Matlabshuxuejianmo_weisoong

这是一个小波去噪的程序，除了实现了最经典的图像去噪算法外，还使用了最新的带有方向性的小波，去噪效果非常好(This is a wavelet denoising procedure, in addition to realize the most classical image denoising algorithms, but also uses the latest with a directional wavelet, denoising effect is very good)

matlab入门学习的好书，很经典的，相信肯定可以对广大学习爱好者有帮助(a good book for matlab learning，i think it will useful for every downloader)

一种电力系统分析工具包，非常实用，包括潮流计算，连续潮流等分析(he PSAT (Power System Analysis Toolbox), simulation model and its analysis steps. The feature, component models, network design of the power system, power flow (PF), continuous power flow (CPF), the small signal stability analysis (SSSA), the simulation of the time domain(TD) and simulation steps of PSAT)

用matlab开发的5个题目的代码，主要是光电综合设计的几个题目(With the development of the five topics matlab code, mainly optoelectronic integrated design of several topics)

To read the handover/softhandover in 3G communication

this is very good book, so i want to upload this here

说明：  matlab图像处理代码,很有帮助的参考程序代码(matlab image processing code, and useful reference code)

在Matlab对FDTD进行仿真，并对一维，二维及三维电磁传播环境分别编写了程序(On the FDTD simulation in Matlab, and the one-dimensional, two-dimensional and three-dimensional electromagnetic propagation environment programs were prepared)

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.)

this program tell about the ofdm reception