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Sampling and Signal Estimation in Computational Optical Sensors

dc.contributor.advisor Brady, David J
dc.contributor.author Shankar, Mohan
dc.date.accessioned 2008-01-02T16:33:37Z
dc.date.available 2008-01-02T16:33:37Z
dc.date.issued 2007-12-14
dc.identifier.uri https://hdl.handle.net/10161/445
dc.description.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.
dc.format.extent 13460154 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Engineering, System Science
dc.subject Engineering, Electronics and Electrical
dc.subject computational sensing
dc.subject computational imaging
dc.subject compression
dc.title Sampling and Signal Estimation in Computational Optical Sensors
dc.type Dissertation
dc.department Electrical and Computer Engineering


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