Time–frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition

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A stochastic approach for generating spectrum compatible fully nonstationary earthquakes (2010, background): “14 where 2 ˆ ( , ) ( , ) g g R R u u G t G t       is the scaled joint time-frequency distribution of the recorded accelerogram [29, 31-33], while ( , ) g S u G t   is the separable power spectral density of the corrective term given by the equation”
Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures (2015, methodology): “The WVD is another signal processing technique used for SHM; however, it introduces cross-term interference between estimated signal components, which impedes efficient estimation of the instantaneous frequencies, besides suffering a liaising problem (Spanos et al 2007).”
Derivation of response spectrum compatible non-stationary stochastic processes relying on Monte Carlo-based peak factor estimation (2012, background): “Arguably, such a consideration is better addressed by means of time-frequency representation techniques (see e.g. Spanos et al. 2007a and Spanos et al. 2007b and references therein), or by means of adaptive (i.e. time-varying) filter models (see e.g. Fan and Ahmadi 1990, Rezaeian and Der Kiureghian…”