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21st International Learning and Technology Conference, L and T 2024

作者： Salem, Nema Haneya, Hala Balbaid, Hanin Asrar, Manal Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia ECoE Effat University Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Artificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different search algorithms within the context of path-planning in the UC Berkeley's PAC-Man's game. The algorithms under consideration include Depth-First Search (DFS), Breadth-First Search (BFS), Uniform Cost Search (UCS), Iterative Deepening Depth First Search (IDDFS), and A⇤ Search. The objective is to identify the most effective algorithm in terms of path-finding performance. The study's findings reveal that the A⇤ search algorithm outperforms the others in terms of score, cost, and node expansion, making it the most suitable choice for finding the shortest path in the PAC-Man's game. © 2024 IEEE.

关键词： Iterative methods

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MIMO IMPEDANCE MODELLING OF GRID FOLLOWING INVERTER WITH LOCAL VOLTAGE CONTROL 14

MIMO IMPEDANCE MODELLING OF GRID FOLLOWING INVERTER WITH LOC...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Saridaki, Georgia Kotsampopoulos, Panos Hatziargyriou, Nikos Luo, Xi Batzelis, Efstratios Singh, Abhinav School of Electrical and Computer Engineering National Technical University of Athens Greece School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9781837242689

In recent years, in response to the global climate crisis, inverter-based energy resources (IBRs) have been massively integrated into power systems. Their effective operation and control lie on a wide variety of inner, outer, and synchronization control loops which have been reported to interact and compromise power system's stability. In this study, a Multi-Input-Multi-Output (MIMO) impedance model for a three-phase grid following inverter with local voltage control has been proposed. This linear time invariant model takes into consideration the delay and the coupling between the control blocks and serves as a simpler and more computationally efficient method to capture oscillations and interactions in time and frequency domain. It can predict instabilities caused by the different control layers along with the frequencies of the oscillations. The effect of the delay, the current control tuning parameters and the interactions between the current controller and the local voltage controller have been highlighted. The results have been validated in time and frequency domain simulations in SIMULINK environment. © The Institution of engineering & Technology 2024.

关键词： Frequency domain analysis

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Multi-UAV Placement for Integrated Access and Backhauling Using LLM-Driven Optimization

Multi-UAV Placement for Integrated Access and Backhauling Us...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Wang, Yuhui Farooq, Junaid Ghazzai, Hakim Setti, Gianluca University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI48126 United States King Abdullah University of Science & Technology Computer Electrical and Mathematical Sciences Division Saudi Arabia

ISBN: (纸本)9798350368369

Unmanned aerial vehicles (UAVs) can enhance wireless access by dynamically positioning themselves closer to users while maintaining a backhaul connection to cellular base stations. In scenarios where users are geographically dispersed, multiple UAVs can be orchestrated to establish multi-hop integrated access and backhaul (IAB) connections. Traditionally, finding optimal UAV placement has required computationally demanding methods, such as combinatorial optimization or reinforcement learning, which are often impractical for real-time applications due to their complexity and training requirements. Additionally, UAV operators may lack the capability to solve complex optimization problems during live operations. This paper presents a novel framework that leverages large language models (LLMs) for optimizing the placement of multiple UAVs through iterative structured prompting. The proposed method achieves near-optimal solutions in significantly fewer iterations compared to traditional methods, making it suitable for real-time deployment without extensive mathematical modeling. Simulation result demonstrate that the proposed method achieves scores over 82% of the theoretical optimal solution while reducing computational time from hours to minutes compared to the baseline deep Q network approach, ensuring robust network connectivity and service quality. The LLM-driven framework simplifies problem-solving for UAV network operators, paving the way for its application in more complex real-world scenarios. © 2025 IEEE.

关键词： Drones

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Optimistic Multi-Agent Policy Gradient 41

Optimistic Multi-Agent Policy Gradient

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41st International Conference on Machine Learning, ICML 2024

作者： Zhao, Wenshuai Zhao, Yi Li, Zhiyuan Kannala, Juho Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Aalto University Finland University of Oulu Finland

Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art *** address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO *** approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in *** optimism prevents individual agents from quickly converging to a local ***, we provide a formal analysis to show that the proposed method retains optimality at a fixed *** extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest. Copyright 2024 by the author(s)

关键词： Multi agent systems

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Texlivernet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation 22

Texlivernet: Leveraging Medical Knowledge and Spatial-Freque...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Jiang, Xiaoyan Zhou, Zhi Wang, Hailing Wang, Guozhong Fang, Zhijun School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798331520526

Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. How-ever, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracting nuanced features, especially for fine-grained segmentation of tumor boundaries and small lesions. To address these limitations, we developed datasets with lesion-specific text annotations for liver tumors and introduced the TexLiverNet model. TexLiverNet employs an agent-based cross-attention module that integrates text features efficiently with visual features, significantly reducing computational costs. Additionally, enhanced spatial and adaptive frequency domain perception is proposed to precisely delineate lesion boundaries, reduce background interference, and recover fine details in small lesions. Comprehensive evaluations on public and private datasets demonstrate that TexLiverNet achieves superior performance compared to current state-of-the-art methods. Code is available at https://***/sky-visionX/TexLiverNet. © 2025 IEEE.

关键词： Image segmentation

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Novel Double-Damped Tuned AC Filters in HVDC Systems

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computers, Materials & Continua 2023年第4期75卷 1467-1482页

作者： Rana Shaheer Mehmood Asif Hussain Usman Ali Muhammad Tariq Mahmood Department of Electrical Engineering University of Management and TechnologyLahore54782Pakistan Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon-si16419Gyeonggi-doKorea School of Computer Science and Engineering Korea University of Technology and Education1600ChungjeolnoByeogchunmyunCheonan31253Korea

This paper presents a performance analysis of novel doubledampedtuned alternating current (AC) filters in high voltage direct current(HVDC) systems. The proposed double-damped tuned AC filters offer theadvantages of improved performance of HVDC systems in terms of betterpower quality, high power factor, and lower total harmonic distortion (THD).The system under analysis consists of an 878 km long HVDC transmissionline connecting converter stations at Matiari and Lahore, two major cities inPakistan. The main focus of this research is to design a novel AC filter usingthe equivalent impedance method of two single-tuned and double-dampedtuned AC filters. Additionally, the impact of the damping resistor on the ACchannel is examined. TheTHDof theHVDCsystem with and without currentAC filters was also compared in this research and a double-damped tuned ACfilter was proposed. The results of the simulation represent that the proposeddouble-damped tuned AC filter is far smaller in size, offers better powerquality, and has a much lower THD compared to the AC filters currently inplace in the converter station. The simulation analysis was carried out utilizingpower systems computer-aided design (PSCAD) software.

关键词： Double-damped tuned AC filters harmonics high voltage DC systems long distance transmission power quality total harmonic distortion

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Reduced-Complexity CRB Optimization for Dual-Function Radar-Communication Systems using Hybrid Linear-Nonlinear Precoding

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IEEE Transactions on Signal Processing 2025年 73卷 2123-2138页

作者： Wen, Cai Chen, Yating Huang, Yan Davidson, Timothy N. Northwest University School of Information Science and Technology Xi’an China Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing China McMaster University Department of Electrical and Computer Engineering Hamilton Canada

This paper proposes a waveform design technique for dual-function radar-communication (DFRC) systems that employ hybrid linear-nonlinear precoding (HLNP). The HLNP signal is a superposition of linear precoding of the communication symbols that enables conventional coherent decoding and a nonlinearly precoded auxiliary signal that introduces additional degrees of design freedom that can be used to improve system performance. Our design goal is to obtain accurate direction of arrival (DOA) estimation and satisfactory waveform ambiguity properties, and hence we optimize a weighted sum of a Cramer-Rao bound (CRB) on DOA estimation and a waveform similarity metric. To simultaneously enable effective communication at the chosen data rates, we set lower bounds on the communication SINRs, and to facilitate implementation, we constrain the total transmission power, the per-antenna power, and the peak-to-average power ratio (PAPR) on each antenna. We deploy successive convex approximation to solve the resultant nonconvex design problem, while leveraging feasible point pursuit to provide a feasible initial point. To reduce the computational cost, we introduce sub-block and null space variants of our design technique. Simulation results verify the effectiveness of the proposed algorithm and its variants, and validate their performance advantages over regular nonlinear precoding schemes. © 1991-2012 IEEE.

关键词： Signaling

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Camera-LiDAR Calibration Using Total Station

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IEEE Access 2025年 13卷 98309-98321页

作者： Noji, Kotaro Koutaki, Gou Noguchi, Shuhei Kumamoto University Department of Computer Science and Electrical Engineering Japan Research and Development Department at ZENRIN Co. Ltd Japan

With the rapid advancements in autonomous driving in recent years, the importance of HD (High Definition) map referenced during autonomous driving has also increased. The mainstream method for creating HD maps involves using data collected from MMS (Mobile Mapping System) equipped with multiple sensors. Accurate fusion of data from multiple sensors is required. Typically, calibration between sensors is done by simultaneously measuring the same object. However, some sensors on MMS have non-overlapping FoV (Field of View), making it difficult to accurately calibrate and fuse their data. In this study, we focus on the fusion of data from a camera and LiDAR with non-overlapping FoVs. By estimating the relative positional information between the camera and LiDAR using two same calibration target by ‘Total station’, we performed data fusion. Originally, the total station are used for civil surveying, but we show that they can be used for camera and LiDAR calibration in computer vision. Specifically, two boards of the same make are placed in the field of view of the camera and LiDAR, and the relative positions of the two boards are matched by a total station, enabling highly accurate camera and LiDAR calibration even when the fields of view do not overlap. In simulation experiments, a translational error of 15.28 mm was achieved, which is approximately 4 mm smaller than the conventional target-based method. © 2013 IEEE.

关键词： Camera-LiDAR calibration non-overlap total station

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

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International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

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