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2011 International Conference on Consumer Electronics, Communications and Networks, CECNet 2011

作者： Zong-Hu, He De-Xiang, Zhang Ling-Jun, Zhang Bao-Hong, Yuan Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei China Institute of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781612844572

Contourlet transform is the combination of the multi-scale analysis and multi-directional analysis in processing high-dimensional signals and has better approximation precision and better sparse description. Firstly, Using the Contourlet transform, several polarimetric images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region variance information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused contourlet coefficients. Experimental results show that the proposed algorithm works better in preserving the edges and texture information compared with the traditional image fusion algorithms. © 2011 IEEE.

关键词： Contourlet transform

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Radar target recognition using SVMs with a wrapper feature selection driven by Immune Clonal Algorithm

Radar target recognition using SVMs with a wrapper feature s...

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13th European Symposium on Artificial Neural Networks, ESANN 2005

作者： Zhang, Xiangrong Wang, Shuang Shan, Tan Jiao, Licheng National Key Lab for Radar Signal Processing Institute of Intelligent Information Processing Xidian University 710071 Xi'an China

ISBN: (纸本)2930307056

A wrapper feature selection method based on Immune Clonal Algorithm for SVM is presented and applied to 1-D images recognition of radar targets in this paper. In the proposed method, the cross-validation is used for feature evaluation in wrapper feature selection step for SVMs. And Immune Clonal Algorithm, which is characterized by rapid convergence to global optimal solution, is applied to find the optimal feature subset. Experimental results on 1-D images of 3 airplanes obtained in a microwave anechoic chamber show the effectiveness of the proposed method.

关键词： Radar target recognition

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A novel adaptive filtering algorithm for SAR speckle reduction

A novel adaptive filtering algorithm for SAR speckle reducti...

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2007 1st Asian and Pacific Conference on Synthetic Aperture Radar, APSAR 2007

作者： Zhu, Jiabing Chen, Renyuan Hong, Yi East China Research Institute of Electronic Engineering China Education Ministry Key Lab. of Intelligent Computing and Signal Processing Anhui University China

ISBN: (纸本)1424411882

The novel adaptive filtering algorithm based on the analysis of the detail images obtained from the wavelet decomposition of the original noisy image is proposed in the paper. The base idea is to compute the wavelet decomposition of the noisy image to reduce the amplitude of insignificant detail coefficients in the wavelet subspaces by selecting an adaptive filter window of size, the adaptive windowing algorithm was introduced where the window size is automatically adjusted depending on the samples statistics in the boundary of this window such as mean z and standard deviation σz, and then utilizing modified 2nd order Lee filtering. One of the advantages of this approach over the traditional speckle filtering method is the fact that textures are preserved while speckle is reduced. Experimental simulations by real SAR images show it are effective. © 2007 IEEE.

关键词： Wavelet decomposition

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A Spectrum Based Algorithm for Image Classification

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电子学报(英文版) 2009年第3期18卷 427-430页

作者： WANG Nian ZHANG Jiang TANG Jun FAN Yizheng LIANG Dong Key Laboratory of Intelligent Computing and Signal Processing Anhui University Education Ministry Hefei 230039 China Hefei Teachers College Hefei 230601 China Department of Mathematics Anhui University Hefei 230039 China

In this paper, a novel algorithm for image classification is presented which uses the projective value of adjacency spectrum as classified samples. Firstly, the eigenvalues of adjacency matrices constructed on the feature point-sets of images are obtained by singular value decomposition. Secondly, the eigenvalues are projected onto the eigenspace by means of the covariance matrix. Finally, image classification is performed by adopting RBF and PNN neural networks as classifiers respectively. Meanwhile, some theoretical analyses are given to support the proposed method.

关键词：图像分类分类算法神经网络分类器矩阵特征值奇异值分解协方差矩阵射影分类特征空间

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Improving PWF method of despeckle in polarimetrie SAR image by fusion based on nonsubsampled contourlet transform

Improving PWF method of despeckle in polarimetrie SAR image ...

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2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008

作者： Zhang, De-Xiang Wu, Xiao-Pei Gao, Qing-Wei Guo, Xiao-Jing The Key Lab. of Intelligent Computing and Signal Processing of Anhui University Hefei China The Institute of Electronic Science Technology of Anhui University Hefei China

ISBN: (纸本)9780769535630

A novel and efficient improving PWF method of speckle reduction in Polarimetrie SAR image by fusion based on nonsubsampled contourlet transform is proposed. First, the three complex elements (HH, HV, and W) of the Polarimetric scattering matrix were decorrelated to form a new elements matrix. Then we show that the sub-band decompositions of the three elements using nonsubsampled contourlet transform, and fusion factor is applied to the decomposed directional high-frequency coefficients to create new sub-band coefficients. Finally, we process and reconstruct multi-resolution contourlet coefficients according to the PWF method. Experimental results show that compared with PWF de-speckling algorithm, the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserves image details, and the significant information of original image like textures and contour details is well maintained. © 2008 IEEE.

关键词： Synthetic aperture radar

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Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 893-913页

作者： Lei Zhang Yutong Liu Haipeng Yang Fan Cheng Qi Liu Xingyi Zhang Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of EducationSchool of Computer Science and TechnologyAnhui UniversityHefeiChina School of Computer Science and Technology University of Science and Technology of ChinaHefeiChina

Influence maximization,whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network,has many applications such as goods advertising and rumour *** the existing influence maximization methods,the community‐based ones can achieve a good balance between effectiveness and ***,this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping *** fact,many nodes in social networks are overlapping ones,which play more important role in influence *** this end,an overlapping community‐based particle swarm opti-mization algorithm named OCPSO for influence maximization in social networks,which can make full use of overlapping nodes,non‐overlapping nodes,and their interactive information is ***,an overlapping community detection algorithm is used to obtain the information of overlapping community structures,based on which three novel evolutionary strategies,such as initialisation,mutation,and local search are designed in OCPSO for better finding influential *** results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts(***,CMA‐IM,CIM,CDH‐SHRINK,CNCG,and CFIN).

关键词： computational intelligence data mining social network

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Enhanced Discriminant Analysis Based Fault Diagnosis Scheme for Marine Diesel Engines 11

Enhanced Discriminant Analysis Based Fault Diagnosis Scheme ...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Zhong, Kai Wang, Jiayi Pan, Donghui Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui Hefei230601 China School of Mathematical Science Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Hefei230601 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Since the marine diesel engine is one of the most popular power equipments for modern shipping, accurate and timely diagnose the faults occurred in diesel engine is extraordinary important for long service life and high reliability. Note that Fisher discriminant analysis (FDA) has been widely applied in fault diagnosis field. However, it is quite susceptible to the small sample size (SSS) problem, which is very common in the running process of diesel engine. In response to the problem, this study pro-poses a new fault classification criterion based exponential discriminant analysis (EDA) scheme for diesel engine fault diagnosis, in which the exponential transformation is carried out first to enlarge the margin between different classes, thus the different fault classes are further separated and the diagnosis capability is enhanced. In addition, the theorems of matrix exponential guarantee that the matrix exp (Sw) is always full-rank, even though the Sw is singular. That means, the novel methodology is still appli-cable when confronts with the SSS problem. Finally, based on the more reasonable fault classification criterion, the simulation results on a real-world two-stroke six-cylinder low-speed marine diesel engine indicate that the EDA based diagnosis scheme achieves higher fault diagnosis accuracy with good robustness and practicability in real-world engineering applications. © 2022 IEEE.

关键词： Failure analysis

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Dual-notch Ultra-wideband Bandpass Filter Based On Folded Dual-mode Resonator

Dual-notch Ultra-wideband Bandpass Filter Based On Folded Du...

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

作者： Li, Qingqing Wu, Xianliang Huang, Zhixiang Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

Aiming at the problem that ultra-wideband systems are easily interfered by narrowband signals, In this paper, a novel ultra-wideband (UWB) bandpass filter with dual-notch characteristics. This filter is mainly composed of Composition of a multimode resonator (MMR)and a folded dual-mode step-impedance resonator. Paralleling T-shaped SIR stubs in the basic MMR structure realizes the introduction of transmission zeros within the passband. At the same time, a pair of high-impedance stubs are also loaded to extend the upper stopband of the filter, and a folded dual-mode stepped impedance resonator is coupled below the filter to generate dual-notch frequencies. Through the electromagnetic simulation software HFSS for simulation optimization, the simulation results show that the passband of the filter is 3.4211.21GHz and its notch frequencies are 8.8GHz and 9.5GHz respectively. Insertion loss below 0.5dB and stopband up to 20GHz (S21 © 2022 IEEE.

关键词： Electromagnetic simulation

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Identifying Disease Modules Based on Connectivity and Semantic Similarities 14th

Identifying Disease Modules Based on Connectivity and Semant...

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Su, Yansen Zhu, Huole Zhang, Lei Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789811534140

The identification of disease modules has attracted increasing attention due to the importance in comprehending pathogenesis of complex diseases. Most of the existing methods were based on the protein-protein interaction (PPI) networks with the incompleteness and incorrectness of the interactome, which results in many disease-related proteins and pathways not within their disease modules. In this paper, we propose a method named IDMCSS to effectively identify disease modules by adding some potential interactions and removing some incorrect interactions in the existing human PPI network. Firstly, the connective similarity and semantic similarity are calculated between the known disease proteins and each of their neighbors. Then, the network is adjusted by adding the interactions with large connective and semantic similarity, and removing those with small connective and semantic similarity. Further, the proteins are sorted in the descending order according to the similarity to the known disease proteins, and the sorted proteins are added into a candidate node set one by one until a certain biological information is not enriched in the candidate node set. Finally, the connected subnetwork with the largest number of nodes in the candidate node set is selected as the disease module. The disease modules identified by IDMCSS involve crucial biological processes of related diseases and can predict 12 targets for drug intervention. The experimental results on asthma demonstrate the effectiveness of the proposed method in comparison to existing algorithms for disease module identification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

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Viewpoint selection for taking a good photograph of architecture 24

Viewpoint selection for taking a good photograph of architec...

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24th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2016

作者： He, Jingwu Zhou, Wenzhe Wang, Linbo Zhang, Hongjie Guo, Yanwen National Key Lab for Novel Software Technology Nanjing University NanJing210023 China MOE Key Laboratory of Intelligent Computing and Signal Processing Institute of Media Computing Anhui University HeFei230039 China

ISBN: (纸本)9783038680246

This paper studies the problem of how to choose the viewpoint for taking good photographs for architecture. We achieve this by learning from professional photographs of world famous landmarks that are available in the Internet. Unlike the previous efforts devoted to photo quality assessment which mainly rely on visual features, we show in this paper combining visual features with geometric features computed on the 3D models can result in a more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 6 world famous architectures as well as their 3D models from Internet. Viewpoint recovery for images is carried out by an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users' viewpoint preference when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues, and perform viewpoint recommendation by learning from both 2D and 3D features, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. © 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.

关键词： Architecture

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