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Analysis of Intrinsic Mode Functions: A PDE Approach [PDF]
IEEE Signal Processing Letters, 2010 The empirical mode decomposition is a powerful tool for signal processing. Because of its original algorithmic, recent works have contributed to its theoretical framework. Following these works, some mathematical contributions on its comprehension and formalism are provided.
Abdel-Ouahab Boudraa
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Analysis of the Intrinsic Mode Functions [PDF]
Constructive Approximation, 2005 The Empirical Mode Decomposition is a process for signals which produces Intrinsic Mode Functions from which instantaneous frequencies may be extracted by simple application of the Hilbert transform. The beauty of this method to generate redundant representations is in its simplicity and its effectiveness.
Robert C Sharpley
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Detection of relevant information in intrinsic mode functions
Visión electrónica, 2020 The empirical mode decomposition (EMD) decomposes a local and adaptive time series into a finite set of intrinsic mode functions (IMF), AM-FM signals that allow to represent a non-linear and non-stationary model with the advantage of not losing the underlying meaning.
Roberto Sebastián Hernández Santander +1 more
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Qualitative assessment of intrinsic mode functions of empirical mode decomposition [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 The 'empirical mode decomposition' (EMD) method has been recently proposed to deal with nonlinear and non- stationary data, which decomposes signals into 'well-behaved' intrinsic mode functions (IMFs). An assessment on the qualitative performance of the EMD method in terms of the degree of signal nature preservation of individual IMF is provided.
Mo Chen 0006 +3 more
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Orthogonal intrinsic mode functions via optimization approach
Journal of Industrial & Management Optimization, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Xinpeng +3 more
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Spectral Correlation Measure for Selecting Intrinsic Mode Functions [PDF]
, 2014 Time series analysis implies extracting relevant features from real-world applications to improve pattern recognition tasks. In that sense, representation methods based on time series decomposition and similarity measures are combined to select representative features with physical interpretability.
Edgar F. Sierra-Alonso +3 more
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A New Definition Of The Intrinsic Mode Function
, 2009 {"references": ["L. Cohen, Time-frequency analysis: theory and applications Prentice-\nHall, Inc., Upper saddle River, NJ, 1995.", "S. Mallat. Wavelet tour of signal processing. Academic Press, San Diego,\nUSA, 1999.", "B. Boashash. Estimating and interpreting the instantaneous frequency of\na signal: Part I Fundamentals. Proc. IEEE 80, 417-430, 1992.",
Zhihua Yang, Lihua Yang
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Intrinsic coupling modes reveal the functional architecture of cortico-tectal networks [PDF]
Science Advances, 2015 Summary In the absence of sensory stimulation or motor output, the brain exhibits complex spatiotemporal patterns of intrinsically generated neural activity. However, little is known about how such patterns of activity are correlated between cortical and subcortical brain areas. Here, we investigate the large-scale correlation structure
Stitt, Iain +6 more
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Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images. [PDF]
Comput Intell Neurosci, 2021 Ding Y.
europepmc +1 more source
Equivalent Effect Function and Fast Intrinsic Mode Decomposition
CoRR, 2011 The Equivalent Effect Function (EEF) is defined as having the identical integral values on the control points of the original time series data; the EEF can be obtained from the derivative of the spline function passing through the integral values on the control points.
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