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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Wudang Xiao Canping Yu Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments.

关键词： Resistance Direction-of-arrival estimation Simulation Noise Estimation Adaptive arrays Information filters

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Wideband Bonding Wire Compensation Structure Design and Electric-thermal Coupling Analysis for Chip Package

Wideband Bonding Wire Compensation Structure Design and Elec...

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Asia-Pacific Conference on Microwave

作者： Pei Ge Hao-Ran Zhu Yu Fa Sun School of Electronic Science and Engineering Anhui University An Hui China Key Laboratory of Intelligent Computing and Signal Processing of Heifei Anhui University An Hui China

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

In this paper, a novel wire-bonding and BGA package is applied to a highly integrated MMIC. The novel compensation component of the bonding wires is composed of a capacitive strip-line and an inductive via below the transmission line. In addition, A large amount of heat is generated by high-intensity chip, leading to a decrease in transmission performance. Multilayered system in package is designed to improve the heat dissipation capability of chip. Multi physics field simulation is introduced to verify the effectiveness of the design. The proposed multilayered system in package is made using LTCC process and loaded with low noise amplifier chip. The return loss of the is better than 10dB and the gain is greater than 10dB in the frequency range of DC-20GHz.

关键词： Low-noise amplifiers System-in-package Power transmission lines Wires Noise Routing Microwave amplifiers Bonding Physics Wideband

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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A Novel Transformed Nested Array with Enhanced DOFs Based on Sum-Difference Coarray

A Novel Transformed Nested Array with Enhanced DOFs Based on...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Wei Xin Yingsong Li Harbin Engineering University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Sum-difference driven coarray (SDCA) was paid great attention in array-signal-processing (ASP). By considering SDCA, the degrees of freedom (DOFs) for sparse arrays can be further improved. Here, a new transformed nested-array (TNA) is constructed, which reduces the element redundancy via rearranging the density of sub-arrays of the constructed new TNA. Compared to former TNAs, the constructed TNA gets higher DOFs and maximization signals, numerical simulation are given for getting its superior behaviors.

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Edge-Preserving Multi-Exposure Fusion with Guided Filtering for Structural Patch Decomposition 6

Edge-Preserving Multi-Exposure Fusion with Guided Filtering ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Zhen, Yang Qi, Junwei Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China College of Computer Science and Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350399059

Multi-exposure image fusion (MEF) is one of the effective ways to obtain high dynamic range (HDR) images. Multi-scale structural patch decomposition based MEF(MSPD) and edge-preserving structural patch decomposition (MESPD) have achieved state-of-the-art fusion quality. MESPD incorporates edge-preserving factors into MSPD to preserve details in a single-scale setting. However, we find that the edge-preserving factors put too much emphasis on the edge, resulting in artifacts on some edges and loss of details. To tackle this issue, guided filter is introduced into MSPD, which is directly used as an edge-preserving filter instead of mean filter to implicitly decompose images. In addition, we have designed a flexible adaptive weight, which can further maintain details of the light and dark areas. Likewise, we inherit MESPD and MSPD to extend our method to multi-scale framework. Extensive experimental results demonstrate our method can produce satisfactory results in both static and dynamic scenes with fewer artifacts and low computational cost. © 2023 IEEE.

关键词： Image fusion

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An Optimization Method Based on Deep Learning for Electromagnetic Inverse Scattering Problems

An Optimization Method Based on Deep Learning for Electromag...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Wangchao Hu Guoda Xie Jinpeng Dai Guilin Hou Xiang Li Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Key Laboratory of Electromagnetic Environmental Sensing Anhui Province Anhui University Hefei China Anhui University Hefei China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, a deep learning optimization method combining U-net model and cycle generative adversarial network (Cycle-GAN) is proposed to efficiently solve the electromagnetic inverse scattering (EMIS) problems. Firstly, a U-net model is used to obtain a rough preliminary reconstruction. Through down-sampling and up-sampling processes, the basic features of the object are extracted and reconstructed from the measured scattered field, providing a coarse dielectric parameter distribution. Then, a Cycle-GAN model further refines the output of the U-net. The Cycle-GAN model, through its adversarial training mechanism involving a generator and a discriminator, enhances the details of the preliminary reconstruction, resulting in a finely detailed dielectric parameter distribution his method improves the problem of rough results obtained by using U-net method alone, and can effectively solve and optimize EMIS problems.

关键词： Deep learning Inverse problems Computational modeling Electromagnetic interference Electromagnetic scattering Optimization methods Feature extraction Dielectric measurement Electromagnetics Image reconstruction

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SGCN: A Scalable Graph Convolutional Network with Graph-Shaped Kernels and Multi-Channels

SSRN

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SSRN 2023年

作者： Huang, Zhenhua Zhou, Wenhao Li, Kunhao Jia, Zhaohong School of Internet Anhui University Feixi Road Hefei230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China

Graph neural networks (GNNs) have shown great success in graph processing. However, current message-passing-based GNNs have limitations in feature aggregations and update mechanisms that rely on a fixed mode, resulting in inadequate representations of the neighborhood structure's richness. Furthermore, the convolution layer in most GNNs lacks flexibility and multi-channels compared to convolutional neural networks (CNNs). CNNs employ multiple channels, where different kernels capture shared patterns of images. To address the limitations, we propose a novel scalable graph convolution network (SGCN). SGCN enhances structural information learning by introducing an expressive feature aggregation mechanism. Drawing inspiration from the design of CNNs, SGCN designs graph-shaped kernels to perform multi-channel convolutions on the subgraph structure. A sampler based on degree centrality is employed to simplify the computation costs. Extensive experiments demonstrate that SGCN achieved state-of-the-art performances on various scales of graph datasets (accuracy improved up to 5.38%). © 2023, The Authors. All rights reserved.

关键词： Convolution

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Nested relation extraction with iterative neural network

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Frontiers of Computer Science 2021年第3期15卷 109-122页

作者： Yixuan CAO Dian CHEN Zhengqi XU Hongwei LI Ping LUO Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Most existing researches on relation extraction focus on binary flat relations like Bomln relation between a Person and a *** a large portion of objective facts de-scribed in natural language are complex,especially in professional documents in fields such as finance and biomedicine that require precise *** example,“the GDP of the United States in 2018 grew 2.9%compared with 2017”describes a growth rate relation between two other relations about the economic index,which is beyond the expressive power of binary flat ***,we propose the nested relation extraction problem and formulate it as a directed acyclic graph(DAG)structure extraction ***,we propose a solution using the Iterative Neural Network which extracts relations layer by *** proposed solution achieves 78.98 and 97.89 FI scores on two nested relation extraction tasks,namely semantic cause-and-efFect relation extraction and formula ***,we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively,which makes the annotation difficult and ***,we extend our model to incorporate a mention-insensitive mode that only requires annotations of relations on entity concepts(instead of exact mentions)while preserving most of its *** mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.

关键词： nested relation extraction mention insensitive relation iterative neural network

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A High Isolation Design Used Between Broadband Dual Polarization Transceiver Antennas

A High Isolation Design Used Between Broadband Dual Polariza...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuke Guo Xiaosheng Zhang Luyu Zhao National Key Laboratory of Radar Detection and Sensing Xidian University Xi'an China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

We proposed a high isolation design between two broadband dual polarization transceiver antennas in this paper. It employs periodic metal isolation walls with equal spacing and height to strongly suppress the low-frequency band (LB) coupling. Covering the vertical and horizontal surfaces of the metal with flexible graphene high resistive metasurfaces that easily conform to the structure improves 40 dB in isolation between different polarizations at 6–18 GHz. This device has less impact on the radiation pattern of the antenna. This design not only enhances the isolation between different polarizations but also optimizes equipment heat dissipation and improves device performance.

关键词： Performance evaluation Heating systems Couplings Graphene Metals Metasurfaces Transceivers Broadband communication Broadband antennas Antenna radiation patterns

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Inductive Network Representation based on Neighbor Aggregation 3

Inductive Network Representation based on Neighbor Aggregati...

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2022 3rd International Conference on Computer Information and Big Data Applications, CIBDA 2022

作者： Xie, Jingsheng Chen, Xi Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China School of Computer Science and Technology Anhui University HefeiAH551 China

ISBN: (纸本)9783800758760

Network representation learning is to learn low dimensional vectors for nodes. It plays a critical role in network analysis. However, most existing network embedding methods focus on embedding the nodes that already exist in the network, and cannot be generalized to newly-added nodes without retraining the learning model. To deal with this problem, the most common idea is to directly aggregate neighbor embeddings as the representation of new nodes, but this may lead to ignoring the particularity of node neighbors themselves. To fill this gap, we present INRL-NA, an innovative method of Inductive Network Representation Learning based on Neighbor Aggregation, which uses the hierarchical information to selectively aggregate neighbor node embeddings, preserving both the structural information and feature information of network. First we build a hierarchical network, then train a refinement model on the hierarchical network, finally after a new node joins the network, we use the hierarchical information and feature information to selectively aggregate neighbor embeddings. The experimental results show that compared with direct aggregation of all neighbor representations, INRLNA has higher representation quality. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Aggregates

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