Results 261 to 270 of about 21,346 (310)

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 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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Digit pipelined discrete wavelet transform

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002
The paper describes a digit pipelined architecture for the 1D discrete wavelet transform, assuming a digit-serial model of computation. The use of simple operations and data movement makes it suitable for VLSI implementation and it can be easily mapped onto fine-grain custom VLSI and FPGA-based architectures.
C. Nagendra, M.J. Irwin, R.M. Owens
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Implementation of discrete wavelet transform

2014 12th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), 2014
The discrete wavelet transform (DWT) has a very wide and important application in digital signal processing. Daubechies order 4 wavelet transform (db4) is elected to discuss in this work. The advantages of DWT are analyzed, and a three-level Mallat algorithm is implemented in this paper, and db4 low-pass and high-pass filters are selected in each level.
Yuanfa Wang, Zunchao Li, Chuang Wang, Lichen Feng, Zhiyun Zhang   +4 more
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The Discrete Wavelet Transform

2004
The MRA structure allows for the convenient, fast, and exact calculation of the wavelet coefficients of an L 2 function by providing a recursion relation between the scaling coefficients at a given scale and the scaling and wavelet coefficients at the next coarser scale. In order to specify this relation, let {V j × be an MRA with scaling function φ(x).
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