Trigonometric

Motorola CDMA Authentication Introduction

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

时间序列ARMA建模，适用于各种ARMA建模，非常好用的通用程序(Time series ARMA modeling, suitable for all kinds of ARMA model, general program is very nice)

波束形成，控制第一旁瓣的幅度，加窗处理，汉明窗，汉宁窗等(Beamforming, amplitude control of the first sidelobe)

Particle Swarm Optimization Simulation

找指紋特徵點 並圈選出來的 matlab 程序(Matlab program to find the fingerprint feature points and circle out)

基于电压定向的矢量控制系统，可以学习一下，里面包括锁相环等环节(Based on voltage oriented vector control system, you can learn, which includes a phase locked loop and other sectors)

CRYPTOGRAHIC ALGORITHM AES USE TO ENCRYPT THE STRING...........

开发circonvt函数来计算序列的圆周移位 编写一个matlab函数，实现快速卷积算法(Development circonvt function to calculate the circumference of the shift sequence write a matlab function, fast convolution algorithm)

说明：  对数值进行分析，AP聚类算法是基于数据点间的"信息传递"的一种聚类算法。与k-均值算法或k中心点算法不同，AP算法不需要在运行算法之前确定聚类的个数。AP算法寻找的"examplars"即聚类中心点是数据集合中实际存在的点，作为每类的代表(For numerical analysis, AP clustering algorithm is based on the "information transfer" between data points. Unlike k-means algorithm or k-center algorithm, AP algorithm does not need to determine the number of clusters before running the algorithm. The "examples" searched by AP algorithm is the actual points in the data set as the representative of each class)