**Department of Electrical and Computer Engineering
The Johns Hopkins University **

**Instructor**- Prof. Trac D. Tran

Address: 310 Barton Hall

Phone: (410) 516-7416

Email: trac@jhu.edu

Office Hours : Thursday 1-3 or by appointment

- Prof. Trac D. Tran

**Lectures**- Thursday Friday, 10:30 - 12:00, Barton 117

**Announcements**- Exam I: Thursday, Oct. 18, 10:30 - 12:00, Barton 117.
- Exam II: Friday, Dec. 14, 10:00 - 12:00, Barton 117.
- Make-up Lectures: Monday, 11/12/2001, and Monday, 11/19/2001, 5:30 - 7:00, Barton 225.

**Homework Assignments**- Problem Set I. Postscript | PDF.
- Problem Set II. Postscript | PDF.
- Problem Set III. Postscript | PDF.
- Problem Set IV. Postscript | PDF.
- Problem Set V. Postscript | PDF.

**Required Texts**- S. Haykin,
*Adaptive Filter Theory*, Prentice Hall, Third Edition, 1996. ISBN 013322760X. - P. S. R. Diniz,
*Adaptive Filtering: Algorithms and Practical Implementations*, Kluwer Academic, 1997. ISBN 0792399129.

- S. Haykin,

**Additional References**(on Library Reserve)- B. Widrow and S. D. Stearns,
*Adaptive Signal Processing*, Prentice Hall, 1985. - J. R. Treichler, C. R. Johnson, Jr., and M. G. Larimore,
*Theory and Design of Adaptive Filters*, Prentice Hall, 2001.

- B. Widrow and S. D. Stearns,

**Course Description**- An introduction to the basic principles, mathematical theory, algorithmic design, and practical implementation of linear adaptive filters. Topics include adaptive least-mean-square and recursive-least-square algorithms, adaptive lattice structures, fast finite-precision implementations, and behavioral analysis.
- Prerequisites: 520.435, basic knowledge of probability and stochastic processes, Matlab programming.

**Tentative Syllabus**- Introduction. Motivation. Applications.
- Fundamentals and Background Material.
- Stationary Stochastic Processes. Signal Modeling and Representation.
- Optimum Linear Filtering. Wiener Filters. Linear Prediction
- Optimization Algorithms. Steepest Descent. Newton Method.

- Least-Mean-Square (LMS) Algorithm.
- General Algorithm. Theory.
- Analysis, Behavior, and Properties.
- LMS Variants: Normalized LMS, Block Adaptive Filters. Subband Adaptive Filters.

- Recursive Least-Squares (RLS) Algorithm.
- Least Squares.
- General Algorithm. Theory.
- Analysis, Behavior, and Properties.
- RLS Variants: Fast Transversal RLS, QR-Based RLS.
- Connection to Kalman Filters.

- Adaptive Lattice Filters.
- RLS Lattice Algorithms.
- Gradient Lattice Algorithms.
- Analysis, Behavior, and Properties.

**Grading**- Exams: 60%
- Homework and Computer Projects: 40%

**Ethics Issues**- Please read the information provided by the Ethics Board.
- Exams will be closed book and closed notes. One 8.5 x 11 handwritten formula sheet will be permitted.
- On homework and projects, you are permitted to discuss the problems for clarification purposes, and to help each other with specific points. However, the overall solution and write-up should be your own work.