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User Grouping-Based Hierarchical Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Assisted Downlink Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Yang, Zhixiang Feng, Lei Zhou, Fanqin Xie, Kunyi Qiu, Xuesong Xiong, Zehui Yuen, Chau Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 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

The joint optimization of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) offers a mutually beneficial solution. In this paper, we study a RIS-assisted downlink user grouping-based hierarchical RSMA communication network, where the base station transmits the common streams and private streams simultaneously to serve multiple users. The sum rate (SR) optimization problem is constructed to jointly optimize user grouping, beamforming, and rate splitting. To tackle the NP-hard problem, a user grouping algorithm is first provided to form the hierarchical RSMA model. Next, an equivalent weighted minimum mean-square error (WMMSE) problem is constructed and a WMMSE-based block coordinate descent (BCD) algorithm is designed to update the remaining variables in an iterated way. Specifically, the optimization of transmit beamforming and the rate splitting matrix is reduced to solving a typical convex quadratically constrained quadratic program problem. The solution for passive beamforming is recovered from the intermediate result obtained by the manifold optimization method. Finally, the process of the BCD algorithm is presented. The numerical results indicate that the proposed scheme evidently surpasses one-layer RSMA scheme, NOMA scheme, SDMA scheme, and no RIS scheme, especially in overloaded scenarios. The proposed scheme further increases the SR of common steams compared to the hierarchical RSMA scheme and significantly reduces computational complexity compared to the generalized RSMA scheme. © 2015 IEEE.

关键词： Mean square error

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Enhancing Breast Cancer Diagnosis with Multi-Resolution Vision Transformers and Robust Decision-Making

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IEEE Access 2025年 13卷 89704-89722页

作者： Sabry, Margo Balaha, Hossam Magdy Ali, Khadiga M. Soliman, Tayseer Hassan A. Gondim, Dibson Ghazal, Mohammed Alghamdi, Norah Saleh El-Baz, Ayman Assiut University Information Systems Department Assiut Egypt University of Louisville J.B. Speed School of Engineering Bioengineering Department Louisville 40292 KY United States Mansoura University Faculty of Medicine Pathology Department Mansoura Egypt University of Louisville Department of Pathology and Laboratory Medicine Louisville KY United States Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh Saudi Arabia

This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. Performance was evaluated via metrics such as precision, recall, the F1 score, accuracy, and specificity across 50 trials with perturbed conditions. The framework achieved a top accuracy of 97.08%, with precision and recall above 94%. The suggested stack configuration outperformed individual models and other stacking configurations, demonstrating balanced performance with minimal variability. Statistical analysis highlighted the reliability and consistency of the framework under perturbed conditions. The multi-resolution ViT-based framework significantly improves BC classification by integrating multiscale analysis and ensemble decision-making. Its high accuracy and robustness make it a valuable tool for reducing interobserver variability in digital pathology workflows. © 2013 IEEE.

关键词： Breast Cancer (BC) computer-Aided Diagnosis (CAD) Fast-Marching Level Set Histopathology Vision Transformer (ViT) Whole Slide Images (WSI)

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Optimizing Bidding Curves for Renewable Energy in Two-Settlement Electricity Markets

arXiv

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arXiv 2025年

作者： Zhao, Dongwei Delikaraoglou, Stefanos Dvorkin, Vladimir Lamadrid, Alberto J.L. Botterud, Audun Argonne National Laboratory LemontIL United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Department of Economics Department of Industrial and Systems Engineering Lehigh University BethlehemPA United States

Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper proposes a new approach for compatible coordination in two-settlement markets based on benchmark bidding curves for variable renewable energy. These curves are optimized based on a bilevel optimization problem, anticipating per-scenario responses of deterministic market-clearing problems and ultimately minimizing the expected cost across day-ahead and real-time markets. Although the general bilevel model is challenging to solve, we theoretically prove that a single-segment bidding curve with a zero bidding price is sufficient to achieve system optimality if the marginal cost of variable renewable energy is zero, thus addressing the computational challenge. In practice, variable renewable energy producers can be allowed to bid multi-segment curves with non-zero prices. We test the bilevel framework for both single- and multiple-segment bidding curves under the assumption of fixed bidding prices. We leverage duality theory and McCormick envelopes to derive the linear programming approximation of the bilevel problem, which scales to practical systems such as a 1576-bus NYISO system. We benchmark the proposed coordination and find absolute dominance over the baseline solution, which assumes that renewables agnostically bid their expected forecasts. We also demonstrate that our proposed scheme provides a good approximation of the least-cost, yet unattainable in practice, stochastic market outcome. © 2025, CC BY.

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Energy-Efficient Over-the-Air Computation for Federated Generative Model Fine-Tuning in Unmanned Vehicle-Assisted Disaster Relief

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhao, Yikun Feng, Lei Zhou, Fanqin Li, Wenjing Xiong, Zehui Du, Hongyang Wu, Celimuge Guo, Song Quek, Tony Q.S Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong University of Electro-Communications Meta-Networking Research Center Tokyo Japan Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong 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

The utilization of emergent generative artificial intelligence (GAI) within the realm of unmanned vehicles (UVs) can boost edge intelligence and potentially enhance the efficiency and effectiveness of rescue operations. However, to facilitate specialized generative edge intelligence in dynamic UV networks, GAI models need to tap into local data and conduct online fine-tuning, and the dispersed distribution of UVs makes distributed fine-tuning paradigms crucial. Although the federated generative model fine-tuning brings a potential solution, the large number of parameter transmissions involved in its fine-tuning process are often constrained by communication bottlenecks, which are more pronounced for resource-constrained UV networks. To cope with these challenges, we introduce an energy-efficient GAI model fine-tuning framework for the hierarchical UV networks, which employs over-the-air technology to save computational resource costs for unmanned aerial vehicle (UAV) during federated aggregation within a federated learning paradigm. Thereafter, we study the resource allocation problem in the fine-tuning process of the generative model and formulate a joint optimization problem for the bandwidth allocation, power control, computation resource allocation, and denoising factor control, aiming to minimize the system energy consumption. Then, we decouple the original problem into four tractable subproblems, and propose a block coordinate descent algorithm to solve them iteratively. Particularly, we derive a closed-form solution for the denoising factor to minimize the local model uploading transmission energy consumption under a specific AirComp communication error threshold. Simulation based on the state-of-the-art generative models shows that our AirComp-assisted federated generative model fine-tuning scheme can achieve satisfactory customized field-of-view image generation capability compared with the traditional fine-tuning scheme in an energy-efficient way. © 2015 I

关键词： Energy efficiency

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Innate Cyber-Immunity and Acquired Cyber-Immunity across Multi-Area Power systems

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IEEE Transactions on Power systems 2025年

作者： Hou, Jiazuo Song, Yue Hou, Yunhe Peng, Jimmy Chih-Hsien National University of Singapore Department of Electrical and Computer Engineering Singapore Tongji University Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology Shanghai201804 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong University of Hong Kong Shenzhen Institute of Research and Innovation Shenzhen518057 China

Organisms have evolved innate and acquired immune systems to defend against pathogens like coronaviruses. Similarly, power networks, threatened by cyber-attacks, desire cyber-immunity. Inspired by the immunology research, this study develops a cyber-immune strategy-characterizing innate cyber-immunity and establishing acquired cyber-immunity-for any selected portion of a power network (termed as power subnetwork) against stealthy false data injection (FDI) cyberattacks. Therein, without extrinsic protection and information asymmetry, this paper for the first time confirms the existence and provides closed-form conditions for power subnetworks that are innately immune to FDI cyber-attacks despite all measurements being compromised. Single-leader-multi-follower bi-level optimization models are then developed to strategically allocate measurement protection, leading to the acquired cyber-immunity, whose conditions for existence and the least cost are explicitly formulated. More importantly, a bi-directional cyber-immunity propagation mechanism is presented to provide the optimal spatial allocation of defense resources across adjacent subnetworks. It also explicitly determines the temporal allocation sequence, revealing its sequential and cumulative impacts on establishing cyber-immunity. This is achieved by designing recursive traversal algorithms, one of which is modelled as a post-order depth-first-search problem of a multi-fork tree. The proposed subnetwork-specific cyber-immune strategy is verified in a modified Australian National Electricity Market. © 1969-2012 IEEE.

关键词： Phishing

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Evaluating Large Language Models in Interaction with Open Government Data 24

Evaluating Large Language Models in Interaction with Open Go...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics

作者： Ioannis Maslaris Areti Karamanou Evangelos Kalampokis Konstantinos Tarabanis Database & Information Retrieval Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece Information Technologies Institute Center for Research & Technology HELLAS (CERTH) Thessaloniki Greece and Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece

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KKL Observer Synthesis for Nonlinear systems via Physics-Informed Learning

arXiv

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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. Control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on Control of Network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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