2011
DOI: 10.1007/s10915-011-9496-0
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Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel

Abstract: The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and an… Show more

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Cited by 34 publications
(31 citation statements)
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“…Mode decomposition is an elementary operation in image and signal processing, and enables essentially all the other processing tasks such as noise removal, image edge detection, distortion restoration, feature extraction, enhancement, segmentation, and pattern recognition. Although there are many mode decomposition techniques such as empirical mode decomposition (EMD), 32 iterative filtering decomposition 42, 70, 71 and wavelets, partial differential equation (PDE) approaches have not been discovered. A major obstacle is due to the limited understanding of high order PDEs.…”
Section: Discussionmentioning
confidence: 99%
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“…Mode decomposition is an elementary operation in image and signal processing, and enables essentially all the other processing tasks such as noise removal, image edge detection, distortion restoration, feature extraction, enhancement, segmentation, and pattern recognition. Although there are many mode decomposition techniques such as empirical mode decomposition (EMD), 32 iterative filtering decomposition 42, 70, 71 and wavelets, partial differential equation (PDE) approaches have not been discovered. A major obstacle is due to the limited understanding of high order PDEs.…”
Section: Discussionmentioning
confidence: 99%
“…Similar to the IFD algorithm, 42, 70 we extract the highest frequency component first, then residual is used as the initial value for u and v n to extract the second highest frequency component. We continue this procedure until all components or intrinsic mode functions are separated.…”
Section: Simplified Models and Computational Algorithmsmentioning
confidence: 99%
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“…Functional modes are mode components which share the same band of frequency as well as same category, i.e., trend, edge, texture, noise etc. A major motivation in this development is the empirical mode decomposition (EMD), which enables the processing of non-uniform and non-stationary signals [32, 57]. However, the PDE transform is typically more robust than the Hilbert-Huang transform [32] and can be easly applied to multi-dimensional data.…”
Section: Introductionmentioning
confidence: 99%