Results 261 to 270 of about 196,483 (296)

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 ...
null Donghoh Kim, null Minjeong Park, null Hee-Seok Oh   +2 more
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Knife Diagnostics with Empirical Mode Decomposition

2015
This paper deals with the condition monitoring of knives via the Empirical Mode Decomposition (EMD). The cutting process is basically transient, thus Fourier Analysis and similar signal processing tools aren’t optimal because they treat signals as they were periodic. EMD is a signal analysis technique which is particularly suited for non-stationary and/
Cotogno M., Cocconcelli M., Rubini R.
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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.
A. Zeiler, R. Faltermeier, I. R. Keck, A. M. Tome, C. G. Puntonet, E. W. Lang   +5 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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Absolute value Empirical Mode Decomposition

2011 4th International Congress on Image and Signal Processing, 2011
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 mean envelopes is directly subtracted from the signals.
JianJia Pan, YuanYan Tang
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Empirical mode decomposition and tissue harmonic imaging

Ultrasound in Medicine & Biology, 2005
Empirical mode decomposition (EMD) is a relatively new technique used in the analysis of nonlinear and nonstationary time series. Previous signal-processing methods used for medical ultrasound have been based on the assumption of a linear time-invariant system. More recently, the technique of tissue harmonic imaging (THI) has become prevalent.
Michael, Bennett, Steve, McLaughlin, Tom, Anderson, Norman, McDicken   +3 more
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BANDWIDTH EMPIRICAL MODE DECOMPOSITION AND ITS APPLICATION

International Journal of Wavelets, Multiresolution and Information Processing, 2008
There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of ...
Xie, Qiwei, Xuan, Bo, Peng, Silong, Li, Jianping, Xu, Weixuan, Han, Hua   +5 more
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Empirical Mode Decomposition Analysis for Visual Stylometry

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
In this paper, we show how the tools of empirical mode decomposition (EMD) analysis can be applied to the problem of “visual stylometry,” generally defined as the development of quantitative tools for the measurement and comparisons of individual style in the visual arts.
Hughes, James Michael, Mao, Dong, Rockmore, Daniel N., Wang, Yang, Wu, Qiang   +4 more
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Enhanced Empirical Mode Decomposition

2008
Empirical mode decomposition (EMD) associated with the Hilbert-Huang transform (HHT) deconstructs a time-series signal into a set of monocomponent signals called intrinsic mode functions (IMF). EMD also acts as a filter limiting the frequency range of each IMF. EMD filtering is less than ideal and can lead to misleading results.
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Empirical mode decomposition improves detection of SSVEP

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
Steady State Visual Evoked Potentials (SSVEPs) have been used to quantify attention-related neural activity to visual targets. This study investigates how empirical mode decomposition (EMD) can improve detection accuracy and rate of SSVEPs. First, the scalp-recorded electroencephalogram (EEG) signals are decomposed into intrinsic mode functions (IMFs ...
Liya, Huang, Xiaoxia, Huang, Yu-Te, Wang, Yijun, Wang, Tzyy-Ping, Jung, Chung-Kuan, Cheng   +5 more
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