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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Chen, X. Chen, X.W. Gu, P.F. He, Z. Wang, J.X. Ding, D.Z. Nanjing University of Science and Technology Department of Communication Engineering Nanjing210094 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210094 China National Key Laboratory of Science and Technology on Space Microwave China Academy of Space Technology Xi'an710100 China

ISBN: (纸本)9781665452366

In the paper, an ultrawideband low-profile miniaturized stealth antenna system built on a multilayer absorbing structure is proposed. Firstly, an ultra-wideband tight dipole (TCA) is selected as the antenna unit in this paper, based on which the steganographic effect is simulated for the free-space infinite array using the master-slave boundary conditions and Floquet port excitation of ANSYS HFSS software After that, a frequency-selective absorption super surface was loaded in the system to achieve an antenna operating band of 2-18 GHz with RCS reduction of 10 dB at 8.5-18 GHz and 5 dB at 18-26 GHz, with an average antenna efficiency of 56.7% (including antenna standing wave loss). © 2022 IEEE.

关键词： Ultra-wideband (UWB)

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Probabilistic Statistical Model of Station Load Based on Improved Cluster Analysis and Kernel Density Estimation

Probabilistic Statistical Model of Station Load Based on Imp...

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Power science and Technology (ICPST), IEEE International Conference on

作者： Jing Wang Qinqin Ma Fei Xu Xi Chen Huaizhao Ji Information and Communication Company State Grid Shaanxi Electric Power Company Xi'an China Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

In order to more accurately describe the load changes in the power system and establish a suitable load model, a probabilistic statistical model of desk load based on improved cluster analysis and nonparametric kernel density estimation is proposed. Firstly, the shortcomings of ISODATA clustering algorithm are analyzed, based on which this algorithm is improved by optimizing the selection of initial clustering centers to enhance the effect of clustering and accelerate the convergence speed of the algorithm, and the improved algorithm is used to cluster the power users and analyze the characteristics of electricity consumption of different users. Then, the probability distributions of different loads in each quarter are calculated by using non-parametric kernel density estimation method, and their probability density curves are extracted. Combined with the actual analysis of the load data of a number of users in a station area, compared with the traditional clustering algorithm in terms of clustering effect, the results verified the superiority of the algorithm proposed in this paper and the application value of the proposed model.

关键词： Analytical models Electricity Clustering algorithms Estimation Predictive models Probabilistic logic Real-time systems

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Efficient Model-Based OPC via Graph Neural Network

Efficient Model-Based OPC via Graph Neural Network

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Electronics Design Automation (ISEDA), International Symposium of

作者： Shuyuan Sun Xuelian Chen Fan Yang Bei Yu Shang Li Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University China Cogenda Inc China Department of Computer Science and Engineering The Chinese University of Hong Kong China

As feature size continues to shrink and light source wavelengths remain unchanged, the optical diffraction effects seriously degrade chip yield. Optical proximity correction (OPC) has become an essential step for chip manufacturability. However, OPC cannot achieve satisfactory mask correction results in an affordable number of iterations on some layouts, delaying the chip design cycle. In this paper, we propose to use the Graph Neural Network (GNN) to predict the initial mask correction and speed up model-based OPC. We design a graph model to represent the chip layout, with segments represented as graph nodes and the diffraction effects between them represented as graph edges. The GNN is used to obtain the embedding of the segment, and we predict the shift value based on its output. The proposed GNN-based predictor enhances the original OPC to find a good correction solution for full-chip-size masks in fewer iterations. compared with the original OPC method, experimental results on 55nm and 32nm full chip designs show that the proposed GNN-based OPC method reduces the number of iterations up to 75% and the acquired mask of comparable quality.

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A Parallel and Pipelined Hardware Architecture for Fractional-Pixel Motion Estimation in AVS3

A Parallel and Pipelined Hardware Architecture for Fractiona...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Zeng, Yuning Zhu, Xizhong Xiang, Guoqing Hao, Zhijian Zhang, Peng Huang, Xiaofeng Yan, Wei Peking University School of Software and Microelectronics Beijing China Advanced Institute of Information Technology Peking University Beijing China Peking University Shenzhen Guraduate School School of Electronic and Computer Engineering Shenzhen China Fudan University State Key Laboratory of ASIC System Shanghai China Hangzhou Dianzi University School of Communication Engineering Hangzhou China

ISBN: (纸本)9781665441544

The latest generation of video coding standards has significantly improved video coding performance. The third generation of audio video coding standards (AVS3) is one of these latest standards. However, due to the more flexible block partition mechanism in AVS3, the computational overhead brought by fractional-pixel motion estimation (FME) increases significantly, making it more difficult to implement a real-time FME hardware. In this paper, a parallel and pipelined hardware architecture supporting block sizes from 4x4 to 64x64 is proposed for FME in AVS3, and it is specially designed for coding tree unit (CTU)-level pipelined architecture of AVS3 encoder. Three necessary modules are developed in this architecture. The first module concerns derivation of motion vector predictors (MVPs). The second module is used to derive fractional-pixel motion vectors (FMVs) of coding units (CUs) partitioned by Binary-tree (BT) and Quad-Tree (QT) mechanisms. And a motion vector substitution (MVS) module is proposed to substitute the FMVs of the remaining CUs with the FMVs derived in the second module. Experimental results show that the proposed methods suffer only 0.62% performance degradation in BD-Rate. Furthermore, the architecture is implemented on XILINX ALVEO U250 FPGA at 400 MHz and the presented FME hardware design can support the real-time encoding of 4K@30fps. © 2022 IEEE.

关键词： Pipelines

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal state update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Fully integrated hybrid microwave photonic receiver

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Photonics Research 2022年第6期10卷 1472-1483页

作者： JIACHEN LI SIGANG YANG HONGWEI CHEN XINGJUN WANG MINGHUA CHEN WEIWEN ZOU Beijing National Research Center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Microwave photonic receivers are a promising candidate in breaking the bandwidth limitation of traditional radio-frequency(RF)*** further balance the performance superiority with the requirements regarding size,weight,and power consumption(SWa P),the implementation of integrated microwave photonic microsystems has been considered an upgrade ***,up to now,to the best of our knowledge,chip-scale fully integrated microwave photonic receivers have not been reported due to the limitation of material *** this paper,we report a fully integrated hybrid microwave photonic receiver(FIH-MWPR)obtained by comprising the indium phosphide(In P)laser chip and the monolithic silicon-on-insulator(SOI)photonic circuit into the same substrate based on the low-coupling-loss micro-optics *** from the integration of all optoelectronic components,the packaged FIH-MWPR exhibits a compact volume of 6 cmand low power consumption of 1.2 *** FIH-MWPR supports a wide operation bandwidth from 2 to 18 ***,its RF-link performance to down-convert the RF signals to the intermediate frequency is experimentally characterized by measuring the link gain,the noise figure,and the spurious-free dynamic range metrics across the whole operation frequency ***,we have utilized it as a de-chirp receiver to process the broadband linear frequencymodulated(LFM)radar echo signals at different frequency bands(S-,C-,X-,and Ku-bands)and successfully demonstrated its high-resolution-ranging *** the best of our knowledge,this is the first realization of a chip-scale broadband fully integrated microwave photonic receiver,which is expected to be an important step in demonstrating the feasibility of all-integrated microwave photonic microsystems oriented to miniaturized application scenarios.

关键词： method microwave bands

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Risk-Aware SDN Defense Framework Against Anti-Honeypot Attacks Using Safe Reinforcement Learning

Risk-Aware SDN Defense Framework Against Anti-Honeypot Attac...

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作者： Gao, Dongying Guo, Caiwei Zhang, Yi Ji, Wen Lv, Zhilei Li, Zheng Zhang, Kunsan Lin, Ruibin State Grid Fujian Information & Telecommunication Company Fuzhou China Department of Information and Communication Engineering & Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China State Grid Zhangzhou Power Supply Company Zhangzhou China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fuzhou China

The development of multiple attack methods by external attackers in recent years poses a huge challenge to the security and efficient operation of software-defined networks (SDN), which are the core of operational controllers and data storage. Therefore, it is critical to ensure that the normal process of network interaction between SDN servers and users is protected from external attacks. In this paper, we propose a risk-aware SDN defense framework based on safe reinforcement learning (SRL) to counter multiple attack actions. Specifically, the defender uses SRL to maximize the utility by choosing to provide a honeypot service or pseudo-honeypot service within predefined security constraints, while the external attacker maximizes the utility by choosing an anti-honeypot attack or masquerade attack. To describe the system risk in detail, we introduce the risk level function to model the simultaneous dynamic attack and defense processes. Simulation results demonstrate that our proposed risk-aware scheme improves the defense utility by 17.5% and 142.4% and reduces the system risk by 42.7% and 59.6% compared to the QLearning scheme and the Random scheme, respectively. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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Recent progress of integrated circuits and optoelectronic chips

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science China(Information sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit photonic neural computing semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic

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Single-Wavelength Net 1 Tb/s Transmission in SMF and 6.4 Tb/s in Weakly-Coupled 7-Core MCF Using a Phase- and Polarization-Diverse Direct Detection Receiver in Jones space

Single-Wavelength Net 1 Tb/s Transmission in SMF and 6.4 Tb/...

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OptoElectronics and communications Conference, OECC

作者： Qi Wu Xueyang Li Yixiao Zhu Chen Cheng Zhaopeng Xu Jiali Li Zhixue He Weisheng Hu Peng Cheng Laboratory Shenzhen China Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China

We demonstrate a record single-wavelength net 1.04-Tb/s transmission over 40-km SMF using 56.5-GHz Jones-space direct-detection receiver with the highest electrical spectral efficiency of 18.4 b/s/Hz. Using the 42-km weakly-coupled 7-core multi-core fiber, we achieve a net 6.49-Tb/s signal transmission for large-scale and high-capacity data center interconnects.

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Non-Uniform 3D Massive MIMO Arrays Topology Optimization for Near-Field communications 35

Non-Uniform 3D Massive MIMO Arrays Topology Optimization for...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio communications, PIMRC 2024

作者： Guo, Yunhui Zhang, Yang Pang, Lihua Liu, Yuanwei Ding, Zhiguo Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Xian University of Science and Technology School of Information Engineering Xian710054 China Khalifa University 6G Center Department of Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362244

In this paper, the design of non-uniform antenna topology for 3D massive MIMO arrays in near-field communications is investigated. Specifically, the near-field spherical wavefront radiation characteristics are considered to accurately model the variations of signal phase across array elements. Subsequently, the closed-form expressions of the per-user signal-to-interference noise ratio (SINR) and achievable sum rate for a multi-user MIMO system with maximum-ratio transmission (MRT) precoding are derived. The focus is on the maximization of the achievable sum rate by optimizing the non-uniform 3D antenna array topology. Since the optimization problem exhibits highly nonlinear and nonconvex characteristics, an enhanced particle swarm optimization (EPSO) algorithm is proposed to effectively solve it. Numerical results demonstrate the superiority of the proposed non-uniform 3D array topology in near-field communications for enhancing the achievable sum rate. © 2024 IEEE.

关键词： Signal to noise ratio

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