Digital Commons Network^™

Automated Intelligent Cueing Device To Improve Ambient Gait Behaviors For Patients With Parkinson's Disease, Nader Naghavi

Doctoral Dissertations

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods …

Unifying Chemistry And Machine Learning For The Study Of Noncovalent Interactions, Jacob A. Townsend

Doctoral Dissertations

Gas separations are in great demand for carbon emission reduction, natural gas purification, oxygen isolation, and much more. Many of these separations rely on cost-prohibitive methods such as cryogenic distillation or strong-binding solvents. As a result, novel materials are being developed to subvert the energetic expense of gas separation processes. These studies focus on improving the performance of alternative materials, including (but not limited to) metal-organic frameworks, covalent organic frameworks, dense polymeric membranes, porous polymers, and ionic liquids.

In this work, the atomistic effects of functional units are explored for gas separations processes using electronic structure theory and machine learning. …

A Datacentric Algorithm For Gamma-Ray Radiation Anomaly Detection In Unknown Background Environments, James M. Ghawaly Jr

Doctoral Dissertations

The detection of anomalous radioactive sources in environments characterized by a high level of variation in the background radiation is a challenging problem in nuclear security. A variety of natural and artificial sources contribute to background radiation dynamics including variations in the absolute and relative concentrations of naturally occurring radioisotopes in different materials, the wet-deposition of $^{222}$Rn daughters during precipitation, and background suppression due to physical objects in the detector scene called ``clutter." This dissertation presents a new datacentric algorithm for radiation anomaly detection in dynamic background environments. The algorithm is based on a custom deep neural autoencoder architecture called …

Improving Convolutional Neural Network Robustness To Adversarial Images Through Image Filtering, Natalie E. Bogda

Masters Theses

The field of computer vision and deep learning is known for its ability to recognize images with extremely high accuracy. Convolutional neural networks exist that can correctly classify 96\% of 1.2 million images of complex scenes. However, with just a few carefully positioned imperceptible changes to the pixels of an input image, an otherwise accurate network will misclassify this almost identical image with high confidence. These perturbed images are known as \textit{adversarial examples} and expose that convolutional neural networks do not necessarily "see" the world in the way that humans do. This work focuses on increasing the robustness of classifiers …

Identifying Smokestacks In Remotely Sensed Imagery Via Deep Learning Algorithms, Kenneth Moss

Masters Theses

Locating smokestacks in remote sensing imagery is a crucial first step to calculating smokestack heights, which allows for the accurate modeling of dioxin pollution spread and the study of resulting health impacts. In the interest of automating this process, this thesis examines deep learning networks and how changes in input datasets and network architecture affect image detection accuracy. This initial image detection serves as the first step in automated object recognition and height calculation. While this is applicable to general land use classification, this study specifically addresses detecting smokestack images. Different dataset scenarios are generated from the massive Functional Map …

Bayesian Topological Machine Learning, Christopher A. Oballe

Doctoral Dissertations

Topological data analysis encompasses a broad set of ideas and techniques that address 1) how to rigorously define and summarize the shape of data, and 2) use these constructs for inference. This dissertation addresses the second problem by developing new inferential tools for topological data analysis and applying them to solve real-world data problems. First, a Bayesian framework to approximate probability distributions of persistence diagrams is established. The key insight underpinning this framework is that persistence diagrams may be viewed as Poisson point processes with prior intensities. With this assumption in hand, one may compute posterior intensities by adopting techniques …

Classification Of Bacterial Motility Using Machine Learning, Yue Ma

Masters Theses

Cells can display a diverse set of motility behaviors, and these behaviors may reflect a cell’s functional state. Automated, and accurate cell motility analysis is essential to cell studies where the analysis of motility pattern is required. The results of such analysis can be used for diagnostic or curative decisions. Deep learning area has made astonishing progresses in the past several years. For computer vision tasks, different convolutional neural networks (CNN) and optimizers have been proposed to fix some problems. For time sequence data, recurrent neural networks (RNN) have been widely used.

This project leveraged on these recent advances to …

Toward More Predictive Models By Leveraging Multimodal Data, Sudarshan Srinivasan

Doctoral Dissertations

Data is often composed of structured and unstructured data. Both forms of data have information that can be exploited by machine learning models to increase their prediction performance on a task. However, integrating the features from both these data forms is a hard, complicated task. This is all the more true for models which operate on time-constraints. Time-constrained models are machine learning models that work on input where time causality has to be maintained such as predicting something in the future based on past data. Most previous work does not have a dedicated pipeline that is generalizable to different tasks …

A Neuroscience-Inspired Approach To Training Spiking Neural Networks, James Michael Ghawaly Jr.

Masters Theses

Spiking neural networks (SNNs) have recently gained a lot of attention for use in low-power neuromorphic and edge computing. On their own, SNNs are difficult to train, owing to their lack of a differentiable activation function and their inherent tendency towards chaotic behavior. This work takes a strictly neuroscience-inspired approach to designing and training SNNs. We demonstrate that the use of neuromodulated synaptic time dependent plasticity (STDP) can be used to create a variety of different learning paradigms including unsupervised learning, semi-supervised learning, and reinforcement learning. In order to tackle the highly dynamic and potentially chaotic spiking behavior of SNNs …