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Random Acquisition in Compressive Sensing: A Comprehensive Overview

Author

Listed:
  • Mahdi Khosravy

    (Osaka University, Japan)

  • Thales Wulfert Cabral

    (State University of Campinas, Brazil)

  • Max Mateus Luiz

    (Federal University of Juiz de Fora, Brazil)

  • Neeraj Gupta

    (Oakland University, USA)

  • Ruben Gonzalez Crespo

    (Universidad Internacional de La Rioja, Spain)

Abstract

Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).

Suggested Citation

  • Mahdi Khosravy & Thales Wulfert Cabral & Max Mateus Luiz & Neeraj Gupta & Ruben Gonzalez Crespo, 2021. "Random Acquisition in Compressive Sensing: A Comprehensive Overview," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 12(3), pages 140-165, July.
    • Handle: RePEc:igg:jaci00:v:12:y:2021:i:3:p:140-165
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