scholarly journals The composition of linear canonical wavelet transforms on generalized function spaces

Filomat ◽  
2020 ◽  
Vol 34 (12) ◽  
pp. 4123-4136
Author(s):  
Akhilesh Prasad ◽  
Z.A. Ansari
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Function Spaces ◽  
Generalized Function ◽  
Test Function ◽  
Boundedness Result

The main goal of this paper is to study the continuity of composition of linear canonical wavelet transform (LCWTs) on generalized test function spaces Lp,A, Gp,A and BA(R3). The boundedness result for composition of linear canonical wavelet transforms on Hps,A is given.

scholarly journals Wavelet transforms in generalized Fock spaces

1997 ◽  
Vol 20 (4) ◽  
pp. 657-672 ◽  
Author(s):  
John Schmeelk ◽  
Arpad Takaci
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Fock Space ◽  
Generalized Function ◽  
Fock Spaces ◽  
The Individual ◽  
Individual Entry

A generalized Fock space is introduced as it was developed by Schmeelk [1-5], also Schmeelk and Takači [6-8]. The wavelet transform is then extended to this generalized Fock space. Since each component of a generalized Fock functional is a generalized function, the wavelet transform acts upon the individual entry much the same as was developed by Mikusinski and Mort [9] based upon earlier work of Mikusinski and Taylor [10]. It is then shown that the generalized wavelet transform applied to a member of our generalized Fock space produces a more appropriate functional for certain appfications.

Continuous Wavelet Transforms of Multivariable Abstract Function Spaces

2010 ◽  
Vol 159 ◽  
pp. 199-204
Author(s):  
Han Zhang Qu ◽  
Jing Yang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Integral Kernel ◽  
Function Spaces ◽  
Continuous Wavelet ◽  
Norm Convergence ◽  
Continuous Wavelet Transforms ◽  
Abstract Function ◽  
One Dimensional ◽  
Abstract Function Spaces

An abstract function space is proposed and discussed. One-dimensional continuous wavelet transform is applied to the continuous wavelet transforms of the multivariable abstract function spaces .The reconstruction formulas of it produced by the integral kernel of the transform multivariable abstract functions and those of it produced by the integral kernel of the multivariable abstract functions which are difference from the transform multivariable abstract functions are obtained in the weak topology as well as in the sense of norm convergence.

scholarly journals Characterization of weighted function spaces in terms of wavelet transforms

2018 ◽  
Vol 37 (4) ◽  
pp. 69-82
Author(s):  
Sanjay Sharma ◽  
Drema Lhamu ◽  
Sunil Kumar Singh
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Wavelet Transforms ◽  
Function Spaces ◽  
Weighted Function ◽  
Weighted Function Spaces ◽  
Weighted Function Space

In this paper, we have characterized a weighted function space $ B_{\omega,\psi}^{p,q}, ~ 1\leq p,q<\infty$ in terms of wavelet transform and shown that the norms on the spaces $B_{\omega,\psi}^{p,q}$  and $\bigwedge_\omega^{p,q}$ (the space defined in terms of differences $\triangle_x$) are equivalent.

The Mehler-Fock-Clifford transform and pseudo-differential operator on function spaces

Filomat ◽  
2019 ◽  
Vol 33 (8) ◽  
pp. 2457-2469
Author(s):  
Akhilesh Prasad ◽  
S.K. Verma
Keyword(s):  
Differential Operator ◽  
Function Spaces ◽  
Pseudo Differential Operator ◽  
Test Function ◽  
Basic Properties ◽  
Cone Function ◽  
Translation Operators

In this article, weintroduce a new index transform associated with the cone function Pi ??-1/2 (2?x), named as Mehler-Fock-Clifford transform and study its some basic properties. Convolution and translation operators are defined and obtained their estimates under Lp(I, x-1/2 dx) norm. The test function spaces G? and F? are introduced and discussed the continuity of the differential operator and MFC-transform on these spaces. Moreover, the pseudo-differential operator (p.d.o.) involving MFC-transform is defined and studied its continuity between G? and F?.

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

Wavelet transform to quantify heart rate variability and to assess its instantaneous changes

1999 ◽  
Vol 86 (3) ◽  
pp. 1081-1091 ◽  
Author(s):  
Vincent Pichot ◽  
Jean-Michel Gaspoz ◽  
Serge Molliex ◽  
Anestis Antoniadis ◽  
Thierry Busso ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Autonomous Nervous System ◽  
Dynamic Changes ◽  
Nonstationary Signals ◽  
Higher Doses

Heart rate variability is a recognized parameter for assessing autonomous nervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics because of limitations inherent in its stationary hypothesis. Conversely, wavelet transform allows analysis of nonstationary signals. We compared the respective yields of Fourier and wavelet transforms in analyzing heart rate variability during dynamic changes in autonomous nervous system balance induced by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a significant increase in heart rate variability, which progressively disappeared after the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autonomous nervous system adaptations induced by both agents and provided novel temporally localized information.

scholarly journals Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Vanishing Moments ◽  
Zero Crossings ◽  
Time Frequency ◽  
Common Time ◽  
Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

Pattern Changes of Time-Shifted Vibration Signals on Wavelet Time-Scale Maps

1995 ◽  
Author(s):  
Da Jun Chen ◽  
Wei Ji Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Wavelet Transforms ◽  
Vibration Signal ◽  
Continuous Wavelet ◽  
Time Axis ◽  
Orthogonal Wavelet ◽  
Signal Component ◽  
Analysis Technique ◽  
Map Analysis

Abstract As a multi-resolution signal decomposition and analysis technique, the wavelet transforms have been already introduced to vibration signal processing. In this paper, a comparison on the time-scale map analysis is made between the discrete and the continuous wavelet transform. The orthogonal wavelet transform decomposes the vibration signal onto a series of orthogonal wavelet functions and the number of wavelets on one wavelet level is different from those on the other levels. Since the grids are unevenly distributed on the time-scale map, it is shown that a representation pattern of a vibration component on the map may be significantly altered or even be broken down into pieces when the signal has a shift along the time axis. On contrary, there is no such uneven distribution of grids on the continuous wavelet time-scale map, so that the representation pattern of a vibration signal component will not change its shape when the signal component shifts along the time axis. Therefore, the patterns in the continuous wavelet time-scale map are more easily recognised by human visual inspection or computerised automatic diagnosis systems. Using a Gaussian enveloped oscillation wavelet, the wavelet transform is capable of retaining the frequency meaning used in the spectral analysis, while making the interpretation of patterns on the time-scale maps easier.

Quantum Wavelet Transforms

2021 ◽  
pp. 193-220
Keyword(s):  
Information Processing ◽  
Quantum Information ◽  
Wavelet Transform ◽  
Tensor Product ◽  
Wavelet Transforms ◽  
Quantum Information Processing ◽  
Complexity Analysis ◽  
Simulation Experiments ◽  
Multi Level

The classical wavelet transform has been widely applied in the information processing field. It implies that quantum wavelet transform (QWT) may play an important role in quantum information processing. This chapter firstly describes the iteration equations of the general QWT using generalized tensor product. Then, Haar QWT (HQWT), Daubechies D4 QWT (DQWT), and their inverse transforms are proposed respectively. Meanwhile, the circuits of the two kinds of multi-level HQWT are designed. What's more, the multi-level DQWT based on the periodization extension is implemented. The complexity analysis shows that the proposed multi-level QWTs on 2n elements can be implemented by O(n3) basic operations. Simulation experiments demonstrate that the proposed QWTs are correct and effective.

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