Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips & Tricks]

Wu, K; Zhang, JA; Guo, YJ

Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips & Tricks]

Wu, K

Zhang, JA Guo, YJ

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Signal Processing Magazine, 2022, 39, (6), pp. 76-84
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Wu, K https://orcid.org/0000-0003-1298-6404
dc.contributor.author	Zhang, JA
dc.contributor.author	Guo, YJ
dc.date.accessioned	2023-01-27T02:00:40Z
dc.date.available	2023-01-27T02:00:40Z
dc.date.issued	2022-11-01
dc.identifier.citation	IEEE Signal Processing Magazine, 2022, 39, (6), pp. 76-84
dc.identifier.issn	1053-5888
dc.identifier.issn	1558-0792
dc.identifier.uri	http://hdl.handle.net/10453/165509
dc.description.abstract	Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1], and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2]; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP210101411
dc.relation.ispartof	IEEE Signal Processing Magazine
dc.relation.isbasedon	10.1109/MSP.2022.3194692
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Networking & Telecommunications
dc.title	Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips & Tricks]
dc.type	Journal Article
utslib.citation.volume	39
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-11-01T00:00:00+1000Z
dc.date.updated	2023-01-27T02:00:39Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	6

Abstract:

Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1], and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2]; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.

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http://hdl.handle.net/10453/165509