FDTDWGFN

weight-based resampling: an application of multinomial resampling. Also a SIR particle filter

分类和回归的工具箱，很好用，精确度很高，我自己很喜欢这个(Classification and regression of the toolbox, useful, high accuracy, I really like this)

THERISHOLDING ALGORITHM FOR FIGURE DETECTION

chapter 1 of the fdtd method for electromagnetics with matlab simulations

利用相机指纹对相机的来源进行源辨认中参考模式噪声的求取(Reference noise)

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.)

基于子空间方法的运动分割技术研究，包GPCA with spectral clustering，RANSAC Local Subspace Affinity (LSA)，三种方法(Motion segmentation technique based on subspace method, including the GPCA with spectral clustering, RANSAC Local Subspace Affinity (LSA))

说明：  thanks it useful ihope to used

介绍了用MUSIC算法进行DOA估计的原理喝仿真结果(Introduction MUSIC algorithm using the principles of the estimated DOA drink simulation results)

说明：  WCDMA系统的simulink仿真，其他的就不用我多说了~(WCDMA system simulink simulation, the other on the no need for me to say ~)