conj

说明：  利用python进行因子分析,因子分析可以进行降维!(Using Python factor analysis, factor analysis can reduce dimensionality!)

计算离散傅里叶变换的一种快速算法，简称FFT。快速傅里叶变换是1965年由J.W.库利和T.W.图基提出的。采用这种算法能使计算机计算离散傅里叶变换所需要的乘法次数大为减少，特别是被变换的抽样点数N越多，FFT算法计算量的节省就越显著。(A computing discrete Fourier transform fast algorithm, referred to as FFT. Fast Fourier Transform in 1965 by the JW Cooley and TW Tuji Ti out. Using this algorithm enables a computer to calculate the number of multiplications needed for the discrete Fourier transform greatly reduced, especially sampling points N is transformed more and save FFT algorithm to calculate the amount of the more remarkable.)

艾特金迭代法求解线性方程组源代码，在程序中修改方程就可以(Aitkin iterative method for solving linear equations source code, the equation can be modified in the program)

这个windows下可视化晶体结构的软件,可用于构造晶体,界面,表面...对于做第一性原理初始模型的构建以及结果的可视化分析特别有用处.(Under the windows crystal structure visualization software can be used for the crystal structure, interface, surface ... do first principles for the construction of the initial model and the results of visual analysis is particularly useful.)

贝塞尔函数与汉克尔函数的Matlab实现(Bessel function and Hankel function of the Matlab implementation)

通过不精确的ALM算法，解析出张量矩阵中的低秩部分和稀疏部分。(via the Inexact ALM method will solve the programming.)

本程序实现了基于最佳维纳滤波器的盲解卷积算法。维纳滤波将地震子波转换为任意所需要的形态。例如，像反滤波一样，可以设计维纳滤波器将地震子波转换为尖脉冲。但维纳滤波不同于反滤波，它是在最小平方的意义上为最 佳。基于最佳纳滤波理论的滤波器算法是人们熟知的维纳一莱文逊(Wiener—Levinson)算法。本程序提供了五种期望输出：零延迟尖脉冲；任一延迟尖脉冲；时间提前了的输入序列；零相位子波；任意期望波形。(Wiener—Levinson algorithm used in wiener filter. matlab with detailed note in Chinese.)

该程序为全向比幅法测角程序，并给出了测角误差分析(The program is omnidirectional amplitude ratio angle measuring program, and the angle error analysis is given)

开关电源电感计算案例，buck降压斩波输出电感的参数计算和选型(inductor parameter calculation of switching power supply)

hartigans Sequential Leader Clustering Algorithm in terms of OpenCV (ver.1.1) Sequential Leader algorithm: Hartigan, J. A. (1975), Clustering Algorithms. John Wiley and Sons, Inc., New York, NY. 1. Select maximum cluster "radius" 2. Start with zero clusters 3. Add each item to be clustered to: * Closest cluster if distance <= r * New cluster if distance to closest cluster > r 4. Compute new center from members in cluster 5. Empty the clusters (keeping the centers) 6. Return to step 3 (until no changes are made)