Use of support vector machines and fabry-perot interferometry to classify states of a laser

<p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Open Access Thesis

Discipline thesis:degree_discipline

Physics and Astronomy

Year dc:date.available

2013

Author dc:creator

• McKinnon, John Motley

Contributors dc:contributor

• David Pawlowski, Ph.D, Chair
• David Johnson, Ph.D. (Michigan Aerospace)
• Ernest Behringer, Ph.D.

• Fabry-Perot interferometers
• support vector machines
• Fourier fit
• Astrophysics and Astronomy

Repository record dc:identifier

https://commons.emich.edu/theses/549

OAI identifier oai:identifier

oai:commons.emich.edu:theses-1922