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Do Not Forget the Stimulus: A Missing Control in Naturalistic Studies of Neural Entrainment. [PDF]

eNeuro
López-Madrona VJ, Pesnot Lerousseau J, Morillon B.   +2 more
europepmc   +1 more source

Diffusion wavelets on connectome: Localizing the sources of diffusion mediating structure-function mapping using graph diffusion wavelets. [PDF]

Netw Neurosci
Jain C, Upadrasta Naga Sita S, Sharma A, Bapi RS.   +3 more
europepmc   +1 more source

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On Sampling Theorem, Wavelets and Wavelet Transforms

Proceedings. IEEE International Symposium on Information Theory, 1993
Summary: The classical Shannon sampling theorem has resulted in many applications and generalizations. From a multiresolution point of view, it provides the sinc scaling function. In this case, for a band-limited signal, its wavelet series transform (WST) coefficients below a certain resolution level can be exactly obtained from the samples with a ...
Xiang-Gen Xia 0001, Zhen Zhang 0010
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Wavelets and Multigrid

SIAM Journal on Scientific Computing, 1993
Some similarities between multiresolution (wavelet) analysis [see \textit{B. Glowinski}, \textit{W. Lawton}, \textit{M. Ravachol} and \textit{E. Tedenbaum}. Wavelet solution of linear and nonlinear elliptic, parabolic and hyperbolic problems in one space dimension, in computing methods in applied sciences and enginneering, Society for Industrial and ...
Briggs, William L., Henson, Van Emden
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Wavelet shrinkage with correlated wavelet coefficients

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002
This paper investigates the statistical characterization of multiscale wavelet coefficients corresponding to random signals and images. Virtually all approaches to wavelet shrinkage model the wavelet coefficients as independent; we challenge that assumption and demonstrate several cases where substantial correlations may be present in the wavelet ...
Zohreh Azimifar, Paul W. Fieguth, Ed Jernigan   +2 more
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Wavelets and Wavelet Transform

2017
Wavelet transforms are the most powerful and the most widely used tool in the field of image processing. Wavelet transform has received considerable attention in the field of image processing due to its flexibility in representing non-stationary image signals and its ability in adapting to human visual characteristics. Wavelet transform is an efficient
Aparna Vyas, Soohwan Yu, Joonki Paik
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Wavelets, Wavelet Filters, and Wavelet Transforms

2013
Spectral characteristics of speech are known to be particularly useful in describing a speech signal such that it can be efficiently reconstructed after coding or identified for recognition. The wavelets are considered one of such efficient methods for representing the spectrum of speech signals.
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Discrete Wavelets and Fast Wavelet Transform

1991
The wavelet analysis, introduced by J. MORLET and Y. MEYER in the middle of the eighties, is a processus of time-frequency (or time-scale) analysis which consists of decomposing a signal into a basis of functions (o jk ) called wavelets. These wavelets are in turn deduced from the analyzing wavelet o by dilatations and translations. More precisely:
Bonnet, Pierre, Rémond, Didier
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The Generalized Wavelets Based on Meyer Wavelet

2009
Starting with investigating the Meyer scaling function, the original Meyer bases are extended to the generalized Meyer wavelet by introducing a time-shift factor into Meyer scaling coefficient functions in this paper. These new wavelets not only inherit many basic properties of the classical Meyer wavelets, such as orthonomality, time-frequency ...
Xudong Teng, Xiao Yuan
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Wavelet and wavelet packet compression of electrocardiograms

IEEE Transactions on Biomedical Engineering, 1997
Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data.
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