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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Jian Yu Mei Yu Yifeng Xue Jufeng Dai School of Electronic and Information Engineering Tianjin University Tianjin China School of Computer Science and Technology Tianjin University Tianjin China School of Software Beijing University of Aeronautics &Astronautics Beijing China

A hierarchy distributed route structure based on IPv6 address in peer to peer systems is proposed in order to solve the problems that physical topology and logical network does not match so as to inefficient search in distributed hash table at present. By building node identifier using IPv6 address and hashing different level clustering identifier to set up hierarchy route, near nodes in physical network are closer in overlay. Smaller hops across domain, lower search latency, and higher efficiency are proved.

关键词： Peer to peer computing Network topology Routing Fingers computer science Space technology Delay effects Circuit topology Buildings Logic

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Application of Multidimensional Support Vector Regression on the EEG source localization

Application of Multidimensional Support Vector Regression on...

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Proceedings of the Biannual World Automation Congress

作者： Jianwei Li Youhua Wang Qing Wu Xueqin Shen Shuping Hou Jinlong An School of Computer Science and Software Hebei University of Technology Tianjin China School of Electrical Engineering and Automation Hebei University of Technology Tianjin China School of Information Engineering Tianjin University of Commerce TJCU Tianjin China

Multidimensional support vector regression (MSVR) with similar iterative re-weight least square (IRWLS) is firstly used in this paper to estimate the location and moment of an equivalent current dipole source in the inverse problem of electroencephalogram (EEG). In order to discover the relationship between the potentials on the scalp and internal source within the brain, the single current dipole source with four-shell concentric sphere model is reconstructed. Our simulation experiments demonstrate that MSVR based on the support vector machine can obtain more robust estimations for EEG source localization problem with equivalent current dipole model.

关键词： Multidimensional systems Electroencephalography Brain modeling Scalp Inverse problems Electrodes Magnetic heads Support vector machines Robustness Current measurement

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Mitigating energy holes based on transmission range adjustment in wireless sensor networks 5

Mitigating energy holes based on transmission range adjustme...

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5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2008

作者： Song, Chao Cao, Jiannong Liu, Ming Zheng, Yuan Gong, Haigang Chen, Guihai School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Computing Internet and Mobile Computing Laboratory Hong Kong Polytechnic University Hong Kong Hong Kong State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

ISBN: (纸本)9789639799264

In a wireless sensor network (WSN), the energy hole problem is a key factor which affects the lifetime of the networks. In a WSN with circular multi-hop deployment (modeled as concentric coronas), sensors in one corona have the same transmission range termed as the transmission range of this corona, and different coronas have different transmission ranges, which compose a list termed as transmission range list. Based on our improved corona model with levels, we propose that a right transmission range of each corona is the decision factor for optimizing network lifetime after nodes deployment. We prove that searching optimal transmission range lists is a multi-objective optimization problem (MOP), which is NP hard. We propose a centralized algorithm and a distributed algorithm to build the transmission range list for different node distributions. The two algorithms can not only reduce the searching complexity but also obtain results approximated to the optimal solution. Furthermore, the simulation results indicate that the network lifetime under our solution approximates to that ensured by the optimal list. Compared with existing algorithms, our solution can make the network lifetime be extended more than two times longer. © Copyright 2008 ICST.

关键词： Sensor nodes

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EEG SOURCE LOCALIZATION OF ERP BASED ON MULTIDIMENSIONAL SUPPORT VECTOR REGRESSION APPROACH

EEG SOURCE LOCALIZATION OF ERP BASED ON MULTIDIMENSIONAL SUP...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： QING WU JIAN-WEI LI JIN-LONG AN YU-FANG WEI YOU-HUA WANG School of Computer Science and Software Hebei University of Technology Tianjin 300130 China School of Electrical Engineering and Automation Hebei University of Technology Tianjin 300130 Chi

A new integrated multi-method system is presented to estimate the location and moment of equivalent current dipole sources of event-related potentials (ERP). In order to handle the large-scale high dimension problems efficiently and quickly, the ISOMAP algorithm was used to find the low dimensional manifolds from recorded EEG. Then, based on reduced dimension data, Multidimensional Support Vector Regression (MSVR) with similar iterative re-weight least square (IRWLS) was used to discover the relationship between the observation potentials on the scalp and the internal sources within the brain. In our experiments, the two current dipole sources with four-shell concentric sphere model were reconstructed. Our experiments demonstrate that MSVR based on the support vector machine can obtain more robust estimations for EEG source localization problem.

关键词： EEG ISOMAP ERP IRWLS Multidimensional support vector regression IRWLS

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Application of Dimensionality Reduction Analysis to Fingerprint Recognition

Application of Dimensionality Reduction Analysis to Fingerpr...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Jing Luo Shuzhong Lin Ming Lei Jianyun Ni Tianjin Polytechnic University Tianjin Tianjin CN Tianjin Key Laboratory for Mechanical & Electrical Equipment Technology Tianjin Professional University Tianjin China School of Computer Science and Technology Tianjin University of Technology Tianjin China School of Electrical Engineering Tianjin University of Technology Tianjin China

Dimensionality reduction is an important issue in Fingerprint recognition that often faces high-dimensional data. Two-dimensional principal component analysis (2DPCA) is one of the most popular methods for dimensionality reduction. A novel fingerprint recognition algorithm using 2DPCA has been proposed in this paper. Firstly, the prime features of original images can be attained by two-lever WT decomposition. Secondly, the features of dimensional reduction are solved by 2DPCA. Finally, fingerprint recognition can be realized by Ellipsoidal Basis Function Neural Network (EBFNN). The algorithm combines the optimization of the 2DPCA and the adaptability of EBFNN. The resulting algorithm is tested on three different fingerprint verification challenge datasets and demonstrates much higher performance in comparison to WT-2DPCA-RBF.

关键词： Fingerprint recognition Neural networks Feature extraction Covariance matrix Principal component analysis Computational intelligence Application software Educational institutions Design automation Laboratories

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An Improved Fingerprint Recognition Algorithm Using EBFNN

An Improved Fingerprint Recognition Algorithm Using EBFNN

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International Conference on Genetic and Evolutionary Computing (WGEC)

作者： Jing Luo Shuzhong Lin Jianyun Ni Ming Lei Tianjin Polytechnic University Tianjin Tianjin CN Tianjin Key Laboratory for Modern Mechanical & Electrical Equipment Technology Tianjin Professional University Tianjin China School of Electrical Engineering Tianjin University of Technology Tianjin China School of Computer Science and Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9780769533346

Radial basis function neural network (RBFNN) is widely applied in pattern recognition. Comparing with the Gaussian function of RBFNN, the ellipsoidal basis function (EBF) can make the partition of input space more specific. Ellipsoidal basis function neural network (EBFNN) is a forward-feedback neural network of which hidden-layer function is EBF, which can make the partition of input space limitary and bounded. A novel fingerprint recognition algorithm using EBFNN has been proposed in this paper. Wavelet transform (WT) is a mathematical tool based on many-layer function decomposition. Firstly, fingerprint features are extracted by six-layer WT decomposition on binary images. Then, the extracted features are input into the designed EBFNN. Finally, EBFNN is trained and fingerprint recognition is accomplished by the trained EBFNN. The resulting algorithm is tested on three different fingerprint verification challenge datasets and shows the algorithm has higher recognition rate and speed than WT-RBF.

关键词： Fingerprint recognition Feature extraction Artificial neural networks Training Algorithm design and analysis Pattern recognition Signal processing algorithms

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An adaptive clustering algorithm based on the possibility clustering and isodata for multispectral image classification 21

An adaptive clustering algorithm based on the possibility cl...

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21st Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2008

作者： Liu, Wenping Hung, Chih-Cheng Kuo, Bor-Chen Coleman, Tommy Department of Digital Media School of Information Beijing Forestry University Beijing China Department of Computer Science North China University of Technology Beijing China School of Computing and Software Engineering Southern Polytechnic State University MariettaGA30060 United States Mathematics Education Department National Taichung University Taichung Taiwan HSCaRS Alabama A and M University NormalAL35762 United States

For a clustering algorithm, the number of clusters is a key parameter since it is directly related to the number of homogenous regions in the given image. Although ISODATA clustering algorithm can determine the number of clusters and cluster centers dynamically, it is challenging to specify so many parameters. The possibility clustering provides the memberships that are interpreted as the degrees of possibility. So the memberships in one class are not related to the memberships in other classes. By taking the advantages of these two clustering algorithms, a new fuzzy clustering algorithm is proposed by combining the possibility clustering and ISODATA clustering algorithm. This new algorithm not only can determine the number of clusters dynamically with the degree of possibility of each date point, but also can reduce the number of input parameters of ISODATA algorithm. The splitting and merging process is evaluated by the possibility distance.

关键词： Clustering algorithms

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Improving Keyphrase Extraction Using Wikipedia Semantics

Improving Keyphrase Extraction Using Wikipedia Semantics

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International Symposium on Intelligent Information Technology Application, IITA

作者： Tianyi Shi Shidou Jiao Junqi Hou Minglu Li Department of Computer Science Shanghai Jiaotong University Shanghai China School of Electronic and Information Engineering Dalian University of Technology Dalian China College of Software Nankai University Tianjin China

Keyphrase extraction plays a key role in various fields such as information retrieval, text classification etc. However, most traditional keyphrase extraction methods relies on word frequency and position instead of document inherent semantic information, often results in inaccurate output. In this paper, we propose a novel automatic keyphrase extraction algorithm using semantic features mined from online Wikipedia. This algorithm first identifies candidate keyphrases based on lexical methods, and then a semantic graph which connects candidate keyphrases with document topics is constructed. Afterwards, a link analysis algorithm is applied to assign semantic feature weight to the candidate keyphrases. Finally, several statistical and semantic features are assembled by a regression model to predict the quality of candidates. Encouraging results are achieved in our experiments which show the effectiveness of our method.

关键词： Wikipedia Data mining Text categorization Search engines Thesauri Information retrieval Algorithm design and analysis Taxonomy Information technology Application software

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High Efficient Intrusion Detection Methodology with Twin Support Vector Machines

High Efficient Intrusion Detection Methodology with Twin Sup...

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International Symposium on Information science and engineering, ISISE

作者： Xuejun Ding Guiling Zhang Yongzhen Ke Baolin Ma Zhichao Li Department of Computer Science Hebei Institute of Architecture and Civil Engineering Zhangjiakou China Department of Computer Science Tianjin Professional University Tianjin China Department of software North Microelectronics Corporation Beijing China Department of Information Management China Central Party School Beijing China

Intrusion detection has become the important component of the network security. Many intelligent intrusion detection models are proposed, but the performance and efficiency are not satisfied to real computer network system. This paper extends these works by applying a new high efficient technique, named twin support vector machines (TWSVM), to intrusion detection. Using the KDD'99 data set collected at MITpsilas Lincoln Labs evaluates the performance and efficiency of the proposed intrusion detection models. The experimental results indicate that the proposed models based on TWSVM is more efficient and has higher detection rate than conventional SVM based model and other models.

关键词： Intrusion detection Support vector machines Data mining Information security Data security computer science Machine intelligence Artificial neural networks Fuzzy systems Clustering algorithms

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Algebraic Criteria for Consensus Problems of Discrete-Time Networked Systems

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IFAC Proceedings Volumes 2008年第2期41卷 1502-1509页

作者： Zonggang Li Yingmin Jia Junping Du Fashan Yu The Seventh Research Division Beihang University Beijing 100083 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

This paper is mainly devoted to the algebraic criteria for consensus problems of discrete-time networked systems with the fixed and switching topology. A special eigenvector ω of the Laplacian matrix is first correlated with the connectivity of a digraph, and then the relations between a class of Laplacian-type matrix and the stochastic matrix are established. Based on these tools, some necessary and/or sufficient algebraic conditions are proposed, which can directly determine whether the consensus problem can be solved or not. Furthermore, it is proved that only the agents corresponding to the positive elements of ω contribute to the group decision value and decide the collective behavior of the system. Particularly for the fixed topology case, it is shown that not only the role of each agent is exactly proportional to the value of the corresponding element of ω but also the group decision value can be calculated by such a vector and the initial states of all agents.

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