Results 251 to 260 of about 197,948 (294)

Flow Empirical Mode Decomposition

2021
Decomposing non-stationary signals using Empirical Mode Decomposition (EMD) highly facilitates signal analyses and processing. According to the original algorithm, EMD decomposes the input signal into useful Intrinsic Mode Functions (IMFs). However, EMD has some drawbacks.
Dário Pedro, Raul Tello Rato, João Pedro Matos-Carvalho, José Manuel Fonseca, André Mora   +4 more
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Online Empirical Mode Decomposition

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
The success of Empirical Mode Decomposition (EMD) resides in its practical approach to dissect non-stationary data. EMD repetitively goes through the entire data span to iteratively extract Intrinsic Mode Functions (IMFs). This approach, however, is not suitable for data stream as the entire data set has to be reconsidered every time a new point is ...
Romain Fontugne, Pierre Borgnat, Patrick Flandrin   +2 more
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Empirical Mode Decomposition - an introduction

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes.
Angela Zeiler, Rupert Faltermeier, Ingo R. Keck, Ana Maria Tomé, Carlos García Puntonet, Elmar Wolfgang Lang   +5 more
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Sliding Empirical Mode Decomposition

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic
Rupert Faltermeier, Angela Zeiler, Ingo R. Keck, Ana Maria Tomé, Alexander Brawanski, Elmar Wolfgang Lang   +5 more
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WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis, 2011
The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely ...
Faltermeier, R, Zeiler, A, Tomé, A. M., Brawanski, A, Lang, E W   +4 more
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NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis, 2010
The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused
Po-Hsiang Tsui, Chien-Cheng Chang, Norden E. Huang   +2 more
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Median ensemble empirical mode decomposition

Signal Processing, 2020
Abstract Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm.
Xun Lang, Naveed ur Rehman, Yufeng Zhang 0002, Lei Xie 0007, Hongye Su   +4 more
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Gap-filling by the empirical mode decomposition

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
We propose a novel gap-filling technique, based on the empirical mode decomposition (EMD). The idea is that a signal with missing data can be decomposed into a set of intrinsic mode functions (IMFs) with missing data. Filling the gaps in each IMF should be easier than filling the gaps in the original signal.
Azadeh Moghtaderi, Pierre Borgnat, Patrick Flandrin   +2 more
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A sampling limit for the empirical mode decomposition

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005., 2006
The aim of this paper is to investigate the effect of sampling on the empirical mode decomposition (EMD). To this end, an experiment utilising linear frequency modulated (LFM) signals was used to simulate different sampling rates. This experiment showed that as the frequency content of the signal (fc) approached the sampling frequency (fs) the EMD ...
Stevenson, Nathan, Mesbah, Mostefa, Boashash, Boualem   +2 more
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Bidimensional Statistical Empirical Mode Decomposition

IEEE Signal Processing Letters, 2012
This letter proposes a new algorithm, termed bidimensional statistical empirical mode decomposition (BSEMD) that adopts a smoothing procedure instead of an interpolation when constructing 2-D upper and lower envelopes. For this purpose, we investigate the sifting process effect of conventional bidimensional empirical mode decomposition (BEMD) on the ...
Donghoh Kim, Minjeong Park, Hee-Seok Oh
openaire   +1 more source