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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jinhu Lu Derong Liu Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy and Sciences Beijing China Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL USA

Over the last few decades, complex networks have been intensively studied throughout many fields of science, especially in biological and engineering sciences. This paper briefly reviews the main advances in the complex biological and engineering networks, aiming to bridge the gap between the complex biological and engineering networks. Biologists pay more attention to the mechanisms and local dynamics of individuals, however, engineers are more interesting in the global dynamical behaviors. It is the time for the biologists and engineers to work together for better understanding the complex networks.

关键词： Biological system modeling Complex networks Mathematical model Biology Genetics Power system modeling Bridge circuits Evolution (biology) computer networks Power engineering computing

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on Image Processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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Integrating Attribute and Status Constraint into the RBAC Model for Access control in Ubiquitous systems

Integrating Attribute and Status Constraint into the RBAC Mo...

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International Conference on Future Generation Communication and Networking (FGCN)

作者： Deqing Zou Jong Hyuk Park Tai-hoon Kim Xueguang Chen Cluster and Grid Computing Laboratory & Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Engineering Kyungnam University Masan Gyeongsangnam South Korea Hannam University South Korea Department of Control Science and Engineering Huazhong University of Science and Technology China

It now becomes a trend to implement the interconnections between resources as well as services in ubiquitous systems. Attribute-based authorization mechanisms, protocols and systems are gaining in popularity, such as SAML, XACML, Shibboleth, etc. However, their management efficiency could be further improved. The Role-based Access control (RBAC) mechanism is widely accepted as a general mechanism for authorization management. However, RBAC is still not flexible enough to address various application scenarios in ubiquitous systems. We propose a new architecture for access control in ubiquitous systems that impose attribute and status constraints on the RBAC model, which can significantly enhance the generality and flexibility of authorization by integrating the advantages of RBAC and attributed- based access control models. Moreover, the state mechanism proposed in this architecture captures the states of the authorization elements so as to reflect the outcomes of the authorization control. Finally, we analyze the flexibility and generality of this architecture in ubiquitous systems.

关键词： Access control Authorization Permission Grid computing Pervasive computing Access protocols Application software Markup languages computer science control system analysis

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NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAGE SEGMENTATION

NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAG...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhoundi-Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

ISBN: (纸本)9781424406715;1424406714

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.

关键词： Biomedical imaging Image segmentation Shape Couplings Brain Magnetic resonance Magnetic resonance imaging Principal component analysis Probability density function Iterative algorithms

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AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY ...

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IEEE International Symposium on Biomedical Imaging

作者： Azadeh Yazdan-Shahmorad Hesamoddin Jahanian Suresh Patel Hamid Soltanian-Zadeh Radiology Department Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA Electrical and Computer Engineering Department Wayne State University Detroit MI USA Biomedical Engineering Department University of Michigan Ann Arbor MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424406715;1424406714

Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.

关键词： Neoplasms Image segmentation Diffusion tensor imaging Clustering algorithms Biopsy Permeability measurement Volume measurement Size measurement Biomembranes Magnetic resonance imaging

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Dimensionality Reduction and Attack Recognition using Neural Network Approaches

Dimensionality Reduction and Attack Recognition using Neural...

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International Joint Conference on Neural Networks (IJCNN)

作者： Vladimir A. Golovko Leanid U. Vaitsekhovich Pavel A. Kochurko Uladzimir S. Rubanau Computer Science Intelligence Information Technologies Department and Laboratory of Articial Neural Networks Brest State Technical University Belarus System Analysis Control and Processing of Information Department of Intelligent Information Technologies Brest State Technical University Belarus Department of Intelligent Information Technologies Brest State Technical University Belarus Information Systems Faculty Brest State Technical University Belarus

Most current Intrusion Detection systems (IDS) examine all data features to detect intrusion. Also existing intrusion detection approaches have some limitations, namely impossibility to process a large number of audit data for realtime operation, low detection and recognition accuracy. To overcome these limitations, we apply modular neural network models to detect and recognize attacks in computer networks. They are based on the combination of principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). PCA networks are employed for important data extraction and to reduce high dimensional data vectors. We present two PCA neural networks for feature extraction: linear PCA (LPCA) and nonlinear PCA (NPCA). MLP is employed to detect and recognize attacks using feature-extracted data instead of original data. The proposed approaches are tested with the help of KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.

关键词： Neural networks Intrusion detection Principal component analysis computer vision computer networks Multi-layer neural network Multilayer perceptrons Data mining Vectors Feature extraction

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Processing of tactile/force measurements for a fully embedded sensor

Processing of tactile/force measurements for a fully embedde...

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2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI

作者： Cannata, Giorgio Maggiali, Marco Mechatronics and Automatic Control Laboratory Department of Communication Computer and System Science University of Genova Via Opera Pia 13 16145 Genova Italy Mechatronics and Automatic Control Laboratory Italian Institute of Technologies University of Genova Via Opera Pia 13 16145 Genova Italy

ISBN: (纸本)1424405661

A new fully embedded tactile/force sensor system to be installed on the phalanges of a robot hand is presented in this paper. Each sensor consists of a distributed array of analog tactile elements and an integrated three-component force transducer with embedded analog and digital electronics. The tactile sensor is a matrix of electrodes etched on a flexible printed circuit board covered by pressure conductive rubber. The force sensor is an off-the-shelf integrated three components micro-joystick. Communications of the sensors with other control modules are implemented with a CAN bus link. A local processing technique is proposed to enable the transmission of both tactile and force data using a single CAN message, and eventually validate through simulative and experimental tests. © 2006 IEEE.

关键词： Embedded systems

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Adaptive tracker for nonlinear time-varying systems against actuator failures

Adaptive tracker for nonlinear time-varying systems against ...

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25th IASTED International Conference on Modelling, Identification, and control, MIC 2006

作者： Tsai, Jason S. H. Wei, Chao-Lung Guo, Shu-Mei Shieh, Leang S. Control System Laboratory Department of Electrical Engineering National Cheng Kung University Tainan 701 Taiwan Department of Computer Science and Information Engineering National Cheng-Kung University Tainan 701 Taiwan Department of Electrical and Computer Engineering University of Houston Houston TX 77204-4005 United States

ISBN: (纸本)0889865515

The adaptive observer for the nonlinear time-varying continuous-time system has been proposed in this paper. Also, a novel state-space adaptive observer-based tracker scheme for nonlinear time-varying sampled-data systems, which have unanticipated decay factors in the actuators, has been proposed in this paper. Here, we utilize the advantage of digital redesign methodology to convert a theoretically well-designed analog controller/observer with a high-gain property into its corresponding low-gain digital controller/observer for a practical implementation.

关键词： Continuous time systems

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A new optimization algorithm based on chaos

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第4期7卷 539-542页

作者： LU Hui-juan ZHANG Huo-ming MA Long-hua Institute of Computer Applications China Jiliang University Hangzhou 310018 China National Laboratory of Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai 200030 China Institute of System Engineering Institute National Laboratory of lndustrial Control Technology Zhejiang University Hangzhou 310027 China

In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.

关键词： Chaos optimization algorithm （COA） Carrier wave two times Multi-variables optimization Carrier wave triple frequency

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Processing of an Embedded Tactile Matrix Sensor

Processing of an Embedded Tactile Matrix Sensor

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IEEE International Conference on Robotics and Biomimetics

作者： Giorgio Cannata Marco Maggiali Mechatronics and Automatic Control Laboratory Department of Communication Computer and System Science University of Genova Genoa Italy Mechatronics and Automatic Control Laboratory Italian Institute of Technologies University of Genova Genoa Italy

A fully embedded tactile/force sensor system to be installed on the phalanges of a robot hand is presented in this paper. The sensor consists of a distributed array of analog tactile elements and an integrated three-component force transducer with embedded analog and digital electronics. The tactile sensor is a matrix of electrodes etched on a flexible printed circuit board covered by pressure conductive rubber. The force sensor is an off-the-shelf integrated three components micro-joystick. Communications of the sensors with other control modules are implemented with a CAN bus link. A local processing technique is proposed to enable the transmission of both tactile and force data using a single CAN message. Simulative and experimental tests validate the proposed method.

关键词： Tactile sensors Force sensors Sensor arrays Transmission line matrix methods Robot sensing systems Transducers Electrodes Etching Flexible printed circuits Rubber

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