A review on Hilbert‐Huang transform: Method and its applications to geophysical studies

By Norden E. Huang, Zhaohua Wu. 2008

1,743 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/3e0e69d17eb683dfb374b5571f4c36de564d40eb

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Citations in the corpus

Bayesian Nonstationary Frequency Analysis of Hydrological Variables 1 (2011, methodology): “…approach for the analysis of hydrometeorological variables include Olsen et al. (1999), Cunderlik et al. (2007), El-Adlouni et al. (2007), Hundecha et al. (2008), El-Adlouni and Ouarda (2008, 2009), Kwon et al. (2008), Cannon (2010) for local frequency analysis, and Cunderlik and Ouarda…”
Multivariate empirical mode decomposition (2010, background): “Owing to no a priori assumptions regarding the data nature, EMD has found applications in the analysis of nonlinear and non-stationary signals (e.g. Duffy 2004; Gautama et al. 2004; Janosi & Muller 2005; Wu & Hu 2006; Huang & Wu 2008; Lin et al. 2009).”
Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool (2011, methodology): “Extracting Individual Components from Synthesized data In many applications listed in [4, 5, 2, 6, 7], the desired end goal is the instantaneous frequency trajectory or profile for the different components.”
EEG artifact removal—state-of-the-art and guidelines (2015, background): “EMD has been criticized due to its low robustness against noise [66] (its performance is greatly influenced by white noise), but has gained a lot of attention in the last few years in many fields [122] and in particular for processing biomedical signals.”
Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method (2009, methodology): “This reprocess not only corrects the non-IMF problem of EEMD, but also leads to new insights into the characteristics of components, as discussed by Huang and Wu.(45) For example, the amplitude of C2 in Fig.”
Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition (2012, methodology): “This is because the trend component might be induced by the inability of the EMD procedure to efficiently capture the IMF components [Lee and Ouarda, 2010], and also because the current trend may be defined only for the time span of the observed data [Wu et al., 2007; Huang and Wu, 2008].”
Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting (2015, methodology): “EEMD is a new noise assisted data analysis method proposed by Huang et al. (Wu et al. 2007; Huang and Wu 2008) to overcome the shortcoming of mode mixing caused by signal intermittency in EMD (Huang et al. 2009).”
A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, methodology): “But for complex data, the wavelet skeleton approach is encumbered by the harmonics due to fixed priori basis functions [33].”
The modulated annual cycle: an alternative reference frame for climate anomalies (2007, methodology): “…and temporally local time series analysis to analyze MAC, particularly, the empirical mode decomposition (Huang et al. 1998, 1999, 2003; Flandrin et al. 2004; Wu and Huang 2004, 2005a; Wu et al. 2007; Huang and Wu 2008) and its most recent improvement, the Ensemble EMD (Wu and Huang 2005b, 2008).”
In situ monitoring of FDM machine condition via acoustic emission (2015, methodology): “Similarly, HHT performs time-frequency-energy analysis based on empirical mode decomposition and Hilbert spectral analysis for non-stationary signals [21, 22].”