ACodeforMatlab

KLT(PCA,EIGEN T, HOTELLING T) for MATLAB

qr factorization using hh algorithm colum pivioting

仿真实现噪声调幅干扰信号,信号形式为一般形式，其中包含(simulation of noise moding jamming signal)

升余弦滚降滤波器，主要完成 Generate coefficients of Nyquist filter(Raised Cosine filter, mainly to complete Generate coefficients of Nyquist filter)

本程序设计的是咬尾编码的turbo码，用matlab编写。咬尾turbo码也即不需要拖尾比特，使得前后两个状态相同，译码采用修正的MAP译码算法。此文件为咬尾编码矩阵生成的测试文件包(This program is designed to encode the turbo tail biting code, written with matlab. Tail biting turbo code that is not trailing bits to make the same before and after the two states, the MAP decoding using modified decoding algorithm. This file is biting the end of the generated test code matrix package)

差分方程求解 实验步骤： 主界面下进入实验五的“差分方程求解”子系统,输入希望看到的输出样点数 输入差分方程系数向量 输入顺序为：，。其中 N+1 为差分方程两边系数最大数目，如果有一边输入系数个数小于 N+1，将按不足系数为零计算。 输入系统初始状态向量 输入顺序为 鼠标单击确定按钮，以数值和图形两种方式显示单位冲激响应和输出响应 3） 确定差分方程形式：y(k)-y(k-1)+0.35y(k-2)=2x(k)-x(k-1)，(Experimental steps for solving differential equations: the main interface into five experimental " differential equation" subsystems, would like to see the output of input sample points difference equation coefficient of input vector input sequence as follows:,. One of N+1 differential equations for the maximum number of factors on both sides, if there is one less than the number of input coefficient of N+1, the calculation will be less than zero coefficient. Initial state vector input system for the importation of the order of the mouse button to click OK to both numerical and graphical display unit impulse response and output in response to 3) to determine the form of differential equation: y (k)-y (k-1)+0.35 y (k-2) = 2x (k)-x (k-1),)

提供了集中语音去噪的matlab代码，并提供了相应的声音文件(Provides a centralized voice denoising matlab code, and provide the corresponding sound files)

几个简单的matlab实例，新手学习matlab可用(A few simple matlab example, matlab novice can learn)

说明：  包含一元线性回归分析、多元线性回归分析、逐步回归分析、非线性回归分析、主成分分析、因子分析、层次聚类分析、判别分析、自相关分析和自回归分析的全套Matlab源代码(MATLAB source code including linear regression analysis, multiple linear regression analysis, stepwise regression analysis, non-linear regression analysis, principal component analysis, factor analysis, hierarchical clustering analysis, discriminant analysis, autocorrelation analysis and autoregressive analysis)

说明：  material property for RCWA calculation