Using an Inductive Learning Algorithm to Improve Antibody Generation in a Single Packet Computer Defense Immune System
Date of Award
Master of Science
Department of Electrical and Computer Engineering
Gregg H. Gunsch, PhD
Coherent optical sources in the mid-infrared region (mid-IR) are important fundamental tools for infrared countermeasures and battlefield remote sensing. Nonlinear optical effects can be applied to convert existing near-IR laser sources to radiate in the mid-IR. This research focused on achieving such a conversion with a quasi-phase matched optical parametric oscillators using orientation-patterned gallium arsenide (OPGaAs), a material that can be quasi-phased matched by periodically reversing the crystal structure during the epitaxial growth process. Although non-linear optical conversion was not ultimately achieved during this research, many valuable lessons were learned from working with this material. This thesis reviews the theory of nonlinear optics and explores the importance of accurate refractive index measurements to proper structure design. The details of four nonlinear optical experiments are presented recommendations are offered for the design of future OPGaAs crystals. Recommendations are also made for improved experimental techniques.
DTIC Accession Number
Aycock, Russell J., "Using an Inductive Learning Algorithm to Improve Antibody Generation in a Single Packet Computer Defense Immune System" (2002). Theses and Dissertations. 4470.