extractimf: Intrinsic Mode Function

In EMD: Empirical Mode Decomposition and Hilbert Spectral Analysis

Description

This function extracts intrinsic mode function from given a signal.

Usage

extractimf(residue, tt=NULL, tol=sd(residue)*0.1^2, max.sift=20, 
    stoprule="type1", boundary="periodic", sm="none", spar=NULL, 
    alpha=NULL, check=FALSE, weight=NULL)

Arguments

residue	observation or signal observed at time tt
tt	observation index or time index
tol	tolerance for stopping rule of sifting. If stoprule=type5, the number of iteration for S stoppage criterion.
max.sift	the maximum number of sifting
stoprule	stopping rule of sifting. The type1 stopping rule indicates that absolute values of envelope mean must be less than the user-specified tolerance level in the sense that the local average of upper and lower envelope is zero. The stopping rules type2, type3, type4 and type5 are the stopping rules given by equation (5.5) of Huang et al. (1998), equation (11a), equation (11b) and S stoppage of Huang and Wu (2008), respectively.
boundary	specifies boundary condition from “none", “wave", “symmetric", “periodic" or “evenodd". See Zeng and He (2004) for evenodd boundary condition.
sm	specifies whether envelop is constructed by interpolation, spline smoothing, kernel smoothing, or local polynomial smoothing. Use “none" for interpolation, “spline" for spline smoothing, “kernel" for kernel smoothing, or “locfit" for local polynomial smoothing. See Kim et al. (2012) for detalis.
spar	specifies user-supplied smoothing parameter of spline smoothing, kernel smoothing, or local polynomial smoothing.
alpha	deprecated.
check	specifies whether the sifting process is displayed. If check=TRUE, click the plotting area to start the next step.
weight	deprecated.

Details

This function extracts intrinsic mode function from given a signal.

Value

imf	imf
residue	residue signal after extracting the finest imf from residue
niter	the number of iteration to obtain the imf

References

Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H. (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society London A, 454, 903–995.

Huang, N. E. and Wu, Z. (2008) A review on Hilbert-Huang Transform: Method and its applications to geophysical studies. Reviews of Geophysics, 46, RG2006.

Kim, D., Kim, K.-O. and Oh, H.-S. (2012) Extending the Scope of Empirical Mode Decomposition using Smoothing. EURASIP Journal on Advances in Signal Processing, 2012:168, doi: 10.1186/1687-6180-2012-168.

Zeng, K and He, M.-X. (2004) A simple boundary process technique for empirical mode decomposition. Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium, 6, 4258–4261.

See Also

extrema, emd.

Examples

### Generating a signal
ndata <- 3000
par(mfrow=c(1,1), mar=c(1,1,1,1))
tt2 <- seq(0, 9, length=ndata)
xt2 <- sin(pi * tt2) + sin(2* pi * tt2) + sin(6 * pi * tt2)  + 0.5 * tt2
plot(tt2, xt2, xlab="", ylab="", type="l", axes=FALSE); box()

### Extracting the first IMF by sifting process
tryimf <- extractimf(xt2, tt2, check=FALSE)

Example output

Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.2-1 (2018-12-20) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps
See www.image.ucar.edu/~nychka/Fields for
 a vignette and other supplements. 
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22

EMD documentation built on Jan. 4, 2022, 1:08 a.m.