Calibration Models and System Development for Compressive Sensing with Micromirror Arrays

Calibration Models and System Development for Compressive Sensing with Micromirror Arrays

Bibliographic Details
Main Author: Profeta, Rebecca L.
Language:English
Published: Wright State University / OhioLINK 2017
Subjects:
Electrical Engineering
Compressive Sensing
Digital Mircromirror Device
Calibration
Bayesian Compressive Sensing
Relevance Vector Machine
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=wright15160282553897
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-wright151602825538972021-08-03T07:05:20Z Calibration Models and System Development for Compressive Sensing with Micromirror Arrays Profeta, Rebecca L. Electrical Engineering Compressive Sensing Digital Mircromirror Device Calibration Bayesian Compressive Sensing Relevance Vector Machine Compressive sensing (CS) is an active research field focused on finding solutions to sparse linear inverse problems, i.e. estimating a signal using fewer linear measurements than there are unknowns. The assumption of signal sparsity makes solutions to this otherwise ill-posed problem possible and has lead to a number of technological innovations such as smaller and less expensive cameras that capture high resolution imagery, low-power radar systems, and accelerated MRI scanners.In this thesis, we present the development of a hardware CS imaging system using a Digital Micromirror Device (DMD) providing spatial light modulation via an array of micromirrors that can be programmatically controlled to produce automated measurements. Additionally, we develop a number of new DMD-specific calibration models intended to capture the physical attributes of micromirrors and the end-to-end data collection system. Algorithms are derived to fit the calibration models from training data, and resultant CS reconstructions demonstrate a substantial reduction in image estimation error while reducing the number of required measurements by fifty percent, relative to current baseline calibration methods. 2017 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright15160282553897 http://rave.ohiolink.edu/etdc/view?acc_num=wright15160282553897 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Electrical Engineering
Compressive Sensing
Digital Mircromirror Device
Calibration
Bayesian Compressive Sensing
Relevance Vector Machine
spellingShingle Electrical Engineering
Compressive Sensing
Digital Mircromirror Device
Calibration
Bayesian Compressive Sensing
Relevance Vector Machine
Profeta, Rebecca L.
Calibration Models and System Development for Compressive Sensing with Micromirror Arrays
author Profeta, Rebecca L.
author_facet Profeta, Rebecca L.
author_sort Profeta, Rebecca L.
title Calibration Models and System Development for Compressive Sensing with Micromirror Arrays
title_short Calibration Models and System Development for Compressive Sensing with Micromirror Arrays
title_full Calibration Models and System Development for Compressive Sensing with Micromirror Arrays
title_fullStr Calibration Models and System Development for Compressive Sensing with Micromirror Arrays
title_full_unstemmed Calibration Models and System Development for Compressive Sensing with Micromirror Arrays
title_sort calibration models and system development for compressive sensing with micromirror arrays
publisher Wright State University / OhioLINK
publishDate 2017
url http://rave.ohiolink.edu/etdc/view?acc_num=wright15160282553897
work_keys_str_mv AT profetarebeccal calibrationmodelsandsystemdevelopmentforcompressivesensingwithmicromirrorarrays
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