Reconstructing Binary Signals from Local Histograms

In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state...

Descripción completa

Detalles Bibliográficos
Autores principales: Sporring, Jon, Darkner, Sune
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953632/
https://www.ncbi.nlm.nih.gov/pubmed/35327943
http://dx.doi.org/10.3390/e24030433
_version_ 1784675899287797760
author Sporring, Jon
Darkner, Sune
author_facet Sporring, Jon
Darkner, Sune
author_sort Sporring, Jon
collection PubMed
description In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state the values for the sizes of the number of unique signals for a given set of histograms, as well as give bounds on the number of metameric classes, where a metameric class is a set of signals larger than one, which has the same set of densely overlapping histograms.
format Online
Article
Text
id pubmed-8953632
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-89536322022-03-26 Reconstructing Binary Signals from Local Histograms Sporring, Jon Darkner, Sune Entropy (Basel) Article In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state the values for the sizes of the number of unique signals for a given set of histograms, as well as give bounds on the number of metameric classes, where a metameric class is a set of signals larger than one, which has the same set of densely overlapping histograms. MDPI 2022-03-21 /pmc/articles/PMC8953632/ /pubmed/35327943 http://dx.doi.org/10.3390/e24030433 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sporring, Jon
Darkner, Sune
Reconstructing Binary Signals from Local Histograms
title Reconstructing Binary Signals from Local Histograms
title_full Reconstructing Binary Signals from Local Histograms
title_fullStr Reconstructing Binary Signals from Local Histograms
title_full_unstemmed Reconstructing Binary Signals from Local Histograms
title_short Reconstructing Binary Signals from Local Histograms
title_sort reconstructing binary signals from local histograms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953632/
https://www.ncbi.nlm.nih.gov/pubmed/35327943
http://dx.doi.org/10.3390/e24030433
work_keys_str_mv AT sporringjon reconstructingbinarysignalsfromlocalhistograms
AT darknersune reconstructingbinarysignalsfromlocalhistograms

Ejemplares similares