9160122010

Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components.

Numerical program on Newton’s forward difference formulae, the use of Sterling formulae & the use of Newton’s backward difference formulae combindly, Trapezoidal rule, Gauss-Seidal method, Jacobi method.

升余弦滚降系统的源程序(用于调制解调系统中波形成型)(Raised Cosine system of the source (for modulation and demodulation waveform prototyping system))

matlab学习，适合初学者对matlab软件进行学习(matlab learning software for beginners to learn matlab)

kaihua-zhang的Fast Compressive Tracking的Matlab实现代码，对理解文章很有帮助。(The code of the paper Fast Compressive Tracking,Kaihua Zhang.)

SAR CS成像算法，学习合成孔径的值得参考。(SAR CS imaging algorithm, it is also useful.)

用于进行图像分类的两个数据，分别为车标和指示牌(Two datasets used to test the accuracy of classification methods)

TRANSIENT RESPONSE OF THE ELECTRICAL CIRUITS USING MATLAB SIMULATION

高等光学仿真源程序，有助于了解光纤光学，主要是光在光纤中传播的模式分布(Higher optical simulation program to help understand fiber optics, light propagation in the fiber mode distribution)

实现PCA(主成分分析)算法，对图像进行主成分分析后根据主成分坐标轴转换原图像坐标(to realize PCA)