Theory and Implementation of an Analog-to-Information Converter using Random Demodulation

By Jason N. Laska, Sami Kirolos, Marco F. Duarte, Tamer Ragheb, Richard Baraniuk, Yehia Massoud. 2007 IEEE International Symposium on Circuits and Systems, 2007

447 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5cece4ae79083d54c20160b675d77da63f8a02da

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In Biomedical Engineering

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Citations in the corpus

Compressive sensing: From theory to applications, a survey (2013, unknown): “The initially developed random demodulator was limited to discrete multi-tone signals and incurred high computational load [81].”
Compressed Synthetic Aperture Radar (2010, background): “The image formation processor produces an image that is a two-dimensional mapping of the illuminated scene [4].”
A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications (2018, background): “Here, φ can be considered as the product of two matrices H and P [18], [19].”
An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (2010, background): “Many applications have been studied, such as reconstruction or denoising of Magnetic Resonance Images (MRI) [29, 30], analog-to-information conversion [31], sensor networks [34, 35], and even homeland security [68].”
On compressive sensing applied to radar (2010, background): “The most obvious application to radar is pulse compression which has been investigated also in some of the cited CS-radar papers and in [17,18].”
Xampling: Analog to digital at sub-Nyquist rates (2009, methodology): “The technique of random demodulation underlying [28], [29] was introduced in [30], where the building blocks were simulated in HSPICE for synthetic inputs, matching the discretized model of [28].”
Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals (2007, unknown): “Indicator sets for vectors and matrices are defined respectively as I(( ¯ v)) = {k | ( ¯ v)(k) 6= 0}, I(( ¯ A)) = {k | (( ¯ A)T )k 6= ( ¯ 0)}.”
Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors (2012, methodology): “analog implementation similar to those described in [18] and”
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors (2008, unknown): “derlying discrete finite-dimensional signal model [19].”
Compressive Radar Imaging (2007, methodology): “Third, there is a subtle tradeoff to optimize between the reduction in sampling rate � N/M� and the dynamic range of the resulting CS system [ 15 ].”
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals (2009, methodology): “The random demodulator was introduced in two earlier papers [39], [ 40 ], which offer preliminary work on the system architecture and experimental studies of the system’s behavior.”
An efficient augmented Lagrangian method with applications to total variation minimization (2013, background): “Nowadays, CS has been widely studied and applied to various fields (see [10, 13, 22–25] for example).”
Fixed-Point Continuation for l1-Minimization: Methodology and Convergence (2008, background): “Applications of compressed sensing include compressive imaging [56, 65, 66], medical imaging [42], multisensor and distributed compressed sensing [1], analog-to-information conversion [36, 37, 38, 59], and missing data recovery [67].”