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Hardware neural network on an SOPC platform

Hardware neural network on an SOPC platform

MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Yifei Ding, Mingyue Hu, Xia Zhou, Yanhong Department of Biomedical Engineering Xianning Institute Xianning Hubei 437100 China College of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong Univ. of Science and Technology Wuhan 430074 China School of Medicine Xianning Institute Xianning Hubei 437100 China

ISBN: (纸本)9780819478078

SOPC (System on Programmable Chip) is an on-chip programmable system based on large scale Field Programmable Arrays (FPGAs). This paper presented an implementation of an SOPC system with a custom hardware neural network using Altera FPGA chip-EP2C35F672C. The embedded Nios processor was used as the test bench. The test result showed that the SOPC Platform with hardware neural network is faster than the software implementation respectively and the accuracy of the design meets the requirement of system. The verified SOPC system can closely model real-world system, which will have wide applications in different areas such as pattern recognition, data mining and signal processing. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Neural networks

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Bioluminescence tomography based on Bayesian approach

Bioluminescence tomography based on Bayesian approach

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

作者： Feng, Jinchao Jia, Kebin Tian, Jie Yan, Guorui Qin, Chenghu The College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation P. O. Box 2728 Beijing 100190 China Life Science Research Center Xidian University Xi'an 710071 China

ISBN: (纸本)9780819475138

As a new mode of molecular imaging, bioluminescence tomography (BLT) will have *** *** on revealing the molecular and cellular information in vivo at the whole-body small animal level because of its high sensitive detection and facile operation. However, BLT is an ill-posed problem, it is necessary to incorporate a priori knowledge into the tomographic algorithm. In this paper, a novel Bayesian reconstruction algorithm for BLT is firstly proposed. In the algorithm, a priori permissible source region strategy is incorporated into the Bayesian network to reduce the ill-posedness of BLT. Then a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of adaptive finite element analysis. Finally, the algorithm maximizes the log posterior probability with respect to a noise parameter and the unknown source density, the distribution of bioluminescent source can be reconstructed. In addition, the novel tomography algorithm based adaptive finite element makes the method more appropriate for complex phantom such as real mouse. In the numerical simulation, a heterogeneous phantom is used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The accurate localization of bioluminescent source and quantitative results show the effectiveness and potential of the tomographic algorithm for BLT. © 2009 SPIE.

关键词： Monte Carlo methods

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M-elite coevolutionary algorithm for numerical optimization

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Ruan Jian Xue Bao/Journal of Software 2009年第11期20卷 2925-2938页

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

The M-elite coevolutionary algorithm (MECA) is proposed for high-dimensional unconstrained numerical optimization problems based on the concept of coevolutionary algorithm and elitist strategy. In the MECA, the individuals with high fitness, called elite population, is considered to play dominant roles in the evolutionary process. The whole population is divided into two subpopulations which are elite population composed of M elites and common population including other individuals, and team members are selected to form M teams by M elites acting as the cores of the M teams (named as core elites) respectively. If the team member selected is another elite individual, it will exchange information with the core elite with the cooperating operation defined in the paper;If the team member is chosen from the common population, it will be led by the core elite with the leading operation. The cooperating and leading operation above are defined by different combinations of several crossover operators or mutation operators. The algorithm is proved to converge to the global optimization solution with probability one. Tests on 15 benchmark problems show that the algorithm can find the global optimal solution or near-optimal solution for most problems tested. Compared with three existing algorithms, MECA achieves an improved accuracy with the same number of function evaluations. Meanwhile, the runtime of MECA is less, even compared with the standard genetic algorithm with the same parameter setting. Moreover, the parameters of the MECA are analyzed in experiments and the results show that MECA is insensitive to parameters and easy to use. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Global optimization

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A New Starry images Matching Method in Dim and Small Space Target Detection

A New Starry Images Matching Method in Dim and Small Space T...

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International Conference on image and Graphics (ICIG)

作者： Yu Zhu Weijun Hu Jun Zhou Feng Duan Jinqiu Sun Lei Jiang Shaanxi Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781424452378

In this paper, a starry image matching method based on isomorphism sub graph and LCS (Longest Common Sub-sequence) is proposed for dim and small space target detecting. A starry image mainly consists of a background with a large number of low-gray pixels and bright but small facular. The relative location of the stars can be regarded as a feature set for matching in the form of a graph. While matching two graphs, the LCS is applied for measuring the similarity and acquiring the isomorphism sub graph. Due to the characteristic of the starry image, this method could deal with the images with more rotation and shift. Experimental results show that this method can provide a good way for the trajectory acquisition of dim and small targets.

关键词： image matching Object detection Extraterrestrial measurements image segmentation Gray-scale Computer graphics Laboratories Speech processing Information processing Computer science

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Three-dimensional localization of in vivo bioluminescent source based on multispectral imaging

Three-dimensional localization of in vivo bioluminescent sou...

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

作者： Feng, Jinchao Jia, Kebin Tian, Jie Yan, Guorui Zhu, Shouping The College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing 100190 China Life Science Research Center Xidian University Xi'an 710071 China

ISBN: (纸本)9780819475138

Bioluminescence tomography (BLT) is a novel in vivo technique in small animal studies, which can reveal the molecular and cellular information at the whole-body small animal level. At present, there is an increasing interest in multispectral bioluminescence tomography, since multispectral data acquisition could improve the BLT performance significantly. In view to the ill-posedness of BLT problem, we develop an optimal permissible source region strategy to constrain the possible solution of the source by utilizing spectrum character of bioluminescent source. Then a linear system to link the measured data with the unknown light source variables is established by utilizing the optimal permissible region strategy based on adaptive finite element analysis. Furthermore, singular value decomposition analysis is used for data dimensionality reduction and improving computational efficiency in multispectral case. The reconstructed speed and stability benefit from adaptive finite element, the permissible region strategy and singular value decomposition. In the numerical simulation, the heterogeneous phantom experiment has been used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The reconstruction results demonstrate the merits and potential of our methodology for localizing bioluminescent source. © 2009 SPIE.

关键词： Singular value decomposition

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Meshless local Petrov-Galerkin method for bioluminescent photon propagation in the biological tissue

Meshless local Petrov-Galerkin method for bioluminescent pho...

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

作者： Qin, Chenghu Tian, Jie Yang, Xin Liu, Kai Feng, Jinchao Xua, Min Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences P. O. Box 2728 Beijing 100190 China Life Science Center Xidian University Xi'an Shaanxi 710071 China The College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100124 China

ISBN: (纸本)9780819475138

As a promising optical molecular imaging modality, bioluminescence tomography (BLT) has attracted remarkable attention for its excellent performance and high cost-effectiveness, which can be employed to speciffically and directly reveal physiological and pathological activities in vivo at molecular and cellular levels. The goal of BLT is to reconstruct the internal bioluminescent light source with surface measurements. Therefore, the calculation of surface light exitance plays an important role in the inverse source reconstruction, whereas photon propagation is complicated because of strongly scattering property of the biological tissue. In this contribution, a novel meshless local Petrov-Galerkin (MLPG) method based on diffusion approximation model is developed to avoid the complex and time-consuming mesh division in the conventional finite element method (FEM), and MLPG requires only a series of discretized nodes without consideration of element information and node connectivity. Compared with other meshless methods based on global weak-form, background cells used for Gauss quadrature are also omitted in the proposed method. In addition, the tissue optical parameters are incorporated as a priori knowledge in this algorithm. Finally, the performance of this method is valuated using two- and three-dimensional numerical simulation experiments. The results demonstrate the effectiveness and feasibility of the presented algorithm to predict boundary bioluminescent light power distribution. © 2009 SPIE.

关键词： Finite element method

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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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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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Blind separation of superimposed images with unknown motions

Blind separation of superimposed images with unknown motions

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kun Gai Zhenwei Shi Changshui Zhang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

We consider the blind separation of source layers from superimposed mixtures thereof, involving unknown motions and unknown mixing coefficients of layers in each mixture. Previous blind separation approaches for such problems assume motions to be uniform translations, and hence are limited for real world applications. In this paper, we develop a sparse blind separation algorithm to estimate both parameterized motions and mixing coefficients. Then, a novel reconstruction approach is presented to recover all layers, by utilizing not only the mixing model but also the statistical properties of natural images. The whole method can handle more general motions than translations, including scalings, rotations and other transformations. In addition, the number of layers is automatically identified, and all layers can be recovered even in the under-determined case where mixtures are fewer than layers. The effectiveness of this technology is shown in the experiments on two simulated mixtures of four layers, real photos containing transparency and reflections, and real crossfade images from videos.

关键词： image reconstruction Motion estimation Optical reflection Cameras Laboratories Space technology Parameter estimation Videos Glass Layout

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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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