Fourier

使用下面的参数设计信道均衡器，其中使用两个独立的随机数发生器，一个用x(n)来表示，用来测试信道；另一个用v(n)来表示，用来模拟接收器中的加性白噪声的影响。序列x(n)是x(n)=±1的伯努利序列，随机变量x(n)具有零均值和单位方差。第二个序列v(n)具有零均值，其方差由信噪比决定。均衡器有11个抽头。(Use the following parameter design of channel equalizer, which uses two separate random number generator, one with x (n) to indicate, to test the channel the other with v (n) to indicate to simulate the receiver in The additive white noise. Sequence x (n) is x (n) = ± 1 of the Bernoulli sequence of random variable x (n) with zero mean and unit variance. The second sequence v (n) has zero mean, its variance decision by the signal to noise ratio. There are 11-tap equalizer.)

说明：  matlab 特征选择matlab源代码(matlab feature selection)

说明：  用于信号滤波，要事先给出滤波器系数，给出心电加上正弦干扰信号的滤波过程。(For signal filtering, to filter coefficients is given in advance, given ECG+ sinusoidal interference signal filtering process.)

一种mos模型建立方法，现在十分流行，希望大家喜欢(NEGF)

说明：  隐马尔可夫模型,浙江大学人工智能研究所.HMM的由来 马尔可夫性和马尔可夫链 HMM实例 HMM的三个基本算法 主要参考文献(Hidden Markov models, Artificial Intelligence Institute of Zhejiang University. HMM Origin Markov and Markov chain HMM example The three basic algorithms HMM Main references)

Matlab functions implementing non-parametric local linear smoothing (LLS) for one-dimensional curve fitting: yi = f(xi) + ei for i = 1, 2, ..., n. The toolbox contains: (i) the usual LLS estimator, see e.g. [1] (ii) a jump-preserving (two-sided) LLS estimator proposed in [2] (iii) a modified version of the two-sided LLS proposed in [3].(Matlab functions implementing non-parametric local linear smoothing (LLS) for one-dimensional curve fitting: yi = f(xi)+ ei for i = 1, 2, ..., n. The toolbox contains: (i) the usual LLS estimator, see e.g. [1] (ii) a jump-preserving (two-sided) LLS estimator proposed in [2] (iii) a modified version of the two-sided LLS proposed in [3].)

快速傅立叶变换（时域抽取基二fft）的源码的分析(Fast Fourier Transform (time-domain extraction base 2 fft) analysis of the source code)

人脸定位代码，短小但精度和速度都很高，matlab版本不能太低(Face location code, a short but very high accuracy and speed,)

Digital signal processing using Matlab

MIMO SDM completed in Matlab