Results 211 to 220 of about 13,612 (265)

A new paper-based approach for teaching Bragg's law. [PDF]

Acta Crystallogr E Crystallogr Commun
Weirich TE.
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A numerical study on the dynamics of SIR epidemic model through Genocchi wavelet collocation method. [PDF]

Sci Rep
Chiranahalli Vijaya DK, Doddabhadrappla Gowda P, Hadimani B.   +2 more
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Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach. [PDF]

Sci Rep
Alzakari SA, Hassairi S, Alhussan AA, Ejbali R.   +3 more
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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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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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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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Wavelet and wavelet Stieltjes transforms

Proceedings of 32nd IEEE Conference on Decision and Control, 2002
Some fundamental and useful properties of wavelet transforms are presented. A unified approach for both discrete and continuous time-frequency localization is introduced. >
T. Bielecki, J. Chen, S. Yau, E.B. Lin
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