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Proceedings Paper

Compressive sampling strategies for integrated microspectrometers
Author(s): David J. Brady; Michael E. Gehm; Nikos Pitsianis; Xiaobai Sun
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Paper Abstract

We consider compressive sensing in the context of optical spectroscopy. With compressive sensing, the ratio between the number of measurements and the number of estimated values is less than one, without compromising the fidelity in estimation. A compressive sensing system is composed of a measurement subsystem that maps a signal to digital data and an inference algorithm that maps the data to a signal estimate. The inference algorithm exploits both the information captured in the measurement and certain a priori information about the signals of interest, while the measurement subsystem provides complementary, signal-specific information at the lowest sampling rate possible. Codesign of the measurement strategies, the model of a priori information, and the inference algorithm is the central problem of system design. This paper describes measurement constraints specific to optical spectrometers, inference models based on physical or statistical characteristics of the signals, as well as linear and nonlinear reconstruction algorithms. We compare the fidelity of sampling and inference strategies over a family of spectral signals.

Paper Details

Date Published: 18 May 2006
PDF: 9 pages
Proc. SPIE 6232, Intelligent Integrated Microsystems, 62320C (18 May 2006); doi: 10.1117/12.666124
Show Author Affiliations
David J. Brady, Duke Univ. (United States)
Michael E. Gehm, Duke Univ. (United States)
Nikos Pitsianis, Duke Univ. (United States)
Xiaobai Sun, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 6232:
Intelligent Integrated Microsystems
Ravindra A. Athale; John C. Zolper, Editor(s)

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