OFDM

数值分析中几种常用的插值算法的matlab源程序。(Numerical analysis of several commonly used source matlab interpolation algorithm.)

利用matlab编写的遗传算法程序，很经典，是从一个毕业的师姐那要过来的，跟大家分享一下(Prepared using matlab genetic algorithm, very classic, is a graduate of the senior sister apprentice that to come and share with you)

说明：  普通机床改造成数控机床,是学生毕业也是经常遇到的课题.(Transformed into ordinary CNC machine tools, graduate students are also frequently encountered issues.)

是2014年的cnn上的一个变化的网络，将卷积层与池化层之间的线性关系变成1×1的卷积层的非线性关系，名为network in network(Is a change in the 2014 cnn on the network layer of the linear convolution and pooled into a non-linear relationship between the layers 11, convolution layer, called the network in network)

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.)

说明：  该代码是多视图聚类的统一图学习框架算法代码，运用Matlab编写，有利于多视图聚类的学习(A Unified Graph Learning Framework for Multi-view Clustering)

RAKE接收机直接序列扩频系统 一个简单的Simulink的直接序列扩频频谱与RAKE接收系统模型。 (For DS- SS systems RAKE receiver RAKE receiver direct sequence spread spectrum system with a simple Simulink spectrum and direct sequence spread spectrum RAKE receiver model.)

说明：  麦克风阵列形成的波束beampattern图形，可调整麦克间距和信号带宽来得到需要的波束图(microphone array beam forming the beampattern graphics, Mike adjustable spacing and the signal bandwidth they need to beam map)

matlab编写的全息程序，主要用于计算全息以及数字再现，(matlab prepared holographic program, mainly used to calculate the hologram and digital reproduction,)

说明：  用来学习贝叶斯网络，包含结构学习和参数学习(For learning Bayesian networks, including structure learning and parameter learning)