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525.721 - Advanced Digital Signal Processing Course Homepage

Instructor Information

Amir-Homayoon Najmi

Email: najmi@jhuapl.edu
Work Phone: 443.778.3320

Dr. Najmi has a B.A. degree in Mathematics from Cambridge University, and a D.Phil. in Theoretical Physics from Oxford University.  He was a Fulbright scholar at the Relativity Centre, University of Texas, a Research Associate and Instructor at the University of Utah and a Research Physicist at Shell Oil Geophysical Research centre prior to joining the Johns Hopkins University APL.  He has published research in wide areas including quantum field theory in cosmological space-times, seismic inverse scattering, and adaptive signal processing applied to electromagnetic waves and biosurveillance.  He has developed and taught courses in Relativity, Astrophysics, Cosmology, Advanced Signal Processing and Wavelet Signal Analysis at the Whiting school, and he is an adjunct associate professor at UMBC where he has taught a course in General Relativity.

Course Information

Course Description

The fundamentals of discrete-time statistical signal processing are presented in this course. Topics include optimal linear filter theory, classical and modern spectrum analysis, adaptive filtering, and the singular value decomposition and its application to least squares problems. Basic concepts of super-resolution methods are described, including an introduction to array processing. Computer experiments using Matlab illustrate some of the signal processing techniques.

Prerequisites

525.414 Probability and Stochastic Processes for Engineers, 525.427 Digital Signal Processing, and the basics of linear algebra.

Course Goal

Thorough understanding of linear systems and signals and the underlying mathematics, including representation and approximation theory in vector spaces [the orthogonality principle, least squares problems, minimum mean square estimation, the Wiener-Hopf equation, linear optima filters, eigrn decomposition methods, the singular value decomposition].  Adaptive linear filters and noise cancellation.  Spectral estimation.

Course Objectives

  • Mathematics of linear systems with deterministic and stochastic inputs.
  • Least squares filtering and minimum mean square filtering.
  • Adaptive linear filters.
  • Spectrum estimation.

When This Course is Typically Offered

Every Summer at Montgomery County campus

Syllabus

Topics Covered

  • linear algebra, Hilbert spaces
  • approximation problem in a Hilbert space
  • least squares filtering
  • minimum mean square filtering
  • Wiener-Hopf equation
  • matrix factorization
  • eigenvalue, eigenvectors, eigen filters
  • singular value decomposition
  • adaptive linear filters [LMS and RLS]
  • modern spectral estimation

Student Assessment Criteria

Homework 50%
computer implementation of algorithms 50%

Computer and Technical Requirements

Familiarity with any computer programming language (MATLAB, IDL, Fortran, C) capable of producing plots required.

Participation Expectations

Alert participation is required.

Textbooks

Textbook information for this course is available online through the MBS Direct Virtual Bookstore.

Course Notes

There are no notes for this course.

Final Words from the Instructor

Textbook:  Mathematical Methods and Algorithms for Signal processing, by Moon and Stirling, Prentice Hall.

(Last Modified: 05-29-2009 at 9:48:26 AM)