Digital Commons Network^™

Customised Investment Optimization Using Genetic Algorithms, Maythaa Al Ali, Shaikha Al Dossari

Theses

Portfolio selection is an important part of fund management as it contributes to investors’ economic growth. Investing is investing money to obtain an additional or specific advantage over money. Investment involves not only profit (return), but also risk that the investor bears. The higher the return an investor expects, the higher the risk the investor takes. Proper portfolio construction can minimize the level of risk to the expected value of an individual stock portfolio. Equity portfolio optimization, therefore, plays an important role in setting an investor’s investment portfolio strategy. In recent decades, there have been great advances in financial mathematics. …

A Novel Analytic Linear Interface Calculation And Its Use In Computational Fluid Dynamics For Enhanced Multiphase Modeling, Simon P. Shipkowski

Theses

Interfacial modeling is of widespread interest in manufacturing, imaging, and scientific applications. Presented here is an interfacial reconstruction method designed for use in Computational Fluid Dynamics (CFD) simulations of nucleate boiling, yet it is more broadly applicable. Nucleate boiling is vital in current and emerging technologies due to the high heat transfer at low temperature. A challenge for multiphase CFD simulation with phase change mass flux is the competing interests of quality mass transfer conservation, resolution of interfaces, and computational time/power. This Piecewise Linear Interface Calculation-Analytic Size Based (PLIC-ASB) method was developed to mitigate this problem in the volume of …

Numerical And Experimental Investigation And Optimization Of Particle Size Distribution In A Medical Jet Nebulizer Using A Multiphase Multiscale Approach, Benjamin Beaudry

Theses

Jet nebulizers are a common form of nebulizer used for aerosol treatment. They are known to function poorly in children under five due to differences in patient attributes. They must reduce their mass median aerodynamic diameter (MMAD) to work more effectively. This allows more medicine to be deposited in the lungs and absorbed. Solving this problem can affect 9 million annual deaths worldwide due to repository disease. While lowering the billions of dollars, the U.S. spends on these deaths. This study focused on the standard Hudson Micro Mist Nebulizer and investigated spray physics using experimental and simulation methods to enhance …

Darshan, Vibhav Kapoor

Theses

The thesis project examines the phenomenon and concept of darshan in the context of photography. Interpolating and extrapolating ideas from tenets of Indian philosophy, it presents a perspective on image-making and its relationship to me as an artist. With the artwork mainly situated in the cultural geographies of India and the United States, the text discusses the role of spirituality, light, colour (blue), and walking in my practice. It also references themes of painting and music – raga and rasa that are constructs of Indian musicology – and how they influence the photographic work.

Accessible And Functional Visualizations For Exploring And Analyzing The Writing And Programming Process, Shaney Flores

Theses

Background. Developing easily decodable and insightful visuals is a challenge in the field of data visualization. This challenge becomes more pronounced when the data is of a complex nature. Good visualizations clearly convey their data using designs with appropriate encoding, visual attributes (i.e., color, shape, size, etc.), and accessibility features (e.g., distinctive colors for color-blind individu- als). One area where well-designed visualizations can make a significant impact is elucidating the learning process. Users ranging from self-taught individuals to students enrolled in coursework could use such visuals to detect problematic areas in their process of learning skills such as writing …

Optical Phased Array For Lidar Systems At Blue Wavelengths, Long H. Nguyen

Theses

Optical Phased Array has become a popular solution for light detection and rang- ing (lidar) due to its compact size, steering ability without physical moving parts, and integration with conventional CMOS manufacturing processes. With the rising demand in advanced driver assistance systems (ADAS) and surveying in agriculture and oceanography industries, there is a need for a durable, high performing, and integrable lidar module. For that, a suitable integrated OPA is required to carry sufficient power and have large steering angle at a particular wavelength range. This thesis is specifically focused on blue wavelengths for use in applications such as aug- …

Measuring Diffusion Through Carbon Nanotube Arrays With Fluorescent Spectroscopy, Michael Machold

Theses

Carbon nanotube (CNT) arrays are emerging as versatile nanostructures that can be used in many applications including gene transfection technology. To understand the transport of molecules through an array of CNTs, diffusion of molecules was studied experimentally and analytically. A procedure using fluorescent spectroscopy was developed to measure the concentration change of fluorescein salt in a two-reservoir system separated by a CNT array. Experiments were conducted to measure how changing the initial concentration gradient and reservoir volume size impacted the diffusivity of the system. For 2 mL reservoirs, diffusivities were measured between 2.56*10^(-11) m^2/s and 3.51*10^(-11) m^2/s. Diffusivities were measured …

Quantifying The Nexus Of Climate, Economy, And Health: A State-Of-The-Art Time Series Approach, Kameron Blair Kinast

Theses

Extreme weather events pose significant threats to human life, the economy, agriculture, and various other socio-economic aspects. This thesis presents a comprehensive analysis of the patterns of climate factors and their impact on the economy and human health using state-of-the-art and emerging statistical machine learning techniques. This research consists of two parts: exploring and comparing the effectiveness of statistical models with respect to climate time series forecasting and analyzing the effects on the economy and human health. The study employs a predominantly computational approach, leveraging R, Python, and Julia to demonstrate the role of statistical computing in understanding climate change …

Lwir Spectral Variability Integration And Improvements To An Earth Observing Statistical Performance Model, Runchen Zhao

Theses

Remote sensing systems can be used to identify objects without physical contact. In hyperspectral remote sensing systems, pixels from a 3D hypercube can be described as a one-dimensional spectra. This one-dimensional spectrum can contain information about surface reflectance and emissivity variation as a function of wavelength, and is often thought of as the materials spectral signature or fingerprint. This spectral signature can be used in applications such as spectral target detection. To study the spectral signatures variation and its impact on target detection, for example, a parameter trade- off study is often needed. In this research, a full-spectrum spectral imaging …

Shiftable Load Forecasting And User-Centric Demand-Side Optimization For Nanogrid, Akash Kumar

Theses

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrid (NG). Despite its perceived advantages, NG’s high load volatility poses risk not only to its stability but also to the associated network. This is further exacerbated by the user’s transient moods and the probabilistic nature of metrological events. Moreover, unoptimized operation of electrical appliances in a NG, especially in peak hours, raises the energy cost substantially. Thus, for risk aversion and cost savings, day-ahead demand-side optimization is highly required, especially for shiftable loads. Moreover, most load centers, aspiring to be NGs, have either limited or no …

End-To-End Network Slicing Using Hypergraph Theory, Omar Abdul-Latif

Theses

Network slicing is the practice of implementing multiple virtualized and independent architectures on a single physical network infrastructure, in a way that allows for multiplexing and efficient use of resources. Network slicing is based on the concept of network virtualization and, when it reaches maturity, is expected to result in complete softwarization of 5G, Beyond-5G (B5G) and 6G networks. This means that future networks will only need minimal physical infrastructure upgrades (mostly in the frontend of the network). Network slicing is identified as one of the key enablers of next generation wireless mobile networks due to its ability to multiplex …

Improved Grover’S Implementation Of Quantum Binary Neural Networks, Brody A. Wrighter

Theses

Binary Neural Networks (BNNs) are the result of a simplification of network parameters in Artificial Neural Networks (ANNs). The computational complexity of training ANNs increases significantly as the size of the network increases. This complexity can be greatly reduced if the parameters of the network are binarized. Binarization, which is a one bit quantization, can also come with complications including quantization error and information loss. The implementation of BNNs on quantum hardware could potentially provide a computational advantage over its classical counterpart. This is due to the fact that binarized parameters fit nicely to the nature of quantum hardware. Quantum …

Curio-Stereo: A Vr Application For The Viewing Of Stereograph Cards, Paige Sandheinrich

Theses

This project discusses the historical significance of stereograph cards as a tool for constructing a shared visual culture in the late 19th and early 20th centuries in the United States and the United Kingdom. Stereographs were widely popularized as an entertainment and educational tool, offering an immersive experience for the viewers. The stereoscopic nature of the images allowed the viewers to immerse themselves in a non-physical reality through visual stimulation, creating a haptic involvement that was unique to the medium. The project of creating a VR application to view stereographs is presented as a potential solution to the problem of …

Evaluating The Performance Of Process-Based And Machine Learning Models For Rainfall-Runoff Simulation With Application Of Satellite And Radar Precipitation Products, Amrit Bhusal

Theses

Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research's high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary …

Single-Mode Distributed-Feedback Lasing In Coupled Dissimilar Photonic Waveguides, Bryce A. Tennant

Theses

Distributed-feedback (DFB) lasers support a wide range of applications including fiber-optic and free-space optical communications, sensing and measurement, military warfare, and manufacturing and metrology. The desire for a reduction in size, weight, power, and cost (SWAP-C) for each of these applications continues to aggressively drive integrated- and silicon-photonic circuit development. Since DFB lasers based on uniform refractive-index diffraction gratings are naturally dual-mode, a variety of techniques have been introduced to impose single-mode operation, including the currently ubiquitous λ/4-grating phase-shift technique. This grating phase shift, however, creates an undesirable peaky-power profile within the laser cavity resulting in nonlinear behavior ultimately limiting …

A Current To Digital Converter For Power Signature Generation Applications, Arunima Aindrila Badhon

Theses

The security of IoT devices is significantly increasing as a consequence of the widespread usage of the Internet of Things (IoT) in applications that include confidential data and implementation of important control decisions using those data. Because of their cheap cost and computational limitations, IoT devices confront significant obstacles in safeguarding. Among the variety of devised tactics analyzing power is one of the most potential strategies to address such challenges. However, due to the size, cost, and power consumption of power analysis devices, this strategy is not suited for many IoT applications. In this thesis, two techniques for collecting power …

A Bit-Exact Hierarchical Systemc Model Of Ppim For Verification And Performance Modeling, Dhana Lavanya Balasubramanian

Theses

The semiconductor industry has seen tremendous advancement lately and is expected to continue to grow since multiple industries like automobile, health care, meteorology, etc are developing dedicated chips with advancements in Artificial Intelligence (AI). These improvements also come with increased complexities. Engineers now require advanced ways to model the designs in addition to existing Hardware Description Languages (HDLs) such as Very High-Speed Integrated Circuit Hardware Description Language (VHDL) and Verilog. One such way to model the designs is by using SystemC, which is a library of C++ classes and macros that can be used similar to an HDL for modeling …

Advanced Control Of Vapor Compression Liquid Chillers, Suliman Awad

Theses

Chillers are one of the most sophisticated and most important equipment in cooling plants. Due to its complexity and importance, a cost effective and accurate control technique is a necessity to ensure system reliability and longevity. As the chiller contains multiple inputs and outputs, two advanced multivariable control techniques were selected to control the chillers cooling capacity, exit chilled liquid temperature of the evaporator as well as to reject disturbances. The advanced techniques covered in this framework are the Linear Quadratic Integral (LQI) and Model Predictive Control (MPC). Both were successfully applied to the chiller’s model for multiple testing cases …

Invariants Of 3-Braid And 4-Braid Links, Mark Essa Sukaiti

Theses

In this study, we established a connection between the Chebyshev polynomial of the first kind and the Jones polynomial of generalized weaving knots of type W(3,n,m).
Through our analysis, we demonstrated that the coefficients of the Jones polynomial of weaving knots are essentially the Whitney numbers of Lucas lattices which allowed us to find an explicit formula for the Alexander polynomial of weaving knots of typeW(3,n).
In addition to confirming Fox’s trapezoidal conjecture, we also discussed the zeroes of the Alexander Polynomial of weaving knots of type W(3,n) as they relate to Hoste’s conjecture. In addition, …

Heat Transfer In Microchannel Compact Heat Exchanger, Mohammed Al-Khateri

Theses

This study aims to investigate heat transfer in a miniaturized channel heat exchanger and effectiveness for given real-life design. The main objective of the study is to develop and build a high-performance heat exchanger for thermal management in automotive application using typical radiator manufacturing techniques. This research uses experimental and model-based investigation. It conceptualizes the Miniaturized Channel Heat Exchangers (MCHX) model-based study for demonstrating the design of miniaturized channel heat exchangers, manufacture, and study the heat exchanger effectiveness by validating the developed mathematical model experimentally. The proposed design of the MCHX is to use extruded tubes containing miniaturized channels as …

Online Fourier Analysis Of Time-Varying Signals In A Real-Time Embedded Environment, Ty Freeman

Theses

A software-based, constant-flow implementation of the radix-2 Cooley-Tukey fast Fourier transform (FFT) algorithm is presented in this paper. The program is built within a real-time embedded environment running FreeRTOS. The system is used for the online frequency anal- ysis of time-varying, one-dimensional signals of an arbitrary length. The system’s design is validated through testing to produce results accurately on signals within specific boundaries. The proposed system has a maximum transform size of 256 samples due to the memory limitations on the development board. The hardware in use is an STM32L476 Nucleo development board which simulates the lightweight, low power, and …

A Study Of Large-Scale Structure Evolution Through Sunyaev-Zel’Dovich Corrections In Massive Clusters, Victoria Butler

Theses

The evolution of large-scale structure is traced by galaxy clusters, which are groups of galaxies gravitationally bound by a shared envelope of dark matter on scales of 1-10 Mpc. They are filled with an intra-cluster medium (ICM) consisting of a diffuse hot plasma with characteristic temperature $10^7K. While ICM models match reasonably well with observations, smaller-scale astrophysical processes in clusters can complicate interpretations of derived thermodynamic properties. These can include cluster-cluster mergers, feedback from active black holes, and accretion, among several others. The complex relationship between these processes and the properties of the ICM must be understood to use clusters …

Quantum Acceleration Of Linear Regression For Artificial Neural Networks, Martin A. Hoffnagle

Theses

Through proofs and small scale implementations, quantum computing has shown potential to provide significant speedups in certain applications such as searches and matrix calculations. Recent library developments have introduced the concept of hybrid quantum-classical compute models where quantum processor units could be used as additional hardware accelerators by classical computers. While these developments have opened the prospect of applying quantum computing to machine learning tasks, there are still many limitations of near and midterm quantum computing. If implemented carefully, the advantages of quantum algorithms could be used to accelerate current machine learning models. In this work, a hybrid quantum-classical model …

Machine Learning Based Framework For Smart Contract Vulnerability Detection In Ethereum Blockchain, Qusai Omar Mustafa Hasan

Theses

Abstract

Blockchain technology is a disruptive technology that revolutionized digital payments and transactions of digital assets. Blockchain transactions operate using smart contracts which are automated software code that facilitates transactions between parties without the need for intermediary systems. Smart contracts have become an increasingly popular means of conducting transactions and executing code in a decentralized manner. As it can be written in various languages which have their flaws in terms of logic and vulnerabilities, also the immutability and autonomy of smart contracts also make them vulnerable to various security threats. Security for smart contracts is essential as exploiting bad logic …

A Graph-Based Approach To Studying The Spread Of Radical Online Sentiment, Le Nguyen

Theses

The spread of radicalization and extremism through the Internet is a growing problem. We are witnessing a rise in online hate groups, inspiring the impressionable and vulnerable population towards extreme actions in the real world. Though the body of research to address these issues is growing in kind, they lack a key understanding of the structure and behavior of online extremist com- munities. In this thesis, we study the structure and behavior of extremist online communities and the spread of hateful sentiments through them to address this gap in the research. We propose a novel Graph-Based Approach to Studying the …

Physiological Signals And The Effects On Prediction Of Future Blood Glucose Values In A Deep Learning Model, Andrew Rearson

Theses

Diabetes is a chronic disease that currently has no cure. However, in the last decade, life- changing technology for people with diabetes has advanced primarily due to new sensors that continuously measure glucose. Individuals with diabetes manually use this data for diabetes management, but the artificial intelligence community has seen the increase in data as an opportunity to work towards automating diabetes control. The project developed deep-learning neural networks incorporating blood glucose measurements measured by a continuous glucose monitor (CGM) and physical activity data from a wearable health sensor (WHS) to predict future glucose values and compare how different configuration …

Ecomves: Enhancing Comves Using Data Piggybacking For Resource Discovery At The Network Edge, Sanzida Hoque

Theses

Over the past few years, Augmented Reality (AR) and Virtual Reality (VR) have emerged as highly popular technologies that demand rapid and efficient processing of data with low latency and high bandwidth, in order to enable seamless real-time interaction between users and the virtual environment. This presents challenges for network infrastructure design, which can be addressed through edge computing. However, edge computing also presents challenges, such as selecting the appropriate edge server for computing tasks in dynamic networks with rapidly changing resource availability. Named Data Networking (NDN) is a potential future Internet architecture that could provide a balanced distribution of …

Care: Towards Customized Assistive Robot-Based Education, Nafisa Abdul Razak Maaz

Theses

Purpose of the Study The aim of this paper is to propose a solution for enhancing the learning experience of elementary school students by combining Artificial Intelligence (AI) and robotics in education. Our AI-powered system aims to classify students according to their intellectual abilities and offer personalized educational materials to assist their progress. Summary We propose using a personalized tutor robot to aid students in their learning. The robot categorizes students into three groups – High-Ability Student (HAS), Average-Ability Student (AAS), or Low-Ability Student (LAS) - based on their performance, facial expressions, and subjective feedback. The robot then displays educational …

Student Grade Prediction Using Machine Learning Methods, Hameed Al-Obaidi

Theses

This capstone project addresses the challenge of students' lack of awareness regarding their academic performance and potential grade declines throughout the semester. The project aims to develop a predictive model that offers students timely warnings about their predicted grades, enabling them to take proactive measures to improve their academic performance. Utilizing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, the project analyzes a comprehensive dataset comprising student demographics, curriculum history, and grades in various subjects. The model undergoes rigorous evaluation and validation to ensure its effectiveness in predicting students' academic outcomes accurately. The findings reveal that the linear regression …

Tourism, Mapping, Retail And Recreational Trails: A Case Study Of Connectivity Between Trails And Adjacent Downtowns In Anniston, Alabama, Usa, Jennifer Green

Theses

This study examines the estimated increase in economic impact on a geography’s local economy by creating cross-marketing efforts between expanding an established outdoor recreation trail to a closely located city downtown commerce district. This research will analyze the resulting potential rise in sales/lodging tax revenues for that city by mapping the trail and downtown district, and cross-marketing the other to users of both or either venue. Potential increase in economic impact will be estimated utilizing IMPLAN methodology by assessing the economic impact on the local economy that a percentage range of increased spending resulting from this cross-marketing effort could generate.