ECE 5/638: Syllabus

Course Information

Location: URBAN 304

Time: 2:00 - 3:50

Units: 4

Contact Information

Instructor: Dr. McNames
Office: FAB 160-12
Office hours: M 10-11, W 4-5
Email: mcnames@pdx.edu

Class Web Site: http://ece.pdx.edu/~ssp

Catalog Description

Unified introduction to the theory, implementation, and applications of statistical signal processing methods. Focus on estimation theory, random signal modeling, characterization of stochastic signals and systems, and nonparametric estimation. Designed to give a solid foundation in the underlying theory balanced with a discussion of the practical advantages and limitations of nonparametric estimation methods.

MTH 343 (Applied Linear Algebra) and ECE 565/665 (Signals and Noise). Should have some proficiency at programming in MATLAB.

Textbook

Statistical and Adaptive Signal Processing, Dimitris G. Manolakis, Vinay K. Ingle, and Stephen M. Kogon, Artech House, Inc., 2005, ISBN 1580536107. Many MATLAB functions used throughout the text are available on the web.

Introduction

We will focus on three fundamental topics:

1. Signal Models: types and properties of statistical models for signals and how they relate to signal processing.
2. Signal Characterization: common second-order methods of characterizing signals including autocorrelation, partial correlation, cross-correlation, power spectral density, and cross-power spectral density.
3. Spectral Estimation: nonparametric methods for estimation of power spectral density, autocorrelation, cross-correlation, transfer functions, and coherence from finite signal samples.

Course Outcomes

1. Understanding of autoregressive, moving average, autoregressive moving average, and general linear models and how they are related.
2. Ability to characterize an estimator.
3. Ability to apply nonparametric techniques to estimate autocorrelation, cross-correlation, and partial correlation
4. Ability to apply nonparametric techniques for spectral estimation including power spectral density, cross-power spectral density, coherence, and transfer function.
5. Ability to conduct and present a peer-reviewed research project orally and in written form.

Assessment

• 15%: Reading quizzes
• 20%: Homework assignments
• 20%: Midterm
• 10%: Final quiz
• 35%: Project
  • 5% draft (completeness)
  • 5% peer review (thoroughness)
  • 5% oral presentation
  • 20% final written report

Grading

		A	>94	A-	>90
B+	>87	B	>84	B-	>80
C+	>77	C	>74	C-	>70
D+	>67	D	>64	D-	>60

If a curve is used to determine the final grades, the cutoff for each letter grade will not be higher than the typical cutoffs listed above.

Notes

• Late homework will receive half credit if turned in 1-7 days late. Homework will not be accepted if more than 7 days late.
• You are strongly encouraged to turn in all homework assignments even if they are partially completed. They count for 20% of your final grade.
• Make up exam will only be given if arranged at least 48 hours in advance.
• Copying or cheating in any way will result in a zero for the test or assignment, may receive an F for the course, may be suspended or expelled from the program.