Semester Award Granted

Spring 2025

Submission Date

May 2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Thesis/Dissertation Advisor [Chair]

Hanqi Zhuang

Thesis/Dissertation Co-Chair

Laurent Cherubin

Abstract

This dissertation examines multidisciplinary methods in marine research, computational modeling, and artificial intelligence. This dissertation tackles significant issues in ecological monitoring, ocean dynamics forecasting, and assistive technology via three independent but interrelated research streams.

The first study examines the localization and distribution of marine organisms along Florida’s East Coast via passive acoustic monitoring methods. The research analyzed more than 65,000 audio recordings to delineate the geographical distribution of several marine creatures, such as black drum, toadfish, Sei whales, North Atlantic right whales, dolphins, and false killer whales. A novel automated detection and localization model was developed, using an adaptive matching filter and Time Difference of Arrival (TDOA) algorithm, which demonstrated accurate marine species tracking with localization errors of about 2 meters across distances of 50 meters.

The second research enhances oceanic modeling with a hybrid deep learning methodology that integrates Empirical Orthogonal Function (EOF) analysis with a Fourier Neural Operator (FNO). This novel technique significantly improves the prediction of ocean velocity fields, demonstrating exceptional performance across several datasets. The suggested EOF-FNO model has exceptional zero-shot super-resolution capabilities and shows improved stability in capturing intricate spatiotemporal marine dynamics, offering a viable computational approach for comprehending oceanic systems.

The last research examines deep learning applications in biometrics, with a particular focus on communication accessibility and security technologies. The research attained exceptional performance in American Sign Language (ASL) recognition and Finger Knuckle Print (FKP) classification via extensive comparative analyses of several neural network architectures. The Vision Mamba (ViM) models have shown exceptional performance, with an accuracy of 99.98% in ASL recognition and 99.1% in biometric identification, underscoring the capabilities of modern deep learning methodologies in assistive and security applications.

These research efforts bring advanced computational approaches that integrate ecological monitoring, deep learning, and biometric innovation. This dissertation integrates modern sensing technologies, complex algorithms, and deep learning methods to provide unique insights and tools for comprehending marine ecosystems, forecasting oceanic incidents, and creating dependable assistive technologies. This work’s highlights the capacity of the advanced computational methods in tackling intricate scientific and technical issues.

Download

Share

COinS