Improvement of Fuzzy Image Contrast Enhancement Using Simulated Ergodic Fuzzy Markov Chains

This paper presents a novel fuzzy enhancement technique using simulated ergodic fuzzy Markov chains for low contrast brain magnetic resonance imaging (MRI). The fuzzy image contrast enhancement is proposed by weighted fuzzy expected value. The membership values are then modified to enhance the image using ergodic fuzzy Markov chains. The qualitative performance of the proposed method is compared to another method in which ergodic fuzzy Markov chains are not considered. The proposed method produces better quality image

Three classifications on branching processes and their behavior for finding the solution of nonlinear integral equations

In this paper, we consider the Monte Carlo method for finding the solution of nonlinear integral equations at a fixed point xo‐ In this method, simulated Galton‐Watson branching process is employed for solving the proposed integral equation. The main goal of this paper is to compare the behavior of three classifications of branching process based on the mean progeny, i.e. the subcritical, critical and supercritical process. First published online: 09 Jun 201

Parallel Monte Carlo algorithms for matrix computations

Available from British Library Document Supply Centre- DSC:DXN057977 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Matrix balancing and robust Monte Carlo algorithm for evaluating dominant eigenpair

Matrix balancing may effect the stability of algorithms in matrix computations and the accuracy of computed solutions. In this paper, we first introduce an algorithm for matrix balancing. Then, using Monte Carlo method we propose a robust algorithm to evaluate dominant eigenpair of a given matrix. Finally, several randomly generated examples are presented to show the efficiency of the new method

Quasi Monte Carlo algorithm for computing smallest and largest generalised eigenvalues

The problem of obtaining the smallest and the largest generalised eigenvalues using quasi Monte Carlo algorithm is considered. We first study the results of Dimov and others using three algorithms based on the power method combined with Monte Carlo and quasi Monte Carlo methods for evaluating extremal eigenvalue of real matrices. We present a quasi Monte Carlo algorithm for computing both the smallest and the largest generalised eigenvalues using Sobol, Halton sequences and the rand function in Matlab. We finally compare the efficiency of three employed generators in our algorithm for different pencils. References Alexandrov V. N., Efficient parallel Monte Carlo Methods for Matrix Computation, Mathematics and computers in Simulation, Elsevier 47 (1998) 113--122, doi:10.1016/S0378-4754(98)00097-4 Chi H., Mascagni M. and Warnock T., On the optimal Halton sequence, Mathematics and Computers in Simulation, 70 (2005) 9--21, doi:10.1016/j.matcom.2005.03.004 Dimov I., Monte Carlo methods for applied scientists, World Scientific Publishing Co., 2008. Dimov I., Alexandrov V. and Karaivanova A., Implementation of Monte Carlo Algorithms for Eigenvalue Problem Using MPI, Recent Advances in Parallel Virtual Machine and Message Passing Interface, Springer, 1998. Fathi Vajargah B. and Mehrdoust F., New Monte Carlo algorithm for obtaining three dominant eigenvalues, Int. J. Appl. Math. 22 (2009), no. 4, 553--559. Kressner D., Numerical Methods for General and Structured Eigenvalue Problems, Springer--Verlag Berlin Heidelberg, 2005. Lemieux C., Monte Carlo and Quasi Monte Carlo Sampling, Springer Science, 2009. Mascagni M. and A. Karaivanova, A Parallel Quasi Monte Carlo Method for Computing Extremal Eigenvalues, Monte Carlo and Quasi Monte Carlo Methods, Springer, 12 (2002) 369--380. Sobol I. M., On quasi Monte Carlo integrations, Mathematics and Computers in Simulation, 47 (1998) 103--112. doi:10.1016/S0378-4754(98)00096-2 Sobol I. M., Quasi Monte Carlo methods, Progress in Nuclear Energy, 24 (1990) 55--61