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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Fekete, Sándor P. Kosfeld, Ramin Kramer, Peter Neutzner, Jonas Rieck, Christian Scheffer, Christian Department of Computer Science TU Braunschweig Germany Department of Electrical Engineering and Computer Science Bochum University of Applied Sciences Germany

ISBN: (纸本)9783959773546

We study Multi-Agent Path Finding for arrangements of labeled agents in the interior of a simply connected domain: Given a unique start and target position for each agent, the goal is to find a sequence of parallel, collision-free agent motions that minimizes the overall time (the makespan) until all agents have reached their respective targets. A natural case is that of a simply connected polygonal domain with axis-parallel boundaries and integer coordinates, i.e., a simple polyomino, which amounts to a simply connected union of lattice unit squares or cells. We focus on the particularly challenging setting of densely packed agents, i.e., one per cell, which strongly restricts the mobility of agents, and requires intricate coordination of motion. We provide a variety of novel results for this problem, including (1) a characterization of polyominoes in which a reconfiguration plan is guaranteed to exist;(2) a characterization of shape parameters that induce worst-case bounds on the makespan;(3) a suite of algorithms to achieve asymptotically worst-case optimal performance with respect to the achievable stretch for cases with severely limited maneuverability. This corresponds to bounding the ratio between obtained makespan and the lower bound provided by the max-min distance between the start and target position of any agent and our shape parameters. Our results extend findings by Demaine et al. [13, 14] who investigated the problem for solid rectangular domains, and in the closely related field of Permutation Routing, as presented by Alpert et al. [6] for convex pieces of grid graphs. © Sándor P. Fekete, Ramin Kosfeld, Peter Kramer, Jonas Neutzner, Christian Rieck, and Christian Scheffer.

关键词： Motion planning

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Energy-Efficient Secure Federated Learning for UAV Swarms 7

Energy-Efficient Secure Federated Learning for UAV Swarms

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7th International Conference on Energy Conservation and Efficiency, ICECE 2024

作者： Afzal, Muhammad Usman Abdellatif, Alaa Awad Information Technology University Electrical Engineering Department Qatar University Computer Science and Engineering Department Qatar

ISBN: (纸本)9798350370898

Unmanned Aerial Vehicles (UAVs) have been recently leveraged in massive amount of Internet of Things (IoT) applications. However, given the stringent limitations of UAVs, investigating their performance in terms of their energy efficiency, tasks accuracy, and security has gained much interest recently. In this paper, we study the deployment of Federated Learning (FL) within the UAV swarms, with the aim of avoiding data exchange between different swarm's members. Specifically, we propose a secure FL framework for UAV swarms, such that the energy efficiency is maximized, while enhancing the learning performance. The suggested approach takes into account the issue of data imbalance in FL in addition to the strict energy and security limits placed on the *** findings show how effective our framework is in terms of security, energy efficiency, and classification accuracy when compared to baseline systems that don't take security or data imbalance into account. © 2024 IEEE.

关键词： Energy efficiency

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Automatic Waterfowl and Habitat Detection using Drone Imagery and Deep Learning

Automatic Waterfowl and Habitat Detection using Drone Imager...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zhao, Andrew Fratila, Andrea Zhang, Yang Zhai, Zhenduo Liu, Zhiguang Shang, Yi University of Illinois Computer Science Department United States University of Missouri Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350324136

The integration of drone technology and Artificial Intelligence (AI) has opened up new possibilities for wildlife conservation and habitat monitoring. In this paper, we present a new system for efficiently and accurately analyzing waterfowl populations and classifying their habitats over large natural areas using drone imagery and deep learning (DL). Given a sequence of drone images captured by a drone along a flight path, the system utilizes customized deep learning models for waterfowl detection and counting, Meta's SAM for image segmentation and customized deep learning models for segment classification, and ChatGPT to generate text-based survey reports. Several image overlap detection methods were developed and compared with. Our experimental results show accurate waterfowl and habitat detection results and improvement over previous work, providing efficient and accurate data analysis for wildlife conservation efforts. © 2024 IEEE.

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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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An Efficient Federated Learning Framework for IoT Intrusion Detection 100

An Efficient Federated Learning Framework for IoT Intrusion ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Chen, Yushen Fang, Fang Wang, Boyu Zhang, Lan Western University Department of Computer Science London Canada Western University Department of Electrical and Computer Engineering London Canada Clemson University Department of Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9798331517786

The exponential growth of the Internet of Things (IoT) ecosystems has raised significant cybersecurity concerns. Deep learning (DL)-based methods have shown promising performance in detecting potential cyber threats in IoT networks. However, as these methods often involve data centralization, they can pose serious data privacy issues for IoT users and increase the communication burden of local networks. Federated learning (FL), as a distributed learning paradigm, enables privacy-preserving training of IoT intrusion detection models by requiring only model updates from IoT devices. However, the resource-constrained nature of IoT devices can significantly decrease FL training efficiencies, such as increased training latency and delayed convergence speed. Moreover, the data heterogeneous issues of IoT devices can also impact the accuracy and robustness of the trained model. To address these challenges, we propose an efficient FL framework, FedKD-Prox, based on federated proximal (FedProx) and knowledge distillation (KD). To improve the prediction accuracy within a limited time budget, the proposed framework aims to efficiently exploit the computation capability of the IoT trainers, reduce the communication overhead of FL, and alleviate the impact of heterogeneous data issues. The simulation results show that FedKD-Prox achieves higher accuracy and improves the robustness of the trained intrusion detection model. © 2024 IEEE.

关键词： Federated learning

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction 35

SCOUT+: Towards Practical Task-Driven Drivers' Gaze Predicti...

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35th IEEE Intelligent Vehicles Symposium, IV 2024

作者： Kotseruba, Iuliia Tsotsos, John K. York University Department Of Electrical Engineering And Computer Science Toronto Canada

ISBN: (纸本)9798350348811

Accurate prediction of drivers' gaze is an important component of vision-based driver monitoring and assistive systems. Of particular interest are safety-critical episodes, such as performing maneuvers or crossing intersections. In such scenarios, drivers' gaze distribution changes significantly and becomes difficult to predict, especially if the task and context information is represented implicitly, as is common in many state-of-the-art models. However, explicit modeling of top-down factors affecting drivers' attention often requires additional information and annotations that may not be readily *** this paper, we address the challenge of effective modeling of task and context with common sources of data for use in practical systems. To this end, we introduce SCOUT+, a task-and context-aware model for drivers' gaze prediction, which leverages route and map information inferred from commonly available GPS data. We evaluate our model on two datasets, DR(eye)VE and BDD-A, and demonstrate that using maps improves results compared to bottom-up models and reaches performance comparable to the top-down model SCOUT which relies on privileged ground truth information. Code is available at https://***/ykotseruba/SCOUT. © 2024 IEEE.

关键词： Forecasting

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Physics-Based Modeling and Characterization of Wide GaN-HEMTs

Physics-Based Modeling and Characterization of Wide GaN-HEMT...

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2025 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications, PAWR 2025

作者： Nouri, Soheil Avval, Amirreza G. El-Ghazaly, Samir M. University of Arkansas Department of Electrical Engineering and Computer Science Fayetteville United States

ISBN: (纸本)9798331507275

In this paper, we model and characterize an unconventionally wide GaN HEMT device. Each finger of the device has a width of 150 μ m which allows the modeling methodology to examine the wave propagation effects at high frequency bands. The transistor simulation employs a hybrid model, integrating a simulator for the transport equations of the semiconductor device and an electromagnetic solver for the extrinsic components. By combining these two elements, we can achieve a more comprehensive and accurate simulation of the transistor's behavior. To assess the accuracy of our modeling technique, we compare the current gain predicted by the model with experimental measurements. The results demonstrate that our model is capable of accurately tracking the reduction in gain at frequencies beyond 50 GHz, highlighting its effectiveness in predicting high-frequency performance. © 2025 IEEE.

关键词： Gallium nitride

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration 5

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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5th National and the 1st International Conference on Applied Research in electrical engineering, AREE 2025

作者： Mousavi, Mohammad Hossein Moradi, Hassan Rouzbehi, Kumars Razi University Department of Computer and Electrical Engineering Kermanshah Iran University of Seville Department of System Engineering and Automatic Control Seville Spain

ISBN: (纸本)9798350377170

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment. © 2025 IEEE.

关键词： Wind power integration

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An Intrusion Detection and Mitigation Framework for Automatic Generation control Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 412-421页

作者： Mohammadi, Fazel Saif, Mehrdad University of New Haven Electrical and Computer Engineering and Computer Science Department West HavenCT06516 United States University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

The main role of Automatic Generation control (AGC) is to maintain power grids frequency within specified operating limits. Due to the fact that AGC is the sole automatic feedback control loop between physical and cyber infrastructure in modern power systems and the data required by the AGC system is transferred to a control center through communication links, it can be highly vulnerable to malicious attacks. Therefore, AGC systems should be well-protected against cyberattacks, e.g., False Data Injection (FDI) attacks. In this paper, an intrusion detection and mitigation framework for AGC systems based on a modified Goertzel algorithm is proposed. Compared with the existing intrusion detection and mitigation strategies, the major superiorities of the proposed framework are less computational burden, high accuracy, and rapid detection and mitigation of FDI attacks, which are considered unknown inputs. The proposed framework is validated on a two-area interconnected power systems model and the IEEE 39-bus test system. The dynamic simulation results under different testing conditions verify the applicability and effectiveness of the proposed framework. © 2023 IEEE.

关键词： Network intrusion

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