IEEE Transactions on Consumer Electronics最新文献_第9页

Quantum-Driven Anomaly Detection Framework for Consumer IoT Cyber-Physical Systems 消费物联网网络物理系统量子驱动异常检测框架

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-09 DOI: 10.1109/TCE.2025.3559432

Khalid Mahmood;Sonia Khan;Mahmood Ul Hassan;Kamran Ahmad Awan;Khursheed Aurangzeb;Muhammad Shahid Anwar

{"title":"Quantum-Driven Anomaly Detection Framework for Consumer IoT Cyber-Physical Systems","authors":"Khalid Mahmood;Sonia Khan;Mahmood Ul Hassan;Kamran Ahmad Awan;Khursheed Aurangzeb;Muhammad Shahid Anwar","doi":"10.1109/TCE.2025.3559432","DOIUrl":"https://doi.org/10.1109/TCE.2025.3559432","url":null,"abstract":"This study aims to enhance the security of Consumer IoT (CIoT) systems by addressing the limitations of traditional anomaly detection approaches. To achieve this, we propose the Quantum-Driven Adaptive Anomaly Detection Framework (Q-ADAPT), a novel model designed to enable real-time anomaly detection through a quantum-inspired adaptive cognitive mapping function. The framework is built upon a multilayered architecture consisting of a Quantum-State Convolutional Layer, Synthetic Verification Layer, and Adaptive Mapping Layer, allowing simultaneous data state analysis and validation against synthetic signals. Q-ADAPT uses an adaptive deep learning model to recognize evolving CIoT behavior patterns, enhancing detection accuracy and resilience under varying noise conditions. The simulation environment spans a time frame of 340 minutes, designed to evaluate the robustness of the model in six distinct scenarios under Gaussian noise. Performance results reveal that Q-ADAPT achieves a detection accuracy of 97.8% in low-complexity environments and maintains 91.3% under high-noise conditions.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4859-4866"},"PeriodicalIF":10.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Untargeted Closed-Box Attack Against Healthcare Image Retrieval via Rank Manipulation 通过秩操纵对医疗保健图像检索的非目标封闭盒攻击

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-09 DOI: 10.1109/TCE.2025.3559103

Wenyun Li;Zheng Zhang;Xiangyuan Lan;Yaowei Wang

{"title":"Untargeted Closed-Box Attack Against Healthcare Image Retrieval via Rank Manipulation","authors":"Wenyun Li;Zheng Zhang;Xiangyuan Lan;Yaowei Wang","doi":"10.1109/TCE.2025.3559103","DOIUrl":"https://doi.org/10.1109/TCE.2025.3559103","url":null,"abstract":"Computer-aided diagnosis always involves a large number of healthcare images, in order to mine such huge medical data, healthcare image retrieval (HIR) attracts a lot of attention from the medical diagnosis research community. However, their security and reliability have yet to be well-studied in the current HIR systems. The closed-box attacks in HIR remain under-explored and challenging, i.e., precisely surrogate stealing without knowing the architecture of the victim model and effective adversarial example generation. In this work, we propose an Untargeted Rank Manipulation Attack (URMA) against deep hashing-based HIR under closed-box scenarios. Specifically, we build a surrogate stealing scheme to explore the correlations between the surrogate model and the original closed box deep hashing model. To enable the attack HIR under the decision-based closed-box setting, we deploy the top-ranking samples returned by the original retrieval models supervising the surrogate model training. Moreover, the designed untargeted embedding generator crafts the high visual quality adversarial example, which lowers the rank of corresponding candidates by adversarial perturbations. When the surrogate model and adversarial generation are adequately trained, the untargeted adversarial attack paradigm is built for deep hashing-based HIR. Extensive experiments validate the efficacy of our URMA with promising attack performance under a closed-box setting on the three public healthcare image datasets. The source code of this paper is available at <uri>https://github.com/li-wenyun/URMA</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4544-4555"},"PeriodicalIF":10.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Blockchain-Based Cross-Domain DDoS Mitigation in Consumer Networks 消费者网络中基于区块链的跨域DDoS缓解

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-09 DOI: 10.1109/TCE.2025.3559451

Hajar Moudoud;Zakaria Abou El Houda;Bouziane Brik

{"title":"A Blockchain-Based Cross-Domain DDoS Mitigation in Consumer Networks","authors":"Hajar Moudoud;Zakaria Abou El Houda;Bouziane Brik","doi":"10.1109/TCE.2025.3559451","DOIUrl":"https://doi.org/10.1109/TCE.2025.3559451","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks pose significant threats to the availability and security of consumer networks and Internet service providers (ISPs). This is a significant concern due to the potential vulnerabilities and security risks associated with the rapid increase in the number of insecure Internet of Things (IoT) devices. Adopting an inter-domain DDoS collaboration strategy is a promising solution to address this issue. However, manual configuration and management of resources across multiple domains can be time-consuming, error-prone, and inefficient. Moreover, the existing inter-domain DDoS mitigation mechanisms (<inline-formula> <tex-math>$i.e$ </tex-math></inline-formula>., Cooperative Defense mechanisms) are facing obstacles due to the lack of incentives for cooperation, low flexibility, and high cost. Most importantly, many of them are centralized, which risks single points of failure, hampering collaboration and resource sharing among Autonomous Systems (ASs). The new emerging techniques, such as Digital-Twin (DT) empowered by Network Function Virtualization (NFV), Software-Defined Networking (SDN), and Blockchain introduce new opportunities for efficient and flexible inter-domain DDoS collaboration <inline-formula> <tex-math>$i.e$ </tex-math></inline-formula>., resources sharing among multiple SDN-based domains. In this context, we propose SecureShare, a novel digital twin-enabled inter-domain DDoS mitigation framework that allows for an efficient, fair, and secure dynamic resource-sharing among SDN-based domains to deal with large-scale DDoS attacks through resource sharing. The deployment of SecureShare is executed within Ethereum’s test network, Sepolia. Furthermore, we performed extensive experiments employing Microsoft Azure Digital Twins (ADT), a platform-as-a-service tool for generating twin graphs of physical objects. The experimental results show that SecureShare achieves promising results in terms of efficiency, security, and flexibility.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"7095-7104"},"PeriodicalIF":10.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems 推进安全自动驾驶汽车系统联邦学习的鲁棒性和隐私性

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-08 DOI: 10.1109/TCE.2025.3558999

Hajar Moudoud;Zakaria Abou El Houda;Bouziane Brik;Mian Ahmad Jan;Bandar Alshawi

{"title":"Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems","authors":"Hajar Moudoud;Zakaria Abou El Houda;Bouziane Brik;Mian Ahmad Jan;Bandar Alshawi","doi":"10.1109/TCE.2025.3558999","DOIUrl":"https://doi.org/10.1109/TCE.2025.3558999","url":null,"abstract":"The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6183-6192"},"PeriodicalIF":10.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HyperEye: A Lightweight Features Fusion Model for Unknown Encrypted Malware Traffic Detection HyperEye：用于未知加密恶意软件流量检测的轻量级特征融合模型

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-08 DOI: 10.1109/TCE.2025.3558353

Xiaodong Zang;Zilong Zheng;Haosheng Zheng;Xuan Liu;Muhammad Khurram Khan;Weiwei Jiang

{"title":"HyperEye: A Lightweight Features Fusion Model for Unknown Encrypted Malware Traffic Detection","authors":"Xiaodong Zang;Zilong Zheng;Haosheng Zheng;Xuan Liu;Muhammad Khurram Khan;Weiwei Jiang","doi":"10.1109/TCE.2025.3558353","DOIUrl":"https://doi.org/10.1109/TCE.2025.3558353","url":null,"abstract":"Recently, effectively identifying encrypted malicious traffic without decryption in consumer applications relies heavily on high-quality labeled traffic datasets. However, this harms models for incorrect labeling and requires more efficient real-time identification of encrypted unknown ones. This paper proposes HyperEye, a real-time, unsupervised, encrypted malicious traffic detection system. It can detect unknown traffic patterns by analyzing the fused traffic features in-depth. Precisely, we extract protocol-agnostic numerical and protocol-specific text features and devise a cross-term fusion algorithm to obtain a comprehensive traffic behavior description. We designed a genetic algorithm-based DBSCAN (GA-DBSCAN) parameter optimization algorithm to enhance the quality and stability in identifying malicious traffic. Extensive experimental results with open-world and real-world datasets demonstrate that our work outperforms other state-of-the-art malware detection systems, achieving an average 11.95% improvement in the F1-score. Besides, experimental results with the real-world dataset demonstrate that our system applies to the dynamic nature of consumer applications and can safeguard users’ data and privacy.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5079-5089"},"PeriodicalIF":10.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum-Resistant Security Framework for Secure and Scalable IoT-Enabled Metaverse Environments 用于安全和可扩展的物联网支持的元环境的抗量子安全框架

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-08 DOI: 10.1109/TCE.2025.3558268

Imran Taj;Muhammad Adnan

{"title":"Quantum-Resistant Security Framework for Secure and Scalable IoT-Enabled Metaverse Environments","authors":"Imran Taj;Muhammad Adnan","doi":"10.1109/TCE.2025.3558268","DOIUrl":"https://doi.org/10.1109/TCE.2025.3558268","url":null,"abstract":"In an era where securing Internet of Things (IoT) devices within Metaverse environments is increasingly critical, existing frameworks often lack robust, quantum-resistant protection suitable for resource-constrained devices. This study aims to develop a comprehensive quantum-resistant security framework designed for IoT-enabled Metaverse applications. Our multilayered architecture incorporates Ideal Coset Lattice Cryptography (ICLC) and a Hypercomplex Multivariate Encryption Scheme (HMES) across the Device, Network, and Metaverse layers. ICLC provides lightweight, quantum-resistant encryption for devices with limited computational resources, while HMES enhances security through complex algebraic structures resistant to quantum attacks. We implement a Zero-Knowledge Proof Authentication mechanism over Hypercomplex Algebras (ZKPHA) to authenticate devices without exposing private keys. An edge computing strategy that employs convex optimization minimizes latency and computational load, ensuring scalability and efficiency. Simulations over a 260-minute period compared our framework with six state-of-the-art methods under various conditions. The results show that our framework reduces the rate of successful cyberattacks on encrypted data to 0.15%, achieves encryption and decryption times of 2.2 milliseconds per operation, and maintains 98.5% system availability during attacks.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5716-5723"},"PeriodicalIF":10.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network 当联邦学习与知识蒸馏相结合，保护消费者边缘网络

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-08 DOI: 10.1109/TCE.2025.3559004

Zakaria Abou El Houda;Hajar Moudoud;Bouziane Brik

{"title":"When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network","authors":"Zakaria Abou El Houda;Hajar Moudoud;Bouziane Brik","doi":"10.1109/TCE.2025.3559004","DOIUrl":"https://doi.org/10.1109/TCE.2025.3559004","url":null,"abstract":"Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"7192-7200"},"PeriodicalIF":10.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated Learning-Based Secure Computing Mechanism for Consumer Internet of Vehicles-Based Transportation Cyber-Physical Systems 基于联邦学习的消费者车联网交通信息物理系统安全计算机制

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-07 DOI: 10.1109/TCE.2025.3552830

Nikhil Kumar Singh;Sakshi Patni;Sunhwan Lim;Joohyung Lee

{"title":"Federated Learning-Based Secure Computing Mechanism for Consumer Internet of Vehicles-Based Transportation Cyber-Physical Systems","authors":"Nikhil Kumar Singh;Sakshi Patni;Sunhwan Lim;Joohyung Lee","doi":"10.1109/TCE.2025.3552830","DOIUrl":"https://doi.org/10.1109/TCE.2025.3552830","url":null,"abstract":"Ensuring reliable and efficient transportation in Cyber-Physical Systems (CPS) requires effective model training across distributed Consumer Internet of Vehicles (CIOV)-based Transportation CPS (TCPS). However, the high mobility of transportation terminals and frequent domain switching during training degrade global model accuracy, while malicious terminals uploading erroneous data further compromise system reliability. To address these challenges, this paper proposes Fed-ECC, a two-tier federated learning (FL)-based edge collaborative computing mechanism for dynamic CIOV-based TCPS. The first tier employs a deep reinforcement learning (DRL)-based clustering algorithm to form edge collaborative computing domains, optimizing terminal selection based on mobility, computational capability, and reliability. The second tier integrates a semi-asynchronous local aggregation mechanism with adaptive aggregation factors and an asynchronous regional aggregation mechanism based on data volume, improving aggregation efficiency and model convergence. Simulation results demonstrate that Fed-ECC enhances global model accuracy by 58.7%, accelerates convergence speed by 57.6%, and achieves 95% accuracy in traffic safety tasks, significantly improving obstacle detection and service reliability. These findings underscore the effectiveness, scalability, and robustness of Fed-ECC in addressing the challenges of high-mobility, large-scale CIOV-based TCPS.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4867-4882"},"PeriodicalIF":10.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics 消费类电子产品基于区块链的分布式协同传感和频谱接入方法

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-07 DOI: 10.1109/TCE.2025.3558629

Yuhuai Peng;Yuan Li;Yu Guo;Dawei Zhang;Fazlullah Khan;Ryan Alturki;Bandar Alshawi

{"title":"A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics","authors":"Yuhuai Peng;Yuan Li;Yu Guo;Dawei Zhang;Fazlullah Khan;Ryan Alturki;Bandar Alshawi","doi":"10.1109/TCE.2025.3558629","DOIUrl":"https://doi.org/10.1109/TCE.2025.3558629","url":null,"abstract":"Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5044-5054"},"PeriodicalIF":10.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Femtosecond Resolution Time-to-Digital Converter Exploiting Near-End Crosstalk Voltage 利用近端串扰电压的飞秒分辨率时间-数字转换器

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-04 DOI: 10.1109/TCE.2025.3557848

Li Xiang;Moke Zhou;Chunxuan Su;Ping Yang

引用次数: 0