LDAandPCA

MINIMUM SHIFT KEYING WAVEFORM IN MATLAB

Congnitive radio math wordh

Project Management with the IBM(R) Rational Unified Process

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.)

说明：  运用高斯积分法计算一维二维三维函数的数值积分(Gaussian integration method using two-dimensional three-dimensional function of one-dimensional numerical integration)

基于认知无线电的扩跳频通信信号的调制识别(Hopping spread based on cognitive radio communication signal modulation recognition)

小波神经网络研究的进展及一种新算法，希望可以抛砖引玉(WNN progress of the studies and a new algorithm, hope to start)

Basic MATLAB Training

出一种实现不同维间估计结果自动配对的二维频率估计算法。首先把二维频率估计问题转化成两个矩阵束的特征值问题。根据矩阵束的特征求出两个矩阵束的公共特征向量，并以此为基础同时求出两个矩阵束的特征值。算法估计误差与现有算法相近，但解决了现有算法普遍存在的配对难题。 (Matrix pencils play an important role in numerical linear algebra. The problem of finding the eigenvalues of a pencil is called the generalized eigenvalue problem. The most popular algorithm for this task is the QZ algorithm, which is an implicit version of the QR algorithm to solve the associated eigenvalue problem B − 1Ax = λx without forming explicitly the matrix B − 1A (which could be impossible or ill-conditioned if B is singular or near-singular) )

make parity matrix in matlab