Compression at the Physical Interface

Healy, Dennis M.; Brady, David J.

doi:10.1109/msp.2007.914996

Cited by 35 publications

(19 citation statements)

References 11 publications

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“…willem@gatech.edu shifting the workload from sensor hardware to software [8][9][10] and is natural in applications where physical measurements are expensive compared to numerical computations. This work explores a variation on this theme: mitigating the computational workload in software instead of the sensing workload in hardware.…”

Section: A Background and Motivationmentioning

confidence: 99%

Compressive matched-field processing

Mantzel

Romberg

Sabra

2012

The Journal of the Acoustical Society of America

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Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented that demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.

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Section: A Background and Motivationmentioning

confidence: 99%

Compressive matched-field processing

Mantzel

Romberg

Sabra

2012

The Journal of the Acoustical Society of America

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“…Other random distributions may also be considered, including matrices with random ± 1 √ m entries. Consequently, a number of new sensing hardware architectures, from analog-to-digital converters to digital cameras, are being developed to take advantage of the benefits of random measurements [6,7].…”

Section: B Compressive Sensing and Randomized Measurementsmentioning

confidence: 99%

On the observability of linear systems from random, compressive measurements

Wakin

Sanandaji

Vincent

2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-Recovering or estimating the initial state of a highdimensional system can require a potentially large number of measurements. In this paper, we explain how this burden can be significantly reduced for certain linear systems when randomized measurement operators are employed. Our work builds upon recent results from the field of Compressive Sensing (CS), in which a high-dimensional signal containing few nonzero entries can be efficiently recovered from a small number of random measurements. In particular, we develop concentration of measure bounds for the observability matrix and explain circumstances under which this matrix can satisfy the Restricted Isometry Property (RIP), which is central to much analysis in CS. We also illustrate our results with a simple case study of a diffusion system. Aside from permitting recovery of sparse initial states, our analysis has potential applications in solving inference problems such as detection and classification of more general initial states.

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“…Being one of the first components in the signal acquisition chain, an ADC can become a bottleneck for the performance of the entire system. While the clock speed of digital computers has been steadily increasing over the last several decades, the conversion speed of ADCs has not been improving at the same rate [1]. The speed limitation of modern ADCs has slowed down progress in applications such as Software Define Radio (SDR) where ultra-fast converters are needed to sample the radio spectrum.…”

Section: Introductionmentioning

confidence: 99%

Parallel architecture for analog-to-information converters via multibit delta-sigma modulation

Leon-Salas

2012

10th IEEE International NEWCAS Conference

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This paper presents a parallel architecture for analog-to-information converters. The converter's architecture shifts most of the computational burden to the digital domain to take full advantage of deep-submicron IC fabrication technologies. The analog part of the converter is composed of a single multibit first-order Delta-Sigma modulator. The proposed architecture is validated through numerical simulations. The results show that signal recovery improves significantly when a 2-bit and a 3-bit Delta-Sigma modulators are employed.

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Compression at the Physical Interface

Cited by 35 publications

References 11 publications

Compressive matched-field processing

Compressive matched-field processing

On the observability of linear systems from random, compressive measurements

Parallel architecture for analog-to-information converters via multibit delta-sigma modulation

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