Time-Frequency decomposition is a signal processing method for analyzing and extracting information from aperiodic signals. Analysis of these signals are ineffective when done using the Fourier transform, instead these signals must be analyzed in the time and frequency domain simultaneously. The current tools for Time-Frequency analysis are either proprietary or computationally expensive making it prohibitive for researchers to use. This thesis investigates the computational aspects of signal processing with a focus on Time-Frequency analysis using wavelets. We develop algorithms that compute and plot the Time-Frequency decomposition automatically, and implement them in C++ as a framework. As a result our framework is significantly faster than MATLAB, and can be easily incorporated into applications that require Time-Frequency analysis. The framework is applied to identify the Event Related Spectral Perturbation of EEG signals; and to vibrational analysis by identifying the mechanical modal parameters of oscillating machines.

Master of Applied Science (MASc)

Thesis