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Flexible Intelligent Metasurfaces for Enhancing MIMO Communications

IEEE Transactions on Communications 2025年

作者： An, Jiancheng Han, Zhu Niyato, Dusit Debbah, Merouane Yuen, Chau Hanzo, Lajos Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to 'morph' its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups. © 1972-2012 IEEE.

关键词： Covariance matrix

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Adaptive Multi-Channel Contrastive Graph Convolutional Network with Graph and Feature Fusion

SSRN

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SSRN 2023年

作者： Zhong, Luying Lu, Jielong Chen, Zhaoliang Song, Na Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China

Multi-view semi-supervised classification is an attractive topic in real-world applications. Due to the powerful capability of gathering information from neighbors, Graph Convolutional Network (GCN) has become a hotspot in the classification task. However, most multi-view classification works based on GCN only assign weights for feature fusion, and directly consider the weighted sum of the adjacency matrices, ignoring the interaction and correlation among features. These may be problematic because aggregating the matrices from less relevant views may destroy the original topology space, leading to undesired performance. To address these issues, this paper proposes an Adaptive Multi-Channel Graph Convolutional Network (AMC-GCN). To extract the interactive information, AMC-GCN designs a deep interactive feature integration network to incorporate consensus and complementary information. To fuse the graph structures, AMC-GCN exploits the relevance between views and imposes an adjacency matrix fusion network to construct multiple GCN channels, thereby delivering valuable information from relevant graphs. To enhance the homogeneity of the framework, AMC-GCN applies a contrastive loss to joint learning during the optimization for classification. With these techniques, AMC-GCN exploits relevant and interactive information between views to promote graph and feature fusion. Extensive experimental results on real-world datasets demonstrate the superiority of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering University of Waterloo Canada

—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%. © 2024, CC BY.

关键词： Diffusion

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Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

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IEEE Transactions on Vehicular Technology 2025年

作者： Tang, Xiao Jiang, Yudan Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu Xi'An Jiaotong University School of Information and Communication Engineering Xi'an710049 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518063 China Northwestern Polytechnical University School of Electronics and Information Xi'an710072 China Xi'An Jiaotong University School of Mechanical Engineering Xi'an710049 China Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination. © 2025 IEEE.

关键词： Beamforming

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Numerical simulation of endovascular treatment options for cerebral aneurysms

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GAMM Mitteilungen 2024年第3期47卷

作者： Frank, Martin Holzberger, Fabian Horvat, Medeea Kirschke, Jan Mayr, Matthias Muhr, Markus Nebulishvili, Natalia Popp, Alexander Schwarting, Julian Wohlmuth, Barbara University of the Bundeswehr Munich Institute for Mathematics and Computer-Based Simulation Neubiberg Germany Technical University of Munich School of Computation Information and Technology Department of Mathematics Chair for Numerical Mathematics Garching Germany Department of Neuroradiology Technical University of Munich Munich Germany University of the Bundeswehr Munich Data Science & Computing Lab Neubiberg Germany

Predicting the long-term success of endovascular interventions in the clinical management of cerebral aneurysms requires detailed insight into the patient-specific physiological conditions. In this work, we not only propose numerical representations of endovascular medical devices such as coils, flow diverters or Woven EndoBridge but also outline numerical models for the prediction of blood flow patterns in the aneurysm cavity right after a surgical intervention. Detailed knowledge about the postsurgical state then lays the basis to assess the chances of a stable occlusion of the aneurysm required for a long-term treatment success. To this end, we propose mathematical and mechanical models of endovascular medical devices made out of thin metal wires. These can then be used for fully resolved flow simulations of the postsurgical blood flow, which in this work will be performed by means of a Lattice Boltzmann method applied to the incompressible Navier–Stokes equations and patient-specific geometries. To probe the suitability of homogenized models, we also investigate poro-elastic models to represent such medical devices. In particular, we examine the validity of this modeling approach for flow diverter placement across the opening of the aneurysm cavity. For both approaches, physiologically meaningful boundary conditions are provided from reduced-order models of the vascular system. The present study demonstrates our capabilities to predict the postsurgical state and lays a solid foundation to tackle the prediction of thrombus formation and, thus, the aneurysm occlusion in a next step. © 2024 The Authors. GAMM - Mitteilungen published by Wiley-VCH GmbH.

关键词： Forecasting

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Simple and High-Precision Hamiltonian Simulation by Compensating Trotter Error with Linear Combination of unitary Operations

引用

PRX Quantum 2025年第1期6卷 010359-010359页

作者： Pei Zeng Jinzhao Sun Liang Jiang Qi Zhao Pritzker School of Molecular Engineering The University of Chicago Illinois 60637 USA Clarendon Laboratory University of Oxford Parks Road Oxford OX1 3PU United Kingdom Blackett Laboratory Imperial College London London SW7 2AZ United Kingdom QICI Quantum Information and Computation Initiative Department of Computer Science School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

Trotter and linear combination of unitary (LCU) operations are two popular Hamiltonian simulation methods. The Trotter method is easy to implement and enjoys good system-size dependence endowed by commutator scaling, while the LCU method admits high-accuracy simulation with a smaller gate cost. We propose Hamiltonian simulation algorithms using LCU to compensate Trotter error, which enjoy both of their advantages. By adding few gates after the Kth-order Trotter formula, we realize a better time scaling than 2Kth-order Trotter. Our first algorithm exponentially improves the accuracy scaling of the Kth-order Trotter formula. For a generic Hamiltonian, the estimated gate counts of the first algorithm can be 2 orders of magnitude smaller than the best analytical bound of fourth-order Trotter formula. In the second algorithm, we consider the detailed structure of Hamiltonians and construct LCU for Trotter errors with commutator scaling. Consequently, for lattice Hamiltonians, the algorithm enjoys almost linear system-size dependence and quadratically improves the accuracy of the Kth-order Trotter. For the lattice system, the second algorithm can achieve 3 to 4 orders of magnitude higher accuracy with the same gate costs as the optimal Trotter algorithm. These algorithms provide an easy-to-implement approach to achieve a low-cost and high-precision Hamiltonian simulation.

关键词： Size distribution

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Machine learning-based classification of medication adherence among patients with noncommunicable diseases

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Informatics in Medicine Unlocked 2025年 52卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Moyo, Tsitsi Fonou Dombeu, Jean Vincent Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura Zimbabwe Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa Cimas Medical Aid Society Borrowdale Office Park Borrowdale Road P. O Box 1243 Harare Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa

Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the

关键词： Chronic disease Decision trees Deep neural network Diabetes Hypertension k-NN Logistic regression Machine learning Medication adherence Naïve bayes Non-communicable diseases Random forest SVM

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Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm

arXiv

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arXiv 2023年

作者： Li, Jinli Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

A high-dimensional and incomplete (HDI) matrix can describe the complex interactions among numerous nodes in various big data-related applications. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm learns a latent factor relying on the stochastic gradient of current instance error only without considering past update information. To address this critical issue, this paper innovatively proposes a Fuzzy PID-incorporated SGD (FPS) algorithm with two-fold ideas: 1) rebuilding the instance learning error by considering the past update information in an efficient way following the principle of PID, and 2) implementing hyper-parameters and gain parameters adaptation following the fuzzy rules. With it, an FPS-incorporated LFA model is further achieved for fast processing an HDI matrix. Empirical studies on six HDI datasets demonstrate that the proposed FPS-incorporated LFA model significantly outperforms the state-of-the-art LFA models in terms of computational efficiency for predicting the missing data of an HDI matrix with competitive accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap Visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

引用

Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress Biomedical imaging

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Challenges and Opportunities of DevOps in Cyber-Physical Production Systems Engineering

Challenges and Opportunities of DevOps in Cyber-Physical Pro...

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Industrial Cyber-Physical Systems (ICPS)

作者： István Koren Felix Rinker Kristof Meixner Jasminka Matevska Jörg Walter Chair of Process and Data Science RWTH Aachen University Aachen Germany Christian Doppler Laboratory SQI Institute of Information Systems Engineering TU Wien Vienna Austria School of Electrical Engineering and Computer Science City University of Applied Sciences Bremen Germany Distributed Computing and Communication OFFIS - Institute for Information Technology Oldenburg Germany

DevOps is a set of practices that combines software development and operations to enable a continuous software product life cycle to improve the quality of software systems. Although DevOps is considered successful for typical software systems, it has not yet been widelyadopted in the area of - Physlcal Production Systems (CPPSs) that integrate physical components with computer-based control and communication systems. Related to industrial settings, many new challenges arise, like long-term investments, missing flexibility in asset-heavy production environments, and the inherent physicality of hardware. This paper examines the use of the DevOps methodology in the manufacturing domain. It identifies and discusses the unique challenges and describes first solution proposals to overcome those challenges, based on literature and experiences from the industry. The article provides useful guidance to researchers and practitioners on potential pitfalls and exciting opportunities.

关键词： Production systems data collection Software systems Hardware Safety Stakeholders Security

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