Qualitative Uncertainty Principles for the Nonisotropic Angular Stockwell Transforms

ABSTRACT The nonisotropic angular Stockwell transform (NAST) employs an angular, scalable and localized window function, to effectively capture directional characteristics and nonisotropic features presented in the signal. In this paper, our main contributions are to derive three distinct qualitative uncertainty principles (UPs) for the NAST: the Benedicks–Amrein–Berther UP, the logarithmic Sobolev‐type UP, and the Donoho–Stark UP. These uncertainty principles provide rather valuable insights into the behavior and limitations of the NAST regarding localization and concentration properties.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-01-22
DOI
https://doi.org/10.1002/mma.10731
Citations
1
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
1.40

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article

Qualitative Uncertainty Principles for the Nonisotropic Angular Stockwell Transforms

Shenzhou Zheng, Xinyu Wang
1 citations
Mathematical Methods in the Applied Sciences
Probabilistic and Robust Engineering Design
1.40
article

Qualitative Uncertainty Principles for the Nonisotropic Angular Stockwell Transforms

Shenzhou Zheng, Xinyu Wang
article en
1 citations

Abstract

ABSTRACT The nonisotropic angular Stockwell transform (NAST) employs an angular, scalable and localized window function, to effectively capture directional characteristics and nonisotropic features presented in the signal. In this paper, our main contributions are to derive three distinct qualitative uncertainty principles (UPs) for the NAST: the Benedicks–Amrein–Berther UP, the logarithmic Sobolev‐type UP, and the Donoho–Stark UP. These uncertainty principles provide rather valuable insights into the behavior and limitations of the NAST regarding localization and concentration properties.

Mathematical Methods in the Applied SciencesVol. 48(8)
Beijing Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 19%
Probabilistic and Robust Engineering Design
1.40
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