地震波程序

这些程序是关于因子图及和积算法的基础程序，对入门有用(These programs are the basic procedures for factor diagrams and accumulation methods, which are useful to the introduction.)

说明：  采用解析法绘制稳定性叶瓣图，构建转速与切削深度之间的关系曲线，为切削参数的合理选择避免发生颤振带来方便。(Analytical method is used to draw the stability leaflet diagram, and the relationship curve between the speed and the cutting depth is constructed, which is convenient for the reasonable selection of cutting parameters to avoid chattering.)

说明：  人工势场法程序代码，用matlab编写，简单易懂，可以设置障碍物信息，增益信息，障碍物影响距离。(the code,you can use it to set the obstale imformation ,the length and so on)

VC实验： 一．实验名称：掌握基于MFC的资源应用 二．实验目的：通过基于MFC编写基于资源的应用程序，进一步掌握资源的应用，并与SDK编程方法比较 三．实验任务： ● 掌握基于MFC的资源应用程序管理 ● 掌握各种资源的类库结构 ● 熟悉资源的应用 四．实验要求： 新建一个对话框，其中有文件，编辑，和帮助菜单，其中在文件菜单中有新建，打开，另存为，页面设置，打印和退出选项。选择文件菜单中的打开时，弹出打开通用对话框，选择文件菜单中的另存为时，弹出打开另存为通用对话框。编辑菜单中有颜色和字体选项，选择编辑菜单中的颜色时，弹出打开通用另存为颜色对话框，选择编辑菜单中的另字体时，弹出打开字体通用对话框。 -err

小甲鱼《零基础入门学习Python》课堂笔记+课后题(Small turtle "zero basic introduction learning Python" class notes + after-school questions)

SH7619 support for the clock framework

RLS算法仿真程序源代码 用RLS算法实现自适应均衡器的MATLAB程序(Implementation of MATLAB program for adaptive equalizer with RLS algorithm)

matlab和xfoil之间的接口，直接通过运行matlab运行xfoil，输入翼型坐标数据得到翼型的气动参数。(The interface between MATLAB and xfoil can directly run xfoil by running matlab, and the aerodynamic parameters of airfoil can be obtained by inputting airfoil coordinate data.)

说明：  hslogic算法仿真，通过MATLAB/Simulink实现QPSK的调制解调模型设计。(Hslogic algorithm simulation, through MATLAB / Simulink to achieve QPSK modulation and demodulation model design.)

说明：  Matlab表情识别，特征脸[1 ]作为面部表情分类的方法。首先，利用训练图像创建低维人脸空间（pca）。这是通过训练图像集主成分分析（PCA）及图片主成分分析（即具有较大特征值的特征向量）获得的。 结果，所有的测试图像以所选择的主成分表示，计算投影图像与所有投影列车图像的欧几里得距离，选择最小值以找出与试验图像最相似的训练图像。(The feature face [1] is used as a facial expression classification method. Firstly, a low-dimensional face space (pca) is created using training images. This is obtained by training principal component analysis (PCA) of image set and principal component analysis of image (i.e. eigenvectors with larger eigenvalues). As a result, all the test images are represented by the selected principal components, the Euclidean distance between the projected image and all the projected train images is calculated, and the minimum value is selected to find the training image most similar to the test image.)