apFFT_Spectrum_Est

利用重力剖面反演水平圆柱的边界及位置，程序抗干扰能力较差，模型计算加入噪音严重影响结果，但可作为算法参考并加以改进。(Tilt-Depth for gravity profile over horizontal cylinder)

Power Factor Improvement of Induction Motor by Using Capacitors Power Factor Improvement of Induction Motor by Using Capacitors

基于hough方法的航迹起始matlab算法(Track initiation method based hough matlab algorithm)

针对MUSIC算法在进行信号波达方向(DOA)估计时谱峰搜索运算量大的问题,提出了一种基于FFT多波束算法与MUSIC算法的联合DOA估计方法。该方法采用FFT多波束算法形成多个波束,近似估计信号DOA,获得对应波束指向,再利用MUSIC算法实现对信号DOA的精确估计。这样就只需在该波束指向对应的空域范围内搜索,减小了DOA估计的运算量。(MUSIC algorithm for performing signal DOA (DOA) estimates peak search operation when big problem, a joint DOA estimation method based on FFT algorithm and multi-beam MUSIC algorithm. The method uses FFT algorithm for multi-beam forming multiple beams, approximate estimate signal DOA, obtain the corresponding beam pointing, reuse MUSIC algorithm for accurate estimation of the signal DOA. This will only search within the beam direction corresponding airspace, reducing the amount of computation DOA estimates.)

this code shows the new filter application ........................................

2nd order Bezier 3d in matlab

一个计算用的matlab小程序，希望大家喜欢，谢谢(A calculation of the matlab small program I hope you like it, thank you)

可以用来方便的进行输入信号的Hilbert-Huang变换，经过了验证，可以使用(Used to implement the input signal of the Hilbert-Huang transform. First proposed calculation method, after validation, can be used.)

模式分类英文版，非常好的machine learning课本(pattern classification， very useful for machine learning.)

克里金法广泛地应用于地下水模拟、土壤制图等领域，是一种很有用的地质统计格网化方法。它首先考虑的是空间属性在空间位置上的变异分布．确定对一个待插点值有影响的距离范围，然后用此范围内的采样点来估计待插点的属性值。该方法在数学上可对所研究的对象提供一种最佳线性无偏估计(某点处的确定值)的方法。它是考虑了信息样品的形状、大小及与待估计块段相互间的空间位置等几何特征以及品位的空间结构之后，为达到线性、无偏和最小估计方差的估计，而对每一个样品赋与一定的系数，最后　进行加权平均来估计块段品位的方法。(Kriging is a technique that provides the Best Linear Unbiased Estimator of the unknown fields (Journel and Huijbregts, 1978 Kitanidis, 1997). It is a local estimator that can provide the interpolation and extrapolation of the originally sparsely sampled data that are assumed to be reasonably characterized by the Intrinsic Statistical Model (ISM). An ISM does not require the quantity of interest to be stationary, i.e. its mean and standard deviation are independent of position, but rather that its covariance function depends on the separation of two data points)