LowVoltageMicroGride

coursework lecture on climate change

该书包含全面的MATLAB函数及其使用方法，可方便查询使用(The book contains a comprehensive MATLAB functions and their use can be easier to find)

matlab实现的短时过零率，mfcc和lpc源代码语音段都有(matlab to achieve short-term zero crossing rate, mfcc and lpc voice segment has source code)

各种信号预处理方法 导数 平滑 傅里叶变换等等(Derivatives of various signal preprocessing method Fourier transform, and so smooth)

利用粒子群算法对直线阵的权系数进行优化，这里只是对相位进行加权，但是程序有很好的宽展功能(Using particle swarm algorithm of linear arrays of the weight coefficient is optimized )

基于Matlab的LDPC码的研究，讲的很好，可以下载看看那(MATLAB ldpc )

一个基于matlab与vc混合编程小例子,对初学者非常有利(a Matlab-based program mixed with vc small examples, very beneficial for beginners)

Hilbert_huang 变换，计算经验模态函数，求瞬时频率(Hilbert_huang transform to calculate the empirical mode function, find the instantaneous frequency)

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.)

图像处理函数 各类函数的具体使用方法介绍(Matlab)