Engineering Commons^™

Design Of Microwave Superconducting Resonators For Materials Characterization, Xinyi Zhao

McKelvey School of Engineering Theses & Dissertations

A resonator is a specialized device capable of storing and transferring energy at precise frequencies. Resonators find widespread use in various fields, such as electrical engineering, physics, and material science, owing to their exceptional ability to accurately measure, filter, and amplify signals. Different types of resonators exist, but coplanar waveguide (CPW) and coupled coplanar waveguide (CCPW) resonators are popular due to their high-frequency operation and easy integration into microfabrication processes.

Soft Electronics And Sensors For Wearable Healthcare Applications, Li-Wei Lo

McKelvey School of Engineering Theses & Dissertations

Wearable electronics are becoming increasingly essential to personalized medicine by collecting and analyzing massive amounts of biological signals from internal organs, muscles, and blood vessels. Conventional rigid electronics may lead to motion artifacts and errors in collected data due to the mismatches in mechanical properties between human skin. Instead, soft wearable electronics provide a better platform and interface that can form intimate contact and conformably adapt to human skin. In this respect, this thesis focuses on new materials formulation, fabrication, characterization of low-cost, high sensitivity and reliable sensors for wearable health monitoring applications.

More specifically, we have studied the silver …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

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 …

Flexible Electronics For Neurological Electronic Skin With Multiple Sensing Modalities, Haochuan Wan

McKelvey School of Engineering Theses & Dissertations

The evolution of electronic skin (E-skin) technology in the past decade has resulted in a great variety of flexible electronic devices that mimic the physical and chemical sensing properties of skin for applications in advanced robotics, prosthetics, and health monitoring technologies. The further advancement of E-skin technology demands closer imitation of skin receptors' transduction mechanisms, simultaneous detection of multiple information from different sources, and the study of transmission, processing and memory of the signals among the neurons. Motivated by such demands, this thesis focuses on design, fabrication, characterization of novel flexible electronic devices and integration of individual devices to realize …

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 …

Long-Term Neural Activity Recorders Using Energy-Based Sensing, Compressive Computation And Data Logging, Darshit Mehta

McKelvey School of Engineering Theses & Dissertations

Insects are ideal candidates for developing bio-robotic systems owing to their ability to thrive in almost any environment. For example, neurons in their exquisite olfactory sensory systems can be tapped to create a sensing platform for standoff chemical monitoring. However, for enabling such cyborg systems, it is vital that the neural activity of a freely behaving organism can be measured for long periods of time. The current state-of-the-art neural recording techniques are power-intensive and they either need batteries, which make them too bulky for insects, or they have to maintain a continuous telemetry link to an external power source which …

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 …

Elucidating And Leveraging Dynamics-Function Relationships In Neural Circuits Through Modeling And Optimal Control, Sruti Mallik

McKelvey School of Engineering Theses & Dissertations

A fundamental research question in neuroscience pertains to understanding how neural networks through their activity encode and decode information. In this research, we build on methods from theoretical domains such as control theory, dynamical systems analysis and reinforcement learning to investigate such questions. Our objective is two-fold: first, to use methods from engineering to identify specific objectives that neural circuits might be optimizing through their spatiotemporal activity patterns, and second, to draw motivation from neuroscience to formulate new engineering principles such as synthesis of dynamical networks for decentralized control applications. We specifically take a top-down, optimization driven approach in our …

Efficient And Scalable Computing For Resource-Constrained Cyber-Physical Systems: A Layered Approach, An Zou

McKelvey School of Engineering Theses & Dissertations

With the evolution of computing and communication technology, cyber-physical systems such as self-driving cars, unmanned aerial vehicles, and mobile cognitive robots are achieving increasing levels of multifunctionality and miniaturization, enabling them to execute versatile tasks in a resource-constrained environment. Therefore, the computing systems that power these resource-constrained cyber-physical systems (RCCPSs) have to achieve high efficiency and scalability. First of all, given a fixed amount of onboard energy, these computing systems should not only be power-efficient but also exhibit sufficiently high performance to gracefully handle complex algorithms for learning-based perception and AI-driven decision-making. Meanwhile, scalability requires that the current computing system …

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 …

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) …

Neural Dynamics, Adaptive Computations, And Sensory Invariance In An Olfactory System, Srinath Nizampatnam

McKelvey School of Engineering Theses & Dissertations

Sensory stimuli evoke spiking activities that are patterned across neurons and time in the early processing stages of olfactory systems. What features of these spatiotemporal neural response patterns encode stimulus-specific information (i.e. ‘neural code’), and how they are translated to generate behavioral output are fundamental questions in systems neuroscience. The objective of this dissertation is to examine this issue in the locust olfactory system. In the locust antennal lobe (analogous to the vertebrate olfactory bulb), a neural circuit directly downstream to the olfactory sensory neurons, even simple stimuli evoke neural responses that are complex and dynamic. We found each odorant …

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 …

Constructing And Analyzing Neural Network Dynamics For Information Objectives And Working Memory, Elham Ghazizadeh Ahsaei

McKelvey School of Engineering Theses & Dissertations

Creation of quantitative models of neural functions and discovery of underlying principles of how neural circuits learn and compute are long-standing challenges in the field of neuroscience. In this work, we blend ideas from computational neuroscience, information and control theories with machine learning to shed light on how certain key functions are encoded through the dynamics of neural circuits. In this regard, we pursue the ‘top-down’ modeling approach of engineering neuroscience to relate brain functions to basic generative dynamical mechanisms. Our approach encapsulates two distinct paradigms in which ‘function’ is understood. In the first part of this research, we explore …

Theory, Design And Implementation Of Energy-Efficient Biotelemetry Using Ultrasound Imaging, Sri Harsha Kondapalli

McKelvey School of Engineering Theses & Dissertations

This dissertation investigates the fundamental limits of energy dissipation in establishing a communication link with implantable medical devices using ultrasound imaging-based biotelemetry.

Ultrasound imaging technology has undergone a revolution during the last decade due to two primary innovations: advances in ultrasonic transducers that can operate over a broad range of frequencies and progresses in high-speed, high-resolution analog-to-digital converters and signal processors. Existing clinical and FDA approved bench-top ultrasound systems cangenerate real-time high-resolution images at frame rates as high as 10000 frames per second. On the other end of the spectrum, portable and hand-held ultrasound systems can generate high-speed real-time scans, …

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 …

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 …

Cognitive Radar Detection In Nonstationary Environments And Target Tracking, Yijian Xiang

McKelvey School of Engineering Theses & Dissertations

Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, …

Joint Estimation Of Attenuation And Scatter For Tomographic Imaging With The Broken Ray Transform, Michael Ray Walker

McKelvey School of Engineering Theses & Dissertations

The single-scatter approximation is fundamental for many tomographic imaging problems. This class broadly includes x-ray scattering imaging and optical scatter imaging for certain media. In all cases, noisy measurements are affected by both local events and nonlocal attenuation. Related applications typically focus on reconstructing one of two images: scatter density or total attenuation. However, both images are media specific. Both images are useful for object identification. Knowledge of one image aides estimation of the other, especially when estimating images from noisy data.Joint image recovery has been demonstrated analytically in the context of the broken ray transform (BRT) for attenuation and …

Self Capacitance Based Wireless Power Transfer For Wearable Electronics: Theory And Implementation, Yarub Omer Alazzawi

McKelvey School of Engineering Theses & Dissertations

Wireless power transfer (WPT)

Kernel Methods For Graph-Structured Data Analysis, Zhen Zhang

McKelvey School of Engineering Theses & Dissertations

Structured data modeled as graphs arise in many application domains, such as computer vision, bioinformatics, and sociology. In this dissertation, we focus on three important topics in graph-structured data analysis: graph comparison, graph embeddings, and graph matching, for all of which we propose effective algorithms by making use of kernel functions and the corresponding reproducing kernel Hilbert spaces.For the first topic, we develop effective graph kernels, named as "RetGK," for quantitatively measuring the similarities between graphs. Graph kernels, which are positive definite functions on graphs, are powerful similarity measures, in the sense that they make various kernel-based learning algorithms, for …

A Modular Approach For Modeling, Detecting, And Tracking Freezing Of Gait In Parkinson Disease Using Inertial Sensors, Prateek Gundannavar Vijay

McKelvey School of Engineering Theses & Dissertations

Parkinson disease, the second most common neurodegenerative disorder, is caused by the loss of dopaminergic subcortical neurons. Approximately 50% of people with Parkinson disease experience freezing of gait (FOG), a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. FOG causes falls and is resistant to medication in more than 50% of cases. FOG episodes can often be interrupted by mechanical interventions (e.g., a verbal reminder to march), but it is often not practical to apply these interventions on demand (e.g., there is not usually another person to detect an FOG episode …

Polarization Division Multiplexing For Optical Data Communications, Darko Ivanovich

McKelvey School of Engineering Theses & Dissertations

Multiple parallel channels are ubiquitous in optical communications, with spatial division multiplexing (separate physical paths) and wavelength division multiplexing (separate optical wavelengths) being the most common forms. In this research work, we investigate the viability of polarization division multiplexing, the separation of distinct parallel optical communication channels through the polarization properties of light. We investigate polarization division multiplexing based optical communication systems in five distinct parts. In the first part of the work, we define a simulation model of two or more linearly polarized optical signals (at different polarization angles) that are transmitted through a common medium (e.g., air), filtered …

Coupled Correlates Of Attention And Consciousness, Ravi Varkki Chacko

McKelvey School of Engineering Theses & Dissertations

Introduction: Brain Computer Interfaces (BCIs) have been shown to restore lost motor function that occurs in stroke using electrophysiological signals. However, little evidence exists for the use of BCIs to restore non-motor stroke deficits, such as the attention deficits seen in hemineglect. Attention is a cognitive function that selects objects or ideas for further neural processing, presumably to facilitate optimal behavior. Developing BCIs for attention is different from developing motor BCIs because attention networks in the brain are more distributed and associative than motor networks. For example, hemineglect patients have reduced levels of arousal, which exacerbates their attentional deficits. More …

Quantitative Hyperspectral Imaging Pipeline To Recover Surface Images From Crism Radiance Data, Linyun He

McKelvey School of Engineering Theses & Dissertations

Hyperspectral data are important for remote applications such as mineralogy, geology, agriculture and surveillance sensing. A general pipeline converting measured hyperspectral radiance to the surface reflectance image can provide planetary scientists with clean, robust and repeatable products to work on.

In this dissertation, the surface single scattering albedos (SSAs), the ratios of scattering eciency to scattering plus absorption eciences of a single particle, are selected to describe the reflectance. Moreover, the IOF, the ratio of measured spectral radiance (in the unit of watts per squared-meter and micrometer) to the solar spectral radiance (in the unit of watts per squared-meter and …

Research Of Electro-Optical Effect In Metal Halide Perovskites By Fabry-Perot Interometer Method, Hanxiang Yin

McKelvey School of Engineering Theses & Dissertations

Perovskites have been investigated a lot by present and reported with outstanding optoelectronic properties. However, so far there is no publications about another important property, the electro-optical (EO) effect which is related to important applications in photolithography. This thesis is mainly mean to calculate the EO constants of one kind of organic perovskite material, CH₃NH₃PbI₃, which has been reported to have good capability of forming film by spin-coating, through the way of putting the film of CH₃NH₃PbI₃ between two layers of metal mirrors to build a Fabry-Perot interferometer and …

Nanopower Analog Frontends For Cyber-Physical Systems, Kenji Aono

McKelvey School of Engineering Theses & Dissertations

In a world that is increasingly dominated by advances made in digital systems, this work will explore the exploiting of naturally occurring physical phenomena to pave the way towards a self-powered sensor for Cyber-Physical Systems (CPS). In general, a sensor frontend can be broken up into a handful of basic stages: transduction, filtering, energy conversion, measurement, and interfacing. One analog artifact that was investigated for filtering was the physical phenomenon of hysteresis induced in current-mode biquads driven near or at their saturation limit. Known as jump resonance, this analog construct facilitates a higher quality factor to be brought about without …

Basis Vector Model Method For Proton Stopping Power Estimation Using Dual-Energy Computed Tomography, Shuangyue Zhang

McKelvey School of Engineering Theses & Dissertations

Accurate estimation of the proton stopping power ratio (SPR) is important for treatment planning and dose prediction for proton beam therapy. The state-of-the-art clinical practice for estimating patient-specific SPR distributions is the stoichiometric calibration method using single-energy computed tomography (SECT) images, which in principle may introduce large intrinsic uncertainties into estimation results. One major factor that limits the performance of SECT-based methods is the Hounsfield unit (HU) degeneracy in the presence of tissue composition variations. Dual-energy computed tomography (DECT) has shown the potential of reducing uncertainties in proton SPR prediction via scanning the patient with two different source energy spectra. …