Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Neural Fields for Tomographic Imaging: with Applications in X-ray Computed Tomography and Synthetic Aperture Sonar

Description
Computed tomography (CT) and synthetic aperture sonar (SAS) are tomographic imaging techniques that are fundamental for applications within medical and remote sensing. Despite their successes, a number of factors constrain their image quality. For example, a time-varying scene during measurement acquisition yields image artifacts. Additionally, factors such as bandlimited or…

Computed tomography (CT) and synthetic aperture sonar (SAS) are tomographic imaging techniques that are fundamental for applications within medical and remote sensing. Despite their successes, a number of factors constrain their image quality. For example, a time-varying scene during measurement acquisition yields image artifacts. Additionally, factors such as bandlimited or sparse measurements limit image resolution. This thesis presents novel algorithms and techniques to account for these factors during image formation and outperform traditional reconstruction methods. In particular, this thesis formulates analysis-by-synthesis optimizations that leverage neural fields to predict the scene and differentiable physics models that incorporate prior knowledge about image formation. The specific contributions include: (1) a method for reconstructing CT measurements from time-varying (non-stationary) scenes; (2) a method for deconvolving SAS images, which benefits image quality; (3) a method that couples neural fields and a differentiable acoustic model for 3D SAS reconstructions.
ContributorsReed, Albert William (Author) / Jayasuriya, Suren (Thesis advisor) / Brown, Daniel C (Committee member) / Dasarathy, Gautam (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2023
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Learning Predictive Models for Assisted Human Biomechanics

Description
This dissertation explores the use of artificial intelligence and machine learningtechniques for the development of controllers for fully-powered robotic prosthetics. The aim of the research is to enable prosthetics to predict future states and control biomechanical properties in both linear and nonlinear fashions, with a particular focus on ergonomics. The research is motivated by…

This dissertation explores the use of artificial intelligence and machine learningtechniques for the development of controllers for fully-powered robotic prosthetics. The aim of the research is to enable prosthetics to predict future states and control biomechanical properties in both linear and nonlinear fashions, with a particular focus on ergonomics. The research is motivated by the need to provide amputees with prosthetic devices that not only replicate the functionality of the missing limb, but also offer a high level of comfort and usability. Traditional prosthetic devices lack the sophistication to adjust to a user’s movement patterns and can cause discomfort and pain over time. The proposed solution involves the development of machine learning-based controllers that can learn from user movements and adjust the prosthetic device’s movements accordingly. The research involves a combination of simulation and real-world testing to evaluate the effectiveness of the proposed approach. The simulation involves the creation of a model of the prosthetic device and the use of machine learning algorithms to train controllers that predict future states and control biomechanical properties. The real- world testing involves the use of human subjects wearing the prosthetic device to evaluate its performance and usability. The research focuses on two main areas: the prediction of future states and the control of biomechanical properties. The prediction of future states involves the development of machine learning algorithms that can analyze a user’s movements and predict the next movements with a high degree of accuracy. The control of biomechanical properties involves the development of algorithms that can adjust the prosthetic device’s movements to ensure maximum comfort and usability for the user. The results of the research show that the use of artificial intelligence and machine learning techniques can significantly improve the performance and usability of pros- thetic devices. The machine learning-based controllers developed in this research are capable of predicting future states and adjusting the prosthetic device’s movements in real-time, leading to a significant improvement in ergonomics and usability. Overall, this dissertation provides a comprehensive analysis of the use of artificial intelligence and machine learning techniques for the development of controllers for fully-powered robotic prosthetics.
ContributorsCLARK, GEOFFEY M (Author) / Ben Amor, Heni (Thesis advisor) / Dasarathy, Gautam (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Ward, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2023