Impact-of-Channel

使用IS-95 FIR滤波器从白噪声中滤得低通噪声,使用matlab2007b编写(Using the IS-95 FIR filter obtained from the white noise in the low-pass noise filter)

通过用remez算法设计的滤波器，此matlab程序可以直接运行(Remez algorithm by using the filter, this procedure can run matlab)

说明：  这是一些滤波的小程序,用MATLAB编的,没有用到MATLAB自身的一些函数(This is a small number of filtering procedures, using MATLAB series, did not use its own MATLAB some function)

说明：  有关于matlab的英文文献，大家可以欣赏欣赏(English literature on matlab, we can appreciate the appreciation)

匹配滤波器的matlab源代码及程序输出结果截图(Matched filter)

DahlUrsin, modeling amplitude versus offset

Smooths a matrix (with/without NaN s) via recursive moving average method and eliminates data gaps. (MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast. MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact. NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN s elements in the vector X the latter interpolates also those NaN s elements surrounded by numeric elements. NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN s in the matrix X the latter interpolates also those NaN elements surrounded by numeric elements.)

column to row source code

用matlab神经网络分析与预测股票的发展趋势，具有较好的预测结果(Matlab neural network analysis and forecasting stock trends, to better predict the results)

simulate mimo system with OneRingSelective channel