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Sampling and Signal Estimation in Computational Optical Sensors

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Date
2007-12-14
Author
Shankar, Mohan
Advisor
Brady, David J
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Abstract
Computational sensing utilizes non-conventional sampling mechanisms along with processing algorithms for accomplishing various sensing tasks. It provides additional flexibility in designing imaging or spectroscopic systems. This dissertation analyzes sampling and signal estimation techniques through three computational sensing systems to accomplish specific tasks. The first is thin long-wave infrared imaging systems through multichannel sampling. Significant reduction in optical system thickness is obtained over a conventional system by modifying conventional sampling mechanisms and applying reconstruction algorithms. In addition, an information theoretic analysis of sampling in conventional as well as multichannel imaging systems is also performed. The feasibility of performing multichannel sampling for imaging is demonstrated using an information theoretic metric. The second system is an application of the multichannel system for the design of compressive low-power video sensors. Two sampling schemes have been demonstrated that utilize spatial as well as temporal aliasing. The third system is a novel computational spectroscopic system for detecting chemicals that utilizes the surface plasmon resonances to encode information about the chemicals that are tested.
Type
Dissertation
Department
Electrical and Computer Engineering
Subject
Engineering, System Science
Engineering, Electronics and Electrical
computational sensing
computational imaging
compression
Permalink
https://hdl.handle.net/10161/445
Citation
Shankar, Mohan (2007). Sampling and Signal Estimation in Computational Optical Sensors. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/445.
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