signal processing - university of torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · class...

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ECE1511---Signal Processing index - Copy.html[9/10/2019 9:22:53 AM] University of Toronto Department of Electrical & Computer Engineering Communications Group ECE 1511S, Fall 2019 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: [email protected] 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 text 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. 20. Place and Time: BF315 (Bankcroft Building), Tuesdays, 12:00-2:00 (starting Sept. 10, 2019) Office hours:mondays 3:00-5:00 pm or by appointment. Tentative Course plan < Sept. 10 Introduction, Discrete Signal Processing and Linear Algebra fundamentals

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Page 1: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and

ECE1511---Signal Processing

index - Copy.html[9/10/2019 9:22:53 AM]

University of TorontoDepartment of Electrical & Computer Engineering

Communications Group

ECE 1511S, Fall 2019

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: [email protected]

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 text 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. 20.

Place and Time: BF315 (Bankcroft Building), Tuesdays, 12:00-2:00 (starting Sept. 10, 2019)

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

Tentative Course plan

<

Sept. 10 Introduction, Discrete Signal Processing and Linear Algebra fundamentals

Page 2: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and

ECE1511---Signal Processing

index - Copy.html[9/10/2019 9:22:53 AM]

Lecture 1(pdf),

Sept. 17 Discrete time random processes and linear filtering

Sept. 24 Discrete time random processes and linear filtering (continued)

Oct. 1 Discrete signal modeling and statistical signal processing

Oct. 8 MSE and Wiener Filtering

Oct. 15 Kalman Filtering,

Oct. 22 Adaptive systems and algorithms (LMS, RLS),

Oct 29 Spectrum Estimation,

Nov. 5 Spectrum Estimation (continued),

Nov. 12 Array Processing,

Nov 19 Special topics: Higher-Order Spectral Analysis (H.O.S.)

Nov. 26 Special Topics:Alpha stable processes and Fractional Lower Order moment analysis

Dec. 3 Special Topics: Gaussian Mixtures

Dec. 10 Presentation of projects (12:00 noon- 2:30 pm)

Dec. 20 Deadline for project reports

Page 3: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 4: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 5: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 6: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 7: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 8: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 9: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 10: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 11: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 12: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 13: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 14: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 15: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 16: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 17: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 18: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 19: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 20: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 21: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 22: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 23: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 24: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
Page 25: Signal Processing - University of Torontodimitris/ece1511/lecture1.pdf · 2019-09-10 · Class Notes for all lectures will be distributed. The notes will be available on line and
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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)
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