ECE 53800 - Digital Signal Processing I

Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing.

Preparation for Course
P: ECE 30100 and 30200

Cr. 3.
Student Learning Outcomes
1. Understand the autocorrelation and covariance methods of estimating the correlation matrix.
2. Know the Discrete Time Fourier Transform (DTFT) and its relationship to the Discrete Fourier Transform (DFT).
3. Comprehend of parametric methods of spectrum estimation, including autoregressive modeling, minimum variance, linear prediction, and eigendecomposition-based methods.
4. Understand nonparametric methods of spectrum estimation, including the periodogram and the correlogram.
5. Understand of linear filters, including the Wiener filter, as applied stochastic to signals.
6. Introduce to adaptive filters, including the method of steepest descent and the least-mean-square (LMS) algorithms.