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2010 18th Iranian Conference on Electrical Engineering, ICEE 2010

作者： Afzali, Maryam Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9781424467600

Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain that harbor seizure focus is a critical step towards treatment of TLE patients. Diffusion tensor imaging (DTI) is a non-invasive method that we use in this work to determine damaged regions of white matter in the brain. To this end, we apply two methods, voxel based morphometry (VBM) and fiber tracking. We use these methods along with two anisotropy maps (EAR and FA) extracted from the DTI data. We use DTI data of 31 subjects (12 normal subjects and 19 TLE patients) for testing and evaluation studies. The VBM method shows that TLE is associated with reduction of both of FA and EAR values in several regions within the temporal lobes. The tractography method shows abnormal diffusion characteristics in three fiber bundles (fornix, uncinate fasciculus, and cingulum). The results show that the EAR maps are less sensitive to noise than the FA maps and the EAR values change more than the FA values in TLE. ©2010 IEEE.

关键词： Diffusion tensor imaging

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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2010 International Conference on Intelligent and Advanced systems, ICIAS 2010

作者： Ramezani, Mahdi Heidari, Amin Fatemizadeh, Emad Soltanian-Zadeh, Hamid Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

ISBN: (纸本)9781424466238

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Brain

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Vision based lane keeping assistance control triggered by a driver inattention monitor

Vision based lane keeping assistance control triggered by a ...

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2010 IEEE International Conference on systems, Man and Cybernetics, SMC 2010

作者： Tunçer, Özgür Güvenç, Levent Coşkun, Fuat Karsligil, Elif Ford Otosan - Gebze Engineering Gebze Kocaeli Turkey ITU Mekar Labs. Automotive Control and Mechatronics Research Center Istanbul Technical University Istanbul Turkey Department of Computer Engineering Intelligent System Laboratory Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9781424465880

Inattentive and impaired drivers are a major cause of road accidents. Especially when impaired by drugs, fatigue or physical handicaps, the skill levels, driving habits, capabilities and decisions of a human driver are adversely affected. In such cases, the ability of a driver to safely operate a vehicle may be augmented considerably by well tuned and driver adaptive warning and assistance systems. Data from 105 drivers were collected in the Drive Safe project on an approximately 30 km route containing both city and highway traffic. The data was used to develop methods for determining inattentive/impaired drivers. This paper is on a lane keeping driver assistant system that is activated once such an inattentive/impaired driver who cannot perform a good task of lane keeping by himself is determined. Robust, parameter space based and velocity scheduled control design techniques carried out in the COMES toolbox are used for designing the lane keeping controller. A camera based image processing algorithm for lane detection and tracking is used. The image processing algorithm and the lane keeping assistant control system are evaluated first using offline simulations and then using more realistic, real time hardware-in-the-loop simulations. While a relatively simple linear model is used for lane keeping controller design, evaluation of the designed controller also uses the high fidelity, high order, realistic and nonlinear vehicle model in Carmaker HiL. A PC is used for processing video frames coming from an in-vehicle camera pointed towards the road ahead. Lane detection and tracking computations including fitting of composite Bezier curves to curved lanes are carried out in this PC. A dSpace microautobox is available for obtaining the lane data from the PC and the Carmaker vehicle data from the dSpace compact simulator used and calculating the required steering actions and sending them to the Carmaker vehicle model. Offline and real time simulation results demonstrate t

关键词： controllers

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A model checking approach for solving epistemic riddles

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Jisuanji Xuebao/Chinese Journal of computers 2010年第3期33卷 406-413页

作者： Luo, Xiang-Yu Su, Kai-Le Gu, Ming Key Laboratory of Security for Information System of Ministry of Education School of Software Tsinghua University Beijing 100084 China School of Computer and Control Guilin University of Electronic Technology Guilin Guangxi 541004 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

This paper aims to model and solve the Sum and Product Riddle in public announcement logic. A dynamic epistemic model is proposed, that is the linear temporal combination of the epistemic model of environment and the epistemic models after each announcement, such that the authors' model checking technique for temporal epistemic logic can be extended to support the modeling and verification of public announcement logic. This model checking method not only can help to find all solutions, but also verify related temporal epistemic properties. Such characteristic is not fully supported by the current version of MCK, MCMAS and DEMO. The authors implemented the proposed method in the symbolic model checker MCTK via OBDD and verified the sum and product riddle. The experimental results show that the proposed method is correct and efficient.

关键词： Model checking

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Robust control for Discrete-Time Networked control systems

Robust Control for Discrete-Time Networked Control Systems

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Dongxiao Wu Jun Wu Sheng Chen State Key Laboratory of Industrial Control Technology　Institute of Cyber-System and Control　Zhejiang School of Electronics and Computer Science University of Southampton Highfield Southampton SO17 1BJ

This paper considers analysis and synthesis of discrete-time networked control systems (NCSs), where the plant has additive uncertainty and the controller is updated with the sensor information at stochastic time intervals. It is shown that the problem is linked to robust control of linear discrete-time stochastic systems and a new small gain theorem is established. Based on this result, sufficient conditions are given for ensuring mean square stability of the NCS, and the genetic algorithm is utilised to design the controller of the NCS based on a linear matrix inequality technique.

关键词： Stochastic systems Networked control systems Delay Stability analysis Robust control Observers Additives

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Distributed simulation system hierarchical design model based on quotient space granular computation

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Zidonghua Xuebao/Acta Automatica Sinica 2010年第7期36卷 923-930页

作者： Chen, Jie Wu, Di Zhang, Juan Department of Automation Beijing Institute of Technology Beijing 100081 China Education Ministry Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China VALORIA Laboratory Department of Computer Science and Technology University of South Brittany Vannes Cedex 56017 France

To answer the problem of ambiguous design levels for large-scale distributed simulation systems, this paper proposes a hierarchical system model based on the quotient space theory. This model consists of a system global design level, an architecture level, and a execution structure level to solve the problem of diverse levels on system design. Furthermore, we deeply analyze the relationship between the granules of different levels in both horizontal and vertical directions, and integratedly consider simulation tasks, resources and services. Finally, the model is verified based on a real simulation system design example, and the results illustrate that the flexibility and reusability of the system can be improved with the application of the hierarchical design model. Copyright © 2010 Acta Automatica Sinica. All rights reserved.

关键词： systems analysis

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A Method for Data Stream Processing Based on Curve Fitting

A Method for Data Stream Processing Based on Curve Fitting

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2010 2nd International Conference on Signal Processing system(2010年信号处理系统国际会议 ICSPS 2010)

作者： Yixu Song Jing Hu Xiaokui Yang Xiufen Xie Jie Fu State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technol Department of Computer Science and Technology University of Science and Technology Beijing Beijing Sany Intelligent Control Equipment CO.LTD Changsha Hunan China

The sampling storage method which used in the current data stream could not respond data tendency effectively. For the problem, this paper presents a new processing method based on curve fitting. A weighted least square principle is used to fit the cached stream data and better model description is obtained. Then the fitting results are analyzed by clustering algorithm, which serves as a classifier for polynomial fitting parameters According to the clustering result, the appropriate window size will be given to fit the periodic stream data. Comparing the function solutions with the actual data, the different methods are adopted to store data according to the comparison result. The experimental results indicate that the proposed method has better fitting accuracy and compression ratio, could meet the requirement of data stream processing. And the data tendency could be responded effectively by the fitting results.

关键词： curve fitting data stream clustering least-square principle

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Internal model control for hysteretic nonlinear systems based on support vector regression

Internal model control for hysteretic nonlinear systems base...

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World Congress on Intelligent control and Automation (WCICA)

作者： Wenguang Zhang Jianqin Mao Yanhua Ma Beijing Key Laboratory of Measurement and Control Technology and System for Industrial Process Control & Computer Engineering School North China Electric Power University Beijing China School of Automation Science and Electrical Engineering Beijing Aeronautics and Astronautics University Beijing China

Aim at the hysteretic nonlinearity characteristic of the giant magnetostrictive actuator in control, an internal model control method based on support vector regression is presented in this paper. Its models are built by support vector regression using the input and output data of the actuator and the internal model control is achieved. The simulation results show the method in this paper has better control precision.

关键词： Support vector machines Actuators Automation Data models Computational modeling Hysteresis Magnetostriction

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Comparison of voxel-based morphometry (VBM) and tractography of diffusion tensor MRI (DT-MRI) in temporal lobe epilepsy

Comparison of voxel-based morphometry (VBM) and tractography...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Maryam Afzali Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence 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)9781424467600

关键词： Tensile stress Magnetic resonance imaging Diffusion tensor imaging Temporal lobe Epilepsy Ear Optical fiber testing Diseases Focusing Medical treatment

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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