Physical Sciences and Mathematics Commons^™

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 …

Computational Design Of Two-Dimensional Transition Metal Dichalcogenide Alloys And Their Applications, John Douglas Cavin

Arts & Sciences Electronic Theses and Dissertations

The discovery of bronze as an alloy of copper and tin is arguably the earliest form of material design, dating back thousands of years. In contrast, two-dimensional materials are new to the 21st century. The research presented in this dissertation is at the intersection of alloying and two-dimensional materials. I specifically study a class of two-dimensional materials known as transition metal dichalcogenides (TMDCs). Because of the large number of transition metals, there are many combinations of TMDCs that can be alloyed, making experimental exploration of the phase space of possible alloys unwieldly. Instead, I have applied first-principles methods to study …

Computational Modelling Enables Robust Multidimensional Nanoscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

The following sections are included:

• Present State of Computational Modelling in Fluorescence Nanoscopy

• Recent Contributions to Computational Modelling in Fluorescence Nanoscopy

• Outlook on Computational Modelling in Fluorescence Nanoscopy

• Acknowledgments

• References

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, …

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) …

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 …

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 …

Surface Modification Of Ii-Vi Semiconducting Nanocrystals, Calynn Morrison

Arts & Sciences Electronic Theses and Dissertations

This dissertation presents the compositional analysis of semiconductor materials by inductively coupled plasma optical emission spectroscopy (ICP-OES), a novel low-temperature shell growth precursor and installation pathway, and L-type for Z-type ligand exchange experiments conducted with four metal dithiocarbamate ligands. The techniques employed in the compositional analysis of semiconductor materials by inductively coupled plasma optical emission spectroscopy (ICP-OES) have a profound influence on the accuracy and reproducibility of the results. In Chapter 3, we describe methods for sample preparation, calibration, standard selection, and data collection. Specific protocols are suggested for the analysis of II-VI compounds and nanocrystals containing the elements Zn, …

Single‐Molecule 3d Orientation Imaging Reveals Nanoscale Compositional Heterogeneity In Lipid Membranes, Jin Lu, Hesam Mazidi, Tianben Ding, Oumeng Zhang, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

In soft matter, thermal energy causes molecules to continuously translate and rotate, even in crowded environments, thereby impacting the spatial organization and function of most molecular assemblies, such as lipid membranes. Directly measuring the orientation and spatial organization of large collections (>3000 molecules μm⁻²) of single molecules with nanoscale resolution remains elusive. In this paper, we utilize SMOLM, single‐molecule orientation localization microscopy, to directly measure the orientation spectra (3D orientation plus “wobble”) of lipophilic probes transiently bound to lipid membranes, revealing that Nile red's (NR) orientation spectra are extremely sensitive to membrane chemical composition. SMOLM images resolve …

Structural Organization And Chemical Activity Revealed By New Developments In Single-Molecule Fluorescence And Orientation Imaging, Tianben Ding

McKelvey School of Engineering Theses & Dissertations

Single-molecule (SM) fluorescence and its localization are important and versatile tools for understanding and quantifying dynamical nanoscale behavior of nanoparticles and biological systems. By actively controlling the concentration of fluorescent molecules and precisely localizing individual single molecules, it is possible to overcome the classical diffraction limit and achieve 'super-resolution' with image resolution on the order of 10 nanometers.

Single molecules also can be considered as nanoscale sensors since their fluorescence changes in response to their local nanoenvironment. This dissertation discusses extending this SM approach to resolve heterogeneity and dynamics of nanoscale materials and biophysical structures by using positions and orientations …

Growth Of Small Particles In Nonequilibrium Plasmas, Necip Berker Üner

McKelvey School of Engineering Theses & Dissertations

Nonequilibrium plasma (NEP) is an extraordinary environment for material synthesis. NEP is comprised of hot electrons with temperatures greater than 10000 K and of cold ions and neutrals that are usually at few hundred kelvins above room temperature. Due to this large difference in species’ temperatures, the assumption of local thermal equilibrium does not hold in NEP. Therefore, NEP can act as a unique processor of mass, and it can transform materials along pathways that are not accessible by methods wherein local thermal equilibrium is valid. For decades, NEPs have been employed in the semiconductor industry to manufacture many thin …

Production Of Medical Radioisotopes Using Titanium Accelerator Targets, Christopher Shaun Loveless

Arts & Sciences Electronic Theses and Dissertations

Theranostic radiopharmaceuticals enable diagnostic imaging and radionuclide therapy in patients using a single molecular agent labeled with a diagnostic-therapeutic pair (e.g., 68Ga/177Lu) or a theranostic radionuclide (e.g., 131I). This theranostic approach can help inform patient-specific treatment plans and improve clinical outcomes. Radionuclide pairs used in theranostic agents fall into two categories: pseudo matched-pairs (e.g., 68Ga/177Lu) and matched-pairs (e.g., 124I/131I). Pseudo matched-pair radionuclides have similar chemistries and pharmacokinetics when bound to the same bioconjugate molecule. In contrast, identical chemistries and pharmacokinetics can be obtained by using the matched-pair radionuclides.

The isotopes of Sc include two diagnostic radioisotopes, 43Sc & 44Sc, and …

Separating Signal From Noise In High-Density Diffuse Optical Tomography, Arefeh Sherafati

Arts & Sciences Electronic Theses and Dissertations

High-density diffuse optical tomography (HD-DOT) is a relatively new neuroimaging technique that detects the changes in hemoglobin concentrations following neuronal activity through the measurement of near-infrared light intensities. Thus, it has the potential to be a surrogate for functional MRI (fMRI) as a more naturalistic, portable, and cost-effective neuroimaging system. As in other neuroimaging modalities, head motion is the most common source of noise in HD-DOT data that results in spurious effects in the functional brain images. Unlike other neuroimaging modalities, data quality assessment methods are still underdeveloped for HD-DOT. Therefore, developing robust motion detection and motion removal methods in …

Convex Relaxations For Particle-Gradient Flow With Applications In Super-Resolution Single-Molecule Localization Microscopy, Hesam Mazidisharfabadi

McKelvey School of Engineering Theses & Dissertations

Single-molecule localization microscopy (SMLM) techniques have become advanced bioanalytical tools by quantifying the positions and orientations of molecules in space and time at the nanoscale. With the noisy and heterogeneous nature of SMLM datasets in mind, we discuss leveraging particle-gradient flow 1) for quantifying the accuracy of localization algorithms with and without ground truth and 2) as a basis for novel, model-driven localization algorithms with empirically robust performance. Using experimental data, we demonstrate that overlapping images of molecules, a typical consequence of densely packed biological structures, cause biases in position estimates and reconstruction artifacts. To minimize such biases, we develop …

Domain Specific Computing In Tightly-Coupled Heterogeneous Systems, Anthony Michael Cabrera

McKelvey School of Engineering Theses & Dissertations

Over the past several decades, researchers and programmers across many disciplines have relied on Moores law and Dennard scaling for increases in compute capability in modern processors. However, recent data suggest that the number of transistors per square inch on integrated circuits is losing pace with Moores laws projection due to the breakdown of Dennard scaling at smaller semiconductor process nodes. This has signaled the beginning of a new “golden age in computer architecture” in which the paradigm will be shifted from improving traditional processor performance for general tasks to architecting hardware that executes a class of applications in a …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Chemistry Of Nanoscale Solids And Organic Matter In Sustainable Water Management Systems, Xuanhao Wu

McKelvey School of Engineering Theses & Dissertations

To alleviate global water scarcity and improve public health, engineered water treatment and management systems have been developed for purifying contaminated water and desalinating brackish or ocean water. These engineered systems provide substantial amounts of potable water and lessen environmental concerns about the release of contaminated water. Wastewater treatment plants (WWTPs), water desalination plants (WDPs), and managed aquifer recharge systems (MARs) are three representative sustainable water management (SWM) systems. But the operation of all three poses two fundamental questions: (1) What is the fate of nanoscale solids (e.g., engineered nanomaterials, naturally occurring nanoparticles) in SWM systems and how will their …

Exploring Usage Of Web Resources Through A Model Of Api Learning, Finn Voichick

McKelvey School of Engineering Theses & Dissertations

Application programming interfaces (APIs) are essential to modern software development, and new APIs are frequently being produced. Consequently, software developers must regularly learn new APIs, which they typically do on the job from online resources rather than in a formal educational context. The Kelleher–Ichinco COIL model, an acronym for “Collection and Organization of Information for Learning,” was recently developed to model the entire API learning process, drawing from information foraging theory, cognitive load theory, and external memory research. We ran an exploratory empirical user study in which participants performed a programming task using the React API with the goal of …

First-Principles Studies Of Anion Engineering In Functional Ceramics, Steven Timothy Hartman

McKelvey School of Engineering Theses & Dissertations

Ceramic materials display a wide variety of valuable properties, such as ferroelectricity, superconductivity, and magnetic ordering, due to the partially covalent bonds which connect the cations and anions. While many breakthroughs have been made by mixing multiple cations on a sublattice, the equivalent mixed-anion ceramics have not received nearly as much attention, despite the key role the anion plays in the materials’ properties. There is great potential for functional ceramics design using anion engineering, which aims to tune the materials properties by adding and removing different types of anions in existing classes of ceramic materials. In this dissertation, I present …

Spectroscopic Investigations Of Excited Charge Carriers In Ii-Vi Nanoparticles, William Matthew Sanderson

Arts & Sciences Electronic Theses and Dissertations

The large absorption cross sections and the tunability of the energetic spacings between the states in the conduction (CB) and valence band (VB) within a semiconductor nanoparticle (NP) make them promising media for capturing electromagnetic radiation and converting it into charge carriers, or electricity. In photovoltaic devices that incorporate semiconductor NPs, it would be ideal if every photon could be absorbed by a NP and the carriers could be collected with perfect efficiency and without loss of energy. The relaxation pathways of the carriers within the NPs down to the band edge and their fate at the band edge contribute …

Exploring Attacks And Defenses In Additive Manufacturing Processes: Implications In Cyber-Physical Security, Nicholas Deily

McKelvey School of Engineering Theses & Dissertations

Many industries are rapidly adopting additive manufacturing (AM) because of the added versatility this technology offers over traditional manufacturing techniques. But with AM, there comes a unique set of security challenges that must be addressed. In particular, the issue of part verification is critically important given the growing reliance of safety-critical systems on 3D printed parts. In this thesis, the current state of part verification technologies will be examined in the con- text of AM-specific geometric-modification attacks, and an automated tool for 3D printed part verification will be presented. This work will cover: 1) the impacts of malicious attacks on …

Development Of Novel Instrumentation And Methods To Investigate The Composition And Phase Partitioning Of Semivolatile And Intermediately Volatile Organic Compounds In Atmospheric Organic Aerosol, Claire Fortenberry

McKelvey School of Engineering Theses & Dissertations

Atmospheric particulate matter (PM) is ubiquitous in both indoor and outdoor air and is generally detrimental to human health. PM composed of particles with aerodynamic diameters less than 2.5 um (PM2.5) are related to adverse health outcomes including heart disease and respiratory disease. Fundamentally, particle physical properties such as size and hygroscopicity are dictated by chemical composition, which can be highly complex, particularly for organic aerosol (OA). In both outdoor and indoor air, OA is composed substantially of intermediately volatile and semivolatile organic compounds (I/SVOCs), which exist in both gas and particle phases under typical atmospheric conditions. The distribution of …

Phantoms To Placentas: Mr Methods For Oxygen Quantification, Kelsey Meinerz

Arts & Sciences Electronic Theses and Dissertations

Molecular oxygen (O2) is vital for efficient energy production and improper oxygenation is a hallmark of disease or metabolic dysfunction. In many pathologies, knowledge of tissue oxygen levels (pO2) could aid in diagnosis and treatment planning. The gold standard for pO2 measures in tissue are implantable probes, which are invasive, require surgery for placement, and are inaccessible to certain regions of the body. Methods for determining pO2 both non-invasively and quantitatively are lacking. The slight paramagnetic nature of O2 provides opportunities to non-invasively characterize pO2 in tissue via magnetic resonance (MR) techniques. As such, O2 can be treated as a …

Elicitation And Aggregation Of Data In Knowledge Intensive Crowdsourcing, Dohoon Kim

All Computer Science and Engineering Research

With the significant advance of internet and connectivity, crowdsourcing gained more popularity and various crowdsourcing platforms emerged. This project focuses on knowledge-intensive crowdsourcing, in which agents are presented with the tasks that require certain knowledge in domain. Knowledge-intensive crowdsourcing requires agents to have experiences on the specific domain. With the constraint of resources and its trait as sourcing from crowd, platform is likely to draw agents with different levels of expertise and knowledge and asking same task can result in bad performance. Some agents can give better information when they are asked with more general question or more knowledge-specific task …

A Virtual 4d Ct Scanner, Xiwen Li

All Computer Science and Engineering Research

4D CT scan is widely used in medical imaging. Images are acquired through phases. In this case, we can track the motion of organs such as heart. However, it also introduces motion artifacts. A lot of research focuses on remove these artifacts. It is difficult to acquire artifact data by a real CT scanner. In this project, we implement a virtual CT machine to simulate the real 4D CT scan. we also conduct experi- ments to check its clinical reality with respect to respiratory and heart motion parameters.

Centrality Of Blockchain, Zixuan Li

All Computer Science and Engineering Research

Decentralization is widely recognized as the property and one of most important advantage of blockchain over legacy systems. However, decentralization is often discussed on the consensus layer and recent research shows the trend of centralization on several subsystem of blockchain. In this project, we measured centralization of Bitcoin and Ethereum on source code, development eco-system, and network node levels. We found that the programming language of project is highly centralized, code clone is very common inside Bitcoin and Ethereum community, and developer contribution distribution is highly centralized. We further discuss how could these centralizations lead to security issues in blockchain. …

Solving Disappearance At Gastech With Visual Analytic Techniques, Saulet Yskak

All Computer Science and Engineering Research

We are living in a society, where images and charts speak louder than words. Therefore, information visualization plays a major role in solving complex problems since it provides a visual summary of data that makes it easier to identify trends and patterns.

In this master project, I propose a web – based visual analytics tool that enables to analyze complex email and time based / event series data. The visual analytics framework uses test data from IEEE VAST Challenge 2014: Mini challenge 1 that concentrated on the disappearance of employees of a fictional GAStech company, but the tool allows users …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross-sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially …

Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy, Hesam Mazidi, Tianben Ding, Arye Nehorai, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

We present a computational method, termed Wasserstein-induced flux (WIF), to robustly quantify the accuracy of individual localizations within a single-molecule localization microscopy (SMLM) dataset without ground- truth knowledge of the sample. WIF relies on the observation that accurate localizations are stable with respect to an arbitrary computational perturbation. Inspired by optimal transport theory, we measure the stability of individual localizations and develop an efficient optimization algorithm to compute WIF. We demonstrate the advantage of WIF in accurately quantifying imaging artifacts in high-density reconstruction of a tubulin network. WIF represents an advance in quantifying systematic errors with unknown and complex distributions, …