Results 241 to 250 of about 375,529 (293)

Wavelet-Based Denoising Optimization for Endoscopic Gastric Slow-Wave Recordings. [PDF]

Healthc Technol Lett
Tremain P, Dowrick JM, Cheng LK, McKeage JW, Saavedra C, Sotelo J, Angeli-Gordon TR.   +6 more
europepmc   +1 more source

Wavelet networks

IEEE Transactions on Neural Networks, 1992
A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions. The basic idea is to replace the neurons by ;wavelons', i.e., computing units obtained by cascading an affine transform and a multidimensional wavelet.
Q, Zhang, A, Benveniste
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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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Wavelet Theory

2011
Wavelet theory lies on the crossroad of pure and computational mathematics, with connections to audio and video signal processing, data compression, and information transmission. The present book is devoted to a systematic exposition of modern wavelet theory.
Novikov Igor, Protasov Vladimir, Skopina Maria   +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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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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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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Wavelets and wavelets-design issues

Proceedings of ICCS '94, 2002
This paper attempts to synthesise the wavelet theories to simple design procedures so that applied researchers can readily select or design wavelets with chosen characteristics for particular applications. The paper highlights the importance of the four most desirable characteristics of wavelets for use in digital signal processing, namely ...
null Thong Nguyen, null Dadang Gunawan
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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 ...
Xia, Xiang-Gen, Zhang, Zhen
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