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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


There is increasing interest in the medical and behavioral health communities towards developing effective strategies for the treatment of chronic diseases. Among these lie adaptive interventions, which consider adjusting treatment dosages over time based on participant response. Control engineering offers a broad-based solution framework for optimizing the effectiveness of such interventions. In this thesis, an approach is proposed to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone, an opioid antagonist, as treatment for a chronic pain condition known as fibromyalgia. System identification techniques are employed to model the dynamics from the daily …

Contributors
Deshpande, Sunil, Rivera, Daniel E., Si, Jennie, et al.
Created Date
2011

This study focuses on state estimation of nonlinear discrete time systems with constraints. Physical processes have inherent in them, constraints on inputs, outputs, states and disturbances. These constraints can provide additional information to the estimator in estimating states from the measured output. Recursive filters such as Kalman Filters or Extended Kalman Filters are commonly used in state estimation; however, they do not allow inclusion of constraints in their formulation. On the other hand, computational complexity of full information estimation (using all measurements) grows with iteration and becomes intractable. One way of formulating the recursive state estimation problem with constraints is …

Contributors
Joshi, Rakesh, Tsakalis, Konstantinos, Rodriguez, Armando, et al.
Created Date
2013

The problem of systematically designing a control system continues to remain a subject of intense research. In this thesis, a very powerful control system design environment for Linear Time-Invariant (LTI) Multiple-Input Multiple-Output (MIMO) plants is presented. The environment has been designed to address a broad set of closed loop metrics and constraints; e.g. weighted H-infinity closed loop performance subject to closed loop frequency and/or time domain constraints (e.g. peak frequency response, peak overshoot, peak controls, etc.). The general problem considered - a generalized weighted mixed-sensitivity problem subject to constraints - permits designers to directly address and tradeoff multivariable properties at …

Contributors
Puttannaiah, Karan, Rodriguez, Armando A, Tsakalis, Konstantinos S, et al.
Created Date
2013

This thesis presents a multi-modal motion tracking system for stroke patient rehabilitation. This system deploys two sensor modules: marker-based motion capture system and inertial measurement unit (IMU). The integrated system provides real-time measurement of the right arm and trunk movement, even in the presence of marker occlusion. The information from the two sensors is fused through quaternion-based recursive filters to promise robust detection of torso compensation (undesired body motion). Since this algorithm allows flexible sensor configurations, it presents a framework for fusing the IMU data and vision data that can adapt to various sensor selection scenarios. The proposed system consequently …

Contributors
Liu, Yangzi, Qian, Gang, Olson, Loren, et al.
Created Date
2010