design

计算两幅图像的峰值信噪比。图像是表示地个像素点所用的二进制位数，即位深。计算PSNR(Calculate PSNR of two images. Image pixel is expressed in a number of bits used, ascended the throne deep. Calculation of PSNR)

Vector control _ SVPWM

本书介绍matblab与c，c++混合编程，适合c和matlab有一定基础的同学参阅(This book describes matblab and c, c++ mixed programming for c and matlab students see there is some basis)

基于SVPWM的三电平逆变的matlab仿真，仿真图是速度、距离、幅度三维图像，isodata 迭代自组织的数据分析，LDPC码的完整的编译码，有CDF三角函数曲线/三维曲线图。(Based on SVPWM three-level inverter matlab simulation, FIG simulation speed, distance, amplitude three-dimensional image, Isodata iterative self-organizing data analysis, Complete codec LDPC code, There CDF trigonometric curve/3D graphs.)

说明：  该程序用BASE-10遗传算法模拟简单函数的优化。首先，我们概述了遗传算法的一些背景信息，并定义了一些术语。接下来，我们将解释如何将该程序用于您自己的目的，并作为一个示例演示如何解决优化问题。(This program simulates the optimzation of a simple function with a base-10 genetic algorithm. First, we outline some background information on GAs and define some terminology. Following this, we explain how to use the program for your own purposes and as an example we show how to solve an optimization problem.)

该程序利用空时二维自适应处理的原理，采用最优处理器的方法，对杂波矩阵进行处理，并能够绘制出二维杂波功率谱图，功率谱亮度图，特征谱图，二维频响图，以及最优化比较图(the code can draw 5 pictures of the Clutter matrix,including :Clutter power spectrum, power spectral brightness diagram, characteristic spectra, two-dimensional frequency response graph and the optimal comparison chart )

mean filter for image

三相零式半波 可控硅整流器 电力电子技术基础例程(Three-phase half-wave Zero silicon-controlled rectifier power electronics technology foundation routine)

精通MATLAB参考源码第10章，喜欢MATLAB和神经网络的可以好好学习下，绝对的好资源！(Chapter X proficient MATLAB reference source, like MATLAB and neural networks can learn, the absolute good resource! )

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)