MATLAB SIMULINK仿真程序 GridConnected_pvInverter_MPPT

中值滤波 原图像画面清晰，添加了椒盐噪声以后图像上出现了明显噪声，经不同模板的中值滤波处理后图像上的噪声被虑去，但使得图像变得模糊，并且，使用的模板越大，去噪声效果越好，同时图像也越模糊。滤波效果明显优于平滑滤波。 (Median filtering the original image, a clear picture of the Salt and pepper noise added after the image there is a clear noise, and by different template after median filtering has been taking into account the noise on the image to go, but makes the image become blurred, and, using the template more large, to noise, the better the effect, while the more blurred the image. Filtering effect is superior to smoothing.)

在所给定的图片上自动标定出嘴的位置，方便不同图像嘴的定位(In the picture given by the location of automatic calibration of the mouth, mouth to facilitate the positioning of different images)

music算法过估计时的情况,角度精度分析(music algorithm than the estimates, angle precision analysis)

海伦方法分析偶极子天线的频率阻抗特性-电阻随频率的变化，电抗随频率的变化(Helen analyze the frequency dipole antenna impedance- resistance changes with frequency, the change of reactance with frequency)

this code is about k mean clustering in Matlab

DBCC算法的具体例子，均是本人自己编写的程序 可以运行( Specific examples of DBCC algorithm)

Algorithm for Generating Random Number with Multi-dimensional Discrete Distribution

OFDM同步技术仿真，带有码间干扰和半码调制，多信道衰落。(OFDM synchronization technology simulation, inter-symbol interference and a half with code modulation, multichannel fading.)

说明：  有限差分法求解一维扩散方程。使用Crank-Nicolson格式求解PDE方程，并与真实解析解u(t,x)=e^(x+0.1t)比较。 一维扩散方程的有限差分法，采用隐式六点差分格式。(Finite difference method for solving one dimensional diffusion equation)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )