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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China Hubei University for Nationalities Enshi Hubei 445000 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478092

It is difficult to meet both direction and curvature constraints for traditional Fast Marching (FM) method in path planning. Based on adjusting the cost function in Eiknoal equation-the control equation for FM, a new model for computing the integrated cost function was presented in this paper. A relationship formula about curvature radius was obtained and three kinds of adjusting strategies were given;two of them were used to modify the route to meet with the requires of turning constraint in this paper. Experiments showed that the improved model can be used to plan the path with FMM for agent such as unmanned aerial vehicle (UAV) or robot, which is limited to pass through the scene matching areas. And our preliminary experiments demonstrated that the strategies are feasible and efficient to obtain path with certain curvature radius. The model can also be used to represent the problem such as an aircraft flying in a flow field. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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A novel path planning method based on path network

A novel path planning method based on path network

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

ISBN: (纸本)9780819478085

Traditional path planning methods are too slow to meet the real-time requirement in practical applications. In order to solve this problem, an idea of path net was proposed in this paper. The path planning procedure is divided into two steps: network segment planning and segment assembling. The first step was done with Fast Marching Method, including port selection of segment and network segment planning. Second step, the A* searching method was chosen to select segments for assembling. Experiments demonstrated that our method can obtain an optimal route in a few of seconds after the start and goal are given while several minutes are needed for traditional methods. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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Multiobjective social evolutionary algorithm based on multi-agent

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Ruan Jian Xue Bao/Journal of Software 2009年第7期20卷 1703-1713页

作者： Pan, Xiao-Ying Liu, Fang Jiao, Li-Cheng Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Computer Science and Engineering Xidian University Xi'an 710071 China

In this paper, a multi-agent social evolutionary algorithm is proposed for multiobjective optimization problems. It completes the search process by the agent evolution. MOMASEA (multi-agent social evolutionary algorithm for multiobjective) defines the trust degree to denote the historical information of agents, and the neighborhood of agent is confirmed by it. According to the characteristic of multiobjective problems, three evolutionary operators are designed to complete the whole evolutionary process. The experimental results show that MOMASEA has a good convergence to the Pareto set. Furthermore, the analysis of the mode for instructs local environment verified that importing acquaintance net model can speed up the convergence effectively. © by Institute of Software, the Chinese Academy of Sciences.

关键词： Multiobjective optimization

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Sliding Mode Tracking for Actuators Comprising Pneumatic Muscle and Torsion Spring

Sliding Mode Tracking for Actuators Comprising Pneumatic Mus...

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2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

作者： Kexin Xing Jian Huang Yongji Wang Jiping He Qi Xu Jun Wu Key Lab.of Image Processing and Intelligent Control Department of Control Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

ISBN: (纸本)9781424447749;9781424447756

In this study, the sliding mode approach is applied to the tracking control problem of a planar arm manipulator system driven by a new type of actuator, which comprises a pneumatic muscle (PM) and a torsion spring. Unlike the traditional agonist/antagonist pneumatic muscle actuator, the PM is arranged in place of bicep and the torsion spring provides opposing torque in the presented actuator. The dynamic model is derived for this system and a sliding mode controller is designed to make the joint angle track a desired trajectory within a guaranteed accuracy even there are modeling uncertainties. A selection method is also proposed to obtain appropriate spring coefficient, which plays an important role in the tracking control task. The effectiveness of the proposed method is confirmed by simulations. The differences between the control results of using our new actuator and that of using the traditional pneumatic muscle actuator in opposing pair configuration are also compared.

关键词： Pneumatic muscle (PM) torsion spring sliding mode control rehabilitation robot

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Study of Double SMO Algorithm Based on Attributes Reduction

Study of Double SMO Algorithm Based on Attributes Reduction

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6th International Symposium on Neural Networks

作者： Chen, Chen Hong, Liu Song, Haigang Chen, Xueguang Hou, TieMin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Bas Res Serv Ministry Sci Beijing 100862 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9783642015090

To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the Support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points' effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.

关键词： Data mining Support vector machine Attribute reduction Training algorithm

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Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model

Variational Bayesian framework for estimating parameters of ...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Babajani-Feremi, Abbas Bowyer, Susan Moran, John Elisevich, Kost Soltanian-Zadeh, Hamid Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States Neuromagnetism Lab. Neurology Department Henry Ford Hospital Detroit MI 48202 United States Department of Neurology Henry Ford Hospital Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

The integrated analysis of the Electroencephalography (EEG), Magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are instrumental for functional neuroimaging of the brain. A bottom-up integrated E/MEG and fMRI model based on physiology as well as a method for estimating its parameters are keys to the integrated analysis. We propose the variational Bayesian expectation maximization (VBEM) method to estimate parameters of our proposed integrated model. VBEM method iteratively optimizes a lower bound on the marginal likelihood. An iteration of the VBEM consists of two steps: a variational Bayesian expectation step implemented using the extended Kalman smoother (EKS) and the posterior probability of the parameters in the previous step, and a variational Bayesian maximization step to estimate the posterior distributions of the parameters. For a given external stimulus, a variety of multi-area models can be considered in which the number of areas and the configuration and strength of connections between the areas are different. The proposed VBEM method can be used to select an optimal model as well as estimate its parameters. The efficiency of the proposed VBEM method is illustrated using simulation and real datasets. The proposed VBEM method can be used to estimate parameters of other non- linear dynamical systems. This study proposes an effective method to integrate E/MEG and fMRI and plans to use these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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Multi-area integrated E/MEG and fMRI modeling

Multi-area integrated E/MEG and fMRI modeling

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Babajani-Feremi, Abbas Bowyer, Susan Moran, John Elisevich, Kost Podell, Kenneth Soltanian-Zadeh, Hamid Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States Neuromagnetism Lab. Neurology Department Henry Ford Hospital Detroit MI 48202 United States Department of Neurology Henry Ford Hospital Detroit MI 48202 United States Division of Neuropsychology and Sports Concussion Safety Program Henry Ford Hospital Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

Functional magnetic resonance imaging (fMRI) has complementary spatiotemporal resolution compared to Electroencephalography (EEG) as well as Magnetoencephalography (MEG). Thus, their integrated analysis should improve the overall resolution. To integrate analysis of E/MEG and fMRI, we extend our previously proposed integrated E/MEG and fMRI neural mass model to a multi-area model by defining two types of connections: the Short-Range Connections (SRCs) between minicolumns within the areas and Long-Range Connections (LRCs) between inter-areas minicolumns. The nonlinear input/output relationship in the proposed model is derived from the state space representation of the multi-area model. The E/MEG signals are originated from the overall synaptic activities of the pyramidal cells of all minicolumns and can be calculated using the lead field matrix (i.e., forward electromagnetic model). The fMRI signal is extracted from the proposed integrated model by calculating the overall neural activities in the areas and using it as the input of the extended balloon model (EBM). Using the simulation results, the capabilities of the proposed model to generate E/MEG and fMRI signals is shown. In addition, changes in the dynamics of the model to variations of its parameters were evaluated and lead us to the appropriate ranges for the parameters. In conclusion, this work proposes an effective method to integrate E/MEG and fMRI for the more effective use of these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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The depth information estimation of microscope defocus image based-on Markov random field

The depth information estimation of microscope defocus image...

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2008 IEEE International Conference on Robotics, Automation and Mechatronics, RAM 2008

作者： Zeng, Xiangjin Huang, Xinhan Wang, Min Peng Li Department of Control Science and Engineering Key Lab. of Image Processing and Intelligence Control HUST Wuhan China

ISBN: (纸本)9781424416769

For depth information estimation of microscope defocus image, a blur parameter model of defocus image based on Markov random field has been present. It converts problem of depth estimation into optimization problem. An improved Iterated Conditional Modes Algorithm has been applied to complete optimization problem, which the select of initial point employed Least squares estimate algorithm prevents that the result gets into local optimization. The experiments and simulations prove that the model and algorithm is efficiency. © 2008 IEEE.

关键词： Microscopes

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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

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Journal of Systems Engineering and Electronics 2009年第3期000卷

作者： Zhang Wenge Liu Fang Jiao Licheng Gao Xinbo School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Ganssian scale mixture (BLS-GSM).

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled secondgeneration bandelet transform Canny operator threshold shrinkage

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DIRECTIONAL CLASSIFICATION OF CORTICAL SIGNALS USING A LIQUID STATE MACHINE

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IFAC Proceedings Volumes 2008年第2期41卷 9596-9600页

作者： Jiangshuai Huang Huijuan Fang Yongji Wang Key Lab. for Image Processing & Intelligent Control of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Liquid State Machine (LSM) is a newly developed computational model with many interesting properties. It has great advantages of dealing with biologic computing when compared to the traditional computational model. In this paper, the LSM was used to deal with the direction classification problem of the spike series which were distilled from the neurons in motor cortex of a monkey. In the output layer, a linear regression and back-propagation are employed as the training algorithms. Compare to outcomes of the two algorithms, it is showed that ideal classification results were derived when using BP as the training algorithm.

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