16QAM

计算CIC补偿滤波器系数方法2 Matlab source code(another method of calculate CIC compensation filter coefficients )

Programa para calculo

实现两种信道环境下的ZF均衡和MMSE均衡，并比较两者的误码率曲线(ZF and MMSE equalization to achieve a balanced two-channel environment, and compare the BER curves)

带通滤波器的matlab代码，根据自选频率进行滤波(bandpass filter basing matlab with slected frequency by user)

本书是《高质量c++编程指南》，林锐博士著，针对编程过程中可能会遇到的若干容易被忽略的问题进行了分析。(This book is "high-quality c++ Programming Guide" Dr. Lin Rui book for several problems easily overlooked programming process may encounter analyzed.)

利用全局控制最小二乘法（FCLS）可以对遥感影像进行丰度反演(Least squares method using the global control (FCLS) remote sensing images can abundance inversion)

在线向量回归分析，有包涵一个例子和内定函数等(On-line vector regression analysis, there is an example of indulgence and unofficially functions, etc.)

本程序采用宽带AR模型的Yule-Walker法的功率谱进行谱估计的(The program uses broadband AR model method Yule-Walker power spectrum estimation spectrum)

image processing and matlab application

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.)