Uncertainty principles for the linear canonical Dunkl wavelet transform

In this paper, we explore various uncertainty principles in the framework of the linear canonical Dunkl transform (LCDT) and the linear canonical Dunkl wavelet transform (LCDWT). We establish fundamental uncertainty principles, including the Heisenberg and entropy uncertainty principles, for both transforms. Additionally, by employing Pitt’s inequality, we derive the logarithmic uncertainty principle, further highlighting the fundamental limits of simultaneous localization in the spatial and frequency domains. Furthermore, we develop the Donoho–Stark and local uncertainty principles specifically for the LCDWT, enriching the theoretical understanding of uncertainty in this setting.

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Publication Details

Journal
Asian-European Journal of Mathematics
Published
2026-02-27
DOI
https://doi.org/10.1142/s179355712650018x
Primary Topic
Mathematical Analysis and Transform Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Uncertainty principles for the linear canonical Dunkl wavelet transform

Satyam Verma, Hatem Mejjaoli, S. Umamaheswari
Asian-European Journal of Mathematics
Mathematical Analysis and Transform Methods
article

Uncertainty principles for the linear canonical Dunkl wavelet transform

Satyam Verma, Hatem Mejjaoli, S. Umamaheswari
article en

Abstract

In this paper, we explore various uncertainty principles in the framework of the linear canonical Dunkl transform (LCDT) and the linear canonical Dunkl wavelet transform (LCDWT). We establish fundamental uncertainty principles, including the Heisenberg and entropy uncertainty principles, for both transforms. Additionally, by employing Pitt’s inequality, we derive the logarithmic uncertainty principle, further highlighting the fundamental limits of simultaneous localization in the spatial and frequency domains. Furthermore, we develop the Donoho–Stark and local uncertainty principles specifically for the LCDWT, enriching the theoretical understanding of uncertainty in this setting.

Asian-European Journal of Mathematics
Taibah University (SA), SRM University (IN)
Reduced inequalities
Openalex Percentile: Top 38%
Mathematical Analysis and Transform Methods
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