Matlab-for-finite-time-program

fo-li迭代算法求解条形腔的模场，利用matlab编程实现(iterative algorithm fo-li strip cavity mode field, the use of matlab programming)

KMV方法计算企业资产和资产波动率 参数说明： E:股权市值 D:负债总额 r：无风险利率 T:企业债期限 EquityTheta：股价波动率

利用差分进化算法（Differential Evolution）实现对经典函数girewangk的验证(The use of differential evolution algorithm (Differential Evolution) to achieve the verification of classical function girewangk)

this code to generate parity check matrix of Low density parity check cod, and encode in order to codeword

LevelSet toolbox by UCSB 提供了水平集计算轮廓的功能(LevelSet toolbox by UCSB provides a level set function of the computation of contour)

说明：  总体最小二乘法的ESPRIT方法，这种算法的估计精度明显好于原来的ESPRIT算法。可以很好的估计出信号的角度。仿真中用的是均匀阵列。(Total least squares method of the ESPRIT method, the algorithm of the estimation accuracy significantly better than the original ESPRIT algorithm. Can be a very good signal to estimate the angle. Simulation using the uniform array.)

非常实用的小波包分解程序，以及实现对小波包分解频带求取频谱和能量谱(Very useful wavelet packet decomposition process, and the realization of the wavelet packet decomposition to obtain the spectrum and the energy spectrum band)

We present a study of a matrix pencil method for esti- mating parameters (frequencies and damping factors) of exponentially damped and/or undamped sinusoids in noise. Comparison of this method to a polynomial method (SVD-Prony method) shows that the matrix pencil method and the polynomial method are two special cases of a matrix prediction approach but the pencil method is more efficient in computation and less restrictive about signal poles. It is found through perturbation analysis and simulation that, for signals with un- known damping factors, the pencil method is less sensitive to noise than the polynomial method. In Appendix A, a new expression of the Cra- mer-Ran bound for the exponential signals is presented.

Principal component analysis for a voice signal

多元线性回归模型的一般形式 参数估计（ OLS估计） 假设检验 预测 (The general form of the multiple linear regression model parameter estimation (OLS estimates) hypothesis testing prediction)