Engineering Commons^™

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Design Of Efficient Algorithms Through Minimization Of Data Transfers, Yong Mo Chong

Electrical & Computer Engineering Theses & Dissertations

This thesis explores the time optimal implementation of computational graphs on a finite register machine. The implementation fully exploits the machine architecture, especially, the number of registers. The derived algorithms allow one to obtain time efficient implementations of a given graph in machines with a known number of registers.

These optimization procedures are applied to digital signal processing graphs. It is shown that the regular structure of these graphs allows one to identify computational kernels which, when used repeatedly, can cover the entire graph. The l- and r-register implementations of Hadamard and Fast Fourier Transforms using various computational kernels are …