MATLAB--FFT

Matlab策略之动量策略，根据前P个月的收益率，选取股票收益率最高和最低的组合买入，并持有Q个月(Matlab strategy momentum strategies, based on the previous month yields P, select the highest and the lowest stock returns a combination of buy and hold Q Months)

2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。 1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。 2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。 (Computer Experiments: 1. Consider an AR process x(n) defined by the difference equation where v(n) is an additive white noise of zero mean and variance .The AR parameters and are both real valued: a) Calculate the noise variance such that the AR process x(n) has unit variance .Hence , generate different realization of the process x (n). b) Given the input x (n), an LMS filter of length M = 2 is used to estimate the unknown AR parameters and . The step size is assigned the value 0.05. Compute and plot the ensemble average curve of and by averaging the value of parameters and over an ensemble of 100 different realization of the filter. Calculate the time constant according to the experiment results and compare with the corresponded theoretical value. c) For one realization of the LMS filter, compute the prediction error And the two tap-weight errors and Using power spectral plots of , show that)

this file implement weighted linear method in pattern recognition

Book fuzzy and neural network Book using Matlab

Tracking of visual phenomena is hard. Very, very hard. And frustrating. You should try not to get discouraged by poor tracking results, but rather concentrate on the specific reasons why your tracker may not be performing well. Is the bad performance predictable from the theoretical properties of the tracker? If so, that s a valuable observation that will serve you well in the future

这是压缩感知领域最新的romp算法。。。。。。。。。。。。。。。。。(this is the code of romp)

说明：  Wiener filtering algorithm(WF) for speech enhancement set parameter values smoothing factor in noise spectrum update smoothing factor in priori update

matlab实现16QAM调制解调 matlab实现16QAM调制解调(ALLIANCES is a recently proposed cooperative random access protocol for wireless networks. In this paper we modify the original model to include user location information. We also derive pair-wise error probability (PEP) under Rayleigh flat fading channel and a power-law attenuation environment. Based on the PEP analysis we propose an optimal relay selection scheme, which achieves significant throughput gains as compared to the random relay selection scheme in the original ALLIANCES.)

说明：  利用在box-muller 和在时域上通过低通滤波器的方法产生多普勒频移的瑞利信道(Using the box-muller, and in the time domain through a low pass filter method produces Doppler frequency shift of the Rayleigh channel)

使用方法如下： --------------------------------- hcmenu1= uicontextmenu uimenu（hcmenu1,...) uimenu（hcmenu1,...) showcontextmenu（hObj，hcmenu1） --------------------------------- 尝试使用实例test_showcontextmenu.m。 这也包含了日常琐碎的一些功能： getParentFigure（hObject） ％的投资回报hObject的父母图 getPositionOnFigure（hObject，单位） ％返回对象的绝对位置上的数字 getRelPosition（hObject，单位） ％的人能（hObject， 定位 ）与特定的 单位 (Use as follows: --------------------------------- hcmenu1 = uicontextmenu uimenu(hcmenu1,...) uimenu(hcmenu1,...) showcontextmenu(hObj,hcmenu1) --------------------------------- Try test_showcontextmenu.m for usage example. This also contains a number of trivial routine functions: getParentFigure(hObject) returns hObject s parent figure getPositionOnFigure( hObject,units ) returns absolute position of object on a figure getRelPosition( hObject,units ) get(hObject, Position ) with specific units )