Engineering Commons^™

Carrier Transport Engineering In Wide Bandgap Semiconductors For Photonic And Memory Device Applications, Ravi Teja Velpula

Dissertations

Wide bandgap (WBG) semiconductors play a crucial role in the current solid-state lighting technology. The AlGaN compound semiconductor is widely used for ultraviolet (UV) light-emitting diodes (LEDs), however, the efficiency of these LEDs is largely in a single-digit percentage range due to several factors. Until recently, AlInN alloy has been relatively unexplored, though it holds potential for light-emitters operating in the visible and UV regions. In this dissertation, the first axial AlInN core-shell nanowire UV LEDs operating in the UV-A and UV-B regions with an internal quantum efficiency (IQE) of 52% are demonstrated. Moreover, the light extraction efficiency of this …

Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz

Dissertations

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, …

Hydrodynamic Investigation Of The Discharge Of Complex Fluids From Dispensing Bottles Using Experimental And Computational Approaches, Baran Teoman

Dissertations

The discharge of non-Newtonian, complex fluids through orifices of industrial tanks, pipes, dispensers, or packaging containers is a ubiquitous but often problematic process because of the complex rheology of such fluids and the geometry of the containers. This, in turn, reduces the discharge rate and results in residual fluid left in the container, often referred to as heel. Heel formation is undesired in general, since it causes loss of valuable material, container fouling, and cross-contamination between batches. Heel may be of significant concern not only in industrial vessels but also in consumer packaging. Despite its relevance, the research in this …

Bioremediation Of Petroleum Hydrocarbons In Coastal Sediments, Charbel Abou Khalil

Dissertations

The biodegradation of dispersed crude oil in the ocean is relatively rapid (a half-life of a few weeks). However, it is often much slower on shorelines, usually attributed to low moisture content, nutrient limitation, and higher oil concentrations in beaches than in dispersed plumes. Another factor may be the increased salinity of the upper intertidal and supratidal zones since these parts of the beach are potentially subject to prolonged evaporation and only intermittent inundation. Therefore, two laboratory experiments are conducted to investigate whether such an increase in porewater salinity results in additional inhibitory effects on oil biodegradation in seashores.

In …

Software Protection And Secure Authentication For Autonomous Vehicular Cloud Computing, Muhammad Hataba

Dissertations

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.

In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our …

Computation Of Risk Measures In Finance And Parallel Real-Time Scheduling, Yajuan Li

Dissertations

Many application areas employ various risk measures, such as a quantile, to assess risks. For example, in finance, risk managers employ a quantile to help determine appropriate levels of capital needed to be able to absorb (with high probability) large unexpected losses in credit portfolios comprising loans, bonds, and other financial instruments subject to default. This dissertation discusses the computation of risk measures in finance and parallel real-time scheduling.

Firstly, two estimation approaches are compared for one risk measure, a quantile, via randomized quasi-Monte Carlo (RQMC) in an asymptotic setting where the number of randomizations for RQMC grows large, but …

Performance Analysis Of The Dominant Mode Rejection Beamformer, Enlong Hu

Dissertations

In array signal processing over challenging environments, due to the non-stationarity nature of data, it is difficult to obtain enough number of data snapshots to construct an adaptive beamformer (ABF) for detecting weak signal embedded in strong interferences. One type of adaptive method targeting for such applications is the dominant mode rejection (DMR) method, which uses a reshaped eigen-decomposition of sample covariance matrix (SCM) to define a subspace containing the dominant interferers to be rejected, thereby allowing it to detect weak signal in the presence of strong interferences. The DMR weight vector takes a form similar to the adaptive minimum …

Low-Reynolds-Number Locomotion Via Reinforcement Learning, Yuexin Liu

Dissertations

This dissertation summarizes computational results from applying reinforcement learning and deep neural network to the designs of artificial microswimmers in the inertialess regime, where the viscous dissipation in the surrounding fluid environment dominates and the swimmer’s inertia is completely negligible. In particular, works in this dissertation consist of four interrelated studies of the design of microswimmers for different tasks: (1) a one-dimensional microswimmer in free-space that moves towards the target via translation, (2) a one-dimensional microswimmer in a periodic domain that rotates to reach the target, (3) a two-dimensional microswimmer that switches gaits to navigate to the designated targets in …

One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin

Dissertations

Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …

Planning Methodology For Alternative Intersection Design And Selection, Liran Chen

Dissertations

The recent publication of the 6th Edition of the Highway Capacity Manual included a chapter on Ramp Terminals and Alternative Intersections that introduces various alternative intersection designs and assesses the performance of Median U-turn, Restricted crossing U-turn and Displaced left-turn intersections. Missing from the literature is an alternative intersection selection tool for identifying whether an alternative intersection would be successful under local conditions. With limited information of organized alternative intersection research, most planners must rely heavily on their personal judgement while selecting the most suitable intersection designs. As appealing as alternative intersections are, there is no comprehensive methodology for planners …

Nondestructive Evaluation Of 3d Printed, Extruded, And Natural Polymer Structures Using Terahertz Spectroscopy And Imaging, Alexander T. Clark

Dissertations

Terahertz (THz) spectroscopy and imaging are considered for the nondestructive evaluation (NDE) of various three-dimensional (3D) printed, extruded, and natural polymer structures. THz radiation is the prime candidate for many NDE challenges due to the added benefits of safety, increased contrast and depth resolution, and optical characteristic visualization when compared to other techniques. THz imaging, using a wide bandwidth pulse-based system, can evaluate the external and internal structure of most nonconductive and nonpolar materials without any permanent effects. NDE images can be created based on THz pulse attributes or a material’s spectroscopic characteristics such as refractive index, attenuation coefficient, or …

Investigation Of Topological Phonons In Acoustic Metamaterials, Wenting Cheng

Dissertations

Topological acoustics is a recent and intense area of research. It merges the knowledge of mathematical topology, condensed matter physics, and acoustics. At the same time, it has been pointed out that quasiperiodicity can greatly enhance the periodic table of topological systems. Because quasiperiodic patterns have an intrinsic global degree of freedom, which exists in the topological space called the hull of a pattern, where the shape traced in this topological space is called the phason. The hull augments the physical space, which opens a door to the physics of the integer quantum Hall effect (IQHE) in arbitrary dimensions. In …

A Self-Learning Intersection Control System For Connected And Automated Vehicles, Ardeshir Mirbakhsh

Dissertations

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the …

Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld

Dissertations

This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.

This work …

Nystrom Methods For High-Order Cq Solutions Of The Wave Equation In Two Dimensions, Erli Wind-Andersen

Dissertations

An investigation of high order Convolution Quadratures (CQ) methods for the solution of the wave equation in unbounded domains in two dimensions is presented. These rely on Nystrom discretizations for the solution of the ensemble of associated Laplace domain modified Helmholtz problems. Two classes of CQ discretizations are considered: one based on linear multistep methods and the other based on Runge-Kutta methods. Both are used in conjunction with Nystrom discretizations based on Alpert and QBX quadratures of Boundary Integral Equation (BIE) formulations of the Laplace domain Helmholtz problems with complex wavenumbers. CQ in conjunction with BIE is an excellent candidate …

Atmospheric Mercury Chemistry: Detection, Kinetics, And Mechanism, Na Mao

Dissertations

The presence of mercury in the environment is of global concern due to its toxicity. The atmosphere is an important transient reservoir for mercury released by human activities and natural sources. The knowledge of atmospheric mercury chemistry is critical for understanding the global biogeochemical cycle. In the atmosphere, mercury primarily exists in three forms: gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM). Over the last decade, the existing knowledge of mercury cycle has dramatically changed: (1) There has been increasing evidence that current detection methods do not accurately quantify gaseous oxidized mercury and a technique which …

Private Information Retrieval And Function Computation For Noncolluding Coded Databases, Sarah A. Obead

Dissertations

The rapid development of information and communication technologies has motivated many data-centric paradigms such as big data and cloud computing. The resulting paradigmatic shift to cloud/network-centric applications and the accessibility of information over public networking platforms has brought information privacy to the focal point of current research challenges. Motivated by the emerging privacy concerns, the problem of private information retrieval (PIR), a standard problem of information privacy that originated in theoretical computer science, has recently attracted much attention in the information theory and coding communities. The goal of PIR is to allow a user to download a message from a …

Reactive Iron Mineral Coatings In Redox Transition Zones Of A Site With Historical Contamination: Abiotic Attenuation, Xin Yin

Dissertations

Reactive iron mineral coatings are found throughout reduction-oxidation (redox) transition zones and play a significant role in contaminant transformation processes. In this study, an 18.3-meter core is collected, subsampled, and preserved under anoxic conditions to maintain its original redox state. Screening analyses are conducted at sampling increments of 5.08 cm in depth for the following: elemental concentrations with X-ray fluorescence (XRF), sediment pH, sediment oxidation-reduction potential (ORP), total volatile organic carbon (TVOC) in the sample headspace, and abundant bacteria (16S rRNA sequencing). Using the Fe and S gradients correlated with microbial data, five RTZs are delineated. To characterize iron mineral …

Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen

Dissertations

Dark patterns are user interfaces purposefully designed to manipulate users into doing something they might not otherwise do for the benefit of an online service. This study investigates the impact of dark patterns on overall user experience and site revisitation in the context of airline websites. In order to assess potential dark pattern effects, two versions of the same airline website were compared: a dark version containing dark pattern elements and a bright version free of manipulative interfaces. User experience for both websites were assessed quantitatively through a survey containing a User Experience Questionnaire (UEQ) and a System Usability Scale …

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …

Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora

Dissertations

Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word …