100

Decompose image into subbands, denoise using BLS-GSM method, and recompose again. fh = decomp_reconst(im,Nsc,filter,block,noise,parent,covariance,optim,sig) im: image Nsc: number of scales filter: type of filter used (see namedFilters) block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood noise: signal with the same autocorrelation as the noise parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood covariance: are we considering covariance or just variance? optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0) sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise) Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero). JPM, Univ. de Granada, 3/03

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)

All zero points for Bessel (zero points for Bessel)

this code is usefull for communication engineering

说明：  MATLAB 16QAM仿真调制与解调及其说明(MATLAB 16QAMdaskljdsajdiosaduiosamdklsajo)

可变形部件模型Deformable Part Models是目前最好的目标检测算法(Deformable component model Deformable Part Models is the best target detection algorithm)

应用数值方法解算一维热传导问题，简单实用，含有源程序和简单说明，参数和变化和调整，可以根据需求进行修改，使用程序：Matlab(Use numirical method to solve 1-D heat transfer problem,the code and paremeter can be modified dependent on your need)

程序生成的随机数,真正的名字叫做“伪随机数”,需要有一个种子来初始化 使用当前时间去初始化种子,就能得到不同的伪随机数序列(Program generates random numbers, the real name is " pseudo-random number," the need for a seed to initialize the current time to initialize the seed, you can get different pseudo-random number sequence)

SVM的一个实例，对不同葡萄酒分类，有原始数据(Matlab code for SVM, classifing types of wine, with original data.)

通过矩不变量法，判断字母是否与原字母相同(letter s distinction)