EVMDM6437_TechRef

Adaptive median filter.

数字图像处理中的有关频域滤波的代码，希望对大家能有所帮助 matlab(Digital image processing in the frequency domain filtering code, and hope we can help matlab)

这个程序是用SLM算法抑制峰均功率比的matlab仿真程序，对100000个OFDM符号进行统计然后表征算法对PAPR的改进程度，绝对可以运行(this process is inhibited by SLM algorithm PAPR of Matlab simulation program, to 100,000 OFDM symbols statistical algorithm then characterization of the degree of the improvement PAPR, absolutely can run)

验证时域采样定理,奈奎斯特定律,奈奎斯特频率(The time domain sampling theorem)

把辨识分成两步进行：第一步：利用相关分析法获得对象的非参数模型（脉冲响应或相关函数）；第二步：利用最小二乘法、辅助变量法或增广最小二乘法等，进一步求的对象的参数模型。如果模型噪声与输入无关，则Cor-ls相关最小二乘法（二步法）可以得到较好的辨识结果。Cor-ls相关最小二乘法（二步法）实质上是先对数据进行一次相关分析，滤除了有色噪声的影响，再利用最小二乘法必然就会改善辨识结果。能适应较宽广的噪声范围，计算量不大，初始值对辨识结果影响较小。但要求输入信号与噪声不相关(The identification is divided into two steps: the first step: using the correlation analysis method for object nonparametric model (impulse response or related function) The second step: using the least squares method, auxiliary variable method or augmented least squares method and so on, further for the parameters of the object model. If the model input noise and independent, the Cor- ls related least squares (two footwork) can get good recognition results. Cor- ls related least squares (two footwork) is essentially the data to a correlation analysis, filter in addition to the influence of colored noise, using least squares inevitable will improve the identification result. To adapt to a wide range of noise and calculation is not big, the initial value to identification results less effect. But for the input signal and noise are not related)

On the Convergence of a Class of Estimation of Distribution Algorithms(On the Convergence of a Class of Estimation Distribution of Algorithms)

this code is for prison problem

使用时域有限差分法编写的计算散射截面的简单程序(Scattering cross section is calculated using a simple program written in finite difference time domain method)

two path fading(qpsk) 这个是我写的关于qpsk的two path fading,站长帮帮忙,我现在很需要一篇<信道估计算法>(基于瑞利衰落信道的LS算法的Matlab程序),后天就要交了,救人一命胜造7级浮屠啊!(two path fading (qpsk) This is what I wrote on the two p qpsk ath fading, the station help out, I am now a need)

MWC sub nyquist sampling Compressive sensing