uncertainty principles
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2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Mawardi Bahri

AbstractThe windowed linear canonical transform is a natural extension of the classical windowed Fourier transform using the linear canonical transform. In the current work, we first remind the reader about the relation between the windowed linear canonical transform and windowed Fourier transform. It is shown that useful relation enables us to provide different proofs of some properties of the windowed linear canonical transform, such as the orthogonality relation, inversion theorem, and complex conjugation. Lastly, we demonstrate some new results concerning several generalizations of the uncertainty principles associated with this transformation.


Author(s):  
Owais Ahmad ◽  
A. Achak ◽  
Neyaz A. Sheikh ◽  
Ujwal Warbhe

The main objective of this paper is to establish Heisenberg’s and Beckner’s uncertainty principles associated with multi-dimensional linear canonical transform.


2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Walid Amghar

In this article, we will recall the main properties of the Fourier transform on the Heisenberg motion group G = ℍ n ⋊ K , where K = U n and ℍ n = ℂ n × ℝ denote the Heisenberg group. Then, we will present some uncertainty principles associated to this transform as Beurling, Hardy, and Gelfand-Shilov.


Author(s):  
Wen-Biao Gao ◽  
Bing-Zhao Li

The windowed offset linear canonical transform (WOLCT) can be identified as a generalization of the windowed linear canonical transform (WLCT). In this paper, we generalize several different uncertainty principles for the WOLCT, including Heisenberg uncertainty principle, Hardy’s uncertainty principle, Donoho–Stark’s uncertainty principle and Nazarov’s uncertainty principle. Finally, as application analogues of the Poisson summation formula and sampling formulas are given.


2021 ◽  
Vol 58 (3) ◽  
pp. 335-357
Author(s):  
Saifallah Ghobber ◽  
Siwar Hkimi ◽  
Slim Omri

The aim of this paper is to prove some uncertainty inequalities for the continuous Hankel wavelet transform, and study the localization operator associated to this transformation.


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