kriging

this is code fore lms noice

设定一个信息码串，产生常见的编码如单极性非归零、双极性非归零、单极性归零、双极性归零、AMI、HDB3码的时域波形；(Set up an information code string to produce a common code such as the unipolar NRZ, bipolar, non-zero, zero unipolar, bipolar zero, AMI, HDB3 code of the time-domain waveform )

说明：  这是我自己编写的一个完整的多进制GF域上RS信道编解码系统。解码采用梅西迭代算法。整个算法模块功能分明，GF域上运算都单独成为子模块，移植性强。还附有测试程序，方便了解各个模块的功能完成情况。(This is my own preparation of a comprehensive multi-band GF RS domain channel codec system. Messi iterative decoding algorithm used. The whole algorithm module function clear, GF domain computing is to become a separate sub-module, transplants strong. Also included testing procedures to facilitate the understanding of various modules of the completion of the function.)

著名的FastICA 算法，最新版本，修改了以前的Bug.(famous Fast algorithm, the latest version, which amended the previous Bug.)

Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

利用matlab对卫星姿态控制进行故障诊断，分别实现了对执行器故障和敏感期故障的诊断(the program is the fault diagnosis for satellites attitude control system, and realize FDI for acutator and sensor fault)

matlab直接打靶法，适用于最优控制。解决小车定点停车问题。(Matlab direct shooting method, applied to the optimal control.Solve the problem of car parking)

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.)

编程实现n进制数转换为m进制数。吴泽乐，吴泽乐(Programming n to m decimal conversion hexadecimal number. WU Ze-Le, WU Ze-Le)

image sharpening with psnr value