PDE_Galerkin

传统的宽带信号中抑制正弦干扰的方法是采用陷波器(notch filter),为此需要精确知道干扰正弦的频率.然而当干扰正弦频率是缓慢变化时,且选频率特性要求十分尖锐时,则最好采用自适应噪声抵消的方法.下图是用一个二阶FIR的LMS自适应滤波器消除正弦干扰的一个方案。该代码实现了以下功能： 1) 借助MATLAB画出了滤波器的误差性能曲面和误差性能曲面的等值曲线； 2） 给出了最陡下降法以及LMS算法的计算公式； 3） 用MATLAB产生了方差为0.05, 均值为0白噪音S(n)，并画出某次采样得到的波形； 4） 根据 2）中的公式，并利用 3）中产生的S(n)，在 1）中的误差性能曲面的等值曲线上叠加画出了采用最陡下降法以及LMS法时H(n)的在叠代过程中的轨迹曲线； 5）用MATLAB计算并画出了LMS法时误差随时间n的变化曲线（对应S(n)的某一次的一次实现）和e(n)波形； 6）在 1）中的误差性能曲面的等值曲线上，叠加画出采用LMS法得到的100次实验中的H(n)的平均值的轨迹曲线； 7）最终对结果进行了分析，具体见压缩包的word文档。 (Wideband signal in the conventional sinusoidal interference suppression method is the use notch filter (notch filter), this requires precise knowledge of the frequency of a sinusoidal interference . However, when the interference frequency sinusoidal changes slowly , and the selected frequency characteristic requirements are very sharp , then best use of adaptive noise cancellation method . following figure is a second-order FIR LMS adaptive filter to eliminate a program sinusoidal interference . The code implements the following functions:   A ) draw with MATLAB error performance of the filter surface and the surface contour error performance curve 2 ) gives the steepest descent method and the LMS algorithm formula 3 ) generated using MATLAB variance of 0.05 with a mean of 0 white noise S (n), and draw a particular sampling waveform 4 ) based on 2 ) of the formula , and use 3 ) generated in S (n), in a ) the error performance curves superimposed on the surface contour pl)

实现dtmf双音多频信号的matlab仿真编码和解码，采用coertzel算法。(Methods of measurement for pushbutton telephone with DTMF )

运用matlab，LBP局部二值模式程序的完整阐释，对学习LBP帮助效果佳(Use matlab, full interpretation of LBP Local Binary Pattern program to help good effect on learning LBP)

阿兰方差的基本实现，可对滤波效果进行检验。(Allan scheme,which can terify the results of denoising in terms of three noise factors.)

1. 按照式计算相似性矩阵 2. 排除自身的相似度 3. 按照式计算归一化矩阵 4. 按照式计算归一化拉普拉斯图矩阵L 5. 计算L的特征向量，将前个特征值最大的向量按列放置成一个矩阵，即依次为前个特征值最大的特征向量 6. 归一化X形成矩阵Y 7. 对矩阵Y按每行为一个数据点，进行k-means聚类，第行所属的类就是原来所属的类。(1. similarity matrix based on formula computation 2. rule out your own similarity. 3. normalized matrix based on formula computation 4. normalized formula Laplasse matrix L according to formula. 5. Calculate the eigenvectors of L. Place the vectors with the largest eigenvalues in columns into a matrix, that is, the eigenvectors with the largest eigenvalues in turn. 6. normalized X formation matrix Y 7. K-means clustering is applied to matrix Y according to one data point per behavior. The first row belongs to the original class.)

the same with the privious one.

Salam Des toolbox que j’ai les télécharger depuis qlq année titre se traitement de signal ou bient signal processing ok

support vector clustering for Matlab tool box

blind image data using RDWT SVD algorithm.watermarking using a wavelet algorithm and a compression (SVD) algorithm....

精通maltab--maltab学习指导辅导练习综合教程用书(Proficient maltab- maltab comprehensive learning tutorial guidance counseling practice books)