Results 251 to 260 of about 196,483 (296)

Quadrivariate Empirical Mode Decomposition

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
We introduce a quadrivariate extension of Empirical Mode Decomposition (EMD) algorithm, termed QEMD, as a tool for the time-frequency analysis of nonlinear and non-stationary signals consisting of up to four channels. The local mean estimation of the quadrivariate signal is based on taking real-valued projections of the input in different directions in
Naveed ur Rehman, Danilo P. Mandic
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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, R. T. Rato, J. P. Matos-Carvalho, José Manuel Fonseca, André Mora   +4 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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Multivariate empirical mode decomposition

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2009
Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis.
Rehman, N., Mandic, D. P.
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Shunt sound decomposition by empirical mode decomposition

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020
Unpredictable disequilibrium syndrome and blood pressure fluctuations can occur during hemodialysis therapy. We have proposed a method for analyzing shunt sounds using EMD to predict these symptoms. The shunt sound is the sound of turbulent blood flow generated in the shunt, which can be measured from the puncture needle of the dialyzer.
Yuki Otake, Osamu Sakata
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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
R. Faltermeier, A. Zeiler, I. R. Keck, A. M. Tome, A. Braw, E. W. Lang   +5 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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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, Lei Xie, Hongye Su   +4 more
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An Improved Empirical Mode Decomposition

2009 2nd International Congress on Image and Signal Processing, 2009
Based on a detailed discussion regarding the attributes of so-called ride waves and the shortages of the classical empirical mode decomposition, this paper presents an improved method. A key point of the proposed method is that the ride waves in a signal should been first removed as independent components before the classical empirical mode ...
Zhihua Yang, Lihua Yang
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Extremum mean empirical mode decomposition

2012 5th International Congress on Image and Signal Processing, 2012
Empirical mode decomposition (EMD) is a data driven processing algorithm, which applies no predetermined filter. It is able to perfectly analyze the nonlinear and nonstationary signals. In EMD decomposition processing, the envelopes are computed by spline interpolation, which is time-consuming.
JianJia Pan, YuanYan Tang
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