embed-rovar

用matlab实现动画的制作，三维、二维制图，图像处理。。。。(Animation, drawing. . .)

matlab使用程序教程，包括四部分：1-32是：图形应用篇；33-66是：界面设计篇；67-84是：图形处理篇；85-100是：数值分析篇(matlab tutorial to use the program, including the four-part :1-32 is: graphics applications, articles 33-66 is: interface design articles 67-84 is: graphics processing articles 85-100 are: Numerical Analysis Section)

该算法建立了格斗空战中本机和目标在进行迎头攻击时的空战三维视图。(The algorithm builds a three-dimensional view of aerial combat air combat aircraft and the target of attacks during head.)

stateflow九州恒润培训教材，重量级资料(stateflow training workshop for you)

matlab 泊肃叶流动 LBM方法，很好用的小程序(LBM matlab Poiseuille flow method, the good little program)

a sample c embedded program for beginners

本人写的中值和均值滤波的代码，主要用于SAR成像滤波，希望管理员给予通过(I wrote the median and mean filtering code, mainly for SAR image filtering, administrators hope to give through)

说明：  有关用MATLAB做GPS星座仿真大家多交流啊相互学习(In connection with the GPS constellation MATLAB simulation you do more exchanges and learn from each other ah)

matlab速度分析程序，用于地震勘探速度分析及动校正（NMO）。(Matlab velocity analysis procedures for seismic velocity analysis and dynamic correction (NMO).)

EMD的Toolbox及使用方法 经验模态分解（Empirical Mode Decomposition, 简称EMD）是由美国NASA的黄锷博士提出的一种信号分析方法.它依据数据自身的时间尺度特征来进行信号分解, 无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点, EMD 方法在理论上可以应用于任何类型的信号的分解, 因而在处理非平稳及非线性数据上, 具有非常明显的优势。所以, EMD方法一经提出就在不同的工程领域得到了迅速有效的应用, 例如用在海洋、大气、天体观测资料与地震记录分析、机械故障诊断、密频动力系统的阻尼识别以及大型土木工程结构的模态参数识别方面。本工具书主要分以下几个部分：导论、非稳态信号介绍、一维时频信号分解、二维时频信号分解、时频影像信息提取等。 (Time frequency toolbox for use with MATLAB The Time-Frequency Toolbox has been mainly developed under the auspices of the French CNRS (Centre National de la Recherche Scientifique). It results from a research effort conducted within its Groupements de Recherche ”Traitement du Signal et Images” (O. Macchi) and ”Information, Signal et Images” (J.-M. Chassery). Parts of the Toolbox have also been developed at Rice University, when one of the authors (PG) was visiting the Department of Electrical and Computer Engineering, supported by NSF. Supporting institutions are gratefully acknowledged, as well as M. Guglielmi, M. Najim, R. Settineri, R.G. Baraniuk, M. Chausse, D. Roche, E. Chassande-Mottin, O. Michel and P. Abry for their help at different phases of the development.)