Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing

Leon Craven; Oliver Nagy; Leif Hanlen

doi:10.1109/AUSCTW.2010.5426774

Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing

Leon Craven^*, Oliver Nagy, Leif Hanlen

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

We show that data reconstruction with analogue-to-information converters can generally be improved by applying a window function. For data recovery via compressed sensing, the choice of window function depends on the number of samples acquired, and any window is better than no window. We also demonstrate that windows can be applied a posteriori in random sampling analogue-to- information converter systems.

Original language	English
Title of host publication	2010 Australian Communications Theory Workshop, AusCTW 2010
Pages	93-96
Number of pages	4
DOIs	https://doi.org/10.1109/AUSCTW.2010.5426774
Publication status	Published - 2010
Event	2010 Australian Communications Theory Workshop, AusCTW 2010 - Canberra, ACT, Australia Duration: 3 Feb 2010 → 5 Feb 2010

Publication series

Name	2010 Australian Communications Theory Workshop, AusCTW 2010

Conference

Conference	2010 Australian Communications Theory Workshop, AusCTW 2010
Country/Territory	Australia
City	Canberra, ACT
Period	3/02/10 → 5/02/10

Access to Document

10.1109/AUSCTW.2010.5426774

Cite this

@inproceedings{bb49b43e21e04e25a5da3022e1a44120,

title = "Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing",

abstract = "We show that data reconstruction with analogue-to-information converters can generally be improved by applying a window function. For data recovery via compressed sensing, the choice of window function depends on the number of samples acquired, and any window is better than no window. We also demonstrate that windows can be applied a posteriori in random sampling analogue-to- information converter systems.",

keywords = "Analogue-to-information conversion, Compressed sensing, Window function",

author = "Leon Craven and Oliver Nagy and Leif Hanlen",

year = "2010",

doi = "10.1109/AUSCTW.2010.5426774",

language = "English",

isbn = "9781424454334",

series = "2010 Australian Communications Theory Workshop, AusCTW 2010",

pages = "93--96",

booktitle = "2010 Australian Communications Theory Workshop, AusCTW 2010",

note = "2010 Australian Communications Theory Workshop, AusCTW 2010 ; Conference date: 03-02-2010 Through 05-02-2010",

}

Craven, L, Nagy, O & Hanlen, L 2010, Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing. in 2010 Australian Communications Theory Workshop, AusCTW 2010., 5426774, 2010 Australian Communications Theory Workshop, AusCTW 2010, pp. 93-96, 2010 Australian Communications Theory Workshop, AusCTW 2010, Canberra, ACT, Australia, 3/02/10. https://doi.org/10.1109/AUSCTW.2010.5426774

Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing. / Craven, Leon; Nagy, Oliver; Hanlen, Leif.
2010 Australian Communications Theory Workshop, AusCTW 2010. 2010. p. 93-96 5426774 (2010 Australian Communications Theory Workshop, AusCTW 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing

AU - Craven, Leon

AU - Nagy, Oliver

AU - Hanlen, Leif

PY - 2010

Y1 - 2010

N2 - We show that data reconstruction with analogue-to-information converters can generally be improved by applying a window function. For data recovery via compressed sensing, the choice of window function depends on the number of samples acquired, and any window is better than no window. We also demonstrate that windows can be applied a posteriori in random sampling analogue-to- information converter systems.

AB - We show that data reconstruction with analogue-to-information converters can generally be improved by applying a window function. For data recovery via compressed sensing, the choice of window function depends on the number of samples acquired, and any window is better than no window. We also demonstrate that windows can be applied a posteriori in random sampling analogue-to- information converter systems.

KW - Analogue-to-information conversion

KW - Compressed sensing

KW - Window function

UR - http://www.scopus.com/inward/record.url?scp=77951485626&partnerID=8YFLogxK

U2 - 10.1109/AUSCTW.2010.5426774

DO - 10.1109/AUSCTW.2010.5426774

M3 - Conference contribution

SN - 9781424454334

T3 - 2010 Australian Communications Theory Workshop, AusCTW 2010

SP - 93

EP - 96

BT - 2010 Australian Communications Theory Workshop, AusCTW 2010

T2 - 2010 Australian Communications Theory Workshop, AusCTW 2010

Y2 - 3 February 2010 through 5 February 2010

ER -

Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this