Decisive arrival law parameters and a general finite capacity queueing problem

This paper addresses the problem of finding the parameters of the arrival law which most significantly influence expected occupation and loss of a finite capacity queue. The input process is supposed to be ergodic and wide sense stationary. We show that it is mostly possible to fit an MMPP(2) to the decisive parameters of observational data. Numerical examples illustrate the importance of the decisive parameters, called key parameters, and also show the accuracy of the proposed fitting procedure. Finally, in the appendix we present the solution of the finite capacity queueing problem with Special Semi Markov Process (SSMP) arrivals and a general service strategy.     

THE ANALYSIS OF THE SEPARATED LAYERS ALGORITHM BY DOMAIN DECOMPOSITION METHOD

From the principle of of the Domain Decomposition Method (DDM), we analyse the 2nd-order linear elliptic partial differential problems and link the Separated-Layers Algorithm (SLA) with DDM. The mathematical properties of SLA and numerical example are presented to obtain satisfactory computation results. For general linear differential ones, also are the structure of SLA and its characteristics discussed.     

Cubic Bézier approximation of a digitized curve

In this paper we present an efficient technique for piecewise cubic Bézier approximation of digitized curve. An adaptive breakpoint detection method divides a digital curve into a number of segments and each segment is approximated by a cubic Bézier curve so that the approximation error is minimized. Initial approximated Bézier control points for each of the segments are obtained by interpolation technique i.e. by the reverse recursion of De Castaljau's algorithm. Two methods, two-dimensional logarithmic search algorithm (TDLSA) and an evolutionary search algorithm (ESA), are introduced to find the best-fit Bézier control points from the approximate interpolated control points. ESA based refinement is proved to be better experimentally. Experimental results show that Bézier approximation of a digitized curve is much more accurate and uses less number of points compared to other approximation techniques.     

Fast Recognition of Fibonacci Cubes

Fibonacci cubes are induced subgraphs of hypercubes based on Fibonacci strings. They were introduced to represent interconnection networks as an alternative to the hypercube networks. We derive a characterization of Fibonacci cubes founded on the concept of resonance graphs. The characterization is the basis for an algorithm which recognizes these graphs in O(mlog n) time. A. Vesel supported by the Ministry of Science of Slovenia under the grant 0101-P-297.     

Distributed multi-join query processing in data grids

Query processing in data grids is a difficult issue due to the heterogeneous, unpredictable and volatile behaviors of the grid resources. Applying join operations on remote relations in data grids is a unique and interesting problem. However, to the best of our knowledge, little is done to date on multi-join query processing in data grids. An approach for processing multi-join queries is proposed in this paper. Firstly, a relation-reduction algorithm for reducing the sizes of operand relations is presented in order to minimize data transmission cost among grid nodes. Then, a method for scheduling computer nodes in data grids is devised to parallel process multi-join queries. Thirdly, an innovative method is developed to efficiently execute join operations in a pipeline fashion. Finally, a complete algorithm for processing multi-join queries is given. Analytical and experimental results show the effectiveness and efficiency of the proposed approach.     

Speed-up fractal image compression with a fuzzy classifier

This paper presents a fractal image compression scheme incorporated with a fuzzy classifier that is optimized by a genetic algorithm. The fractal image compression scheme requires to find matching range blocks to domain blocks from all the possible division of an image into subblocks. With suitable classification of the subblocks by a fuzzy classifier we can reduce the search time for this matching process so as to speedup the encoding process in the scheme. Implementation results show that by introducing three image classes and using fuzzy classifier optimized by a genetic algorithm the encoding process can be speedup by about 40% of an unclassified encoding system.     

Mesh Simplification Based on Super-Face and Genetic Algorithm in Reverse Engineering

Mesh simplification is one of the most important links in the context of reverse engineering. It is also an important consideration in computer graphics. This paper presents a novel and original method of triangular mesh reduction. A new concept of super-face is defined. In order to construct a super-face, a way to calculate the scattered point curvature is discussed. The growth of a seed point and the combination of different regions are also necessary for the construction of a super-face. When the hole left by the deletion of super-face is retriangulated a genetic algorithm is used that has been modified for our specific problem. Then the different weights to balance the optimisation of the new triangular mesh can be adjusted.     

Huffman algebras for independent random variables

Based on a rearrangement inequality by Hardy, Littlewood, and Polya, we define two-operator algebras for independent random variables. These algebras are called Huffman algebras since the Huffman algorithm on these algebras produces an optimal binary tree that minimizes the weighted lengths of leaves. Many examples of such algebras are given. For the case with random weights of the leaves, we prove the optimality of the tree constructed by the power-of-2 rule, i.e., the Huffman algorithm assuming identical weights, when the weights of the leaves are independent and identically distributed.     

Steady-state weights solution to affine projection algorithm

A new expression of the weights update equation for the affine projection algorithm (APA) is proposed that improves the convergence rate of an adaptive filter,particularly for highly colored input sign...     

基于Matlab的线性规划问题求解

线性规划是运筹学中研究较早、发展较快、应用广泛、方法成熟的一个重要分支,它是辅助人们进行科学管理的一种重要的数学方法.文章首先介绍了线性规划的基本概念及标准形式,着重讨论了线性规划问题的三种常用解法:单纯形法、直接搜索法以及遗传算法,最后在Matlab R2009a环境下进行了仿真.通过结果可以看出,用Matlab求解线性规划问题,可以避免手工的烦琐计算,大大地提高工作效率和结果的准确性.