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该程序是基于MATLAB编写的对信号进行分析的随机信号通过线性系统，包括时域、频域等多项分析(signal and system analysis)

说明：  这是国外一本经典教材附带的程序，通信系统的matlab实现，所含例子丰富(This is a classic foreign materials attached to the procedures, communications systems to achieve matlab contains abundant examples of)

主元分析法的简单程序，可以以此为基础进步发展改进。(Principal component analysis of a simple procedure that can improve as a basis for progress and development.)

速度分析，形成速度谱，加入了各向异性参数(Velocity analysis, the formation of the velocity spectrum, joined the anisotropy parameter)

IEEE 802.3 - 2012 Section Six IEEE Standard for Ethernet

ANC 在控制滤波器的输出 附加一个与参考信号 不相关的零均值高斯白噪声对次级通道在线建模 (active noise control algorithm with secondary path identification)

系统讲述了matlab在数学建模中的应用，有助于良好的掌握matlab的工具箱使用和进一步了解matlab的强大功能。(Matlab system described in the application of mathematical modeling to help a good grasp of the matlab toolbox to learn more about the use and power of matlab.)

一束5W 的线偏振光以Φ = 45 度方位角振动，垂直入射到玻璃 - 空气表面，该光束波长0.6μm，玻璃介质折射率1.54 @0.6m，当入射角θ从0-70 度变化时，通过给定条件，分别完成如下要求： 画出该光束反射光的光强曲线（I-θ）； 分析该反射光束的偏振态变化，画出偏振方向变化曲线（φ-θ）和偏振光束相位变化曲线 （Φ-θ）。 （注：光束从光密到光疏的界面，在入射角θ 从0-70 度变化中，包括了临界角θc）(Bunch of linearly polarized light 5W to Φ = 45 degrees azimuth vibration, normal incidence to the glass- air surface, the beam wavelength 0.6μm, glass refractive index 1.54 @ 0.6m, when the incident angle θ changes from 0-70 degrees By the given conditions, namely the completion of the following requirements: Draw the reflected light beam intensity curve (I-θ) analysis of changes in the polarization state of the reflected beam, draw the polarization direction of the curve (φ-θ) and polarized beam phase curve (Φ-θ). (Note: the light beam from the optical density of the interface is sparse, the incident angle θ  variation from 0-70, including the critical angle θc))

采用双位方程计算对称阵子天线的输入阻抗方向图,并进行频率扫描，给出了输入阻抗随着频率变话的曲线(MOM to calculate the dipole antenna)

说明：  k均值聚类算法（k-means clustering algorithm）是一种迭代求解的聚类分析算法，其步骤是，预将数据分为K组，则随机选取K个对象作为初始的聚类中心，然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。(The k-means clustering algorithm is an iteratively solved clustering algorithm in which the data is pre-divided into K groups, the K objects are randomly selected as the initial clustering center, and then the distance between each object and the seed clustering center is calculated, and each object is assigned to the clustering center closest to it.)