School of Electrical, Computer and Energy Engineering
PhD Final Oral Defense
Biology-Based Matched Signal Processing and Physics-Based Modeling For Improved
Detection Applications
by
Brian N. O’Donnell
November 10, 2014
3:00PM
ISTB4 492
Committee:
Dr. Antonia Papandreou-Suppappola (chair)
Dr. Cihan Tepedelenlioglu
Dr. Daniel Bliss
Dr. Stephen Johnson
Dr. Narayan Kovvali
Abstract
Peptide microarrays have been used in molecular biology to profile immune responses
and develop diagnostic tools. When the microarrays are printed with random peptide
sequences, they can be used to identify antigen antibody binding patterns or
immunosignatures. In this thesis, an advanced signal processing method is proposed to
estimate epitope antigen sub-sequences as well as identify mimotope antigen subsequences that mimic the structure of epitopes from random-sequence peptide
microarrays. The method first maps peptide sequences to linear expansions of highlylocalized one-dimensional (1-D) time-varying signal, and uses a time-frequency
processing technique to detect recurring patterns in sub-sequences. This technique is
matched to the
aforementioned mapping scheme, and it allows for an inherent analysis on how
substitutions in the sub-sequences can affect antibody binding strength. The performance
of the proposed method is demonstrated by estimating epitopes and identifying potential
mimotopes for eight monoclonal antibody samples.
The proposed mapping is generalized to express information on a protein's sequence
location, structure and function onto a highly localized 3-D Gaussian waveform. In
particular, as analysis of protein homology has shown that incorporating different kinds
of information into an alignment process can yield more robust alignment algorithms, a
pairwise protein structure alignment method is proposed based on a joint similarity
measure of multiple mapped protein attributes. The 3-D mapping allocates protein
properties into distinct regions in the time-frequency plane in order to simplify the
alignment process by including all relevant information into a single, highly customizable
waveform. Simulations demonstrate the improved performance of the joint alignment
approach to infer relationships between proteins, and provide information on mutations
that cause changes to both the sequence and structure of a protein.
In addition to the biology-based signal processing methods, a statistical method is
considered that uses a physics-based model to improve processing performance. In
particular, an externally developed physics-based model for sea clutter is examined when
detecting a low radar cross-section target in heavy sea clutter. This novel model includes
a process that generates random dynamic sea clutter based on the governing physics of
water gravity and capillary waves and a finite-difference time-domain electromagnetics
simulations process based on Maxwell's equations propagating the radar signal. A
subspace clutter suppression detector is applied to remove dominant clutter eigenmodes,
and its improved performance over matched filtering is demonstrated using simulations.