publication . Article . Other literature type . Preprint . 2015

Introducing libeemd: a program package for performing the ensemble empirical mode decomposition

Esa Räsänen; Jouni Helske; P. J. J. Luukko;
Open Access English
  • Published: 12 Jul 2015
  • Publisher: Springer
Abstract
Comment: The final publication is available at Springer via https://dx.doi.org/10.1007/s00180-015-0603-9
Subjects
free text keywords: Hilbert–Huang transform, intrinsic mode function, time series analysis, adaptive data analysis, noise-assisted data analysis, detrending, Hilbert-Huang transform; Intrinsic mode function; Time series analysis; Adaptive data analysis; Noise-assisted data analysis; Detrending, Probability Theory and Statistics, Sannolikhetsteori och statistik, Statistics, Probability and Uncertainty, Statistics and Probability, Computational Mathematics, Statistics - Computation
Funded by
EC| CRONOS
Project
CRONOS
Time dynamics and ContROl in naNOStructures for magnetic recording and energy applications
  • Funder: European Commission (EC)
  • Project Code: 280879
  • Funding stream: FP7 | SP1 | NMP
26 references, page 1 of 2

Bowman DC, Lees JM (2014) The Hilbert-Huang Transform: Tools and Methods. R version 3.1.0 (2014-04-10)

Chang LW, Lo MT, Anssari N, Hsu KH, Huang N, Hwu WM (2011) Parallel implementation of multi-dimensional ensemble empirical mode decomposition. In: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pp 1621{1624

Chen Q, Huang N, Riemenschneider S, Xu Y (2006) A B-spline approach for empirical mode decompositions. Adv Comput Math 24:171{195

Colominas MA, Schlotthauer G, Torres ME, Flandrin P (2012) Noise-assisted EMD methods in action. Adv Adapt Data Anal 04:1250,025

Datig M, Schlurmann T (2004) Performance and limitations of the Hilbert{ Huang transformation (HHT) with an application to irregular water waves. Ocean Eng 31:1783{1834 [OpenAIRE]

Eddelbuettel D (2013) Seamless R and C++ Integration with Rcpp. Springer, New York [OpenAIRE]

Eddelbuettel D, Francois R (2011) Rcpp: Seamless R and C++ integration. Journal of Statistical Software 40(8):1{18, URL http://www.jstatsoft. org/v40/i08/

Engeln-Mullges G, Uhlig F (1996) Numerical Algorithms With C. SpringerVerlag Berlin Heidelberg

Galassi M, Davies J, Theiler J, Gough B, Jungman G, Alken P, Booth M, Rossi F (2009) GNU Scienti c Library Reference Manual. Network Theory Limited, UK, URL http://www.gnu.org/software/gsl/

Huang H, Pan J (2006) Speech pitch determination based on Hilbert-Huang transform. Signal Process 86:792{803

Huang N, Shen S (2005) The Hilbert-Huang transform and its applications. Interdisciplinary Mathematical Sciences, World Scienti c Publishing Company, Inc.

Huang NE, Wu Z (2008) A review on Hilbert-Huang transform: method and its applications to geophysical studies. Rev Geophys 46

Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc Lond A Mat 454:903{995

Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: The Hilbert spectrum. Annu Rev Fluid Mech 31:417{457

Huang NE, Wu MLC, Long SR, Shen SSP, Qu W, Gloersen P, Fan KL (2003) A con dence limit for the empirical mode decomposition and Hilbert spectral analysis. P Roy Soc Lond A Mat 459:2317{2345

26 references, page 1 of 2
Abstract
Comment: The final publication is available at Springer via https://dx.doi.org/10.1007/s00180-015-0603-9
Subjects
free text keywords: Hilbert–Huang transform, intrinsic mode function, time series analysis, adaptive data analysis, noise-assisted data analysis, detrending, Hilbert-Huang transform; Intrinsic mode function; Time series analysis; Adaptive data analysis; Noise-assisted data analysis; Detrending, Probability Theory and Statistics, Sannolikhetsteori och statistik, Statistics, Probability and Uncertainty, Statistics and Probability, Computational Mathematics, Statistics - Computation
Funded by
EC| CRONOS
Project
CRONOS
Time dynamics and ContROl in naNOStructures for magnetic recording and energy applications
  • Funder: European Commission (EC)
  • Project Code: 280879
  • Funding stream: FP7 | SP1 | NMP
26 references, page 1 of 2

Bowman DC, Lees JM (2014) The Hilbert-Huang Transform: Tools and Methods. R version 3.1.0 (2014-04-10)

Chang LW, Lo MT, Anssari N, Hsu KH, Huang N, Hwu WM (2011) Parallel implementation of multi-dimensional ensemble empirical mode decomposition. In: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pp 1621{1624

Chen Q, Huang N, Riemenschneider S, Xu Y (2006) A B-spline approach for empirical mode decompositions. Adv Comput Math 24:171{195

Colominas MA, Schlotthauer G, Torres ME, Flandrin P (2012) Noise-assisted EMD methods in action. Adv Adapt Data Anal 04:1250,025

Datig M, Schlurmann T (2004) Performance and limitations of the Hilbert{ Huang transformation (HHT) with an application to irregular water waves. Ocean Eng 31:1783{1834 [OpenAIRE]

Eddelbuettel D (2013) Seamless R and C++ Integration with Rcpp. Springer, New York [OpenAIRE]

Eddelbuettel D, Francois R (2011) Rcpp: Seamless R and C++ integration. Journal of Statistical Software 40(8):1{18, URL http://www.jstatsoft. org/v40/i08/

Engeln-Mullges G, Uhlig F (1996) Numerical Algorithms With C. SpringerVerlag Berlin Heidelberg

Galassi M, Davies J, Theiler J, Gough B, Jungman G, Alken P, Booth M, Rossi F (2009) GNU Scienti c Library Reference Manual. Network Theory Limited, UK, URL http://www.gnu.org/software/gsl/

Huang H, Pan J (2006) Speech pitch determination based on Hilbert-Huang transform. Signal Process 86:792{803

Huang N, Shen S (2005) The Hilbert-Huang transform and its applications. Interdisciplinary Mathematical Sciences, World Scienti c Publishing Company, Inc.

Huang NE, Wu Z (2008) A review on Hilbert-Huang transform: method and its applications to geophysical studies. Rev Geophys 46

Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc Lond A Mat 454:903{995

Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: The Hilbert spectrum. Annu Rev Fluid Mech 31:417{457

Huang NE, Wu MLC, Long SR, Shen SSP, Qu W, Gloersen P, Fan KL (2003) A con dence limit for the empirical mode decomposition and Hilbert spectral analysis. P Roy Soc Lond A Mat 459:2317{2345

26 references, page 1 of 2
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