matlab_jiemi_code

ABC到dq坐标的转换，独立模块，可以单独调用，在MATLAB2010下运行，很好使(ABC to dq coordinate conversion, independent modules that can be called on in MATLAB2010 run, so good)

Unscented Kalman filter and Kalman Filter

一本介绍matlab GUI编程的书，里面包括了各种控件的编程，比如菜单、编辑框、按钮、列表框等。(An introduction matlab GUI programming books, which include a variety of control programming, such as menus, edit boxes, buttons, list boxes and so on.)

利用频域并行的方法对GPS的C码捕获的方法进行了仿真。直接解压就可以了。(frequency domain using the method of parallel GPS C code acquisition of the simulation methods. Decompression directly on it.)

医学影像处理，图像处理相关的图像处理学习指导(Medical Image Processing, Image Processing Image Processing related study guide)

8psk的蒙特卡洛仿真8psk的蒙特卡洛仿真(8psk Monte Carlo simulation with matlab)

说明：  离散信号MATLAB分析的好程序！分析细致，有助于学习！频谱细化(MATLAB analysis of discrete signal a good program! Detailed analysis will help to learn! Spectrum refinement)

Gaussian noise refers to its obey gaussian probability density function (i.e., normal distribution) of the noise. If a noise, its amplitude distribution obeys the gaussian distribution, and its power spectral density is uniformly distributed, has described it as a gaussian white noise. Were not associated with the second order moments of the gaussian white noise, a moment is constant, 所谓高斯噪声是指它的概率密度函数服从高斯分布（即正态分布）的一类噪声。如果一个噪声，它的幅度分布服从高斯分布，而它的功率谱密度又是均匀分布的，则称它为高斯白噪声。高斯白噪声的二阶矩不相关，一阶矩为常数，是指先后信号在时间上的相关性。高斯白噪声包括热噪声和散粒噪声。 (Gaussian noise refers to its obey gaussian probability density function (i.e., normal distribution) of the noise. If a noise, its amplitude distribution obeys the gaussian distribution, and its power spectral density is uniformly distributed, has described it as a gaussian white noise. Were not associated with the second order moments of the gaussian white noise, a moment is constant, refers to the correlation of signal in time. White gaussian noise including thermal noise and shot noise. )

Retrieve the character data from the second child of the first employee. T.

一个主成分分析方法的matlab实现，比较经典齐全的源码(A matlab Implementation of principal component analysis)