ECE1511---Signal Processing

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University of TorontoDepartment of Electrical & Computer Engineering

Communications Group

ECE 1511S, Fall 2017

Signal Processing Course URL:http://portal.utoronto.ca/...

Instructor: Prof. D. Hatzinakos BAHEN BUILDING, 40 St George Str., Room 4144 Tel: 978-1613, E-mail: dimitris@comm.utoronto.ca

Overview: The course deals with some basic and some advanced topics in the area of digital signal processing. Emphasis is given to statistical signal processing with applications.

Text: No specific text will be assigned. Several sources will be recommended for reading. Class Notes for all lectures will be distributed. The notes will be available on line and can be downloaded from the course website.

Recommended references: 1. Monson Hayes,Statistical Digital Signal Processing and Modeling, Wiley, 1996

2. Charles W. Therrien, Discrete Random Signals and Statistical Signal Processing, Prentice Hall, 1992

3. D. Manolakis, V. Ingle and S. Kogon, Statistical and adaptive signal processing, Artech House, 2005

Grading: Weekly homework (50%) One to two problems or computer exercises will be assigned during each lecture. A report is due a week later

Project (50%, presentation: 15%, final report: 35%) Student proposed individual projects. Students are expected to make a presentation on their project during the last two lectures. Interactive discussion and feedback from the class is expected. Final project reports are due on Dec. 13.

Place and Time: BA4164 (BAHEN Building), Wednesdays, 12:30-2:30 (starting Sept. 13, 2013)

Office hours:Tuesdays 2:00-3:00 pm or by appointment.

Tentative Course plan

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Sept. 13 Introduction, Discrete Signal Processing and Linear Algebra fundamentals (pdf), (pdf), (pdf)

ECE1511---Signal Processing

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Sept. 20 Discrete time random processes and linear filtering

Sept. 27 Discrete time random processes and linear filtering (continued)

Oct. 4 Discrete signal modeling and statistical signal processing

Oct. 1 MSE and Wiener Filtering

Oct. 18 Kalman Filtering,

Oct. 25 Adaptive systems and algorithms (LMS, RLS),

Nov 1 Spectrum Estimation,

Nov. 8 Spectrum Estimation (continued),

Nov. 15 Array Processing,

Nov 22 Special topics: Higher-Order Spectral Analysis (H.O.S.)

Nov. 29> Special Topics:Alpha stable processes and Fractional Lower Order moment analysis

Dec. 6 Presentation of course projects

Dec. 13 Deadline for project reports

dimitris

Note

f(x) is strictly convex over a closed interval [a,b] if, for any two points x1 and x2 in [a,b] and for any scalar c such that 0<= c <=1, then f(c x1 +(1-c) x2)<cf(x1)+(1-a)f(x2)