diffnorm

this file is used to simulation of dam break problem in MATLAB codes

This is approximate entropy and sample entropy article. It will be helpful for motor behavior and rehabilitation study.

A program that can easily caculate the fractal dimension. You need only input the parameter and then push the button.(A program that can easily caculate the frac tal dimension. You need only input the paramete r and then push the button.)

读取卫星钟钟误差和IGS站接收机钟差的程序(Read the satellite Zhong Zhong error and IGS stations receiver clock error program)

thats to create aplication of hotel

Digital image processing matlab problem

MIDACO是一般的优化问题求解器。 MIDACO可应用于连续（NLP），离散/整数（IP）和混合整数（MINLP）的问题。问题可能被限制在平等和/或不等式约束。 MIDACO适合多达数百至几千优化变量的问题。 MIDACO实现了一个自由衍生物，启发式算法的处理方法处理的问题，因为黑盒可含有关键功能特性，如非凸性，不连续或随机噪声。(MIDACO is a solver for general optimization problems. MIDACO can be applied to continuous (NLP), discrete/integer (IP) and mixed integer (MINLP) problems. Problems may be restricted to equality and/or inequality constraints. MIDACO is suitable for problems with up to several hundreds to some thousands of optimization variables. MIDACO implements a derivative-free, heuristic algorithm that treats the problem as black-box which may contain critical function properties such as non-convexity, discontinuities or stochastic noise.)

选取读入一份彩色图像，在运行Untitled.m程序时：先用鼠标交互的选取一个颜色区域作为你要与其他色彩分割的目标，，鼠标选取好后，按ENTER健后出现进行分割出现结果。 (Select a color image read in the run Untitled.m program: the first interaction with the mouse, select a color region as the color you want to split with other goals, the mouse selection is completed, press ENTER health appeared after the results of segmentation .)

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)

几种分类器NB SVM　ＫＮＮ　效果不错　(Several classifier NB SVM KNN good results)