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Dec 11, 2024
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EE 6733 - Advanced Topics in Signal Processing
Digital filter designs. Discrete random signals. Linear prediction and the Wiener filter. Stochastic gradient methods, least-squares and Kalman filter, SVD, super-resolution algorithms, current research problems.
Requisites: EE 6713
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes:
- Ability to apply least-squares and Kalman filtering algorithms.
- Ability to apply super-resolution algorithms.
- Ability to characterize DT filtering of random processes.
- Ability to conduct eigen- and spectrum analysis of WSS processes.
- Ability to design IIR and FIR filters to specifications.
- Ability to design and analyze stochastic gradient filters.
- Ability to imply SVD in filter design.
- Ability to specify Wiener filters and linear predictors.
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