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新型广义傅里叶表示法和相位变换。

Novel generalized Fourier representations and phase transforms.

作者信息

Singh Pushpendra

机构信息

Department of ECE, National Institute of Technology Hamirpur, India.

出版信息

Digit Signal Process. 2020 Nov;106:102830. doi: 10.1016/j.dsp.2020.102830. Epub 2020 Aug 10.

DOI:10.1016/j.dsp.2020.102830
PMID:32834705
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7416779/
Abstract

The Fourier representations (FRs) are indispensable mathematical formulations for modeling and analysis of physical phenomena and engineering systems. This study presents a new set of generalized Fourier representations (GFRs) and phase transforms (PTs). The PTs are special cases of the GFRs and true generalizations of the Hilbert transforms. In particular, the Fourier transform based kernel of the PT is derived and its various properties are discussed. The time derivative and integral, including fractional order, of a signal are obtained using the GFR. It is demonstrated that the general class of time-invariant and time-variant filtering operations, analog and digital modulations can be obtained from the proposed GFR. A narrowband Fourier representation for the time-frequency analysis of a signal is also presented using the GFR. A discrete cosine transform based implementation, to avoid end artifacts due to discontinuities present in the both ends of a signal, is proposed. A fractional-delay in a discrete-time signal using the FR is introduced. The fast Fourier transform implementation of all the proposed representations is developed. Moreover, using the analytic wavelet transform, a wavelet phase transform (WPT) is proposed to obtain a desired phase-shift in a signal under-analysis. A wavelet quadrature transform (WQT) is also presented which is a special case of the WPT with a phase-shift of radians. Thus, a wavelet analytic signal representation is derived from the WQT. Theoretical analysis and numerical experiments are conducted to evaluate effectiveness of the proposed methods.

摘要

傅里叶表示(FRs)是用于对物理现象和工程系统进行建模与分析的不可或缺的数学公式。本研究提出了一组新的广义傅里叶表示(GFRs)和相位变换(PTs)。PTs是GFRs的特殊情况,是希尔伯特变换的真正推广。特别地,推导了基于傅里叶变换的PT核,并讨论了其各种性质。利用GFR获得信号的时间导数和积分,包括分数阶的。结果表明,时不变和时变滤波操作、模拟和数字调制的一般类别可以从所提出的GFR中获得。还使用GFR提出了一种用于信号时频分析的窄带傅里叶表示。提出了一种基于离散余弦变换的实现方法,以避免由于信号两端存在的不连续性而产生的端部伪影。引入了使用FR的离散时间信号中的分数延迟。开发了所有提出的表示的快速傅里叶变换实现。此外,利用解析小波变换,提出了一种小波相位变换(WPT),以在所分析的信号中获得所需的相位偏移。还提出了一种小波正交变换(WQT),它是WPT的一种特殊情况,相位偏移为 弧度。因此,从小波正交变换中导出了小波解析信号表示。进行了理论分析和数值实验,以评估所提出方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/d037c2bc6e0c/gr013_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/902345161a1a/gr001_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/e1bcb7876325/gr002_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/a48b31ef960e/gr003_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/67be192edb1f/gr004_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/1e0dfa77a81e/gr005_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/1bda151892ba/gr006_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/ff45d8820c01/gr007_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/79f7ef32d05a/gr008_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/7548b0b95ceb/gr009_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/b6843dd1aecd/gr010_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/16db78046182/gr011_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/13247d1ac0f8/gr012_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/d037c2bc6e0c/gr013_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/902345161a1a/gr001_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/e1bcb7876325/gr002_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/a48b31ef960e/gr003_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/67be192edb1f/gr004_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/1e0dfa77a81e/gr005_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/1bda151892ba/gr006_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/ff45d8820c01/gr007_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/79f7ef32d05a/gr008_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/7548b0b95ceb/gr009_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/b6843dd1aecd/gr010_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/16db78046182/gr011_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/13247d1ac0f8/gr012_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a2b/7416779/d037c2bc6e0c/gr013_lrg.jpg

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本文引用的文献

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A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.一种用于癫痫发作和无癫痫发作 EEG 信号分类的新型信号建模方法。
IEEE Trans Neural Syst Rehabil Eng. 2018 May;26(5):925-935. doi: 10.1109/TNSRE.2018.2818123.
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The Fourier decomposition method for nonlinear and non-stationary time series analysis.
用于非线性和非平稳时间序列分析的傅里叶分解方法。
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