Visualizing long vectors of measurements by use of the Hilbert curve.
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| Title: | Visualizing long vectors of measurements by use of the Hilbert curve. |
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| Authors: | Estevez-Rams, E.1,2 estevez@imre.oc.uh.cu, Perez-Demydenko, C.2, Aragón Fernández, B.3, Lora-Serrano, R.4 |
| Source: | Computer Physics Communications. Dec2015, Vol. 197, p118-127. 10p. |
| Subjects: | Vector analysis, Hilbert space, Mathematical transformations, Ising model, Mathematical sequences |
| Abstract: | The use of Hilbert curves to visualize massive vector of data is revisited following previous authors. The Hilbert curve mapping preserves locality and makes meaningful representation of the data. We call such visualization as Hilbert plots. The combination of a Hilbert plot with its Fourier transform allows to identify patterns in the underlying data sequence. The use of different granularity representation also allows to identify periodic intervals within the data. Data from different sources are presented: periodic, aperiodic, logistic map and 1/2-Ising model. A real data example from the study of heartbeat data is also discussed. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Physics Communications is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 109980332 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Visualizing long vectors of measurements by use of the Hilbert curve. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Estevez-Rams%2C+E%2E%22">Estevez-Rams, E.</searchLink><relatesTo>1,2</relatesTo><i> estevez@imre.oc.uh.cu</i><br /><searchLink fieldCode="AR" term="%22Perez-Demydenko%2C+C%2E%22">Perez-Demydenko, C.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Aragón+Fernández%2C+B%2E%22">Aragón Fernández, B.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Lora-Serrano%2C+R%2E%22">Lora-Serrano, R.</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Physics+Communications%22">Computer Physics Communications</searchLink>. Dec2015, Vol. 197, p118-127. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vector+analysis%22">Vector analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert+space%22">Hilbert space</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+transformations%22">Mathematical transformations</searchLink><br /><searchLink fieldCode="DE" term="%22Ising+model%22">Ising model</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+sequences%22">Mathematical sequences</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The use of Hilbert curves to visualize massive vector of data is revisited following previous authors. The Hilbert curve mapping preserves locality and makes meaningful representation of the data. We call such visualization as Hilbert plots. The combination of a Hilbert plot with its Fourier transform allows to identify patterns in the underlying data sequence. The use of different granularity representation also allows to identify periodic intervals within the data. Data from different sources are presented: periodic, aperiodic, logistic map and 1/2-Ising model. A real data example from the study of heartbeat data is also discussed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Physics Communications is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.cpc.2015.08.019 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 118 Subjects: – SubjectFull: Vector analysis Type: general – SubjectFull: Hilbert space Type: general – SubjectFull: Mathematical transformations Type: general – SubjectFull: Ising model Type: general – SubjectFull: Mathematical sequences Type: general Titles: – TitleFull: Visualizing long vectors of measurements by use of the Hilbert curve. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Estevez-Rams, E. – PersonEntity: Name: NameFull: Perez-Demydenko, C. – PersonEntity: Name: NameFull: Aragón Fernández, B. – PersonEntity: Name: NameFull: Lora-Serrano, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00104655 Numbering: – Type: volume Value: 197 Titles: – TitleFull: Computer Physics Communications Type: main |
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