Prewitt

在输入输出信号中加入高斯白噪声。这是在很多工程领域必须加入的。此文件是matlab中信号加入高斯白噪声的文件(In the input and output signals in white Gaussian noise. This is in many engineering fields must be added. This file is in matlab adding Gaussian white noise signal file)

Genetic Algorith program in Matlab (m file)

层次分析法（AHP）是进行评估的一种常用方法，其中层次分析法中求随机一致性指标是该方法的关键一步。本程序即实现的求解PI的程序(AHP is a common method of assessment, in which AHP seek random consistency index is a key step in the method. This procedure that is implemented procedures for solving PI)

编写的数值分析power方法matlab程序(numerical method for power method )

Evaluates the finite element linear basis function. and sets up the geometric data.

Calendar Application” Using C/C++

基于功率谱相减的语音消噪增强法程序，采用硬阈值法(Power spectrum subtraction based speech denoising Enhancement Act program, using hard-threshold)

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.)

优点：1.对于信噪比高的信号滤波效果好； ％ 2.对于边沿的保护强过阈值滤波，不会产生阈值滤波情况下的过于平滑与Gibbs现象。 ％缺点：1.由于对边沿信号没做任何处理，所以边沿可能会有脉冲噪声保留下来； ％ 2.计算相关系数中，如果计算出来的小波系数点位置偏差大，则相关系数计算受影响； ％ 3.需要迭代运算，迭代的噪声能量阈值选取很重要，这里以开始段无信号处估计噪声； ％ 4.需要迭代运算，所以运算量比阈值法大； ％ 5.受分解层次影响，在大尺度上小波系数点位置偏差更大，相关系数计算不准确。 ％需要具体调整的地方：1.分解的尺度；()

MATLAB基础和应用实例精粹(经典之作）(The essence of basic and applied examples MATLAB (classic))