线性尺度不变信号、系统和变换的统一框架:教程

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-11-26 DOI:10.1016/j.dsp.2024.104880
Anubha Gupta , Pushpendra Singh , Priya Aggarwal , Shiv Dutt Joshi
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引用次数: 0

摘要

本文从系统理论的角度提出了线性尺度不变信号、系统和变换的统一框架。这项工作是与线性平移不变系统和变换相关的理论的尺度对应。与用于研究线性移位或时不变系统的傅里叶变换和拉普拉斯变换类似,Mellin变换用于研究尺度不变系统。然而,与平移不变量理论不同的是,尺度不变量系统和变换的相关理论到目前为止还没有一个统一的方法。在这项工作中,我们从信号处理的角度提出了这一理论,其中我们将尺度不变变换的发展作为一个系统的进展,从尺度周期信号的尺度级数到尺度非周期信号的尺度不变变换。我们还提出了几个例子来说明所提出的理论的效用。
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Unified framework for linear scale invariant signals, systems, and transforms: A tutorial
This paper presents a unified framework for linear scale invariant signals, systems, and transforms from a system theoretic perspective. The work is the scale counterpart of the theory related to linear shift invariant systems and transforms. Similar to Fourier and Laplace transforms that are used to study linear shift or time invariant systems, Mellin transform is used to study scale invariant systems. However, unlike the shift invariant theory, the theory related to scale invariant systems and transforms has so far not been presented with a unified approach. In this work, we present this theory from signal processing viewpoint, where we present the development of scale invariant transform as a systematic progression from scale series for scale periodic signals to scale invariant transform for scale aperiodic signals. We also present a few examples to illustrate the utility of the presented theory.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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