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在matlab中，基于GUI工具箱，实现了数字滤波器和均衡器的功能(A digital filter and equalizer function, GUI-based toolbox in matlab)

Matlab 命令大全详细介绍了Matlab中常用函数的用法以及应用举例，包括数值计算函数、数学变换函数以及绘图函数等(The book of the usage of Matlab functions details the application of commonly used functions in Matlab and application examples, including the numerical functions, mathematical transformation functions and graphics functions and so on )

这篇文章也很不错，是有关DVV滴定的，最近一直在看，希望对感兴趣的朋友有帮助！谢谢！(This article is also very good, is about the DVV titration, has recently been looking at, want to help friends interested! Thank you!)

微卫星SSR的常用软件，可以分析相关处理数据(Microsatellite SSR commonly used software, related processing data can be analyzed )

本文将介绍模糊控制的思想,模糊控制的matlab/simulink 2012b仿真以及模糊控制器的C语言代码导出操作.这里没有复杂的理论公式,只有简单的语言描述和具体实现过程.(This article will introduce the idea of fuzzy control, fuzzy control matlab/simulink 2012b simulation and fuzzy controller C language code export operation.There is no complex theoretical formula, only a simple language description and specific implementation process.)

costas 环路 simulink 仿真文件(costas loop simulation simulink file)

用matlab来实现的衍射模拟实验，便于教学中立体的观看实验结果，这是很好的(using Matlab to achieve the diffraction experiments to facilitate the teaching of stereo viewing the experimental results, This is a good)

以D-fft方法计算任意物屏的近场与远场衍射(The fresnel and fraunhofer diffraction of arbitrary screen calculated by D- FFT method)

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.)

Program to look all the paths from N sources each other.