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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Thermal Management Approaches for Power Electronic Building Blocks and Power Corridors

Thermal Management Approaches for Power Electronic Building ...

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IEEE Electric Ship Technologies Symposium, ESTS

作者： J. C. Ordonez C. Sailabada J. Chalfant C. Chryssostomidis C. Li K. Luo E. Santi B. Tian A. Biglo N. Rajagopal J. Stewart C. DiMarino Department of Mechanical Engineering Center for Advanced Power Systems Florida State University Tallahassee FL MIT Sea Grant Design Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Mechanical Engineering University of South Carolina Columbia SC Department of Electrical Engineering University of South Carolina Columbia SC Center for Power Electronics Systems (CPES) Virginia Polytechnic Institute and State University Arlington VA USA

This paper presents an overview of thermal management solutions that are being investigated for power electronic building blocks and their integration into power corridors, both of which are seen as enablers of flexible and reconfigurable power distribution systems in the next generation Navy ships. Air, liquid, two-phase, and indirect cooling approaches are discussed in the context of specific building block configurations built and designed at CPES (Virginia Tech). The paper concludes with an overview of ongoing efforts towards the design, construction, and testing of technology prototypes.

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National laboratory, Lemont, USA." Argonne National laboratory was left off the original publication. © The Author(s) 2025.

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Feedback-based Optimal Dispatch for Virtual Power Plants in Active Distribution Networks 5

Feedback-based Optimal Dispatch for Virtual Power Plants in ...

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Xu, Tong Zeng, Xuesong Wu, Wenchuan Energy Planning and Research Institute Southwest Electric Power Design Institute Co. Ltd. China Power Engineering Consulting Group Chengdu China Department of Electrical Engineering State Key Laboratory of Power Systems Tsinghua University Beijing China

ISBN: (纸本)9781665434256

Virtual power plants (VPPs) play an increasingly important role in power systems as they provide flexibility and efficiency for active distribution networks and serve as a great tool of energy management for the power grid penetrated with plenty of distributed energy resources (DERs) in the future. This article aims to propose a novel optimal dispatch method for VPP to realize real-time autonomous optimization. The proposed method combines the advantage of model-based method and data-driven method to provide instant optimal points trajectory based on system feedback, i.e., measurements. Firstly, the approximated mapping from control variables to state variables is established. Then a zeroth order optimization method is presented to calculate real-time command of DERs. The proposed strategy is adaptively compatible with the sudden change of the system and provide effective voltage support verified by the simulation in the unbalanced distribution networks. © 2021 IEEE

关键词： Virtual Power Plants

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On the potential of lateral BJTs and SiGe HBTs in advanced CMOS technologies

On the potential of lateral BJTs and SiGe HBTs in advanced C...

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Pacific Rim Meeting on Electrochemical and Solid State Science 2020, PRiME 200

作者： Suligoj, Tomislav Žilak, Josip Osrečki, Željko Koričić, Marko Micro and Nano Electronics Laboratory Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Croatia

ISBN: (纸本)9781607689003

The potential of lateral bipolar transistors, based on Horizontal Current Bipolar Transistor (HCBT), in scaled CMOS technologies are examined by TCAD simulations. Thorough and consistent simulations show that SiGe HCBT can achieve fT/fmax of 358/490 GHz outperforming standard vertical-current HBT with fT/fmax of 348/423 GHz, assuming the same intrinsic transistor doping profile. By optimizing the extrinsic base shape and width, the characteristics of SiGe HCBT can be further improved. SiGe HCBTs in bulk silicon substrates result in better performance comparing to the transistors in SOI substrates. © The Electrochemical Society

关键词： Si-Ge alloys

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent systems and Mixture of Experts

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the architectural designs, operational procedures, and inherent advantages of using MAS and MoE in generative AI to explore its functionality and applications fully. Next, we review the applications of MAS and MoE frameworks in content generation and resource allocation, emphasizing their impact on networking operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal policy optimization (MoE-PPO) framework for 3D object generation and data transfer scenarios. The framework uses MAS for dynamic task coordination of each network service provider agent and MoE for expert-driven execution of respective tasks, thereby improving overall system efficiency and adaptability. The simulation results demonstrate the effectiveness of our proposed framework and significantly improve the performance indicators under different network conditions. Finally, we outline potential future research directions. © 2024, CC BY.

关键词： Multi agent systems

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A Self-Calibration Logic Circuit Agnostic To Offset Calibration Technique For High-Precision Dynamic Comparator

A Self-Calibration Logic Circuit Agnostic To Offset Calibrat...

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Women in Technology Conference (WINTECHCON), 2022 IEEE

作者： Nidhi Sharma Rajesh K. Srivastava Vinayak Hande Deep Sehgal Devarshi Mrinal Das Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar India Semi-conductor Laboratory Ministry of Electronics & Information Technology (MeitY) S.A.S Nagar India Integrated Systems and Circuit Design Carinthia University of Applied Sciences Villach Austria

This paper proposes a calibration technique agnostic calibration logic circuitry for the self-calibration of the high-precision dynamic comparator, which can be used in major self-calibration techniques to activate the calibration and offset polarity change detection for completion of the calibration. This paper also briefly reviews the different comparator offset cancellation techniques, i.e., auto-zeroing, self-calibration, and chopper stabilization. The proposed calibration logic is implemented in a dynamic comparator’s effective trans-conductance controlled offset calibration in SCL 180 nm technology. The standard deviation of the input-referred offset of the comparator is reduced to 445 μV from 2.85 mV with a complete calibration range of 5σ offset, therefore qualifying it for high-speed flash ADCs and asynchronous SAR ADCs. The power consumption at 1 GHz operating frequency is 154.6 μW.

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Plasmon hybridization induced by quasi bound state in the continuum of graphene metasurfaces oriented for high-accuracy polarization-insensitive two-dimensional sensors

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Chinese Optics Letters 2022年第4期20卷 65-70页

作者： Xiuyu Wang Jihong Xin Qun Ren Haocheng Cai Jiaqi Han Chengyi Tian Pengcheng Zhang Lijie Jiang Zhihao Lan Jianwei You Wei E.I.Sha Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology School of MicroelectronicsTianjin UniversityTianjin 300072China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education School of Electronic EngineeringXidian UniversityXi'an 710071China Huawei Technologies Company Ltd. Shanghai 518129China Department of Electronic and Electrical Engineering University College LondonLondon WC1E7JEUK Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China

Plasmonics could provide compact and powerful solutions for manipulating light in deep-subwavelength dimensions,which is promising for a great range of nanophotonic technologies such as plasmonic rulers and ***,the effective area of enhanced localized field induced by surface plasmon polaritons is typically restricted to the structural *** this work,we propose a method to generate high quality-factor extended electromagnetic fields via hybridizing the superradiant state and the quasi bound state in the continuum of graphene *** coupling interaction involved operates as a three-level system with multiple sharp resonances immune to the polarization,which holds great promise for developing nanodevices with high sensing capacity in two dimensions.

关键词： plasmon hybridization quasi bound state in the continuum high-Q sensing

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