EE 648 Digital Signal Processing II


Course Information

3 credit hours

Prerequisites: EE638 or consent of the instructor.

Description: In this course, a number of advanced topics in digital signal processing are covered. The emphasis is on fast transforms and algorithms, adaptive signal processing, multidimensional and multirate signal processing, inverse problems, nonlinear filtering, time-frequency methods, and processing of signals carried by propagating waves.

Text: Course notes and handouts

References:

P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall, 1993
C. K. Chui, An Introduction to Wavelets, Academic Press, 1992.
C. K. Chui, Wavelets: A Tutorial in Theory and Application, Academic Press, 1992.
M. Vetterli and J. Kovacevic, Wavelets and Subband Coding, Prentice Hall, 1995.
J. W. Woods (editor), Subband Image Coding, Kluwer, 1991.
R. K. Young, Wavelet Theory and Its Applications, Kluwer, 1993.
J.S. Lim and A.V. Oppenheim, Advanced Topics in Signal Processing, Prentice Hall, 1988.
R. E. Blahut, Fast Algorithms for Digital Signal Processing, Addison-Wesley, 1982.
D. E. Dudgeon, R.M. Mersereau, Multidimensional Digital Signal Processing, Prentice Hall, 1984.
R. E. Cochiere, L. R. Rabiner, Multirate Digital Signal Processing, Prentice Hall, 1983.
S. Haykin, Adaptive Filter Theory, Prentice Hall, 1986.
S. Y. Kung, VLSI Signal Processing, Prentice Hall.

Tenative Course OutLine:

Background material in algebra and number theory

Multirate Signal Processing (sampling rate conversion, FIR filters for interpolation and decimation), quadrature-mirror filters, multistage implementations, signal processing based on decimation and interpolation)

Wavelets, time-frequency methods

Inverse Problems (iterative signal restoration algorithms, reconstruction of nonuniformly sampled signals, regularization

Nonlinear Filtering (rank order filters, multistage nonlinear filters, transform domain nonlinearities

Adaptive Signal Processing (LMS-Newton algorithm, Kalman filter, adaptive lattice structures, multidimensional problems, nonlinear adaptive systems, multistage adaptive systems)

Multidimensional spectral estimation, beaming, seismic wave migration

Multidimensional (MD) Signal Processing (multidimensional transforms, design and implementation of MD filters, stability)

High Order Signal Processing (bispectrum, cumulants, system identification and spectral analysis) VLSI Signal Processing (mappings, hardware algorithms, and transformations)

Exams and/or course projects


Class Information and Web Links

More information about the class and links to other pages is available here.
This page was last updated on August 19, 1996 at 2:45PM EST.

Professor Edward J. Delp