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IEEE International Conference on Systems, Man and Cybernetics

作者： Yangyang Li Licheng Jiao Jingjing Zhao Qiuyi Wu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of ¿prematurity¿ makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the ¿prematurity¿ problem in genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.

关键词： Multicast algorithms Competitive intelligence Laboratories Information processing Evolutionary computation Routing Genetic algorithms Quality of service Quantum computing Heuristic algorithms

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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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Reconstruction of images from compressive sensing based on the stagewise fast LASSO

Reconstruction of images from compressive sensing based on t...

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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： Wu, Jiao Liu, Fang Jiao, Licheng School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China College of Sciences China Jiliang University Hangzhou 310018 China

ISBN: (纸本)9780819478092

Compressive sensing (CS) is a theory about that one may achieve a nearly exact signal reconstruction from the fewer samples, if the signal is sparse or compressible under some basis. The reconstruction of signal can be obtained by solving a convex program, which is equivalent to a LASSO problem with l1-formulation. In this paper, we propose a stage-wise fast LASSO (StF-LASSO) algorithm for the image reconstruction from CS. It uses an insensitive Huber loss function to the objective function of LASSO, and iteratively builds the decision function and updates the parameters by introducing a stagewise fast learning strategy. Simulation studies in the CS reconstruction of the natural images and SAR images widely applied in practice demonstrate that the good reconstruction performance both in evaluation indexes and visual effect can be achieved by StF-LASSO with the fast recovered speed among the algorithms which have been implemented in our simulations in most of the cases. Theoretical analysis and experiments show that StF-LASSO is a CS reconstruction algorithm with the low complexity and stability. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Compressed sensing

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Needle segmentation in 3D ultrasound images based on phase-grouping

Needle segmentation in 3D ultrasound images based on phase-g...

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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

作者： Zhao, Siying Qiu, Wu Ming, Yuchi Ding, Mingyue Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Hubei 10086 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Hubei 10086 China

ISBN: (纸本)9780819478085

Three Dimensional (3D) ultrasound images can provide spatial information to help doctors locate the needle position precisely in ultrasound-guided surgery. In this paper, we present a method called "3D Phase-grouping" to segment the needle inside the patient. The method is an extension from 2D phase-grouping to the 3D case via a new mathematic model-the outer products of adjacent orientation vectors. Firstly, the voxels with the same outer products of the gradient orientation vectors of the adjacent points are divided into Line Support Regions (LSR). Then, the needle is extracted with the 3D Least-Squares line fitting method in the maximal LSR. Synthetic and 3D ultrasound phantom data were tested. The segmentation results were evaluated by the angular deviation, position deviation and computational time. Compared with the 3D Hough transform, 3D Phase-Grouping is more accurate and faster without using pre-information. The computational complexity and robustness of the algorithm remain to be our future research topics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Needles

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Decentralized adaptive stabilization of nonlinear interconnected stochastic systems by output feedback

Decentralized adaptive stabilization of nonlinear interconne...

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International Conference on control and Automation (ICCA)

作者： Huijin Fan Li Xu The Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology China The Department of Electronics and Information Systems Akita Prefectural University Akita Japan

ISBN: (纸本)9781424447060

This paper studies the stabilization problem for a class of stochastic nonlinear interconnected systems with unknown parameters involved. By employing filtered transformation, the stochastic Lyapunov-like theorem and the back-stepping design technique, a decentralized output-feedback controller and parameter adaptive law are proposed. It is proved that the closed-loop, large-scale, interconnected stochastic systems is globally asymptotically stable in probability.

关键词： Stochastic systems Output feedback control systems Adaptive control Stochastic processes Interconnected systems Nonlinear systems Programmable control Differential equations Nonlinear control systems

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M-elitist evolutionary algorithm and its application to V-BLAST system

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2009年第10期31卷 2443-2448页

作者： Mu, Cai-Hong Jiao, Li-Cheng Liu, Yi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China State Key Laboratory of Integrated Service Networks Xidian University Xi'an 710071 China School of Electronic Engineering Xidian University Xi'an 710071 China

A new algorithm named as M-elitist Evolutionary Algorithm (MEA) is presented with low complexity and high performance to approach the performance of Maximum-Likelihood(ML) detection, for solving the problem of the high complexity of ML detection in real-time Vertical- Bell laboratories LAyered Space-Time (V-BLAST) communication system. The simulation of one knapsack problem validates the effectiveness of MEA to solve combinatorial optimization problems. Furthermore, the simulation of V-BLAST communication system shows that the MEA-based detection algorithm can approach the performance of ML well, and is superior to the detection algorithm based on standard genetic algorithm and that based on clonal selection algorithm as well as some classical ones.

关键词： Combinatorial optimization

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Solving 3-coloring problem by tissue P systems with cell separation

Solving 3-coloring problem by tissue P systems with cell sep...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Shuo Wang Zhengke Miao Xiaolong Shi Zheng Zhang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Department of Mathematics XuZhou Normal University of China Xuzhou China

Tissue P systems are a class of distributed and parallel computing models inspired by intercellular communication and cooperation between neurons. An interesting variant of tissue P systems is known as tissue P systems with cell separation, which is endowed with the ability of generating an exponential workspace in a linear time and in this way getting the possibility to solve computationally hard problems in polynomial time. In this paper, a uniform solution to 3-coloring problem by tissue P systems with cell separation is presented, which is linear in terms of the number of vertices and the number of edges.

关键词： Polynomials Biomembranes NP-complete problem Distributed computing Neurons Tree graphs image processing intelligent control Mathematics Mathematical model

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Solving multidimensional 0–1 knapsack problem by tissue P systems with cell division

Solving multidimensional 0–1 knapsack problem by tissue P s...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Juanjuan He Zhengke Miao Zheng Zhang Xiaolong Shi Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Department of Mathematics XuZhou Normal University of China Xuzhou China

Tissue P systems with cell division are biologically inspired theoretical models of distributed and parallel computing, which provide the possibility to solve computationally hard problems in polynomial time. In this paper, we present a semi-uniform solution to the multidimensional 0-1 knapsack problem by such kind of devices, which is linear in terms of the number of variables, the number of constraint inequalities, and all coefficients related with variables.

关键词： Multidimensional systems Biomembranes Biology computing Polynomials Biological tissues Cells (biology) Biological system modeling Helium image processing intelligent control

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Path Integral Based Contour Extraction Method Using Multilevel Metropolis Sampling

Path Integral Based Contour Extraction Method Using Multilev...

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7th International Conference on Photonics and Imaging in Biology and Medicine

作者： Yangguang, Sun Tian, Lan Xiaowei, Fu Mingyue, Ding Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475398

In this paper we proposed a novel path integral method using multilevel Metropolis sampling to extract the contours of interested objects of medical images, which is a quantum statistical approach inspired by the essential characteristics of quantum. The implemented multilevel Metropolis sampling is a fast approach to the sampling problem and can be used to sample any distribution function and calculate all actions for the movements of quantum particle towards the position with high probability density likely to be accepted. Comparing with traditional deformable models based on classical mechanics which are sensitive to the initialization of contour and difficult to adapt topology changes of the contour, our work has the capability to break through the limitations of deformable models and makes the boundary extraction in high noise and low contrast of medical images possible. Experiments were performed with synthetic and medical images and the feasibility and potential of our method was demonstrated. © 2009 SPIE.

关键词： Distribution functions

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Deterministic algorithm for the reordering problem using tile assembly

Deterministic algorithm for the reordering problem using til...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Yufang Huang Jin Xu Zhen Cheng Zhihua Chen Xuncai Zhang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China School of Electronics Engineering and Computer Science Peking University Beijing China

Algorithmic self-assembly, a generalization of crystal growth, has been proposed as a mechanism for the bottom-up fabrication of autonomous DNA computation. In theory, growth can be programmed by designing a set of molecular tiles with binding interactions that enforce assembly rules. There are many interesting applications of the reordering problem in Mathematics, as well as Computer Sciences. It is a useful primitive operation for computation and construction in the field of science and engineering. It is worth exploring more efficient approach to solve this problem. In this paper, we provide a description of a novel method for the reordering problem by use of the programmable tile assembly model. We propose the thought of block assembly model which takes full advantage of the huge parallelism inherent in the tile assembly. On the basis of the structural property, we delicately devise a rule to produce a long reporter strand which can connect all the inputs of the given problem with the final results. It is proved that this method can be extended to any reordering problem with arbitrary size in theory. The great advantage of our schema is that it only needs a constant number of tile types and in time linear with the size of the input problem to solve any reordering problem.

关键词： Tiles Assembly Self-assembly DNA computing Molecular computing Fabrication Logic Biology computing Laboratories intelligent control

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