Final-Project--Codeareport1

此程序是关于经济学中布莱克斯科尔斯模型 的matlab实现(This procedure creates a three-dimensional plot showing how gamma changes relative to price for a Black-Scholes option. Recall that gamma is the second derivative of the option price relative to the underlying security price. The plot shows a three-dimensional surface whose z-value is the gamma of an option as price (x-axis) and time (y-axis) vary. It adds yet a fourth dimension by showing option delta (the first derivative of option price to security price) as the color of the surface)

INface toolbox for illumination invariant face recognition

说明：  LDPC编码经过AWGN信道后的高斯消元法的译码过程的MATLAB仿真(LDPC codes through AWGN channel Gaussian elimination method, after the decoding process of MATLAB simulation)

sp CURRENT SOURCE INVERTER

P wave q wave r wave location detection

QPSK for AWGN and Rayleign fading channel example

无人驾驶汽车模型预测控制的算例代码，非常实用。(The code of the model predictive control of the unmanned vehicle)

实现多小波变换的MATLAB工具箱库函数(multi-wavelet transform MATLAB Toolbox library functions)

Matlab Simulink modal for Stiction ( Data Driven Model)

ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。 基本思想： 在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。 (Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach. Basic idea: The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions. After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour. Literature: - Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)