SRAPN

《MATLAB R2008图形与动画编程实例教程》的源代码--第四章(" MATLAB R2008 examples of graphics and animation programming tutorial" source code- Chapter IV)

导航电文的数据收集及仿真，可读取导航文件并处理相关数据。(Navigation message data collection And simulation. Can read documents and data processing navigation.)

均匀白噪声、高斯白噪声信号波形，及其自相关函数和频谱密度函数图形(The wave of Even White noise and Gaussian noise,and their Autocorrelation Function and Spectrum Density. )

The latest generation of high-end video cards off er considerable computing power using their 100–200 on-card processors, 0.3–1.0+ GB of RAM, and fast inter-processor communications. One promising application of this Graphics Processing Unit (GPU) computing capability is through Matlab and Matlab mex functions. With a properly developed mex function, the user-friendly Matlab interface can be used to perform behind-the-scenes parallel computations on the GPU. (The latest generation of high-end video cards offer considerable computing power using their 100-200 on-card processors, 0.3-1.0+ GB of RAM, and fast inter-processor communications. One promising application of this Graphics Processing Unit (GPU) computing capability is through Matlab and Matlab mex functions. With a properly developed mex function, the user-friendly Matlab interface can be used to perform behind-the-scenes parallel computations on the GPU.)

用matlab 生成规则的LDPC码的校验矩阵H(Matlab to generate regular LDPC code check matrix H)

通过该例子熟悉雷诺方程，仔细体会雷诺方程的求解过程，仅供参考(Through this example, familiar with the Reynolds equation, carefully understand the solution process of the Reynolds equation, for reference only.)

一个用matlab做的电话声学回声消除程序(Using matlab to do a telephone acoustic echo cancellation procedures)

matlab code for HTK MFCC MATLAB

一个动态copula的例子，利用patton2004,2006a，2006b中的方法(example for dynamic copula)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)