GCC

Numerical approximation rn of the definite integral from a to b that is obtained with the aid of Romberg s method with n rows and n columns.

本程序实现了一维稳态无源项的对流-扩散方程的数值解法，采用的离散格式包括中心差分，一阶迎风，混合格式与QUICK格式。并画出其与精确解的比较。(This program implements a one-dimensional steady-state convection passive items- diffusion equations numerical solution, a discrete format used include center differential, the first order upwind, mixed format QUICK format. And draw the comparison with the exact solution.)

说明：  HSMM的matlab实现，可用于状态识别和寿命预测(HSMM with matlab ,which can be used for remaining useful life pridection.)

中频数字正交采样，并进行滤波，得出基带信号(IF quadrature sampling and filtering)

motion of charged particle in em field

逆变器仿真程序,模拟AC-DC，需要者可以参考(Inverter simulation, simulation of AC- DC, those can be reference )

matlab应用，一个小波变换的滤波实例应用。(matlab application, a wavelet transform filter example application.)

EMD程序中缺少的EXTR部分程序 接在function后边运行就可以了(EMD program, the lack of EXTR part of the procedures connected to the function behind the run on it)

基于matlab的简单的人脸识别的代码,希望对大家有用(face detection)

这是TD算法的逆过程，首先将时间序列，划分为相邻点的短序列，当然此时的拟合误差为0，因为第一点和第二点的连线，原始点都落在线段上。将相邻两个线段连接起来，此时每条线段包含三个原始点，计算中间那个点的拟合误差。这样，所有这些三个点的线段中的中间点的拟合误差计算出来后，找出误差最小且误差小于阀值R的分段，作为第一条包含三个点的线段。 在上面的基础上，第一条分段同样的和相邻线段连接，然后计算每一条分段的拟合误差，再找出误差最小且小于阀值R的分段，作为第二个分段。依次方式循环，直到所有分段的拟合误差都小于阀值R，分段结束。 (This is the reverse process of TD algorithm, the first time series is divided into short sequences adjacent dots, of course fitting error is zero at this time, because the first and second points of connection, the original point fall line on. The two adjacent segments are connected, then each segment contains three original point, the intermediate fitting error calculated that point. Thus, after all these segments fitting error of three points in the intermediate point calculated to find the minimum error and the error is less than the threshold R segment, as the first line contains three points. Based on the above, the first segment and the adjacent line segment connecting the same, and then calculate a fitting error for each segment, and then find the smallest error and less than the threshold R segment, as the second segment . Followed by a cyclic manner until all segments fitting error is less than the threshold R, segment ends.)