Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Birkhäuser Boston eBooks

The Wavelet Transform and Time-Frequency Analysis

Leon W. Cohen

Published Jan 1, 2003• 15 citations
Publisher

Abstract

We present a general method for calculating relevant moments of the wavelet transform.Explicit results are given for the time, frequency, and scale moments.Using the results obtained, we show that the wavelet transform has unique characteristics that are not possessed by other methods.We discuss whether these unusual characteristics are physically or mathematically important.Exactly solvable examples are given, and the results are contrasted to those of the standard methods such as the spectrogram and the Wigner distribution.

DOI
10.1007/978-1-4612-0025-3_1

Related papers

Time-frequency characterization of random systems

Lorenzo Galleani, Leon W. Cohen

We have previously presented a method to write equations for the Wigner distribution corresponding to the solution of a linear differential equation. We extend this method to arbitrary time-frequency distributions, the Short Time Fourier Transform, and the wavelet transform. Also, we apply the method to stochastic signals and address a number of applications including Brownian motion. We obtain the Wigner distribution for Brownian motion by solving the Langevin equation and also by using our method. We obtain exact solutions.

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE•Dec 31, 2003•4 citations

An uncertainty principle for the basic wavelet transform

Javid Ahmad Ganie, Renu Jain

The aim of this paper is to derive the uncertainty principle which has implications in signal analysis and in quantum mechanics. First, we derive the definition of the wavelet transform in [Formula: see text]-calculus by using some weight function. Certain properties like linearity, scaling, translation, etc. were discussed. Later on, to illustrate this integral transform several results were derived for the effectiveness and performance of the proposed method. Also, this paper surveys recent applications to establish generalized uncertainty principle.

International Journal of Wavelets Multiresolution and Information Processing•Feb 24, 2021•4 citations

Higher-dimensional wavelets

Jean-Pierre Antoine, Romain Murenzi, Pierre Vandergheynst +1 more

In the previous chapters, we have thoroughly discussed the 2-D CWT and some of its applications. Then we have made the connection with the group theoretical origins of the method, thus establishing a general framework, based on the coherent state formalism. In the present chapter, we will apply the same technique to a number of different situations involving higher dimensions: wavelets in 3-D space ℝ 3 , wavelets in ℝ n ( n > 3), and wavelets on the 2-sphere S 2 . Then, in the next chapter, we will treat time-dependent wavelets, that is, wavelets on space–time, designed for motion analysis. In all cases, the technique is the same. First one identifies the manifold on which the signals are defined and the appropriate group of transformations acting on the latter. Next one chooses a square integrable representation of that group, possibly modulo some subgroup. Then one constructs wavelets as admissible vectors and derives the corresponding wavelet transform. Three-dimensional wavelets Some physical phenomena are intrinsically multiscale and three-dimensional. Typical examples may be found in fluid dynamics, for instance the appearance of coherent structures in turbulent flows, or the disentangling of a wave train in (mostly underwater) acoustics, as discussed above. In such cases, a 3-D wavelet analysis is clearly more adequate and likely to yield a deeper understanding [56].

Cambridge University Press eBooks•Sep 23, 2004•0 citations

Topics in time-frequency representations

Leon W. Cohen

We discuss some issues relevant to time-frequency analysis. Among them are, time-frequency spatial/spatial-frequency representations, generalized marginals and generalized characteristic functions. We derive representations that jointly involve the four variables, time, frequency, space, and spatial frequency. We apply these representations to wave packet motion. For the study on generalized marginals we show that there is an inherent difference between the standard methods of transforming variables and in time- frequency analysis. We use the method of generalized characteristic function operator method to generalize Taylor's theorem.

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE•Oct 22, 1996•5 citations

The Uncertainty Principle for the Short-Time Fourier Transform and Wavelet Transform

Leon W. Cohen

We obtain both a global and local uncertainty principle for the short-time Fourier transform. Explicit expressions are obtained for the uncertainty product in terms of the uncertainty product of the signal and window. Although the uncertainty product of the spectrogram is not the sum of the uncertainty products of the window and signal, nonetheless, we show that the global uncertainty product has to be greater than one. We also derive local uncertainty relations which indicate how the local spread in time and frequency are related at a particlar time–frequency point. In addition, we obtain general results for the time, scale, and frequency moments of the scalogram in terms of the moments of the signal and mother wavelet. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Birkhäuser Boston eBooks•Jan 1, 2001•29 citations

Moment quantization and (A-adic) discrete-continuous wavelet transform theory

Carlos R. Handy, Romain Murenzi

We establish how moment quantization directly leads to a complete ( A -adic) discrete-continuous wavelet transform theory in contrast to the approximate reconstruction methods published earlier (1996 Phys. Rev. A 54 3754, 1997 J. Phys. A: Math. Gen. 30 4709).

Journal of Physics A Mathematical and General•Nov 8, 1999•8 citations
We present a general method for calculating relevant moments of the wavelet transform.Explicit results are given for the time, frequency, and scale moments.Using the results obtained, we show that the wavelet transform has unique characteristics that are not possessed by other methods.We discuss whether these unusual characteristics are physically or mathematically important.Exactly solvable examples are given, and the results are contrasted to those of the standard methods such as the spectrogram and the Wigner distribution.
We have previously presented a method to write equations for the Wigner distribution corresponding to the solution of a linear differential equation. We extend this method to arbitrary time-frequency distributions, the Short Time Fourier Transform, and the wavelet transform. Also, we apply the method to stochastic signals and address a number of applications including Brownian motion. We obtain the Wigner distribution for Brownian motion by solving the Langevin equation and also by using our method. We obtain exact solutions.
The aim of this paper is to derive the uncertainty principle which has implications in signal analysis and in quantum mechanics. First, we derive the definition of the wavelet transform in [Formula: see text]-calculus by using some weight function. Certain properties like linearity, scaling, translation, etc. were discussed. Later on, to illustrate this integral transform several results were derived for the effectiveness and performance of the proposed method. Also, this paper surveys recent applications to establish generalized uncertainty principle.
In the previous chapters, we have thoroughly discussed the 2-D CWT and some of its applications. Then we have made the connection with the group theoretical origins of the method, thus establishing a general framework, based on the coherent state formalism. In the present chapter, we will apply the same technique to a number of different situations involving higher dimensions: wavelets in 3-D space ℝ 3 , wavelets in ℝ n ( n > 3), and wavelets on the 2-sphere S 2 . Then, in the next chapter, we will treat time-dependent wavelets, that is, wavelets on space–time, designed for motion analysis. In all cases, the technique is the same. First one identifies the manifold on which the signals are defined and the appropriate group of transformations acting on the latter. Next one chooses a square integrable representation of that group, possibly modulo some subgroup. Then one constructs wavelets as admissible vectors and derives the corresponding wavelet transform. Three-dimensional wavelets Some physical phenomena are intrinsically multiscale and three-dimensional. Typical examples may be found in fluid dynamics, for instance the appearance of coherent structures in turbulent flows, or the disentangling of a wave train in (mostly underwater) acoustics, as discussed above. In such cases, a 3-D wavelet analysis is clearly more adequate and likely to yield a deeper understanding [56].
We discuss some issues relevant to time-frequency analysis. Among them are, time-frequency spatial/spatial-frequency representations, generalized marginals and generalized characteristic functions. We derive representations that jointly involve the four variables, time, frequency, space, and spatial frequency. We apply these representations to wave packet motion. For the study on generalized marginals we show that there is an inherent difference between the standard methods of transforming variables and in time- frequency analysis. We use the method of generalized characteristic function operator method to generalize Taylor's theorem.
We obtain both a global and local uncertainty principle for the short-time Fourier transform. Explicit expressions are obtained for the uncertainty product in terms of the uncertainty product of the signal and window. Although the uncertainty product of the spectrogram is not the sum of the uncertainty products of the window and signal, nonetheless, we show that the global uncertainty product has to be greater than one. We also derive local uncertainty relations which indicate how the local spread in time and frequency are related at a particlar time–frequency point. In addition, we obtain general results for the time, scale, and frequency moments of the scalogram in terms of the moments of the signal and mother wavelet. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.