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Matlab计算卫星角度包括仰角、方位角、相对方位角和基线倾角 (satang calculation of satellite angles: elevation, azimuth, and also relative azimuth and baseline orientation )

Capacity of a MIMO System vs. C/N Relatioship

一个智能天线处理程序源码，能有效提高算法性能，很实用。(A smart antenna processing source code, can effectively improve the algorithm performance, very practical.)

说明：  用MATLAB编写的svm源程序，可以实现支持向量机。(Prepared using MATLAB source SVM, support vector machine can be achieved.)

power system mmc hvdc .you can easy learn. tt,s a wonderful artticle for you to learn.~~~~~~~~~~~(power system mmc hvdc .you can easy learn. tt,s a wonderful artticle for you to learn.~~~~~~~~~~~~)

It contains the information about the performance of the fractal image compression

matlab经典算法程序，插值拟合，回归等(matlab classic algorithm, interpolation fitting, regression，etc。)

The Haar transform can be expressed in the matrix form as T = HIHT (inverse transform HTTH) where I is an NxN image matrix, H is an NxN transformation matrix and T is the resulting NxN transform. For Haar transform, the transformation matrix H contains the Haar basis functions hk(x). They are defined on a continuous, closed interval, x -[0, 1] for k = 0,1,2 ...,N -1 where N = 2n.( The Haar transform can be expressed in the matrix form as T = HIHT (inverse transform HTTH) where I is an NxN image matrix, H is an NxN transformation matrix and T is the resulting NxN transform. For Haar transform, the transformation matrix H contains the Haar basis functions hk(x). They are defined on a continuous, closed interval, x-[0, 1] for k = 0,1,2 ...,N-1 where N = 2n.)

输入信号为方波信号+高斯白噪声，方波信号基 频为1kHz，幅值为1，高斯白噪声方差为0.2， 均值为0，采样频率为20kHz。试用最大熵估计 法估计此信号的AR模型及其功率谱，并分析结 果(The input signal is a square wave signal+ Gaussian white noise, square wave signal fundamental frequency 1kHz, amplitude is a Gaussian white noise variance is 0.2, the mean is 0, the sampling frequency is 20kHz. Try maximum entropy estimation method to estimate the AR model of this signal and its power spectrum, and analyze the results)

这是基于压缩感知的稀疏自适应算法，可以直接运行，没有任何问题(This is based on compressed sensing sparse adaptive algorithm can be run directly without any problems)