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IEEE Electron Device Letters 2025年第6期46卷 904-907页

作者： Hao, Weiquan Li, Xunyu Pan, Zijin Miao, Runyu Yue, Zijian Wang, Albert University of California Department of Electrical and Computer Engineering RiversideCA92521 United States

Analog, mixed-signal and radio-frequency (AMX/RF) integrated circuits (IC) are sensitive to noises and interferers. Such crosstalk is particularly problematic to complex chips in advanced technologies. Global flying noises coupling through complex metal interconnects in the backend-of-the-line (BEOL) in CMOS become dominant that cannot be isolated using traditional in-substrate noise-isolation techniques. This letter reports a novel layout-only transistor-level stacked-via metal tube structure in standard CMOS BEOL to suppress global flying noises on a chip. The new technique is validated in simulation and measurement using 38GHz single-pole double-throw (SPDT) traveling wave switch circuits fabricated in a foundry 45nm silicon-on-insulator (SOI) CMOS technology, demonstrating ~7 dB global noise reduction. © 1980-2012 IEEE.

关键词： CMOS integrated circuits

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GraphAush: Combining Adversarial Learning and Graph Embedding for a Novel Shilling Attack Model Towards Robust Recommender Systems 11

GraphAush: Combining Adversarial Learning and Graph Embeddin...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Barnett, Clayton Kashef, Rasha Toronto Metropolitan University Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350367300

Recommender systems (RS) are integral to modern e-commerce and content platforms. Yet, their reliance on user-item interaction data makes them vulnerable to shilling attacks, where fake data is injected to manipulate recommendations. Traditional shilling attack strategies utilize basic statistical properties of user-item data to create deceptive profiles. Still, recent advancements have shifted towards model-based attacks leveraging machine learning to enhance effectiveness and evade detection. This paper introduces GraphAush, a novel neural shilling attack model that employs Generative Adversarial Networks (GANs) with a novel generator shilling loss tailored to manipulate the user-item interaction graph. By incorporating a novel generator shilling loss function that leverages Node2Vec embeddings, GraphAush optimizes fake profile generation to maximize the effectiveness of attacks while minimizing detectability. This method overcomes the limitations of previous models, which either require intricate knowledge of the target RS or use indirect graph-based approaches. The efficacy of GraphAush was validated through experiments with multiple benchmark datasets, revealing its strong performance against traditional heuristic and other GAN-based attack methods. This innovative approach highlights a significant advancement in adversarial techniques for RS and sets a new benchmark for evaluating shilling attack strategies. © 2024 IEEE.

关键词： Generative adversarial networks

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Guest Editorial: EV charging impacts on power system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2024年第6期18卷 1091-1094页

作者： [Anonymous] Department of Electrical Engineering and Automation Aalto University Espoo Finland School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Electrical Engineering São Paulo State University Ilha Solteira Brazil Electrical Engineering Department Sharif University of Technology Tehran Iran School of Industrial Engineering University of Castilla-La Mancha Ciudad Real Spain

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D-band Stacked Common-Base Power Amplifier with 19.2dBm oP1dBin 55 nm SiGe BiCMOS 50

D-band Stacked Common-Base Power Amplifier with 19.2dBm oP1d...

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50th IEEE European Solid-State Electronics Research Conference, ESSERC 2024

作者： Bilato, Andrea Pirbazari, Mahmoud M. Mazzanti, Andrea University of Pavia Computer and Biomedical Engineering Dept. of Electrical Italy

ISBN: (纸本)9798350388138

This paper presents the design and experimental validation of a stacked common-base power amplifier (PA) tailored for D-band communication systems. Leveraging 55 nm SiGe BiCMOS technology, the proposed PA architecture combines the superior gain compression performance of the common-base topology with the higher voltage swing provided by stacked-transistor power amplifiers. Experimental results demonstrate a peak output power of 19.7 dBm with only a 0.5 dB lower 1-dB compression point (o P1dB) of 19.2 dBm at 135 GHz. The power added efficiency (PAE) reaches 12.1% at saturation and 11.5% at o P1dB. © 2024 IEEE.

关键词： BiCMOS technology

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Comparison of Two-Wheel and Four-Wheel Steering Using Event-Triggered Predictive Motion Control and Scale Vehicles

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ASME Letters in Dynamic Systems and Control 2024年第3期4卷 031002页

作者： Zhou, Zhaodong Rother, Christopher Chen, Jun Department of Electrical and Computer Engineering Oakland University RochesterMI48309 United States

This study compares the trajectory tracking performance of two- and four-wheel steering systems, especially under normal driving conditions. Specifically, the lateral motion is controlled by an event-triggered model predictive control (MPC), which activates either when consecutive control steps surpass the predictive horizon or when tracking error exceeds a predetermined lateral offset. Using a modified 1/10th scale Tamiya TT-02 RC car as a test platform, the tracking performance of both two- and four-wheel systems are evaluated. Results from the experiments highlight the better tracking performance of the four-wheel steering system over the traditional two-wheel systems and demonstrate the benefit of using event-triggered MPC for lateral motion control even under normal driving conditions, contrary to common belief that four-wheel steering systems are beneficial only in tight steering maneuvers. © 2024 by ASME.

关键词： Model predictive control

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Digital Twin-Empowered Interference Management for Multi-hop Internet-of-Vehicles Networks Over Millimeter Wave Bands

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IEEE Internet of Things Journal 2025年第11期12卷 17807-17827页

作者： Elloumi, Mohamed Kaddoum, Georges Hassan, Md. Zoheb MontréalQC Canada Université Laval Electrical and Computer Engineering Department QC Canada

The Internet of Vehicles (IoV) generates massive data traffic and demands reliable end-to-end connectivity to achieve multi-Gbps throughput between vehicles and roadside units. Millimeter-wave (mmWave) bands, with their abundant bandwidth, are promising for high-throughput IoV networks. However, in this context, significant propagation losses, intermittent line-of-sight availability, and dynamic topology changes due to vehicle mobility present critical challenges. This paper introduces RAVEN (Resource Allocation for VEhicular Networks), a centralized resource management framework designed to address these challenges effectively. RAVEN leverages a digital twin network (DTN) to optimize the end-to-end system capacity of multi-hop mmWave IoV networks by effectively managing co-channel interference among vehicles. RAVEN comprises the following three steps: (i) a channel prediction step that utilizes DTN's awareness of vehicular mobility and environmental contexts to predict site-specific channel gains for vehicular communication links;(ii) a clustering step that partitions vehicles into non-overlapping clusters, allowing vehicles within each cluster to share the same mmWave channel for data transmission, while simultaneously reducing co-channel interference;(iii) a multi-hop connectivity optimization step that provides a connected vehicular networking topology by jointly optimizing vehicle-to-vehicle and vehicle-to-infrastructure connectivity using a graph theory approach. A proof-of-concept of RAVEN is developed by implementing a DTN on the Microsoft Azure Digital Twins platform while integrating real-world vehicular mobility traces, edge-cloud collaboration, and parallel computing. Extensive simulations demonstrate that RAVEN outperforms several benchmark schemes, and offers scalability and near real-time decision-making capabilities for managing interference in large-scale IoV networks. © 2014 IEEE.

关键词： Resource allocation

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Energy-Efficient and Robust Communication in Multi-Connectivity Enabled mmWave Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Kim, Jungjun Lee, Jihwan Bahk, Saewoong Department of Electrical and Computer Engineering and INMC Seoul National University Seoul Republic of Korea

The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that achieves over 98% optimality for small-sized networks. More importantly, we evaluate in realistic scenarios using a 3-D ray-tracing simulator and confirm that performs very well even in urban terrain environments with buildings. IEEE

关键词： Convex optimization

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Exploration, Collaboration, and Applications in Multi-Agent Reinforcement Learning

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Performance Evaluation Review 2025年第3期52卷 31-34页

作者： Chen, Jingdi Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

In recent years, the field of humancentric decision-making has emerged as a critical area of research, driven by its potential to fundamentally reshape how decisions are made across a variety of complex systems. Human-centric decision-making is characterized by considerable variability, which can be both advantageous and problematic. Variability is beneficial in contexts like brainstorming or problem-solving, where diverse perspectives lead to innovation. However, in situations where consistency is crucial- such as public health, safety, and fairness-this variability can have negative consequences. For instance, when radiologists interpret the same mammogram differently, it can result in unnecessary medical procedures. Similarly, in military operations, inconsistent judgments by sonar technicians regarding potential threats can jeopardize both crew safety and mission success. This highlights the critical need for a deeper understanding of decisions where the stakes are high. Thus, it is important to deepen our understanding of the decision-making process in completed and dynamic scenarios that involve multiple decisionmakers. This should guide the creation of validated methods to identify, measure, model, and adjust variability in judgments to improve decision-making in multi-agent environments. © 2025 Copyright is held by the owner/author(s).

关键词： Reinforcement learning

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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Optimized Temporary Frequency Support for Wind Power Plants Considering Expanded Operational Region of Wind Turbines

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 51-64页

作者： Zhengyang Hu Bingtuan Gao Zhao Xu Sufan Jiang School of Electrical Engineering Southeast UniversityNanjing210096China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996 USA

Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated operations remain insufficiently investigated,highlighting the potential for further improvement of the frequency *** paper proposes a bi-level optimized temporary frequency support(OTFS)strategy for a *** implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine(WT)controllers and the central WPP ***,to exploit the frequency support capability of WTs,the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT *** approach synergizes the WTs'temporary frequency support with the secondary frequency control of synchronous generators,enabling WTs to release more kinetic energy without causing a secondary frequency ***,a model predictive control strategy is developed for the WPP *** strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy ***,comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.

关键词： Active power control model predictive control optimized temporary frequency support wind power plant wind turbine

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