matlab_fem

说明：   simulink相关的一些非常经典的案例，涉及到了基本的使用和深入的使用。(simulink relevant to some of the most classic cases, involving the use of basic and in-depth use.)

The Seismic Unix Project is partially supported by the CWP Consortium Project. In the past, the Seismic Unix Project has received partial support from the Gas Research Institute (GRI) and the Society of Exploration Geophysicists Foundation.

dsp源代码 matlab c 代码 代码 代码(dsp c Matlab source cod e cod e cod e)

该文给出了一种利用matlab系统实现频谱分析与显示的方法。该方法对语音信号进行基于FFT的短时频谱分析，频谱图的伪彩色映射及显示。(This paper presents a system implementation using matlab spectrum analysis and display methods. The method is based on the speech signal short-time FFT spectrum analysis, spectrum and display pseudo-color mapping.)

实现了对语音信号的STFT处理，用matlab(Realization of the speech signal STFT processing, using matlab)

It is project report for MATLAB GUI designed for performance evaluation of three phase induction motor. This report illustrated the implementation and use of the GUI

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)

一种简单的模糊逻辑工具箱设置，两个输入，一个输出，隶属度函数为7(A simple set of fuzzy logic toolbox, two inputs, one output membership function for the 7)

说明：  社会保障行业研究,方法论研究,仅供参考.(Social Security industry research, methodological research is for reference only)

Frequency-Domain multivariate analysis