Results 261 to 270 of about 215,660 (296)

Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions

IEEE Geoscience and Remote Sensing Letters, 2011
This letter proposes the use of denoising in conjunction with 2-D empirical mode decomposition (2D-EMD) of hyperspectral image bands for higher classification accuracy. Initially, 2D-EMD is performed to hyperspectral image bands for decomposition into intrinsic mode functions (IMFs). Then, denoising is applied to the first IMF of each band because this
Begüm Demir, Sarp Ertürk, M. Kemal Güllü   +2 more
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INTRINSIC MODE FUNCTIONS OF EARTHQUAKE SLIP DISTRIBUTION

Advances in Adaptive Data Analysis, 2010
In this paper, empirical mode decomposition technique is used to analyze the spatial slip distribution of five past earthquakes. It is shown that the finite fault slip models exhibit five empirical modes of oscillation. The last intrinsic mode is positive and characterizes the non-stationary mean of the slip distribution.
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The structures of some typical intrinsic mode functions

Mathematical Methods in the Applied Sciences, 2012
The empirical mode decomposition is a powerful tool for analyzing nonlinear and nonstationary data. In this method, one of the main conceptual innovations is the introduction of intrinsic mode functions (IMFs), which arise as basic modes from the application of the empirical mode decomposition to signals.
Yang, Zhijing, Yang, Lihua
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Noise-assisted intrinsic mode function coherence in seizure anticipation

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. We explore the applicability of noise-assisted Ensemble Empirical Mode Decomposition (EEMD) for patient-specific seizure anticipation.
Daniel W. Moller, Alan W. L. Chiu
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An intrinsic mode function basis dictionary for auditory signal processing

2014 International Conference on Audio, Language and Image Processing, 2014
As one important field of sparse representation, the research of dictionary learning attracts most researchers interest in signal processing study. Empirical Mode Decomposition (EMD), as an efficient and adaptive signal decomposition method that depends completely on the signal, is considered as an innovative and appropriative the basis function theory.
Chang Gao, Haifeng Li 0001, Lin Ma 0003
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Two-Dimensional Curvature-Based Analysis of Intrinsic Mode Functions

IEEE Signal Processing Letters, 2018
A novel approach in modeling the empirical mode decomposition (EMD) is proposed here, allowing a perfect image recovery and, for instance, a straightforward extension for multidimensional $\mathbb{R}^{n}$ signals. In fact, thanks to a new sifting process modeling, where the two-dimensional (2-D) local mean envelope is formulated with the ...
El-Hadji Samba Diop, Radjesvarane Alexandre, Abdel-Ouahab Boudraa   +2 more
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Denoise of ECG Based on Weighted Sum of Intrinsic Mode Functions

2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2013
Denoise of ECG using weighted sum of intrinsic mode functions (IMFs) with weights determined by the distance metric between each IMF and the noise is proposed in this paper. Traditionally, approaches of the like are achieved by subtracting some IMFs directly from the noisy ECG.
Ching-Haur Chang, Shin-Da Lee
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Hilbert spectral analysis of vowels using intrinsic mode functions

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 2015
In recent work, we presented mathematical theory and algorithms for time-frequency analysis of non-stationary signals. In that work, we generalized the definition of the Hilbert spectrum by using a superposition of complex AM-FM components parameterized by the Instantaneous Amplitude (IA) and Instantaneous Frequency (IF).
Steven Sandoval, Phillip L. De Leon, Julie M. Liss   +2 more
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Empirical Mode Decomposition for Signal Preprocessing and Classification of Intrinsic Mode Functions

Pattern Recognition and Image Analysis, 2018
Empirical mode decomposition (EMD) is an adaptive, data-driven technique for processing and analyzing various types of non-stationary signals. EMD is a powerful and effective tool for signal preprocessing (denoising, detrending, regularity estimation) and time-frequency analysis. This paper discusses pattern discovery in signals via EMD. New approaches
D. M. Klionskiy, D. I. Kaplun, V. V. Geppener   +2 more
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On selecting relevant intrinsic mode functions in empirical mode decomposition: An energy-based approach

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.
Baptista de Souza, Douglas, Chanussot, Jocelyn, Favre, Anne-Catherine   +2 more
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