view2dm123

fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b) with parameters: u,b format: result = fit_ML_laplace( x,hAx ) input: x - vector, samples with laplace distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields u,b - fitted parameters CRB_b - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

Compressive Sampling是最近讨论非常热烈的数据处理方法。用一个与变换基不相关的观测矩阵将变换所得到的高维信号投影到一个低维空间上，然后通过优化求解从这些少量的投影中以高概率重构出原信号。(Compressive Sampling is a very lively discussion of recent data processing method. A base with the transformation matrix will not change the relevant observation obtained a high-dimensional signal projected onto low-dimensional space, and then solved by optimizing the projection from these small reconstructed with high probability in the original signal.)

给出具体的电源规划问题解决方案，采用matlab程序语言进行编写。(The specific solution of power planning problem is given, and the program language of MATLAB is used to compile it.)

经典的基于短时能量和短时平均过零率的双门限检测方法(Classic double threshold detection method based on short-time energy and short-time average zero crossing rate)

基于PSO（粒子群算法）的列车速度优化，matlab实现。(Train speed optimization based on PSO (particle swarm optimization) is realized by matlab.)

Differential Evolution for optimal power flow

how to deploy nodes for energy efficient protocol based on hierarchical routing

这是matlab的一些程序希望对大家有用，谢谢了(This is some of the procedures of the matlab hope that useful, thank you)

根据Lucas的论文建立偏微分的模型 并且求解PDE 该PDEs描述了固液分离的萃取过程(Lucas papers, the partial differential model and solve the PDE PDEs to describe the extraction process of the solid-liquid separation of)

最大似然估计 以最快的速度实现参数估计准确率极高(Maximum likelihood estimation with the fastest speed to achieve a high accuracy rate parameter estimation)