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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 4Dynamic Programming
- 1AI
- 1Artificial Intelligence
- 1Automated Planning and Scheduling
- 1Cognitive Radio Networks
- 1Convolutional Neural Networks
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Automated Coordination of Distributed Energy Resources using Local Energy Markets and Reinforcement Learning
DownloadFall 2024
The conventional unidirectional model of the electricity grid operations is no longer sufficient. The continued proliferation of distributed energy resources and the resultant surge in net load variability at the grid edge necessitates deploying adequate demand response methods. This thesis...
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Spring 2013
Cost overrun and schedule slippage are common problems for mega industrial construction projects. Lack of effective planning and scheduling tools is identified as a major contributing factor to poor project performance. Planning and scheduling tools should be custom designed to address the...
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Extending Differentiable Programming to include Non-differentiable Modules using Differentiable Bypass for Combining Convolutional Neural Networks and Dynamic Programming into an End-to-end Trainable Framework
DownloadSpring 2019
Differentiable Programming is the paradigm where different functions or modules are combined into a unified pipeline with the purpose of applying end-to-end learning or optimization. A natural impediment is the non-differentiability characteristic of many modules. This thesis proposes a new way...
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Fall 2015
We study a channel selection problem in cognitive radio with imperfect spectrum sensing. In this problem, a secondary (unlicensed) user must select a subset of M channels out of N channels to sense. The user then accesses up to K ≤ M channels that were sensed free. The objective is to maximize...