dcspeedcontrol

3GPP标准交织器，南京邮电大学，通信工程系(3GPP standard interleaver)

通过BUTTERWORTH滤波器,讲语音信号中的噪声过滤掉.(By BUTTERWORTH filter, speaking voice signal in the noise filtered out.)

Stud. Appl. Math. 118, 153-197 (2007)关于solitons的一篇文章，位图figure5的编程(Stud. Appl. Math. 118, 153-197 (2007) on the solitons in an article, bitmap figure5 Programming)

这本书是专门针对MatlabGUI编程的，简单易懂，里面有大量实例。(This book is designed for programming MatlabGUI, easy-to-understand, there are a large number of examples.)

基于matlab的数字滤波器设计的文献大全(Matlab digital filter design based literature Daquan)

用matlab编写的用于医学图像。很实用。大家可以参考参考。(Using matlab to prepare for the license plates feature extraction. Very practical. Members can refer to reference)

说明：  都是关于空间普估计算法，其中有解相干和非相干算法以及它们之间的对比( the spatial spectrum estimation)

基于matlab 模拟仿真FDTD算法对印刷电路板表面的传输特性(Based on MATLAB simulation FDTD algorithm on the surface of the printed circuit board transmission characteristics)

包括各种智能算法的详细介绍和具体的实例分析，附带matlab的源码，是不多的学习资料.(The analysis includes a variety of intelligent algorithms are detailed and specific examples, with Matlab source code, is a few learning materials.)

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.)