Date Approved
7-15-2013
Date Posted
4-7-2014
Degree Type
Open Access Thesis
Degree Name
Master of Science (MS)
Department or School
Physics and Astronomy
Committee Member
David Pawlowski, Ph.D, Chair
Committee Member
David Johnson, Ph.D. (Michigan Aerospace)
Committee Member
Ernest Behringer, Ph.D.
Committee Member
Marshall Thomsen, Ph.D.
Abstract
This thesis develops an algorithm that can determine if a laser is functioning correctly over a long period of time. A Fourier fit is created to model fringe profiles from a Fabry-Perot interferometer, and singular value decomposition is used to reduce noise in each signal. Levenberg-Marquardt gradient descent is performed to correctly locate the center of each image and to optimize each fit with respect to the spatial frequency. The Fourier fit is used to extract important information from each image to be used for separating the image types from one another. Principal component analysis is used to reduce the dimensionality of the data set and to plot a projection of the data using its first two principal components. It is determined that the image data are not linearly separable and require a non-linear support vector network to complete the classification of each image type.
Recommended Citation
McKinnon, John Motley, "Use of support vector machines and fabry-perot interferometry to classify states of a laser" (2013). Master's Theses and Doctoral Dissertations. 549.
https://commons.emich.edu/theses/549