Sparse Signal Reconstruction using Weight Point Algorithm

Usman, Koredianto; Gunawan, Hendra; Suksmono, Andriyan B.

doi:10.5614/itbj.ict.res.appl.2018.12.1.3

Jurnal SINTA 2

Sparse Signal Reconstruction using Weight Point Algorithm

Journal of ICT Research and Applications (JICTRA) · Vol. 12 No. 1 (2018) · pp. 35–53

DOI: 10.5614/itbj.ict.res.appl.2018.12.1.3
ISSN: 2338-5499, 2337-5787
Published: 2018-04-30
Publisher: DRI - ITB
Language: eng

Abstract

In this paper we propose a new approach of the compressive sensing (CS) reconstruction problem based on a geometrical interpretation of l1-norm minimization. By taking a large l1-norm value at the initial step, the intersection of l1-norm and the constraint curves forms a convex polytope and by exploiting the fact that any convex combination of the polytope's vertexes gives a new point that has a smaller l1-norm, we are able to derive a new algorithm to solve the CS reconstruction problem. Compared to the greedy algorithm, this algorithm has better performance, especially in highly coherent environments. Compared to the convex optimization, the proposed algorithm has simpler computation requirements. We tested the capability of this algorithm in reconstructing a randomly down-sampled version of the Dow Jones Industrial Average (DJIA) index. The proposed algorithm achieved a good result but only works on real-valued signals.

Keywords

Collaboration network of the authors

10 authors · 17 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

Bandung Institute of Technology
Telkom University
Indonesian Institute of Sciences

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Sugihartono Sugihartono	Bandung Institute of Technology	2
Rina Pudji Astuti	Bandung Institute of Technology	1
Adit Kurniawan	Bandung Institute of Technology	1
Agus Subekti	Indonesian Institute of Sciences	1
Nana Rachmana Syambas	Bandung Institute of Technology	1
Ledya Novamizanti	Telkom University	1
Afif Priyambodo	Telkom University	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Sparse Signal Reconstruction using Weight Point Algorithm</dc:title>
	<dc:creator>Usman, Koredianto</dc:creator>
	<dc:creator>Gunawan, Hendra</dc:creator>
	<dc:creator>Suksmono, Andriyan B.</dc:creator>
	<dc:subject xml:lang="en-US">compressive sampling</dc:subject>
	<dc:subject xml:lang="en-US">convex combination</dc:subject>
	<dc:subject xml:lang="en-US">convex polytope</dc:subject>
	<dc:subject xml:lang="en-US">sparse reconstruction</dc:subject>
	<dc:subject xml:lang="en-US">l1-norm</dc:subject>
	<dc:subject xml:lang="en-US">weight point.</dc:subject>
	<dc:description xml:lang="en-US">In this paper we propose a new approach of the compressive sensing (CS) reconstruction problem based on a geometrical interpretation of l1-norm minimization. By taking a large l1-norm value at the initial step, the intersection of l1-norm and the constraint curves forms a convex polytope and by exploiting the fact that any convex combination of the polytope's vertexes gives a new point that has a smaller l1-norm, we are able to derive a new algorithm to solve the CS reconstruction problem. Compared to the greedy algorithm, this algorithm has better performance, especially in highly coherent environments. Compared to the convex optimization, the proposed algorithm has simpler computation requirements. We tested the capability of this algorithm in reconstructing a randomly down-sampled version of the Dow Jones Industrial Average (DJIA) index. The proposed algorithm achieved a good result but only works on real-valued signals.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2018-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/3393</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2018.12.1.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 12 No. 1 (2018); 35-53</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2018.12.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/3393/3252</dc:relation>
</oai_dc:dc>