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A CRITERION FOR SELECTING RELEVANT INTRINSIC MODE FUNCTIONS IN EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2010Information extraction from time series has traditionally been done with Fourier analysis, which use stationary sines and cosines as basis functions. However, data that come from most natural phenomena are mostly nonstationary. A totally adaptive alternative method has been developed called the Hilbert–Huang transform (HHT), which involves generating ...
Nii Attoh-Okine
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Decomposition of functions into pairs of intrinsic mode functions
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2008The intrinsic mode functions (IMFs) arise as basic modes from the application of the empirical mode decomposition (EMD) to functions or signals. In this procedure, instantaneous frequencies are subsequently extracted from the IMFs by the simple application of the Hilbert transform, thereby providing a multiscale analysis of the signal's ...
Vatchev, Vesselin, Sharpley, Robert
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2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Although the empirical mode decomposition is a powerful tool for analyzing complicated datasets, many irrelevant intrinsic mode functions may appear in the decomposition. In this paper, we develop an energy-based method to detect relevant intrinsic mode functions.
Douglas Baptista De Souza +2 more
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Although the empirical mode decomposition is a powerful tool for analyzing complicated datasets, many irrelevant intrinsic mode functions may appear in the decomposition. In this paper, we develop an energy-based method to detect relevant intrinsic mode functions.
Douglas Baptista De Souza +2 more
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A De-noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions
IEEE Signal Processing Letters, 2016Empirical mode decomposition is a nonparametric adaptive tool that decomposes signals into a set of zero-mean modes called intrinsic mode functions (IMFs) that can be used to denoise a signal by selecting the relevant (noise-free) modes. In this paper, the statistical properties of IMFs, produced by a range of signal distributions, are examined ...
H Al-Badrawi, Nicholas J Kirsch
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Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions [PDF]
Mechanical Systems and Signal Processing, 2011 Signal processing is an important tool for diagnostics of mechanical systems. Many different techniques are available to process experimental signals, among others: FFT, wavelet transform, cepstrum, demodulation analysis, second order ciclostationarity ...
Roberto Ricci, Paolo Pennacchi
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Advances in Adaptive Data Analysis, 2010
Empirical Mode Decomposition (EMD) has been widely used to analyze non-stationary and nonlinear signal by decomposing data into a series of intrinsic mode functions (IMFs) and a trend function through sifting processes. For lack of a firm mathematical foundation, the implementation of EMD is still empirical and ad hoc.
Gang Wang +4 more
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Empirical Mode Decomposition (EMD) has been widely used to analyze non-stationary and nonlinear signal by decomposing data into a series of intrinsic mode functions (IMFs) and a trend function through sifting processes. For lack of a firm mathematical foundation, the implementation of EMD is still empirical and ad hoc.
Gang Wang +4 more
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A PDE characterization of the intrinsic mode functions
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009For the first time, a proof of the sifting process (SP) and so the empirical mode decomposition (EMD), is given. For doing this, lower and upper envelopes are modeled in a more convenient way that helps us prove the convergence of the SP towards a solution of a partial differential equation (PDE).
El-Hadji Samba Diop +2 more
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Intrinsic mode functions for gait recognition
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004Gait recognition is an attractive biometric as it is unobtrusive and can be used for recognition from a distance. A number of methods have been proposed by different researchers in the recent past for this purpose. Most of these methods analyze gait as a linear and stationary signal.
Prem Kuchi, Sethuraman Panchanathan
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