Extended Upscale and Downscale Representation with Cascade Arrangement

Doan, QM; Dinh, TH; Trung, NL; Nguyen, DN; Singh, AK; Lin, C-T

Extended Upscale and Downscale Representation with Cascade Arrangement

Doan, QM Dinh, TH Trung, NL Nguyen, DN Singh, AK Lin, C-T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023, 2023-July, pp. 715-719
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Doan, QM
dc.contributor.author	Dinh, TH
dc.contributor.author	Trung, NL
dc.contributor.author	Nguyen, DN
dc.contributor.author	Singh, AK
dc.contributor.author	Lin, C-T
dc.date	2023-07-02
dc.date.accessioned	2024-02-15T11:48:03Z
dc.date.available	2024-02-15T11:48:03Z
dc.date.issued	2023-01-01
dc.identifier.citation	2023 IEEE Statistical Signal Processing Workshop (SSP), 2023, 2023-July, pp. 715-719
dc.identifier.isbn	9781665452458
dc.identifier.issn	2373-0803
dc.identifier.uri	http://hdl.handle.net/10453/175696
dc.description.abstract	Smoothing filters are widely used in EEG signal processing for noise removal while preserving important features. Unlike common approaches in the time domain, a recent effective algorithm using the Upscale and Downscale Representation (UDR) technique has been introduced to process the signal in the image domain. The idea of UDR is to visualize the input with an appropriate line width, convert it to a binary image, and then smooth it by skeletonizing the signal object to a unit width and projecting it back to the time domain. We propose in this paper a cascaded UDR (CUDR) where the interested signal is filtered twice. CUDR's performance is verified on simulated data with added white Gaussian noise and compared with the cascaded arrangement of some conventional techniques. Experimental results have demonstrated the outperformance of CUDR in terms of the fitting error when dealing with noisy signals, especially at a low signal-to-noise ratio.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE Statistical Signal Processing Workshop (SSP)
dc.relation.ispartof	22nd IEEE Statistical Signal Processing Workshop (SSP)
dc.relation.ispartofseries	IEEE/SP Workshop on Statistical Signal Processing
dc.relation.isbasedon	10.1109/ssp53291.2023.10207993
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Extended Upscale and Downscale Representation with Cascade Arrangement
dc.type	Conference Proceeding
utslib.citation.volume	2023-July
utslib.location.activity	VIETNAM, VNU Univ Engn & Technol, Hanoi
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-08-09T00:00:00+1000Z
dc.date.updated	2024-02-15T11:48:02Z
pubs.finish-date	2023-07-05
pubs.publication-status	Published
pubs.start-date	2023-07-02
pubs.volume	2023-July

Abstract:

Smoothing filters are widely used in EEG signal processing for noise removal while preserving important features. Unlike common approaches in the time domain, a recent effective algorithm using the Upscale and Downscale Representation (UDR) technique has been introduced to process the signal in the image domain. The idea of UDR is to visualize the input with an appropriate line width, convert it to a binary image, and then smooth it by skeletonizing the signal object to a unit width and projecting it back to the time domain. We propose in this paper a cascaded UDR (CUDR) where the interested signal is filtered twice. CUDR's performance is verified on simulated data with added white Gaussian noise and compared with the cascaded arrangement of some conventional techniques. Experimental results have demonstrated the outperformance of CUDR in terms of the fitting error when dealing with noisy signals, especially at a low signal-to-noise ratio.

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http://hdl.handle.net/10453/175696