Results 251 to 260 of about 254,430 (319)

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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Hardware implementation of Discrete Wavelet Transform and Inverse Discrete Wavelet Transform on FPGA

2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010
In this paper, hardware implementation of the Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based on FPGA is explained. DWT and IDWT algorithms are implemented on the Altera Cyclone-II FPGA. Filtering processes of rows and columns are seriatim applied as in level-by-level architecture. But both addressing for read/write
Çavuşlu, Mehmet Ali, Karakaya, Fuat
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Discrete Wavelet Transform

2010
According to the definition of the continuous wavelet transform (CWT) given in (3.7), Chap. 3, the scale parameter s and translation parameter \(\tau\) can be varied continuously. As a result, performing the CWT on a signal will lead to the generation of redundant information.
Robert X. Gao, Ruqiang Yan
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Discrete Wavelet Transform

1998
Abstract In the wavelet reconstruction formulae studied so far, the discretization of the corresponding wavelet transforms must be made directly by computing the relevant integrals for all the necessary values of rotation parameters. In Euclidean theory, however, a multiresolution analysis for performing discrete wavelet analysis and ...
W Freeden, T Gervens, M Schreiner
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Discrete Wavelet Transforms using Daubechies Wavelet

IETE Journal of Research, 2001
The Wavelet co-efficients have to be calculated using sampled version of basis functions. As an attempt to compute the Wavelet co-efficients and to find the mother function co-efficients from discrete Wavelet Transform an 8 bit data vector has been used and also the input 8 bit data vector has been derived from only the 4 bit data which is the result ...
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Spherical wavelet transform and its discretization

Advances in Computational Mathematics, 1996
Starting from continuous wavelet transform on the sphere the authors describe a continuous version of spherical multiresolution. Next, using a scale discretization they construct spherical counterparts to wavelet packets and scale discrete wavelets.
Freeden, Willi, Windheuser, U.
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Discrete Wavelet Transforms in Walsh Analysis

Journal of Mathematical Sciences, 2021
This paper presents a review of discrete wavelet transforms defined through generalized Walsh functions, including orthogonal discrete wavelet transform, biorthogonal discrete wavelet transform, nonstationary discrete wavelet transform, and periodic discrete wavelet transform, and show their applications in image processing, compression of fractal ...
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The Discrete Wavelet Transform

2013
Introduction Here we introduce the discrete wavelet transform (DWT), which is the basic tool needed for studying time series via wavelets and plays a role analogous to that of the discrete Fourier transform in spectral analysis. We assume only that the reader is familiar with the basic ideas from linear filtering theory and linear algebra
Donald B. Percival, Andrew T. Walden
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Process Identification Using Discrete Wavelet Transforms

IFAC Proceedings Volumes, 1994
Abstract A time-frequency domain identification methodology using wavelet transforms is developed. This approach allows incorporation of both time-domain and frequency-domain information into identification, and also combines some advantages of each approach. Identification methodology using time-domain and frequency-domain wavelets are developed and
S. PALAVAJJHALA, R.L. MOTARD, B. JOSEPH
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Initialization of orthogonal discrete wavelet transforms

IEEE Transactions on Signal Processing, 2000
Summary: The symptotic formulae of both the approximation error and the systematic error of a special prefilter projection and the quantitative estimates of the upper bounds of the errors are obtained. In addition, it is shown that for the Daubechies' orthogonal wavelet basis, the estimated constant is optimal.
Zhang, Jiankang, Bao, Zheng
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