corr_fading

该文件能够实现盲信号的分离，分离效果很好，输入为两种信号的非线性叠加，经过仿真效果很明显！(The document can achieve blind signal separation, separation a good effect, input non-linear superposition of two kinds of signals, through simulation effect is obvious!)

该工具箱为时频分析中的一类重要变换,离散时频分布的工具箱,其中提供基本算法的程序.可直接应用(the Toolbox too frequency analysis of a kind of important transformation, discrete time-frequency distribution of the toolkit, providing basic arithmetic procedures. can be directly applied)

Computational modeling

用Baum-Welch算法来迭代估计一个隐马尔科夫模型（HMM）的初始状态概率分布以及其状态转移概率矩阵。其中文件有mainfile_B_W.m为主函数，Baum_Welch.m为Baum-Welch算法迭代函数，Forward_variable.m与Backward_variable.m与Gamma_variable.m与Ksi_variable.m是需要计算的四种因子，B_pdf.m为混淆散射概率密度函数。(It s Baum-Welch algorithm for iteratively estimating the initial states probability distribution and state transition probability matrix. In the folder, "mainfile_B_W.m" is the main function, and "Baum_Welch.m" is the iteration Baum-Welch function. Also these four file "Forward_variable.m " "Backward_variable.m" "Gamma_variable.m" "Ksi_variable.m" are used for calculating crucial factors. "B_pdf.m" is the emission probability distrubution function.)

Polynomial filter (includes Savizky-Golay filter)

有限元单元划分 checkgeometry(Finite Element unit division checkgeometry)

阵元间隔为半波长的均匀分布16元线列阵，利用均匀加权常规波束。远场有5个互不相关的目标源发射信号，每个源相对基阵的方位分别为-40，-42，-5，30和33，且每个源信号到达基阵时的信噪比相等，均为10dB。分别对波束域和阵元域MUSIC算法进行比较。 (Array element spacing of half wavelength for the uniform distribution of 16 per linear array, using conventional beam evenly weighted. Far-field has five separate objectives of the source of emission signals, each source array of the relative position of-40,-42,-5,30 and 33, and each source signal to noise ratio at the time of Scientology Front equal, are 10dB. Separately on the beam element domain and the domain MUSIC algorithm for comparison.)

multi media card application note

completing missing images with painting

实现ID3算法，在结果中以树表示出来。决策树是对数据进行分类，以此达到预测的目的。该决策树方法先根据训练集数据形成决策树，如果该树不能对所有对象给出正确的分类，那么选择一些例外加入到训练集数据中，重复该过程一直到形成正确的决策集。(ID3 algorithm to achieve, in the results that come out to the tree. Decision tree is to classify the data, thus achieving the purpose of prediction. The decision tree training set of data according to the formation of the first decision tree, if the tree can not give the correct classification of all objects, then select a number of exceptions to the training set data, repeat the process until the correct decision set.)