24_point

对图像实现骨架化操作、提取边界、执行图像开运算度、增强图像对比度并提取文本图像中的某些字符对象。(Implementation of the image skeleton operation, extraction of the border, the implementation of open computing degree images to enhance image contrast and to extract the text of some of the characters in the image object。)

说明：  模拟退火GUI演示，利用模拟退火算法求全局最大值最小值(Simulated annealing GUI demonstration, the use of simulated annealing algorithm seeking the global maximum value of the minimum)

从前用matlab编的人脸识别的特征提取以及识别程序，希望有用(The previous series of face recognition using matlab feature extraction and identification procedures, seek to help)

基于遗传算法的多目标函数优化问题的一种有效方法(An effective method for multi-objective optimization problem based on genetic algorithm function)

calssification algorithm2222

配电网潮流计算分层前推回代法 基于分层模型的潮流计算方法 (Distribution power flow calculation stratified forward and backward substitution method)

Matlab下的数字信号处理示例，各种数字信号处理的实现(Digital signal processing under Matlab example, the realization of various digital signal processing)

MATLAB遗传算法OX变换的源代码(OX Transform MATLAB genetic algorithm source code)

说明：  四面体有限元程序,单元刚度的计算及总刚度矩征集成(tetrahedral finite element program modules stiffness of the total stiffness and assembly into Moment)

实验一 双极性矩形随机信号的归一化功率谱密度一 1.1 功率谱密度简介 平稳过程的任何一个非零样本函数的持续时间为无限长，显然都不满足绝对可积和总能量有限的条件。因此，它的傅里叶变换不存在即没有频谱函数。所以我们用功率谱密度来表述其频谱特性。 随机过程的任一实现是一个确定的功率型信号。而对于任意的确定功率信号f(t)，它的功率谱密度为： 式中， 是f(t)的截短函数 对应的频谱函数。f(t)是平稳随机过程 的一个实现。而随机过程某一个实现的功率谱密度不能作为过程的功率谱密度。过程的功率谱密度应该看作是任一实现的功率谱密度的统计平均，即 虽然该式给出了平稳随机过程的功率谱密度，但我们通常都不利用这个式子来计算功率谱。我们知道，确知的非周期功率信号的自相关函数与功率谱密度是一对傅里叶变换。对于平稳随机过程，也有类似的关系，即 和 对于平稳随机过程我们通常先求出其自相关函数再利用上式求出其功率谱密度。 1.2 实验要求  1.了解平稳随机信号功率谱的概念及计算方法  2.仿真不同占空比，等概、非等概双极性矩形随机信号的归一化功率谱密度  3.分析不同信号所包含的频谱分量，有无直流分量和定时分量信息 (A pair of rectangular random experiment polarity signal sample return a nonzero function of any duration power spectral density of a 1.1 power spectral density of a stationary process Introduction of infinite length, apparently not satisfied absolutely integrable and the total energy of the limited conditions. Therefore, it is the Fourier transform of the spectrum that does not function does not exist. Therefore, we use the power spectral density to express their spectral characteristics. Any random process is a realization of a certain type signal power. For arbitrarily determined power signal f (t), its power spectral density is: wherein, is f (t) is a function corresponding to the truncated spectrum function. f (t) is a stationary random process of realization. The random process to achieve a certain power spectral density can not serve as the power spectral density of the process. Power spectral density process should be seen as a statistical average power spectral density of any r)