Wavelets

By M. Greiner, Peter Lipa, P. Carruthers. 2018

Subfields: Numerical Methods & Scientific Computing

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In Numerical Methods & Scientific Computing

Citations in the corpus

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (2016, methodology): “On the other hand, we will also improve this framework with tools developed in GSP, including graph wavelet operators [14, 9, 11, 6, 30], spectral graph coarsening techniques [33], and improved localization properties [36, 26, 27].”
On the optimal estimation of probability measures in weak and strong topologies (2013, background): “However, various estimators, pn have been proposed in literature to estimate p, the popular ones being the kernel density estimator and wavelet estimator, which under suitable conditions have been shown to be optimal with respect to the L loss (1 ≤ r ≤ ∞) in the sense of achieving the minimax rate o”
Optimal selection of wavelet basis function applied to ECG signal denoising (2006, background): “All simulations are performed in MATLAB [16].”
Diverging Moments and Parameter Estimation (2005, result): “This result is similar to the one we got in [1] where we showed that using log-periodograms (Welch estimator) to analyze processes with spectra of the type ΓX(f) ∼ σ(2)|f |−α leads to a systematic bias on the estimate of α.”
Weak convergence and spectrality of infinite convolutions (2022, background): “The study of spectral sets is also closely related to Gabor bases [42] and wavelets [53].”
A CAS Wavelet Method for Solving Nonlinear Fredholm Integro- Differential Equations of Fractional Order (2011, background): “Wavelets theory is a new and emerging area in mathematical research, it is very successfully used in signal analysis for waveform representation and segmentations, timefrequency analysis and fast algorithms for easy implementation [9, 1].”
Sharp weighted estimates for vector-valued singular integral operators and commutators (2003, background): “For more information on this subject see [1],[4],[6] or [9].”
Mathematical Methods for Supervised Learning (2005, background): “In this case, the approximation classes are again known (see [16]), but we mention only the case r = 1.”
Cycles and trends in the Czech temperature series using wavelet transforms (2004, background): “These conditions are described in detail by Percival (2002), for example.”
Deep Haar Scattering Networks (2015, background): “Haar wavelets can easily be constructed on graphs [7, 26].”
Audio-Visual Automatic Speech Recognition: An Overview (2004, methodology): “As an alternative to PCA, a number of popular linear image transforms (Gonzalez and Wintz, 1977) have been used in place of P ), the discrete wavelet transform (DWT -Daubechies, 1992) in others (Potamianos et al., 1998), and the Hadamard and Haar transforms by Scanlon and Reilly (2001).”
On multilinear singular integrals of Calderón-Zygmund type (2002, background): “This last point of view was pioneered and extensively pursued by Coifman and Meyer in [13], [14], [15], [16], and [47].”
Wavelets and Subband Coding (2013, background): “Another, more recent, tutorial book by Meyer gives an excellent overview of the history of the subject, its mathematical implications and current applications [195].”
Wavelets on irregular grids with arbitrary dilation matrices and frame atoms for L2(Rd) (2004, methodology): “For the case of orthogona wavelets, Yang Wang [48] has recently considered wavelet sets associated with arbitrary fam invertible matrices and irregular sets of translates.”