examples-5

this project is my first project. this is simple game .

图片播放软件，含翻页，自动播放，播放速度控制，旋转（任意角度），放大，缩小等(Photo playback software, including flip, auto play, playback speed control, rotation (any angle), zoom, etc.)

说明：  该代码是一个基于MATLAB语言编辑的四面体有限元长应变四面体算法，包含一个例子很实用，性能稳定。感谢大家的分享。(This code is a kind of constrain Tetrahedral fem compution on MATLAB。there are total code and an example in cube。It is very practical and stable。thank you for your exchanging。 )

说明：  这个源码提供了如何利用高斯混合模型去做影像辨识的模型分类(the source how to use a Gaussian mixture model to do imaging Identification Classification Model)

高斯背景建模源代码，实现背景建模，但前景处理效果不好(Gaussian background modeling source code to achieve the background modeling, but the outlook is not good treatment effect)

应用MATLAB工具箱在VC++中实现神经网络的程序设计(MATLAB toolbox application in VC++ in the realization of neural network programming)

一种流形学习算法（很好用），包括主成分分析、因子分析、贝叶斯分析，包含收发两个客户端的链路级通信程序。( A fluid manifold learning algorithm (good use), Including principal component analysis, factor analysis, Bayesian analysis, Contains two clients receive link-level communications program.)

Bloom效果(附代码), 很酷的一个效果，效率不算高。互相交流一下。看看吧！(Bloom effect (with code), a cool effect, efficiency is not high. Exchange about. Let' s see it!)

基于TMS320C54x 实现πP4- DQPSK 调制 信息产业部电子第十研究所(Based on the TMS320C54x implementation πP4-DQPSK modulation 10th Electronics Institute of the Ministry of Information Industry)

这篇文章是盲信号处理，因为是英文文章，所以大家看内容就好了。(Principal component analysis (PCA) is a mainstay of modern data analysis- a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.)