shujufenxinyd

这是一个简单的FFT为无电位1 +1 +1 Ð 薛定谔方程的光束传播方法。如果该软件灵活，允许引入的术语（如果是极少数需要包括色散效应高阶导数）。例如，如果一个人渴望解决的一个方程的形式： （四/ dz的+ Ð ^ 2/dx ^ 2 - 0.25 * Ð ^ 3/dx ^ 3）== 0 Ÿ 它可以解决使用以下代码： Ž = linspace（0,1,512） x = linspace（-5,5,1024） psi0 =进出口（- x的。^ 2） Ð = D_lateral1D（十，2）-0.25 * D_lateral1D（十，3） 字段= LinearBPM（psi0，有D，Z） 凡D_lateral1D是一个函数，提供“取代”的微分算子。 这个函数是在一个非线性的BPM，我曾对（分步，谱方法等），将提交给我，当我完成他们的文件和组织功能更复杂的设置非常初期阶段。 (This is a simple FFT based beam propagation method for potential-free 1+1+1D Schrodinger equation. The software if flexible and allows to introduce high-order derivatives in the term (very handful if is needed to include dispersion effects). For example, if one desires to solve an equation in the form: (d/dz+ d^2/dx^2- 0.25*d^3/dx^3)Y == 0 It can be solved using the following code: z=linspace(0,1,512) x=linspace(-5,5,1024) psi0=exp(-x.^2) D=D_lateral1D(x,2)-0.25*D_lateral1D(x,3) field=LinearBPM(psi0,D,z) Where D_lateral1D is a provided function that "replaces" the derivative operator. This function is a very early stage in a more complex set of non-linear BPM functions that I have made (split-step, spectral methods, etc.) and I will submit them when I finish their documentation and organization. Sorry for my English, I m eager for your comments. )

fft_cos_sin系数生成表，测试用，由他人提供的()

图像重采样程序，可以实现图像任意大小的尺寸调整，采用分片重采样方法(Image resampling process, image of any size can be adjusted, subdivision resampling methods )

说明：  matlab 环境下编的时钟程序代码，业余爱好的(MATLAB，clock)

Improved differential evolution algorithm to optimize the hybrid particle swarm optimization intelligent optimization algorithm source code

说明：   使用二分法对样本集进行剪辑，剪辑法的思想，就是将样本集分成训练集与考试集， 利用训练集样本对考试集的样本进行分类（使用近邻法），如果考试集中某个样本分类错误的话，将这个样本删除。在该函数中，使用最近邻法，只进行一次剪辑（遍历完考试集中的样本以后退出）。还有一种重复剪辑法（适用于样本比较多的情况），把样本随机分为多个样本集，将相邻的两个样本前一个作为考试集，后一个作为训练集，调用二分剪辑。所有的样本子集剪辑完毕以后，在递归调用，直到没有样本被剪辑掉,没得讲，重复剪辑的效果肯定好一些。 (The sample sets using the dichotomy of the clips, editing law idea is to sample set into training set and test set, using the training set samples to the test set of samples (using the nearest neighbor algorithm), if the examination focused on a sample of classification error then delete this sample. In the function, use the nearest neighbor method, only once clips (focused on a sample of traversal finished after the exit exam). There is also a repeat clip method (samples are more applicable to the case), the samples were randomly divided into multiple sample set, the two adjacent samples of the former one as a test set, the latter one as a training set, call the two sub-clips. All the samples were a subset of the clips after they have finished, in the recursive call, until there are no samples were clip out, did not have to say that the effect of repeated clips definitely better.)

关于Arduino书,程序，要点，起步等级(A book about Arduino)

分形方法描述了物体的粗糙度，可以结合实体造型软件使用(fractal interpolated)

itti 1988的实验 ，显著性检测，很好的学习资料(itti 1988 experiment, a significant detection, good learning materials)

计算旅行商问题的一个ＧＵＩ程序，浙江大学的学生设计的～～～～～(Calculation of a GUI program traveling salesman problem)