Publication Date
2017
Document Type
Thesis
Committee Members
Joshua Ash (Advisor), Arnab Shaw (Committee Member), Vince Velten (Committee Member)
Degree Name
Master of Science in Electrical Engineering (MSEE)
Abstract
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.
Page Count
81
Department or Program
Department of Electrical Engineering
Year Degree Awarded
2017
Copyright
Copyright 2017, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0002-8253-3243
