level_set_interp

本书主要是介绍了matlab程序设计的教案。(This book mainly introduces the matlab program to design the curriculum.)

建立了Buck-Boost降压-升压斩波电路仿真的模型。(Buck-Boost buck- boost chopper circuit simulation model.)

Subject of this thesis is elemental description of DVB digital television and digitalization process of its terrestrial transmission according to DVB-T standard. The thesis characterizes in detail RF transmission channels used for wireless distribute of television. These channels ale mathematically described and their effect to OFDM signal is simulated in the MATLAB environment.

说明：  本程序一维数据的EM算法实现，稍作修改就可以成为多维数据的EM算法实现。(EM algorithm for one-dimensional data of the program implementation, minor modifications can become the cube of the EM algorithm.)

说明：  计算全息中的非聂耳全息的数字全息图构建与重现(CGH in the number of non-Nie Er holographic hologram construction and reproduce)

这是matlalb控制系统仿真域实例一书中的各章节代码，有需要的朋友欢迎下载。(Control System Simulation with MATLAB example explanation (with the book CD-ROM) code sections)

How to image processing using matlab: load image, BW, edge detection, noise remover, sharpening by any method by simple code (lp2m aray jakarta)

matlab解决多参数优化问题，已经调通过，可以直接使用。并且目标函数有微分构成。(matlab to solve the multi-parameter optimization problems, have been transferred through, can be used directly. And the objective function differential constitute.)

检测概率PD与输入信噪比r的关系（理论曲线） 采样点数：N=100；噪声方差：sigma^2=1 ( Probability of detection PD relationship with the input signal to noise ratio of r (theoretical curves) sampling points: N = 100 noise variance: sigma ^ 2 = 1 )

说明：  本设计为基于MATLAB特征点匹配的指纹识别系统。带有一个GUI界面。主要对指纹图像进行三方面处理：图像预处理、特征提取和特征匹配。图像预处理包括四个步骤：图像分割、滤波增强、二值化、细化，对指纹图像进行预处理后，去除了原图像的冗余部分，方便后续的识别处理；特征提取主要是提取指纹图像细化后的端点和分叉点；特征匹配是利用两个指纹的图像进行特征点比较，来确定两幅图像是否来自于同一手指。(This design is a fingerprint recognition system based on MATLAB feature point matching. With a GUI interface. Fingerprint image is mainly processed in three aspects: image preprocessing, feature extraction and feature matching. Image preprocessing includes four steps: image segmentation, filter enhancement, binarization and thinning. After preprocessing the fingerprint image, the redundant part of the original image is removed to facilitate the subsequent identification processing; feature extraction is mainly to extract the refined endpoint and bifurcation point of fingerprint image; feature matching is to determine the two images by comparing the feature points of two fingerprint images Is it from the same finger.)