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Multiuser Communications with Movable-Antenna Base Station: Joint Antenna Positioning, Receive Combining, and Power Control

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arXiv 2023年

作者： Xiao, Zhenyu Pi, Xiangyu Zhu, Lipeng Xia, Xiang-Gen Zhang, Rui The School of Electronic and Information Engineering Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Movable antenna (MA) is an emerging technology which enables a local movement of the antenna in the transmitter/receiver region for improving the channel condition and communication performance. In this paper, we study the deployment of multiple MAs at the base station (BS) for enhancing the multiuser communication performance. First, we model the multiuser channel in the uplink to characterize the wireless channel variation due to MAs’ movements at the BS. Then, an optimization problem is formulated to maximize the minimum achievable rate among multiple users for MA-aided uplink multiuser communications by jointly optimizing the MAs’ positions, their receive combining at the BS, and the transmit power of users, under the constraints of finite moving region for MAs, minimum inter-MA distance, and maximum transmit power of each user. To solve this challenging non-convex optimization problem, a two-loop iterative algorithm is proposed by leveraging the particle swarm optimization (PSO) method. Specifically, the outer-loop updates the positions of a set of particles, where each particle’s position represents one realization of the antenna position vector (APV) of all MAs. The inner-loop implements the fitness evaluation for each particle in terms of the max-min achievable rate of multiple users with its corresponding APV, where the receive combining matrix of the BS and the transmit power of each user are optimized by applying the block coordinate descent (BCD) technique. Simulation results show that the antenna position optimization for MAs-aided BSs can significantly improve the rate performance as compared to conventional BSs with fixed-position antennas (FPAs). Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

Joint Task Scheduling and Container Image Caching in Edge Computing

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arXiv 2023年

作者： Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian Institute of Artificial Intelligence and Future Networks Beijing Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

Stacking-Based Attention Temporal Convolutional Network for Action Segmentation

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Stacking-Based Attention Temporal Convolutional Network for ...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liu Yang Yu Jiang Junkun Hong Zhenjie Wu Zhan Yang Jun Long School of Computer Science and Engineering Central South University China Big Data Institute Central South University Central South University China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Central South University Changsha China

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution Networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional Network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast.

关键词： Convolution Stacking Semantics Acoustics Convolutional neural networks Speech processing

Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning

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arXiv 2023年

作者： Li, Xueyuan Deng, Ruining Tang, Yucheng Bao, Shunxing Yang, Haichun Huo, Yuankai Data Science Institute Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States NVIDIA Corporation RedmondWA United States Department of Electrical and Computer Engineering Vanderbilt University Medical Center NashvilleTN United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN37232 United States

Precise identification of multiple cell classes in high-resolution Giga-pixel whole slide imaging (WSI) is critical for various clinical scenarios. Building an AI model for this purpose typically requires pixel-level annotations, which are often unscalable and must be done by skilled domain experts (e.g., pathologists). However, these annotations can be prone to errors, especially when distinguishing between intricate cell types (e.g., podocytes and mesangial cells) using only visual inspection. Interestingly, a recent study showed that lay annotators, when using extra immunofluorescence (IF) images for reference (referred to as molecular-empowered learning), can sometimes outperform domain experts in labeling. Despite this, the resource-intensive task of manual delineation remains a necessity during the annotation process. In this paper, we explore the potential of bypassing pixel-level delineation by employing the recent segment anything model (SAM) on weak box annotation in a zero-shot learning approach. Specifically, we harness SAM’s ability to produce pixel-level annotations from box annotations and utilize these SAM-generated labels to train a segmentation model. Our findings show that the proposed SAM-assisted molecular-empowered learning (SAM-L) can diminish the labeling efforts for lay annotators by only requiring weak box annotations. This is achieved without compromising annotation accuracy or the performance of the deep learning-based segmentation. This research represents a significant advancement in democratizing the annotation process for training pathological image segmentation, relying solely on non-expert annotators. Copyright © 2023, The Authors. All rights reserved.

关键词： Image annotation

data-mechanism-driven Product Performance Optimization with Multiple Parameters Under Uncertainties in Manufacturing Automation Systems

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Data-mechanism-driven Product Performance Optimization with ...

IEEE International Conference on Automation science and engineering (CASE)

作者： Kaiyue Cui Zhaoxi Hong Xiuju Song Mengchu Zhou Zhiwu Li Yixiong Feng Jianrong Tan State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Ningbo Innovation Center Zhejiang University Ningbo China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Institute of Systems Engineering Macau University of Science and Technology Macau China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

High-end equipment with robust and excellent performance is of great significance to manufacturing automation systems. Product parameters are the foundation of performance. Usually, there is a lack of clear physical models to reveal the relationship between parameters and performance. Simulation is an important way to understand the design results of product performance parameters. However, simulations are often particularly time and resource-consuming. To address these issues, this work proposes a data-mechanism-driven product performance optimization method, which introduces Taguchi' method, nonparametric correlation testing, and multiple attribute decision making (MADM), to efficiently obtain the optimal parameter scheme. A novel MADM method is designed, which is combined with the Multi-Objective Optimization on the basis of a Ratio Analysis plus the full MULTIplicative form (MULTIMOORA) and the Variable Neighborhood Search. It has been proven that it performs better than the MULTIMOORA based on the well-known Simulated Annealing and Tabu search. Finally, taking the cutting unit of EBZ200i as a case study, we have successfully obtained the optimal parameter scheme that comprehensively performs better in terms of wear performance, robustness, and environmental economic performance.

关键词： Uncertainty Correlation computer aided software engineering Decision making Simulated annealing Robustness Environmental economics Optimization Manufacturing automation Testing

Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving data Fusion in Healthcare Internet of Things

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IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul02841 Korea Republic of

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： Sensitive data

AI-Optimized Placement and Routing for PCB Design

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AI-Optimized Placement and Routing for PCB Design

International Conference on science Technology engineering and Management (ICONSTEM)

作者： P. Sathyaraj S. Arulkumar A. Ajina Kedir Beshir L. Umasankar A.N. Arularasan Department of Electronics and Communication Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electrical and Computer Engineering Wachemo University Ethiopia Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India Department of Electrical and Electronics Engineering S.A. Engineering College Tiruvallur Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics.

关键词： Machine learning algorithms System performance Machine learning Routing Rendering (computer graphics) Real-time systems Reliability

Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning

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arXiv 2023年

作者： Lin, Ya-Wei Eileen Coifman, Ronald R. Mishne, Gal Talmon, Ronen Viterbi Faculty of Electrical and Computer Engineering Tech-nion Haifa Israel Department of Mathematics Yale University New HavenCT United States Halicioǧlu Data Science Institute University of California San Diego La Jolla CA United States

Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Our method relies on combining diffusion geometry, a central approach to manifold learning, and hyperbolic geometry. Specifically, using diffusion geometry, we build multi-scale densities on the data, aimed to reveal their hierarchical structure, and then embed them into a product of hyperbolic spaces. We show theoretically that our embedding and distance recover the underlying hierarchical structure. In addition, we demonstrate the efficacy of the proposed method and its advantages compared to existing methods on graph embedding benchmarks and hierarchical datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

Intervention of resource allocation strategies on spatial spread of epidemic

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Research Square

Research Square 2021年

作者： Zhang, Kebo Han, Yuexing Gou, Min Wang, Bing School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

Medical resources are crucial in mitigating the epidemic, especially during pandemics such as the ongoing COVID-19. Thereby, reasonable resource deployment inevitably plays a significant role in suppressing the epidemic under limited resources. When an epidemic breaks out, people can produce resources for self-protection, or donate resources to help others. That is, the exchange of resources also affects the transmission between individuals, thus, altering the epidemic dynamics. To understand factors on resource deployment and the interplay between resource and transmission we construct a metapopulation network model with resource allocation. Our results indicate actively or promptly donating resources is not helpful to suppress the epidemic under both homogeneous population distribution (HOD) and heterogeneous population distribution (HED). Besides, strengthening the speed of resource production can significantly increase the recovery rate so that reduce the final outbreak size. These results may provide policy guidance towards epidemic containment. © 2021, CC BY.

关键词： Resource allocation