Physical Sciences and Mathematics Commons^™

On The Study Of Age-Related Physiological Decline In C. Elegans, Drew Benjamin Sinha

McKelvey School of Engineering Theses & Dissertations

Aging decline is a universal and unescapable phenomenon; as organisms reach maturity and continue living, physiological function inevitably declines, resulting in mortality. While the study of mortality has been long studied, technical and practical challenges have limited the equally important study of how/when individuals deteriorate and what types of factors affect that deterioration. This gap in knowledge is not only evident in a relative lack of empirical data on physiological decline, but considerable debate around the analysis and conceptual interpretations of the little data that is available.

In this dissertation, I use quantitative reasoning and analysis of longitudinal data to …

Electrodeless Electrochemistry Enabled By Nonthermal Plasma, Harold Oldham

McKelvey School of Engineering Theses & Dissertations

The increasing availability and decreasing cost of electricity generated by renewable resources have motivated research into electrified chemical processing, whereby electrical energy is used to drive chemical transformations. Electricity-intensive processing techniques such as electrochemistry using solid electrodes has attracted attention in this context for the synthesis of organic compounds, such as high-value pharmaceuticals and renewable chemical production. Selective chemical transformations are achieved in conventional aqueous electrochemical systems by using external circuitry to bias solid electrodes, allowing for the preferential transfer of electrons between the electrode-liquid interface. Despite having the ability to promote controlled electrochemical reactions, configurations using solid electrodes are …

Interfacial Engineering And Photoelectrochemistry Of Patterned Metal/Semiconductor Heterostructures, Che Tan

McKelvey School of Engineering Theses & Dissertations

Photoelectrochemical (PEC) cells enable the conversion of solar energy into storable fuels, which is critical in overcoming the intermittent nature of this largest renewable source. However, the majority of semiconductors used as photoelectrodes in these cells have low conversion efficiencies and/or stabilities. Silicon (Si) is an attractive semiconductor material for photoelectrodes, but the development of efficient Si-based photoanodes is challenging due to their instability in alkaline solutions. Thus, one focus of this dissertation is the design and fabrication of highly stable nickel (Ni)-patterned Si photoanodes through interfacial engineering of the barrier heights. Recently, hot carriers in plasmonic metal nanostructures have …

The Challenges Of Applying Computational Legal Analysis To Mhealth Security And Privacy Regulations, Brian Tung

McKelvey School of Engineering Theses & Dissertations

As our world has grown in complexity, so have our laws. By one measure, the United States Code has grown over 30x as long since 1935, and the 186,000-page Code of Federal Regulations has grown almost 10x in length since 1938. Our growing legal system is too complicated; it’s impossible for people to know all the laws that apply to them. However, people are still subject to the law, even if they are unfamiliar with it. Therein lies the need for computational legal analysis. Tools of computation (e.g., data visualization, algorithms, and artificial intelligence) have the potential to transform civic …

Machine Learning In Complex Scientific Domains: Hospitalization Records, Drug Interactions, Predictive Modeling And Fairness For Class Imbalanced Data, Arghya Datta

McKelvey School of Engineering Theses & Dissertations

Machine learning has demonstrated potential in analyzing large, complex datasets and has become ubiquitous across many fields of scientific research. As machine learning is actively deployed in many complex and critical domains, it is essential for machine learning to engage with domain expertise to aid in knowledge discovery as well as address challenges in predictive modeling in complex domains. Domain expertise represents an essential and elaborate collection of knowledge that is often under-utilized when applying machine learning in complex domains. In this dissertation, I have addressed existing challenges regarding knowledge discovery in complex domains via engagement with domain expertise, particularly …

Community Detection In Complex Networks, Zhenqi Lu

McKelvey School of Engineering Theses & Dissertations

Network science plays a central role in understanding and modeling complex systems in many disciplines, including physics, sociology, biology, computer science, economics, politics, and neuroscience. By studying networks, we can gain a deep understanding of the behavior of the systems they represent. Many networks exhibit community structure, i.e., they have clusters of nodes that are locally densely interconnected. These communities manifest the hierarchical organization of the objects in systems, and detecting communities greatly facilitates the study of the organization and structure of complex systems.

Most existing community-detection methods consider low-order connection patterns, at the level of individual links. But high-order …

Photoacoustic Imaging, Feature Extraction, And Machine Learning Implementation For Ovarian And Colorectal Cancer Diagnosis, Eghbal Amidi

McKelvey School of Engineering Theses & Dissertations

Among all cancers related to women’s reproductive systems, ovarian cancer has the highest mortality rate. Pelvic examination, transvaginal ultrasound (TVUS), and blood testing for cancer antigen 125 (CA-125), are the conventional screening tools for ovarian cancer, but they offer very low specificity. Other tools, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), also have limitations in detecting small lesions. In the USA, considering men and women separately, colorectal cancer is the third most common cause of death related to cancer; for men and women combined, it is the second leading cause of cancer deaths. …

Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao

McKelvey School of Engineering Theses & Dissertations

Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart devices and autonomous cars to crucial infrastructures. Despite such critical importance, conventional design strategies of AMS circuits still follow an expensive and time-consuming manual process and are unable to meet the exponentially-growing productivity demands from industry and satisfy the rapidly-changing design specifications from many emerging applications. Design automation of AMS IC is thus the key to tackling these challenges and has been …

Bayesian Quadrature With Prior Information: Modeling And Policies, Henry Chai

McKelvey School of Engineering Theses & Dissertations

Quadrature is the problem of estimating intractable integrals. Such integrals regularly arise in engineering and the natural sciences, especially when Bayesian methods are applied; examples include model evidences, normalizing constants and marginal distributions. This dissertation explores Bayesian quadrature, a probabilistic, model-based quadrature method. Specifically, we study different ways in which Bayesian quadrature can be adapted to account for different kinds of prior information one may have about the task. We demonstrate that by taking into account prior knowledge, Bayesian quadrature can outperform commonly used numerical methods that are agnostic to prior knowledge, such as Monte Carlo based integration. We focus …

Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee

McKelvey School of Engineering Theses & Dissertations

Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …

A Neuromorphic Machine Learning Framework Based On The Growth Transform Dynamical System, Ahana Gangopadhyay

McKelvey School of Engineering Theses & Dissertations

As computation increasingly moves from the cloud to the source of data collection, there is a growing demand for specialized machine learning algorithms that can perform learning and inference at the edge in energy and resource-constrained environments. In this regard, we can take inspiration from small biological systems like insect brains that exhibit high energy-efficiency within a small form-factor, and show superior cognitive performance using fewer, coarser neural operations (action potentials or spikes) than the high-precision floating-point operations used in deep learning platforms. Attempts at bridging this gap using neuromorphic hardware has produced silicon brains that are orders of magnitude …

Preference Elicitation In Constraint-Based Models: Models, Algorithms, And Applications, Atena M. Tabakhi

McKelvey School of Engineering Theses & Dissertations

Constraint-based models offer powerful approaches for describing and resolving many combinatorial optimization problems in a centralized or distributed environment. In such models, the goal is to find a value assignment to a set of variables given a set of preferences expressed by means of cost functions such that the sum over all costs is optimized. The importance of constraint-based models is outlined by the impact of their applications in a wide range of agent-based systems. Many real-life combinatorial problems can be naturally formalized using constraint-based models. Examples of such applications are supply-chain management, roster scheduling, meeting scheduling, combinatorial auctions, bioinformatics, …

Non-Hermitian Physics And Engineering In Whispering Gallery Mode Microresonators, Changqing Wang

McKelvey School of Engineering Theses & Dissertations

Non-Hermitian physics describes the behaviors of open systems which have interactions with the environment. It can be applied to a wide range of classical and quantum systems. Exotic physical phenomena are unveiled in such non-Hermitian systems, especially around a singular point in the parameter space, i.e., the exceptional point (EP), where the eigenvalues and the associated eigenvectors are degenerate. A plethora of demonstrations have been found in optics and photonics, where the non-Hermitian effects are ubiquitous due to the existence of optical dissipation or amplification. In particular, whispering gallery mode (WGM) resonators are ideal candidates for studying light-matter interactions in …

Reasoning About Scene And Image Structure For Computer Vision, Zhihao Xia

McKelvey School of Engineering Theses & Dissertations

The wide availability of cheap consumer cameras has democratized photography for novices and experts alike, with more than a trillion photographs taken each year. While many of these cameras---especially those on mobile phones---have inexpensive optics and make imperfect measurements, the use of modern computational techniques can allow the recovery of high-quality photographs as well as of scene attributes.

In this dissertation, we explore algorithms to infer a wide variety of physical and visual properties of the world, including color, geometry, reflectance etc., from images taken by casual photographers in unconstrained settings. We specifically focus on neural network-based methods, while incorporating …

Algebraic, Computational, And Data-Driven Methods For Control-Theoretic Analysis And Learning Of Ensemble Systems, Wei Miao

McKelvey School of Engineering Theses & Dissertations

In this thesis, we study a class of problems involving a population of dynamical systems under a common control signal, namely, ensemble systems, through both control-theoretic and data-driven perspectives. These problems are stemmed from the growing need to understand and manipulate large collections of dynamical systems in emerging scientific areas such as quantum control, neuroscience, and magnetic resonance imaging. We examine fundamental control-theoretic properties such as ensemble controllability of ensemble systems and ensemble reachability of ensemble states, and propose ensemble control design approaches to devise control signals that steer ensemble systems to desired profiles. We show that these control-theoretic properties …

Improving Additional Adversarial Robustness For Classification, Michael Guo

McKelvey School of Engineering Theses & Dissertations

Although neural networks have achieved remarkable success on classification, adversarial robustness is still a significant concern. There are now a series of approaches for designing adversarial examples and methods to defending against them. This paper consists of two projects. In our first work, we propose an approach by leveraging cognitive salience to enhance additional robustness on top of these methods. Specifically, for image classification, we split an image into the foreground (salient region) and background (the rest) and allow significantly larger adversarial perturbations in the background to produce stronger attacks. Furthermore, we show that adversarial training with dual-perturbation attacks yield …

Real-Time Virtualization And Coordination For Edge Computing, Haoran Li

McKelvey School of Engineering Theses & Dissertations

Recent years have witnessed the emergence of edge computing as an enabling platform for time-sensitive services. However, existing edge computing platforms face a multitude of challenges in meeting the latency requirements of time-sensitive applications. (1) Traditional real-time virtualization platforms require offline configuration of the scheduling parameters of virtual machines (VMs) based on their worst-case workloads. However, this static approach results in pessimistic resource allocation when the workloads in the VMs change dynamically. (2) Edge computing operators must deliver consistent tail latency performance for time-sensitive applications deployed on different edge sites. Traditionally, significant effort is required to test, tune, and configure …

Towards Deploying Robust Machine Learning Systems, Liang Tong

McKelvey School of Engineering Theses & Dissertations

Machine learning (ML) has come to be widely used in a broad array of settings, including important security applications such as network intrusion, fraud, and malware detection, as well as other high-stakes settings, such as autonomous driving. A general approach is to extract a set of features, or numerical attributes, of entities in question, collect a training data set of labeled examples (for example, indicating which instances are malicious and which are benign), learn a model which labels previously unseen instances presented in terms of their extracted features, and then investigate alerts raised by instances predicted as malicious. Despite the …

Aerosol Vapor Synthesis Of Organic Processable Pedot Particles And Measuring Electric Conductivity Using A 3d Printed Probe Station, Yang Lu

McKelvey School of Engineering Theses & Dissertations

Conducting polymers are organic semiconductors characterized by conjugated backbones (alternating single-double bonds) that enable mixed ionic-electronic conductivity. Their polymeric nature, tunable band structure and reversible redox capability have demonstrated fundamental advances in the fields ranging from electrochemical energy storage, sensing, to electro/photo catalysis and neuromorphic engineering. Conjugated backbones, the origin of all the unique physical and chemical properties associated with conducting polymers, prevent their solubility due to high lattice energy which hinders processing. Current solution utilizes a long-chain polymer (PSS) as dopants to render conducting polymer water dispersible (PEDOT:PSS). Nonetheless, PSS is highly acidic and hydrophilic limiting applicability with acid-incompatible …

A Collaborative Knowledge-Based Security Risk Assessments Solution Using Blockchains, Tara Thaer Salman

McKelvey School of Engineering Theses & Dissertations

Artificial intelligence and machine learning have recently gained wide adaptation in building intelligent yet simple and proactive security risk assessment solutions. Intrusion identification, malware detection, and threat intelligence are examples of security risk assessment applications that have been revolutionized with these breakthrough technologies. With the increased risk and severity of cyber-attacks and the distributed nature of modern threats and vulnerabilities, it becomes critical to pose a distributed intelligent assessment solution that evaluates security risks collaboratively. Blockchain, as a decade-old successful distributed ledger technology, has the potential to build such collaborative solutions. However, in order to be used for such solutions, …

Interaction Of Aqueous U(Vi) With Goethite, Montmorillonite, And Uo2(S), Anshuman Satpathy

McKelvey School of Engineering Theses & Dissertations

Uranium contamination in subsurface environments is a matter of great concern throughout the world. Fate and transport of uranium in the subsurface can be controlled by U(VI) adsorption and reduction onto common iron (oxy)hydroxides and clay minerals. Aqueous U(VI) can also exchange uranium atoms with solids comprised of uranium which can potentially lead to changes in the morphology of the uranium-containing solids and affect their stability. First, the performance of multiple surface complexation models (SCMs) on adsorption of U(VI) onto goethite was analyzed for a broad range of input conditions. Individual models could fit the data for which they were …

Stochastic Goal Recognition Design, Christabel Wayllace

McKelvey School of Engineering Theses & Dissertations

Goal Recognition Design (GRD) is the problem of finding the least amount of environment modifications to force an acting agent to reveal its goal as early as possible. Figuring out an agent’s goal by observing its behavior is a problem studied in Psychology, Economics, and Artificial Intelligence, where it is known as goal recognition. Contrary to most common approaches where the focus is on finding faster algorithms to detect the goal, GRD takes an offline approach and focuses on environment design to facilitate goal recognition. This thesis investigates GRD problems when action outcomes are stochastic, which is the case of …

Assessment And Diagnosis Of Human Colorectal And Ovarian Cancer Using Optical Imaging And Computer-Aided Diagnosis, Yifeng Zeng

McKelvey School of Engineering Theses & Dissertations

Tissue optical scattering has recently emerged as an important diagnosis parameter associated with early tumor development and progression. To characterize the differences between benign and malignant colorectal tissues, we have created an automated optical scattering coefficient mapping algorithm using an optical coherence tomography (OCT) system. A novel feature called the angular spectrum index quantifies the scattering coefficient distribution. In addition to scattering, subsurface morphological changes are also associated with the development of colorectal cancer. We have observed a specific mucosa structure indicating normal human colorectal tissue, and have developed a real-time pattern recognition neural network to localize this specific structure …

Data Driven Architectural Geometric And Photometric Modeling, Huayi Zeng

McKelvey School of Engineering Theses & Dissertations

This thesis presents novel algorithms of architectural modeling, a crucial computer vision task to understand architectures by parsing visual input such as image or sensor data into digital representations. Compelling modeling algorithms serve as the fundamental block for a wide range of applications, including augmented reality, digital mapping, virtual simulation. While numerous handcrafted approaches have been proposed, the problem remains challenging in various difficult cases, such as occlusions and imperfect input. This dissertation studies four novel data-driven methods to perform high quality modeling. I first propose two geometric architectural modeling algorithms to recover the geometric primitives from aerial and facade …

Optical And Physicochemical Properties Of Atmospherically Processed Brown Carbon Using Novel First-Principle Instrumentation, Benjamin Sumlin

McKelvey School of Engineering Theses & Dissertations

Atmospheric processing of brown carbon (BrC) – a class of spherical, internally-mixed, light-absorbing organic aerosol – emitted from smoldering biomass combustion is an understudied phenomenon with implications for climate science, air quality models, and satellite retrieval algorithms. BrC aerosols have received significant attention as a strong contributor to atmospheric light absorption in the shorter visible wavelengths and a driver of UV photochemistry. Their complex refractive indices (m=n+ik), size distributions, and carbon oxidation states dictate their optical properties, atmospheric residence times, and chemical interactions, respectively. There is currently a gap in our understanding of these fundamental particle properties and their evolution …

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou

McKelvey School of Engineering Theses & Dissertations

It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …

Sustainable Bioproduction By Rhodopseudomonas Palustris Tie-1 Through Metabolic Engineering, Wei Bai

McKelvey School of Engineering Theses & Dissertations

The heavy reliance of the petroleum industry for raw material and the rising atmospheric CO2 caused by this reliance have driven the research and development of sustainable alternatives. Microbial production of chemicals, such as fuel and plastic, has been viewed as a feasible method. The wide selection of substrates by microbes enables them to produce chemicals using naturally abundant material or industrial waste, such as CO2, making the production sustainable. Compared to the model organisms such as Escherichia coli, Saccharomyces cerevisiae, many non-model organisms have a broader selection for carbon, electron, and nitrogen sources, making them great candidates for sustainable …

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

Mapping Transcription Factor Networks And Elucidating Their Biological Determinants, Yiming Kang

McKelvey School of Engineering Theses & Dissertations

A central goal in systems biology is to accurately map the transcription factor (TF) network of a cell. Such a network map is a key component for many downstream applications, from developmental biology to transcriptome engineering, and from disease modeling to drug discovery. Building a reliable network map requires a wide range of data sources including TF binding locations and gene expression data after direct TF perturbations. However, we are facing two roadblocks. First, rich resources are available only for a few well-studied systems and cannot be easily replicated for new organisms or cell types. Second, when TF binding and …