quexianjiance

signal processing about OFDM simulation

This file permits to solve the equation type Ax=b by the iterative methods.

对一维脉冲信号可去除基底，且做结构函数分析。如果是二维信号，可进行动态谱分析，进行二维高斯函数拟合(Can remove the base and do the structure function analysis of the one-dimensional pulse signal. If it is a two-dimensional signal dynamic spectral analysis, two-dimensional Gaussian function fitting)

this program shows temperature of the initional points

一个新设计的OTSU算法，使用方法如下：％ load clown ％ subplot(221) ％ imshow(X/max(X(:))) ％ title( Original , FontWeight , bold ) ％ for n = 2:4 ％ Iseg = otsu(X,n) ％ subplot(2,2,n), colormap(gray) ％ imshow(Iseg) ％ title([ n = int2str(n)], FontWeight , bold ) ％ end(OTSU design a new algorithm, using the method as follows: load clown subplot (221) imshow (X/max (X (:))) title (Original, FontWeight, bold) for n = 2:4 Iseg = otsu (X, n) subplot (2,2, n), colormap (gray) imshow (Iseg) title ([n = int2str (n)], FontWeight, bold) end)

interop between C# and matlab

完成对excel的读取、删除、添加、修改等的功能，利用matlab开发，编写语言，并与word里面的excel连到了一块(Complete the Excel function such as read, delete, add, modify, using MATLAB to develop, written language, and the Excel and Word attached to adatabase is attached to a piece of)

multiobjective optimization

simple ac transformer circuit

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)