Physical Sciences and Mathematics Commons^™

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Visual Analytics And Modeling Of Materials Property Data, Diwas Bhattarai

LSU Doctoral Dissertations

Due to significant advancements in experimental and computational techniques, materials data are abundant. To facilitate data-driven research, it calls for a system for managing and sharing data and supporting a set of tools for effective data analysis and modeling. Generally, a given material property M can be considered as a multivariate data problem. The dimensions of M are the values of the property itself, the conditions (pressure P, temperature T, and multi-component composition X) that control the concerned property, and relevant metadata I (source, date).

Here we present a comprehensive database considering both experimental and computational sources …

Advanced Communication And Sensing Protocols Using Twisted Light And Engineered Quantum Statistics, Michelle L. Lollie

LSU Doctoral Dissertations

Advanced performance of modern technology at a fundamental physical level is driving new innovations in communication, sensing capability, and information processing. Key to this improvement is the ability to harness the power of physical phenomena at the quantum mechanical level, where light and light-matter interactions produce technological advancement not realizable by classical means. Theoretical investigation into quantum computing, sensing capability beyond classical limits, and quantum information has prompted experimental work to bring state-of-the-art quantum systems to the forefront for commercial use. This dissertation contributes to the latter portion of the work. A set of preliminaries is included highlighting pertinent physical …

Diffusional Fractionation Of Helium Isotopes In Silicate Melts, Haiyang Luo, Bijaya Karki, Dipta B. Ghosh, Huiming Bao

Faculty Publications

Estimating Helium (He) concentration and isotope composition of the mantle requires quantifying He loss during magma degassing. The knowledge of diffusional He isotope fractionation in silicate melts may be essential to constrain the He loss. Isotopic mass dependence of He diffusion can be empirically expressed as D3He/D4He = (4/3)^β, where D is the diffusivity of a He isotope. However, no studies have reported any β values for He in silicate melts due to technical challenges in both experiments and computations. Here, molecular dynamics simulations based on deep neural network potentials trained by ab initio data …

Computer Simulations Of Diffusional Isotope Effects And Dynamical Properties Of Silicate Melts, Haiyang Luo

LSU Doctoral Dissertations

Silicate melts have served as transport agents in the chemical and thermal evolution of Earth. Diffusional isotope effect in silicate melts is the key to interpret isotope variations in lots of geological samples. Isotopic mass dependence of diffusion is commonly expressed as (Di/Dj)=(mj/mi)^β, where Di and Dj are diffusion coefficients of two isotopes whose masses are mi and mj. However, how the dimensionless empirical parameter β depends on temperature, pressure, and composition remains poorly constrained. Viscosity and electrical conductivity are two fundamental dynamical properties of silicate melts needed to constrain melt distribution in Earth's interior but remain unclear for most …

Machine Learning Methods For Depression Detection Using Smri And Rs-Fmri Images, Marzieh Sadat Mousavian

LSU Doctoral Dissertations

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data.

In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either …

Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick

LSU Doctoral Dissertations

The dissertation focuses on western region of Southwest Pacific Ocean (SWPO)

basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed

and modeled data. The classification-based machine learning approach

identifies the synoptic geophysical and aerosol environment favorable or unfavorable

for TC intensification and intensity change prior to landfall incorporating

observational and satellite data. A multiple poisson regression model with varying

temporal monthly lags was used to build a relationship between the number of

monthly TC days with basin wide average dust aerosol optical depth (AOD), sea

surface temperature (SST), and upper ocean temperature (UOT). This idea …

Habitat Associations And Reproduction Of Fishes On The Northwestern Gulf Of Mexico Shelf Edge, Elizabeth Marie Keller

LSU Doctoral Dissertations

Several of the northwestern Gulf of Mexico (GOM) shelf-edge banks provide critical hard bottom habitat for coral and fish communities, supporting a wide diversity of ecologically and economically important species. These sites may be fish aggregation and spawning sites and provide important habitat for fish growth and reproduction. Already designated as habitat areas of particular concern, many of these banks are also under consideration for inclusion in the expansion of the Flower Garden Banks National Marine Sanctuary. This project aimed to gain a more comprehensive understanding of the communities and fish species on shelf-edge banks by way of gonad histology, …

The Importance Of Landscape Position Information And Elevation Uncertainty For Barrier Island Habitat Mapping And Modeling, Nicholas Matthew Enwright

LSU Doctoral Dissertations

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Landscape position, such as elevation and distance from shore, influences habitat coverage on barrier islands by regulating exposure to abiotic factors, including waves, tides, and salt spray. Geographers commonly use aerial topographic lidar data for extracting landscape position information. However, researchers rarely consider lidar elevation uncertainty when using automated processes for extracting elevation-dependent habitats from lidar data. …

Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga

LSU Master's Theses

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different …

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …

Using Tourmaline As An Indicator Of Provenance: Development And Application Of A Statistical Approach Using Random Forests, Erin Lael Walden

LSU Master's Theses

Tourmaline is a petrologic indicator mineral that is the major repository of boron in the earth’s crust. It forms readily when boron is present, accommodating multiple cations and anions with multiple possible substitutions for each site in the crystal structure. It is stable over a wide variety of pressures and temperatures, from near-surface P/T conditions to greater than 950 C and 7 GPa. It records information about conditions of formation, as well as pressure and temperature. Due to its resistance to chemical or physical weathering, and the negligible diffusion of elements in the crystal lattice, information about provenance is preserved. …

Seavipers - Computer Vision And Inertial Position Reference Sensor System (Cviprss), Justin Lee Erdman

LSU Doctoral Dissertations

This work describes the design and development of an optical, Computer Vision (CV) based sensor for use as a Position Reference System (PRS) in Dynamic Positioning (DP). Using a combination of robotics and CV techniques, the sensor provides range and heading information to a selected reference object. The proposed optical system is superior to existing ones because it does not depend upon special reflectors nor does it require a lengthy set-up time. This system, the Computer Vision and Inertial Position Reference Sensor System (CVIPRSS, pronounced \nickname), combines a laser rangefinder, infrared camera, and a pan--tilt unit with the robust TLD …

The Gaussian Radon Transform For Banach Spaces, Irina Holmes

LSU Doctoral Dissertations

The classical Radon transform can be thought of as a way to obtain the density of an n-dimensional object from its (n-1)-dimensional sections in diff_x001B_erent directions. A generalization of this transform to infi_x001C_nite-dimensional spaces has the potential to allow one to obtain a function de_x001C_fined on an infi_x001C_nite-dimensional space from its conditional expectations. We work within a standard framework in in_x001C_finite-dimensional analysis, that of abstract Wiener spaces, developed by L. Gross. The main obstacle in infinite dimensions is the absence of a useful version of Lebesgue measure. To overcome this, we work with Gaussian measures. Specifically, we construct Gaussian measures …

On Identifying Critical Nuggets Of Information During Classification Task, David Sathiaraj

LSU Doctoral Dissertations

In large databases, there may exist critical nuggets - small collections of records or instances that contain domain-specific important information. This information can be used for future decision making such as labeling of critical, unlabeled data records and improving classification results by reducing false positive and false negative errors. In recent years, data mining efforts have focussed on pattern and outlier detection methods. However, not much effort has been dedicated to finding critical nuggets within a data set. This work introduces the idea of critical nuggets, proposes an innovative domain-independent method to measure criticality, suggests a heuristic to reduce the …

Ensemble Methods For Malware Diagnosis Based On One-Class Svms, Xing An

LSU Master's Theses

Malware diagnosis is one of today’s most popular topics of machine learning. Instead of simply applying all the classical classification algorithms to the problem and claim the highest accuracy as the result of prediction, which is the typical approach adopted by studies of this kind, we stick to the Support Vector Machine (SVM) classifier and based on our observation of some principles of learning, characteristics of statistics and the behavior of SVM, we employed a number of the potential preprocessing or ensemble methods including rescaling, bagging and clustering that may enhance the performance to the classical algorithm. We implemented the …

Knowledge-Based Methods For Automatic Extraction Of Domain-Specific Ontologies, Janardhana R. Punuru

LSU Doctoral Dissertations

Semantic web technology aims at developing methodologies for representing large amount of knowledge in web accessible form. The semantics of knowledge should be easy to interpret and understand by computer programs, so that sharing and utilizing knowledge across the Web would be possible. Domain specific ontologies form the basis for knowledge representation in the semantic web. Research on automated development of ontologies from texts has become increasingly important because manual construction of ontologies is labor intensive and costly, and, at the same time, large amount of texts for individual domains is already available in electronic form. However, automatic extraction of …

Learning Discrete Hidden Markov Models From State Distribution Vectors, Luis G. Moscovich

LSU Doctoral Dissertations

Hidden Markov Models (HMMs) are probabilistic models that have been widely applied to a number of fields since their inception in the late 1960’s. Computational Biology, Image Processing, and Signal Processing, are but a few of the application areas of HMMs. In this dissertation, we develop several new efficient learning algorithms for learning HMM parameters. First, we propose a new polynomial-time algorithm for supervised learning of the parameters of a first order HMM from a state probability distribution (SD) oracle. The SD oracle provides the learner with the state distribution vector corresponding to a query string. We prove the correctness …

Machine Learning Techniques For Efficient Query Processing In Kowledge Base Systems, Kevin Paul Grant

LSU Doctoral Dissertations

In this dissertation we propose a new technique for efficient query processing in knowledge base systems. Query processing in knowledge base systems poses strong computational challenges because of the presence of combinatorial explosion. This arises because at any point during query processing there may be too many subqueries available for further exploration. Overcoming this difficulty requires effective mechanisms for choosing from among these subqueries good subqueries for further processing. Inspired by existing works on stochastic logic programs, compositional modeling and probabilistic heuristic estimates we create a new, nondeterministic method to accomplish the task of subquery selection for query processing. Specifically, …