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Full-Text Articles in Engineering
Quantifying And Reversing Compensatory Movements By Persons Post-Stroke In The Ambient Setting, Aaron Miller
Quantifying And Reversing Compensatory Movements By Persons Post-Stroke In The Ambient Setting, Aaron Miller
Doctoral Dissertations
Nearly 800,000 people in the United States suffer stroke annually. Following the onset of stroke, survivors will exhibit deficits, such as hemiplegia, which will limit their function and ability to perform activities of daily living (ADLs). In order to regain independence, many stroke survivors will employ maladaptive compensatory strategies to help with the completion of tasks. Compensation is generally defined as any performance of a task that is different than the way it may have been performed before the onset of a neurodegenerative disorder. While for some severely impaired individuals, compensation may be necessary, for most these maladaptive strategies ultimately …
Machine Learning With Topological Data Analysis, Ephraim Robert Love
Machine Learning With Topological Data Analysis, Ephraim Robert Love
Doctoral Dissertations
Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …
Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach
Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach
Masters Theses
As convolutional neural networks become more prevalent in research and real world applications, the need for them to be faster and more robust will be a constant battle. This thesis investigates the effect of degradation being introduced to an image prior to object recognition with a convolutional neural network. As well as experimenting with methods to reduce the degradation and improve performance. Gaussian smoothing and additive Gaussian noise are both analyzed degradation models within this thesis and are reduced with Gaussian and Butterworth masks using unsharp masking and smoothing, respectively. The results show that each degradation is disruptive to the …