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IEEE Conference on Global Communications (GLOBECOM)

作者： Hanzhi Yu Yuchen Liu Mingzhe Chen Department of Electrical and Computer Engineering and Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Accuracy Simulation Optimization methods Estimation Information processing Logic gates Prediction algorithms Servers Synchronization Resource management

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On the Secrecy Performance of Reconfigurable Intelligent Surfaces-Assisted Satellite Networks Under Shadow-Rician Channels

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6794-6808页

作者： Le, Si-Phu Nguyen, Tan N. Le-Tien, Thuong Duy, Tran Trung Nguyen, Tien-Tung Ng, Derrick Wing Kwan Tu, Lam-Thanh Voznak, Miroslav Ton Duc Thang University Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ho Chi Minh72900 Viet Nam VSB-Technical University of Ostrava Department of Telecommunications Faculty of Electrical Engineering and Computer Science Ostrava70800 Czech Republic University of Technology Vietnam National University Ho Chi Minh72900 Viet Nam Posts and Telecommunications Institute of Technology Faculty of Telecommunications 2 Ho Chi Minh72900 Viet Nam Industrial University of Ho Chi Minh City Faculty of Electronics Technology Ho Chi Minh70000 Viet Nam University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this study, we investigate the secrecy performance of reconfigurable intelligent surfaces-assisted satellite (RISAS) networks. Specifically, we analytically derive closed-form expressions of the secrecy outage probability (SOP) and the tight lower bound secrecy rate (SR) exploiting the moment matching method (MMM). Furthermore, we explore the asymptotic behavior of the SOP as the satellite transmit power is sufficiently large, offering valuable insights into network performance. Numerical simulations are conducted to validate the established framework and elucidate the influence of critical parameters such as transmission distance, transmit power, and the number of reconfigurable intelligent surfaces (RIS) elements on SOP and SR. Our findings highlight the efficacy of increasing the number of phase shifters of the RIS to mitigate the adverse effects of fading, shadowing, and transmission environments. Finally, we unveil the adaptability of the proposed networks, illustrating their suitability for various satellite systems, including low earth orbit (LEO) and geostationary earth orbit (GEO). Among these, LEO offers superior performance due to its shorter transmission distance compared to medium earth orbit (MEO) and GEO. © 1965-2011 IEEE.

关键词： Satellite communication systems

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Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

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computers, Materials & Continua 2022年第11期73卷 2223-2239页

作者： Abdul Saboor Mohd Fadzil Hassan Rehan Akbar Erwin Susanto Syed Nasir Mehmood Shah Muhammad Aadil Siddiqui Saeed Ahmed Magsi High-Performance Cloud Computing Centre(HPC3) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia School of Electrical Engineering Telkom UniversityBandungIndonesia KICSIT Institute of Space Technology(IST)IslamabadPakistan Department of Electrical and Electronics Engineering Universiti Teknologi PETRONASSeri IskandarPerakMalaysia

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud ***,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location ***,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud ***,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security *** this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and *** underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center *** preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

关键词： Root of Trust(RoT) Trusted Platform Module(TPM) cryptoprocessor microservices Hardware Security Modules(HSM) DevOps

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On the computational and statistical complexity of over-parameterized matrix sensing

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 8006-8052页

作者： Jiacheng Zhuo Jeongyeol Kwon Nhat Ho Constantine Caramanis Department of Computer Science University of Texas Austin TX Wisconsin Institute for Discovery University of Wisconsin-Madison Madison WI Department of Statistics and Data Sciences University of Texas Austin TX Department of Electrical and Computer Engineering University of Texas Austin TX

We consider solving the low-rank matrix sensing problem with the Factorized Gradient Descent (FGD) method when the specified rank is larger than the true rank. We refer to this as over-parameterized matrix sensing. If the ground truth signal X* ∈ ℝd×d is of rank r, but we try to recover it using FF&Tau where F ∈ ℝd×k and k > r, the existing statistical analysis either no longer holds or produces a vacuous statistical error upper bound (infinity) due to the at local curvature of the loss function around the global maxima. By decomposing the factorized matrix F into separate column spaces to capture the impact of using k > r, we show that ||FtFt - X*||2F converges sub-linearly to a statistical error of Õ(kdσ2/n) after $\tilde{\mathcal{O}}(\frac{\sigma_{r}}{\sigma}\sqrt{\frac{n}{d}})$ iterations, where Ft is the output of FGD after t iterations, σ2 is the variance of the observation noise, σr is the r-th largest eigenvalue of X*, and n is the number of samples. With a precise characterization of the convergence behavior and the statistical error, our results, therefore, offer a comprehensive picture of the statistical and computational complexity if we solve the over-parameterized matrix sensing problem with FGD.

关键词： computational complexity statistical complexity over-parameterization matrix sensing matrix regression factorized gradient descent

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

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IEEE Internet of Things Journal 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT's accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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Fractional-order Modified Windkessel Model of the Human Arterial Vascular System

Fractional-order Modified Windkessel Model of the Human Arte...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Bahloul, Mohamed A. Aboelkassem, Yasser Belkhatir, Zehor Laleg-Kirati, Taous-Meriem Alfaisal University College of Engineering Electrical Engineering Department Riyadh11533 Saudi Arabia University of Michigan-Flint College of Innovation and Technology MI48502 United States University of Michigan Michigan Institute for Data Science Ann ArborMI48109 United States University of Southampton Digital Health & Biomedical Engineering Group School of Electronics and Computer Science Southampton United Kingdom Computer Electrical and Mathematical Sciences and Engineering Makkah Saudi Arabia National Institute for Research in Digital Science and Technology Paris-Saclay France

ISBN: (纸本)9798350300260

This paper explores fractional-order derivatives to model the apparent arterial compliance dynamics in human vascular aging subjects. The proposed model employs fractional-order capacitor (FOC) elements that combine complex and frequency-dependence characteristics of proximal and distal arterial compliances. The FOC modeling approach accounted for both resistive and capacitive properties. The mathematical derivation has been combined into a global arterial lumped parameter representation forming a novel fractional-order modified arterial Windkessel representation. The model is then validated using aortic pressure and flow rate data acquired from human subjects at different ages. The results show that the FOC model is an accurate and flexible approach that can estimate arterial compliance dynamics and central blood pressure distribution while maintaining a low model complexity. © 2023 IEEE.

关键词： aortic input impedance Arterial Wind-kessel Fractional calculus fractional-order capacitor vascular compliance

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Automation Through Machine Learning: Reinvigorating Cyber Security 15

Automation Through Machine Learning: Reinvigorating Cyber Se...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bagade, Jayashree V. Dari, Sukhvinder Singh Dhivrani, Rekha Chowhan, Santosh Ravivarman, G. Janakiraman, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Symbiosis International Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats. © 2024 IEEE.

关键词： Cyber Instructions Malicious Procedures Threats

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Advances and Open Challenges in Federated Foundation Models

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IEEE Communications Surveys and Tutorials 2025年

作者： Ren, Chao Yu, Han Peng, Hongyi Tang, Xiaoli Zhao, Bo Yi, Liping Tan, Alysa Ziying Gao, Yulan Li, Anran Li, Xiaoxiao Li, Zengxiang Yang, Qiang School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden College of Computing and Data Science Nanyang Technological University Singapore College of Computer Nankai University China School of Medicine Yale University United States Digital Research Institute of Enn Group Langfang China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong WeBank Shenzhen China

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field. © 2025 IEEE.

关键词： Federated learning

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