MATLABstudy

关于matlab计算与仿真的ppt,关于matlab计算与仿真的ppt(a ppt about the simulation and computation of matlab)

本程序是分析不同结构树的峰均功率比的仿真程序(This procedure is to analyze the structure of trees of different peak to average power ratio of the simulation program)

用MATLAB程序打开数字地震数据记录SAC格式。(using Matlab, it can open the data of seismic wave.)

UN AUTRE MONDE DE LA VISION.............MATLAB..................SEGMENTATION

modeling induction motor. very easy to use

％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)

dpsk的matlab实现，该程序含有BER的绘制方法(the dpsk program under the circumstance of matlab including BER)

LTE下行链路OFDM中的调制方式64QAM(LTE downlink OFDM Modulation of 64QAM)

求均值 方差 标准差 用matlab编写的 (Average variance standard deviation using matlab)

用于图像识别和特征提取时的主成分分析程序，采用Matlab编写。(for image recognition and feature extraction of principal component analysis procedures, the preparation of Matlab.)