PotentialFlows

％ BackgroundRemoval=[true],false ％ Gain=[tsquare],linear ％ BandPass=[paul],fircls ％ CenterFrequency, auto (determined using pburg) ％ BandWidth=auto (a fraction of the CenterFrequency default=0.25) ％ ContrastStretch=[true],false ％ HilbertAmplitude=[true],false ％ HorizontalStacking=1 (a number of traces) ％( BackgroundRemoval = [true], false Gain = [tsquare], linear BandPass = [paul], fircls CenterFrequency, auto (determined using pburg) BandWidth = auto (a fraction of the CenterFrequency default = 0.25) ContrastStretch = [true], false HilbertAmplitude = [true], false HorizontalStacking = 1 (a number of traces))

解方程，可为相关学习者或者matlab独立编程人员提供参考(Solution of equation for the relevant independent learners or matlab programmers reference)

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运用插值的全矢量有限差分法求解阶跃光波导本征模式以及等效折射率 (Using the interpolation of full vector finite difference method to solve step eigen mode optical waveguide and equivalent refractive index )

LDA简单的小程序，线性判别，用于分类的(LDA simple little program, linear discriminant for classification)

calls all the tests.

说明：  压缩包里包含了无约束优化问题常用的几种求解方法的源程序：变量轮换法（variable_rotation.m）、最速下降法（steepest_descent.m）、修正牛顿法（modified_newton.m）、共轭梯度法（conjugate_gradient.m）。另外，coefficient_matrix.m为目标函数系数获得矩阵，minval.m为最小值计算函数，gradient.m为梯度计算函数(Compression bag contains unconstrained optimization problems of several commonly used method of source: variable rotation Act (variable_rotation.m), steepest descent method (steepest_descent.m), as amended Newton (modified_newton.m), conjugate gradient method (conjugate_gradient.m). In addition, coefficient_matrix.m as the objective function coefficients obtained matrix, minval.m function for the minimum calculation, gradient.m for gradient calculation function)

Driver for receiving and demuxing event-channel signals.

matlab求解非齐次poisson方程的fft快速算法，经测试在自己的机器上能算到对区域进行7000次左右的分段。(matlab fft fast algorithm for solving non-homogeneous poisson equation has been tested on the machine count to about 7000 times the segmented area.)

对KMEANS算法进行matlab实现，有数据支持(On KMEANS algorithm matlab realize, there are data to support)