at-risk students and enabling personalized interventions. The model combines classification algorithms with rough set-based decision rules to analyze complex student data, including academic performance, behavior patterns, and levels of engagement. The ML layered approach detects patterns and outliers, supporting data-driven decisions to improve student well-being and educational outcomes. Evaluation on the Open University Learning Analytics Dataset (OULAD) demonstrated high accuracy (97.85%) in predicting student outcomes and precision (94.62%) in identifying students needing support. The hybrid approach outperformed conventional methods by approximately 15%, showcasing its transformative potential. This framework effectively monitors student performance and enables customized interventions to meet individual learning needs, fostering a more supportive educational environment.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"80834-80846"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943742","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

Leveraging Priority Queueing in IoT-Edge-Fog-Cloud Infrastructures for Efficient Healthcare Monitoring

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565679

Dang Van Anh;Van-Hau Nguyen

{"title":"Leveraging Priority Queueing in IoT-Edge-Fog-Cloud Infrastructures for Efficient Healthcare Monitoring","authors":"Dang Van Anh;Van-Hau Nguyen","doi":"10.1109/ACCESS.2025.3565679","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565679","url":null,"abstract":"The rapid growth of the Internet of Healthcare Things (IoHT) has led to challenges in real-time processing, prioritization, and resource allocation of heterogeneous healthcare data. Existing edge-fog-cloud approaches often fail to effectively handle critical medical events and ensure timely interventions. This paper presents a novel IoHT framework that integrates an M/M/C/K priority queue model (M: Markovian arrival/service rates, C: servers, K: capacity) with a three-tier edge-fog-cloud architecture. The proposed approach introduces a dynamic priority assignment mechanism that leverages real-time patient data for swift processing of critical events and an adaptive resource allocation strategy that optimizes performance under varying workloads. Simulations and real-world case studies demonstrate the framework’s superiority, achieving a 30% reduction in average response time for critical events and a 25% improvement in resource utilization compared to state-of-the-art methods. Contributions include: 1) a novel M/M/C/K priority queue model integrated with edge-fog-cloud architecture; 2) dynamic priority assignment and adaptive resource allocation strategies; and 3) comprehensive evaluation through simulations and case studies. By addressing key challenges in IoHT data processing and prioritization, this work enables the development of efficient, responsive, and reliable IoHT systems for timely and personalized healthcare interventions, ultimately improving patient outcomes and quality of care.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"80461-80477"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943880","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

Machine Learning-Driven QoT Prediction for Enhanced Optical Networks in DWDM System

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565502

Sudha Sakthivel;Mohammad Riyaz Belgaum;Aznida Abu Bakar Sajak;Muhammad Mansoor Alam;Mazliham Mohd Su'ud

{"title":"Machine Learning-Driven QoT Prediction for Enhanced Optical Networks in DWDM System","authors":"Sudha Sakthivel;Mohammad Riyaz Belgaum;Aznida Abu Bakar Sajak;Muhammad Mansoor Alam;Mazliham Mohd Su'ud","doi":"10.1109/ACCESS.2025.3565502","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565502","url":null,"abstract":"This research demonstrated the machine learning (ML) classifiers with regression learning to improve an optical system’s quality of transmission (QoT). In Optical Communication, the data can be communicated from source to destination through the established lightpaths. However, as the signal traverses the optical links and devices, its QoT may deteriorate due to various impairments. The QoT is an essential component that determines the connectivity of an optical network. Therefore, ensuring a QoT guarantee is necessary to establish a successful lightpath. Predicting the QoT before establishing lightpaths can guide the routing and allocation of resources required for the lightpaths. In this research, using ML models an appropriate QoT analytical prediction model is developed computationally. Simulations were conducted at a 10 Gbps data rate per channel for 64-channel DWDM systems. The proposed model significantly improves in detecting fiber nonlinearity, and performance was studied using Q-factor, BER, and noise power. The results indicate that the SVM-based classifier with regression learning performs better than any other classifiers discussed in this research. This study assesses the efficiency of the proposed ML models in predicting the QoT for established lightpaths. Results indicate that all the ML classifiers with Regression models can accurately predict the transmission quality for over 90% of lightpaths. However, the proposed SVM-based classifier with a regression model demonstrates superior generalization, with a nearly perfect QoT prediction rate of around 99% for the established lightpaths. In the network planning stage, residual margins are added to compensate for inaccuracies, which ensures accurate signal reception. The proposed ML model achieved a lightpath residual margin with a 0.7dB error.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"80445-80460"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979908","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943894","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

Dataset Construction and Effectiveness Evaluation of Spoken-Emotion Recognition for Human Machine Interaction 面向人机交互的语音情感识别数据集构建与有效性评价

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-04-29 DOI: 10.1109/ACCESS.2025.3565537

Mitsuki Okayama;Tatsuhito Hasegawa

{"title":"Dataset Construction and Effectiveness Evaluation of Spoken-Emotion Recognition for Human Machine Interaction","authors":"Mitsuki Okayama;Tatsuhito Hasegawa","doi":"10.1109/ACCESS.2025.3565537","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565537","url":null,"abstract":"The widespread use of large language models (LLMs) and voice-based agents has rapidly expanded Human-Computer Interaction (HCI) through spoken dialogue. To achieve more natural communication, nonverbal cues—especially those tied to emotional states—are critical and have been studied via deep learning. However, three key challenges persist in existing emotion recognition datasets: 1) most assume human-to-human interaction, neglecting shifts in speech patterns when users address a machine, 2) many include acted emotional expressions that differ from genuine internal states, and 3) even non-acted datasets often rely on third-party labels, creating potential mismatches with speakers’ actual emotions. Prior studies report that agreement between external labels and speakers’ internal states can be as low as 60–70%. To address these gaps, we present the VR-Self-Annotation Emotion Dataset (VSAED), consisting of 1,352 naturally induced and non-acted Japanese utterances (1.5 hours). Each utterance is labeled with self-reported internal emotional states spanning six categories. We investigated: 1) how effectively non-acted, machine-oriented speech conveys internal emotions, 2) whether speakers alter expressions when aware of an emotion recognition system, and 3) whether specific conditions yield notably high accuracy. In experiments using a HuBERT-based classifier, we achieve around 40% recognition accuracy, underscoring the complexity of capturing subtle internal emotions. These findings highlight the importance of domain-specific datasets for human-machine interactions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"79084-79097"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949331","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