搜索资源列表
HHT-Transform-Toolbox
- matlab信号分析工具箱 HHT EMD 非常好的数字信号处理工具-matlab signal analysis toolbox HHT EMD very good digital signal processing tools
emdinmatlab
- 摘要:经验模态分解方法(EMD )在非平稳信号的分析和处理中起着重要的作用 ,为了能够方便的使用EMD方法对信号进行处理 ,现labVIEW 虚拟仪器开发平台良好的用户图形界面和TLAB 软件强大的数值分析功能相结合 ,利用 Lab V IEW调用 MATLAB实现EMD信号处理方法.仿真结果表明对信号进行EMD分解后,使得瞬时频率具有了物理意义,但只是对信号进行了初步处理 ,可根据实际需要进行相应后续处理 。-Abstract: The empirical mode decomposition
emdfenjie
- 对采集到的振动信号进行EMD分解，得到一系列频率不同的经验模态函数IMF分量，可以用来滤波和提取原信号的特征。-The collected vibration signals of EMD, get a series of different frequency components of IMF empirical mode function can be used to filter and extract features of the original signal.
High_orderandEMD
- High_orderandEMD.rar包函数了高阶累计量的所所以求法，EMD经验模态分量等算法，是用来分析非平稳随变量信号的最常用的好方法，在此献给信号处理的研究者们。-High_orderandEMD.rar package cumulative amount of higher-order functions are therefore seeking method, EMD empirical mode component of the algorithm is a good metho
ECG-denoise
- ECG质量好心电图是由医生解释利用和识别的生理和病理现象。然而，在实际生活中，心电图记录往往是由文物损坏几个方法已被开发用于ECG增强。在本文中，我们提出了一种基于最近一个新的心电图增强方法开发经验模式分解（EMD）。提出基于EMD方法能够消除双方的高频噪声和BW 以最小的信号失真-Good quality electrocardiogram ECG interpretation by a physician use and identification of physiological and
eemd
- 本代码是eemd的原始代码，关于emd的注释已经很多，eemd无非是多次emd取平均，需要的自己拿去-This code is eemd original code has a lot of comments about emd, eemd is nothing more than taking multiple emd average, take their own needs
boundary_conditions_averextr
- boundary_conditions_isbm emd等模态分解用的边界条件处理-boundary_conditions_isbm ： boundary conditions for handling emd..mode destruction
boundary_conditions_charalen
- boundary_conditions ： boundary conditions for handling emd..mode destruction-boundary_condition ： boundary conditions for handling emd..mode destruction
EMD_TOOLBOX-pack_emd
- emd tool,由著名的patrick flandrin编译-emd tool, patrick flandrin compilation
HHT2
- Hilbert变换只是单纯地求信号的瞬时振幅，频率和相位，有可能出现没有意义的负频率；HHT变换先将信号进行EMD分解，得到的是各个不同尺度的分量，对每一个分量进行Hilbert变换后得到的是有实际意义的瞬时频率。 -imagesc(t,fliplr((1:size(im,1))/(2*size(im,1))),im,[inf,0]) set(gca, YDir , normal ) xlabel([ time ]) ylabel([ normalized f
Similarity
- 研究两幅图像匹配相似度衡量的方法，利用直方图的相关知识，采用相关，卡方，直方图相交,Bhattacharyyahe和EMD方法实现对两幅图像的相似度衡量。-Study two images matching similarity measure method, using the histogram of the relevant knowledge, using relevant, chi-square, histogram intersection, Bhattacharyyahe and
eemd
- 该程序主要完成了EMD算法的实现，并附有例子程序进行测试，有助于初学者了解该算法-The program is mainly done to realize EMD algorithm, together with examples of procedures for testing, to help beginners understand the algorithm
EMDs
- 对信号进行emd处理,分解得到若干固有模态函数-Emd the signal processing, decomposition of certain intrinsic mode functions
prediction-methods-for-hydrology
- 径流预报常用的几种模型：AR模型，BP模型，RBF模型，GM(1,N)模型；预报数据预处理方法：自相关函数以及偏自相关函数确定法；EMD方法-Several commonly used runoff forecasting model: AR model, BP model, RBF model, GM (1, N) model forecast data preprocessing methods: autocorrelation function and partial autocorre
package_emd
- EMD对图像进行处理，与传统的小波去噪进行比较具有明显优势-EMD for image processing, and compares traditional wavelet de-noising has obvious advantages
81278654emdppt
- emd分解运用PPT进行展示，便于初学者能够更加容易理解其过程-PPT presentation using EMD decomposition process, easy for beginners to understand the process
pint_2014_Applied-Acoustics
- The main problem in dustrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sour
RidgeEEMD
- R-EEMD facilitates nonuniform and trial-dependent weights obtained by an optimization procedure during ensemble combination and results in less decomposition errors when compared with the conventional ensemble empirical mode decomposition tec
Vibration-Monitoring
- The HHT represents a time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instan eous frequency components within the signal through an Empirical Mode Decomposition (EMD) process. The analytical background of t
emd-intrduction
- 法国人的翻译：经验模态分解和算法,很好的说明文档-French translation: empirical mode decomposition and algorithms, good documentation