EE 7733 - Time Frequency Analysis and Wavelet Signal Processing and Applications

Good knowledge of Fourier transforms, properties, basic DSP: sampling, DTFT, and discrete time filtering (grad DSP course EE6713) required. Structured to cover two areas: the broad area of time-frequency (TF) analysis, and the focused application of wavelets to various signal processing tasks. In TF analysis, covers the fundamental need for this type of analysis, the uncertainty principle, densities, characteristic functions, and mathematical representations, the short-time Fourier transform and Spectrogram, the Wigner distribution, other TF distributions, and some TF distribution construction methods. Various examples will be used to illustrate the power and challenges of TF analysis. In the wavelet section, connects TF analysis to the use of wavelets, and covers multiresolution analysis, 1D and 2D compression of signals and images, noise reduction, and signal modulation. MATLAB Wavelet Toolbox used to implement, study, and visualize the operation of wavelet filter banks.

Requisites
EE 6713
Credit Hours: 3.0
Repeat/Retake Information: May not be retaken.

Lecture/Lab Hours: 3.0 lecture

Eligible grades: A-F,WP,WF,FN,FS,AU,I