Matlab

关于Matlab的编程，由浅入深，很好的参考书，(On Matlab programming, Deep good reference book,)

matlab的经典算法程序。包括差值拟合与规划问题；绘图；数据分析与解方程等。还有ppt说明。(classic algorithm matlab program. Including the difference between the fitting and planning issues drawing data analysis and solution of equations and so on. There ppt instructions.)

Recursive least square in matlab

说明：  AP算法源码代 运行可以画出图 AP算法源码代 运行可以画出图(AP algorithm source code to run on behalf of AP algorithm source code can be drawn diagram can be drawn map on behalf of running)

Matlab 加入高斯白噪声，并计算加入噪声后信号的自相关 和加入噪声后信号和理想信号的互相关 ，将以上参数代入函数中进行维纳滤波。(Matlab Gaussian white noise, and calculated after adding noise autocorrelation and added noise signal and the ideal signal cross-correlation signal, the above parameters into function Wiener filtering.)

matlab code mobile communications

Matlab和C的混编，用于SAR图像去相干斑，将处理加性噪声的BM3D推广到SAR。(Functions for Matlab for the denoising of a SAR image corrupted by multiplicative speckle noise with the technique described in "A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage", written by S. Parrilli, M. Poderico, C.V. Angelino and L. Verdoliva)

Probability hypothesis density filter for MTT tracking

Direct torque control based neural netwoek

费希尔线性判别分析代码 Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and -1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem( Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and-1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem)