array-signal-processing

The MATLAB interface makes it possible to use and control a precompiled solver from MATLAB...

使用Matlab实现，包括一维特征最小错误率bayes分类器；二维特征最小错误率bayes分类器；二维特征最小风险bayes分类器以及使用的数据集合。(Using the Matlab implementation, including the minimum error rate of one-dimensional characteristics of bayes classifier two-dimensional characteristics of the minimum error rate bayes classifier two-dimensional characteristics of the minimum risk bayes classifier and the use of data collection.)

rand, randn, randint.... you can understand my code

用蒙特卡罗仿真研究一个通信系统的误比特率BER。假设通信系统满足条件：信源输出的数据符号是相互独立和等概的；在发射机中没有进行脉冲形成；信道是AWGN信道(Using Monte Carlo simulation of a communication system bit error rate BER. Suppose communications systems to meet the conditions: the data source output symbols are independent, and so almost the no pulse in the transmitter in the form channel is AWGN channel)

基于matlab/simulink的SVPWM仿真建模(Based on matlab/simulink for SVPWM simulation modeling)

分形的练习一 ①Koch曲线 用复数的方法来迭代Koch曲线 clear i 防止i被重新赋值 A=[0 1] 初始A是连接（0，0）与（1，0）的线段 t=exp(i*pi/3) n=2 n是迭代次数 for j=0:n A=A/3 a=ones(1,2*4^j) A=[A (t*A+a/3) (A/t+(1/2+sqrt(3)/6*i)*a) A+2/3*a] end plot(real(A),imag(A)) axis([0 1 -0.1 0.8]) ②Sierpinski三角形 A=[0 1 0.5 0 0 1] 初始化A n=3 迭代次数 for i=1:n A=A/2 b=zeros(1,3^i) c=ones(1,3^i)/2 A=[A A+[c b] A+[c/2 c]] end for i=1:3^n patch(A(1,3*i-2:3*i),A(2,3*i-2:3*i), b ) patch填充函数 end (Fractal Exercise One The ① Koch curve Plural iteration Koch curve clear i to prevent i is reassigned A = [0 1] initial A is a connection (0,0) and (1,0) of the segments t = exp (i* pi/3) n = 2 n is the number of iterations for j = 0: n A = A/3 a = ones (1,2* 4 ^ j) A = [A (t* A+ a/3) (A/t+ (1/2+ sqrt (3)/6* i)* a) A+2/3* a] end plot (real (A), imag (A)) axis ([0 1-0.1 0.8])   ② Sierpinski triangle A = [0 1 0.5 0 0 1] initialized A n = 3 the number of iterations. for i = 1: n A = A/2 b = zeros (1,3 ^ i) c = ones (1,3 ^ i)/2 A = [A A+ [c b] A+ [c/2 c]] end for i = 1:3 ^ n patch (A (1,3* i-2: 3* i), A (2,3* i-2: 3* i), b ) patch filled function end)

ldpc decoding and encoding

Linear Discriminant Analysis

说明：  LMS/F组合算法由Shao-jen Lin和J.G.Jarris提出，它计算量增加不多却同时兼有LMS和LMF算法的优点。即收敛速度和稳态性能比基本LMS算法好，稳定性比LMF算法好。(LMS/F combination algorithm by the Shao-jen Lin, and JGJarris proposed, it is increasing much more calculations with both LMS and the LMF of the method. Ie, the convergence speed and steady-state performance better than the basic LMS algorithm, stability, better than the LMF algorithm.)

应用sift算子进行图像匹配，效果不错。。。(sift is used to match two picture，the result is good...)