Engineering Commons^™

Low Impedance, Durable, Self-Adhesive Hydrogel Epidermal Electrodes For Electrophysiology Recording, Naiyan Wu

McKelvey School of Engineering Theses & Dissertations

Traditional electrodes used for electrophysiology recording, characterized by their hard, dry, and inanimate nature, are fundamentally mismatched with the soft, moist, and bioactive characteristics of biological tissues, leading to suboptimal skin-electrode interfaces. Hydrogel materials, mirroring the high water content and biocompatibility of biological tissues, emerge as promising candidates for epidermal electronic materials due to their adjustable physicochemical properties. However, challenges such as inadequate electrical conductivity, elevated skin impedance, unreliable adhesion in moist conditions, and performance decline from dehydration have significantly restricted the efficacy and applicability of hydrogel-based electrodes. In this thesis, we report a high-performance hydrogel epidermal electrode patch for …

Estimating And Detecting Slow-Wave Events In Eeg Signals, Zhenghao Xiong

McKelvey School of Engineering Theses & Dissertations

Slow wave activity (SWA) is an electroencephalogram (EEG) pattern commonly occurring during anesthesia and deep sleep, and is hence a candidate biomarker to quantify such states and understand their connection to various phenotypes. SWA consists of individual slow waves (ISW), high-amplitude deflections lasting for approximately 0.5 to 1 second, and occurring quasi-periodically. This latter fact poses a challenge for conventional power spectral density EEG analysis methods that perform best when there is persistency of oscillatory activity. In this work, we pursue a time-domain detection framework for identifying and quantifying ISWs as a metric for SWA. Our method works, in essence, …

Watch: A Distributed Clock Time Offset Estimation Tool On The Platform For Open Wireless Data-Driven Experimental Research, Cassie Jeng

McKelvey School of Engineering Theses & Dissertations

The synchronization of the clocks used at different devices across space is of critical importance in wireless communications networks. Each device’s local clock differs slightly, affecting the times at which packets are transmitted from different nodes in the network. This thesis provides experimentation and software development on POWDER, the Platform for Open, Wireless Data-driven Experimental Research, an open wireless testbed across the University of Utah campus. We build upon Shout, a suite of Python scripts that allow devices to iteratively transmit and receive with each other and save the collected data. We introduce WATCH, an experimental method to estimate clock …

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 …

Design Of An Offner-Chrisp Imaging Spectrometer For A Planetary Fluorescence Instrument, Tristan Wells Carlson

McKelvey School of Engineering Theses & Dissertations

Spectrometers have been an integral part of space exploration in the late 20th and 21st centuries and will continue to provide quantitative measurements to answer exciting questions like, “Is or was there life on other planets?” PERISCOPE, Probe for Exploring Regolith and Ice by Subsurface Classification of Organics, Polycyclic aromatic hydrocarbons, and Elements, is a next generation spectrometer designed to explore icy worlds like Europa. It uses time-resolved ultraviolet (UV) fluorescence spectroscopy, a technique that identifies organic molecules, polycyclic aromatic hydrocarbons (PAH), and rare earth elements (REE). Photon wavelength discrimination is very important for this technique and is accomplished by …

Locating Unknown Interference Sources With Time Difference Of Arrival Estimates, Chia Ying Kuo

McKelvey School of Engineering Theses & Dissertations

Adaptive spectrum sharing between different systems and operators is being deployed in order to make use of the wireless spectrum more efficiently. However, when the spectrum is shared, it can create situations in which an operator is unable to determine the identity of an interferer transmitting an unknown signal. This is the situation in which the POWDER testbed found itself in, starting in late 2021. This thesis provides general-purpose tools for operators to locate an unknown signal source in real-world outdoor environments. We used cross-correlation between the signals measured at multiple time-synchronized base stations to estimate the time difference of …

Design & Analysis Of Mixed-Mode Integrated Circuit For Pulse-Shape Discrimination, Bryan Orabutt

McKelvey School of Engineering Theses & Dissertations

In nuclear science experiments it is usually necessary to determine the type of radiation, its energy and direction with considerable accuracy. The detection of neutrons and discriminating them from gamma rays is particularly difficult. A popular method of doing so is to measure characteristics intrinsic to the pulse shape of each radiation type in order to perform pulse-shape discrimination (PSD).

Historically, PSD capable systems have been designed with two approaches in mind: specialized analog circuitry, or digital signal processing (DSP). In this work we propose a PSD capable circuit topology using techniques from both the analog and DSP domains. We …

Numerical Investigations Of Logic Gates And Transistors In Quantum And Classical Information Processing, Anthony Corbin

McKelvey School of Engineering Theses & Dissertations

The development of numerical analysis techniques for electromagnetics and quantum mechanics problems has dramatically increased the rate of new discoveries. Propelled by the exponential growth of computers over the past decades, numerical software has brought the ability to reliably experiment with new ideas and predict new phenomena, even before the physical realizations of these ideas in the labs. This work will demonstrate the application of this capability by applying numerical analysis techniques to design and analyze new devices. First, a system of optical logic gates using a nonlinear metallodielectric grating is completely designed and simulated using numerical analysis. Second, the …

Short-Term Memory And Olfactory Signal Processing, Lijun Zhang

McKelvey School of Engineering Theses & Dissertations

Modern neural recording methodologies, including multi-electrode and optical recordings, allow us to monitor the large population of neurons with high temporal resolution. Such recordings provide rich datasets that are expected to understand better how information about the external world is internally represented and how these representations are altered over time. Achieving this goal requires the development of novel pattern recognition methods and/or the application of existing statistical methods in novel ways to gain insights into basic neural computational principles. In this dissertation, I will take this data-driven approach to dissect the role of short-term memory in olfactory signal processing in …

Nevr: Learning Continuous Neural Video Representation With Local Feature Codes For Video Interpolation, Wentao Shangguan

McKelvey School of Engineering Theses & Dissertations

Video frame interpolation aims to synthesis a non-exists intermediate frame guided by two successive frames. Recently, some work shows excellent results in learning continuous representation of temporally-varying 3D objects with neural field (NF), which could be used for interpolating the original video. However, these methods require several videos from different viewing angles, the information of camera poses, learning for each specific scene, and achieving sub-optimal results for video frame interpolation. To this end, we propose a new learning neural field representation-based model, Neural Video Representation (NeVR) to learn a continuous representation of videos for high-quality video interpolation. Unlike the traditional …

Scatter Estimation And Correction For Experimental And Simulated Data In Multi-Slice Computed Tomography Using Machine Learning And Minimum Least Squares Methods, Cornelia Wang

McKelvey School of Engineering Theses & Dissertations

Current research aims to reduce the stopping power ratio prediction error in the inputs to the proton therapy planning process to less than 1%, which allows for improved radiation therapy planning. Our present study on reducing SPR error neglects the effect of scattering, which can increase SPR error by as much as 1-1.5%. The idea is that for each source-to-detector pair, 24 mm collimation data is close to 3 mm collimation data but with increased signal due to scattering. The goal is to estimate 3 mm collimation data from 24 mm collimation data. Pairs of sinograms, both experimental data and …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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, …

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, …

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 …

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 …

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 …

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 …