Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool

By Ingrid Daubechies, Jianfeng Lu, Hau‐Tieng Wu. 2011

2,284 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e7506895bb22230f1a6ece241fbb1893263093f5

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In Civil and Structural Engineering

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Citations in the corpus

Synchroextracting Transform (2017, methodology): “However, considering the calculation error and that the SEO’s real part needs to be utilized in practical applications [6], [17], it is suggested that (25) be rewritten as the following equation:”
Nonlinear Chirp Mode Decomposition: A Variational Method (2017, background): “These modes usually exhibit poor energy concentration in a TFR, and show low correlation with the original signal.”
Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures (2015, methodology): “Recently introduced by Daubechies et al (2011), SWT is a new time-frequency analysis method capable of analyzing non-stationary, nonlinear, and noisy signals.”
Variational Mode Decomposition (2014, methodology): “Note that the newer de ﬁ nition of signal components is slightly more restrictive than the original one: while all signals adhering to the above IMF de ﬁ nition satisfy the original EMD mode properties, the converse is not necessarily true [13].”
Stock closing price prediction based on sentiment analysis and LSTM (2019, background): “The main purpose is to gradually decompose the ﬂuctuations of different scales of a stationary or non-stationary complex signal to obtain a series of intrinsic mode functions (IMF) with better performance and a single residual term [15].”
Second-Order Synchrosqueezing Transform or Invertible Reassignment? Towards Ideal Time-Frequency Representations (2015, methodology): “Following [1], the authors in [20], [19] proposed to neglect operator , and to reassign only the complex coefÞcients according to the map , which reads: (8) Knowing , the th mode can then be retrieved by considering: (9) as explained in more details in Section III.”
A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021, methodology): “Constrained by the di ﬃ culty in extracting useful features directly from the raw vibration signal [24], we apply synchrosqueezed wavelet transform (SWT) [29] to obtain time–frequency representations (TFRs).”
Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013, methodology): “The fixed-basis STFT and WT could not cope with nonstationarity and produced harmonics and spurious frequencies, while the SST— a “posterior” method that reallocates WT coefficients to have concentrated T-F representation—was not immune to these effects either [21].”
Multisynchrosqueezing Transform (2019, methodology): “In [7] and [8], it has been proved that putting ε̃ = ε1/3 and assuming ε being sufficiently small, for |G(t, ω)| ≥ ε̃, we have the approximation”
General linear chirplet transform (2016, positive): “Recently, with the development of modern signal processing technology, Maes and Daubechies proposed a phase-based reassignment technique that they termed synchrosqueezing transform (SST), with additional advantage of allowing for straightforward extraction and reconstruction [8,9].”
Empirical Wavelet Transform (2013, methodology): “In this paper, as we will use the Fourier formalism in Section III, we adopt the description used in [4] which is slightly different from the original used in [9].”
The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications (2011, positive): “In this paper, we focus on the original, CWT-based approach studied in [15,14], although an STFT-based alternative was developed in [44] and other variants are also possible.”