DOA_simple

Implemented Gamma correction using matlab

极有用的数据处理程序，方便处理实验数据，并取得数学表达式(matlab)(very useful data processing procedures, facilitate the processing of experimental data and mathematical expression made (Matlab))

功能简单，实现了加减乘除，取余等简单功能。适合初学者使用。(Features simple addition and subtraction multiplication and division achieved, take over other simple functions. Suitable for beginners.)

王建卫编的《MATLAB+7.X程序设计》书配套Matlab源代码及习题答案,相当不错的一本书.(Source codes and exercises answer of the book-"Matlab+7.X programming language" writeen by Jianwei Wang,It s a good book.)

MIT人工智能实验室的目标识别的源码，MATLAB版本的，非常适合计算机视觉方面的研究使用。(MIT artificial intelligence laboratory identification of the target source code, version of the MATLAB, very suitable for computer vision of study.)

线性方程组的数值解法，包括高斯列主元消去法 ,Jacobi迭代法,Gauss--Saidel迭代法,超松弛迭代法(The numerical solution of linear equations, including gaussian pca elimination method, Jacobi iteration method, Gauss- Saidel iteration method, overrelaxation iteration method)

利用MATLAB来实现离散傅里叶变换和离散傅里叶逆变换，N为离散傅里叶变换长度，形式为xk=IDFT（xn，N）(To achieve the discrete Fu Liye transform and discrete Fu Liye transform by using MATLAB, N as the discrete Fu Liye transform length, form xk=IDFT (xn, N))

功能描述：测试Levinson-Durbin算法 文件名：L_D_sim.m 测试用例： (1)x(n)+0.81*x(n-2)=u(n)，u(n)为高斯白噪声 (2)x(n)=u(n)+u(n-1)+u(n-2),u(n)为高斯白噪声 文件输出：两种测试用例情况下的功率谱 调用函数：function [A,E] = Levinson_Durbin_Algo(R,P) 被调用：无 作者：mingcheng 编写时间：2009-11-13 修改时间：2009-11-13 版本：V1.0 ( Function Description: Test Levinson-Durbin algorithm file name: L_D_sim.m test case: (1) x (n)+0.81* x (n-2) = u (n), u (n) is Gaussian white noise (2) x (n) = u (n)+ u (n-1)+ u (n-2), u (n) is Gaussian white noise file output: two tests use case in case of power spectrum call the function: function [A, E] = Levinson_Durbin_Algo (R, P) is called: No of: mingcheng write time :2009-11-13 modified :2009-11-13 version: V1.0 )

前馈ANC系统的模型，但是，不详细，可以参考！(the model of two earphone active noise control)

FTF自适应算法 求：自适应滤波器的两个权值ω0 (n)和ω1(n)分别收敛于0.2和0.7的收敛轨迹，并与LMS算法进行对比。(FTF adaptive algorithm Requirements: two weights ω0 is (n) of the adaptive filter and ω1 (n) converges to 0.2 and 0.7, convergence trajectory, and compared with the LMS algorithm.)