Framework of compressive sampling with its applications to one- and two-dimensional signals

Rachit Patel, Prabhat Thakur, Sapna Katiyar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Compressive sampling emerged as a very useful random protocol and has become an active research area for almost a decade. Compressive sampling allows us to sample a signal below Shannon Nyquist rate and assures its successful reconstruction with some limitations on signal, that is, signal should be sparse in some domain. In this paper, we have used compressive sampling for an arbitrary one-dimensional signal and two-dimensional image signal compression and successfully reconstructed them by solving L1-norm optimization problems. We also have showed that compressive sampling can be implemented if a signal is sparse and incoherent through simulations. Further, we have analyzed the effect of noise on the recovery.

Original languageEnglish
Title of host publicationProceedings of the International Congress on Information and Communication Technology, ICICT 2015
EditorsDurgesh Kumar Mishra, Suresh Chandra Satapathy, Yogesh Chandra Bhatt, Amit Joshi
PublisherSpringer Verlag
Pages11-20
Number of pages10
ISBN (Print)9789811007668
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventInternational Congress on Information and Communication Technology, ICICT 2015 - Udaipur, India
Duration: 9 Oct 201510 Oct 2015

Publication series

NameAdvances in Intelligent Systems and Computing
Volume438
ISSN (Print)2194-5357

Conference

ConferenceInternational Congress on Information and Communication Technology, ICICT 2015
Country/TerritoryIndia
CityUdaipur
Period9/10/1510/10/15

Keywords

  • Basis function
  • Compressive sampling
  • Incoherent signal
  • L1-norm
  • Sparse signal

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science

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