Mathematica-function

该程序设计了一种PID控制器，可以通过比例积分微风三个参数有效的消除系统的扰动。(The program design of a PID controller, you can breeze through the PI system to effectively eliminate the three parameters of the disturbance.)

#include <iostream.h> int main() { int i(0),sum(0) while (i<=100) { sum=sum+i ++i }cout<<"sum="<<sum<<endl return 0； }

matlab source code example for solving a problem such as partial differential eqaution , ordinar differential equation , matric etc.

基于小波变换的图像增强，包含整个处理过程(Image enhancement based on wavelet transform, including the whole process)

说明：  采用Matlab编写的QP算法，并且在QP算法做了一些改进，加速了收敛性(Written using Matlab QP algorithm, and in the QP algorithm made some improvements to accelerate the convergence)

自己收集的粒子滤波程序，包括基本的程序，卡尔曼滤波以及改进的算法。(Their own procedures for the collection of the particle filter.)

实现mcmc中的metropolitan抽样,工作环境R2010B(Achieved in the metropolitan mcmc sampling, the working environment R2010B)

matlab code for water fall cognitive radio

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)

power flow in power systems