Reconstructing Binary Signals from Local Histograms

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Reconstructing Binary Signals from Local Histograms. / Sporring, Jon; Darkner, Sune.

In: Entropy, Vol. 24, No. 3, 433, 21.03.2022, p. 1-11.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Sporring, J & Darkner, S 2022, 'Reconstructing Binary Signals from Local Histograms', Entropy, vol. 24, no. 3, 433, pp. 1-11. <http://10.3390/e24030433>

APA

Sporring, J., & Darkner, S. (2022). Reconstructing Binary Signals from Local Histograms. Entropy, 24(3), 1-11. [433]. http://10.3390/e24030433

Vancouver

Sporring J, Darkner S. Reconstructing Binary Signals from Local Histograms. Entropy. 2022 Mar 21;24(3):1-11. 433.

Author

Sporring, Jon ; Darkner, Sune. / Reconstructing Binary Signals from Local Histograms. In: Entropy. 2022 ; Vol. 24, No. 3. pp. 1-11.

Bibtex

@article{a18a7cda60cd4881b627953ab2ca1d0e,
title = "Reconstructing Binary Signals from Local Histograms",
abstract = "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.",
author = "Jon Sporring and Sune Darkner",
year = "2022",
month = mar,
day = "21",
language = "English",
volume = "24",
pages = "1--11",
journal = "Entropy",
issn = "1099-4300",
publisher = "MDPI AG",
number = "3",

}

RIS

TY - JOUR

T1 - Reconstructing Binary Signals from Local Histograms

AU - Sporring, Jon

AU - Darkner, Sune

PY - 2022/3/21

Y1 - 2022/3/21

N2 - 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.

AB - 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.

M3 - Journal article

VL - 24

SP - 1

EP - 11

JO - Entropy

JF - Entropy

SN - 1099-4300

IS - 3

M1 - 433

ER -

ID: 300706180