ECE1511---Signal Processing

file:///C|/Users/Dimitris/Documents/ece1511/index.html[2021-01-02 1:48:52 PM]

University of TorontoDepartment of Electrical & Computer Engineering

Communications Group

ECE 1511S, Winter 2021

Signal ProcessingCourse URL:https://q.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 digitalsignal 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 andcan be downloaded from the course website.

Recommendedtextreferences:

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. 20.

Place andTime: All lectures and office hours will be delivered on line , synchronously with Quercus

BBCollaborate. Tuesdays, 2:00-4:00 (Toronto time, First lecture on Jan. 12, 2021)

Office hours:Mondays 3:00-5:00 pm or by appointment.

Tentative Course plan

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ECE1511---Signal Processing

file:///C|/Users/Dimitris/Documents/ece1511/index.html[2021-01-02 1:48:52 PM]

January 12,2021

Introduction, Discrete Signal Processing and Linear Algebra fundamentals Lecture 1(pdf),

January19,2021

Discrete time random processes and linear filtering Lecture 2(pdf), Problem set 1(pdf) (Due date: ), Problem set 1 solutions(pdf),

January26,2021

Discrete time random processes and linear filtering (continued)

February 2,2021

Discrete signal modeling and statistical signal processing

February 9,2021

MSE and Wiener Filtering

February 23,2021

Kalman Filtering,

March 2, 2021 Adaptive systems and algorithms (LMS, RLS),

March 9, 2021 Spectrum Estimation,Deadline for project approval,

March 16, 2021 Spectrum Estimation (continued),

March 23, 2021 Array Processing,

March 30, 2021 Special topics: Higher-Order Spectral Analysis (H.O.S.) ,

April 6,2021 Special Topics:Alpha stable processes and Fractional Lower Order momentanalysis >,

April 13, 2021 No class

April 20, 2021 Presentation of projects

April 27, 2021 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)