Adaptive non-uniform partition algorithm based on linear canonical transform

Weikang Zhao, KinTak U and Huibin Luo

Chaos, Solitons & Fractals, 2022, vol. 163, issue C

Abstract: This paper proposes a new signal representation method of adaptive non-uniform partition algorithm based on linear canonical transform, which can partition the signal in independent regions to further improve the reconstruction quality of the signal thus extending its application range. That means the partial signal in each sub-region can be approximated by a determined linear canonical transform series after applying the least squares approximation. The partition process is guided by a set control error so that it can more effectively reflect the regional characteristics of the signal. Compared with the signal reconstructed effect by both the original adaptive non-uniform partition algorithm and Fourier transform through simulation experiments, the results prove that the proposed algorithm has better performance in signal representation and reconstruction under the same approximation condition. On the other hand, through the comparison of simulation experiments with wavelet transform and the original rectangular non-uniform partition algorithm for one-dimensional signal denoising, it can be concluded that the proposed method has certain potential in signal denoising.

Keywords: Linear canonical transform; Adaptive non-uniform partition algorithm; Signal denoising; Least squares approximation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:163:y:2022:i:c:s0960077922007536

DOI: 10.1016/j.chaos.2022.112561

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