possion

The local binary pattern (LBP) is a simple yet very efficient operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, for example, by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. LBP is also very flexible: it can be easily adapted to different types of problems and used together with other image descriptors

managment of risk : value at risk report and program

说明：  如何在一个方形区域画一个半径为小数的圆，也要求画出方形区域，并指出圆的位置(how to plot a circle and a square,please point the location of the circle)

simulink的简单应用和一些简单的simulink通信方面的仿真实例,还有一些简单的入门课件(simulink)

This file help us how to plot CDF Function which used to plot PAPR of OFDM system. It use Matlab to run.

一，二，三次拟合，并得到R方的检验。输入任何矩阵，得出险要的值。(One, two, three fitting and get R-square test. Enter any matrix derived advantageous values.)

This Program is Simulink for the characterstics idetification of the designed Solar Cell Models

compensation of dead time effect of the inverter dead time effect causes the impact of the control performance of the INVERTER system, and pointed out that the dead time compensation, the key lies in the current phase of the acquisition, in order to overcome the practical zero of the current ambiguity of the system is proposed based on the two-phase stationary coordinate system under the feedforward dead-time compensation method. by the offset voltage of the three-phase output current cycle Fourier transform is found only compensation 1, 5, 7 harmonic components to eliminate the dead time effect. simulation and experimental results verify the correctness and feasibility of this approach.

遗传算法求解非线性规划问题的两个MATLAB源程序(Genetic Algorithm for nonlinear programming problems two MATLAB source)

Classification algorithm based on support vector machine, after test results, well, and achieved very good classification results