| 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 text references: | 1. Monson Hayes, |
| 2. Charles W. Therrien, |
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| 3. D. Manolakis, V. Ingle and S. Kogon, |
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| 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 or group 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: |
Wednesdays, 1:00-3:00 pm (Toronto time, First lecture on January 7, 2026), room ES1047 |
| Office hours:By appointment. |
| January 7, 2026 | Introduction, Discrete Signal Processing and Linear Algebra fundamentals Lecture 1(pdf), |
| January 14 2026 | Discrete time random processes and linear filtering
Lecture 2(pdf), Problem set 1(pdf) (Due date: January 21, 2026 ), Problem set 1 solutions(pdf), |
| January 21, 2026 | Discrete time random processes and linear filtering (continued) Lecture 3(pdf), Problem set 2(pdf) (Due date: January 28, 2026 ), Problem set 2 solutions(pdf), |
| January 28, 2026 | Discrete signal modeling and statistical signal processing Lecture 4(pdf), Problem set 3(pdf) (Due date: February 4, 2026 ), , |
| February 4, 2026 | MSE and Wiener Filtering Lecture 5(pdf), Problem set 4(pdf) (Due date: February 11, 2026), Term Project info(pdf) |
| February 11, 2026 | Kalman Filtering, Lecture 6(pdf), Problem set 5(pdf) (Due date: February 25, 2026), |
| February 25, 2026 | Adaptive systems and algorithms (LMS, RLS), Lecture 7(pdf), Problem set 6(pdf) (Due date: March 4, 2026) |
| March 4, 2026 | Spectrum Estimation, Deadline for project approval, Lecture 8(pdf), Problem set 7(pdf) (Due date: March 18, 2026), project descriptions and group names are due. |
| March 11, 2026 | Spectrum Estimation (continued), Lecture 9(pdf) |
| March 18, 2026 | Array Processing, Lecture 10(pdf), Problem set 8(pdf) (Due date: March 25, 2026 |
| March 25, 2026 | Special topics: Higher-Order Spectral Analysis (H.O.S.) , Lecture 11 (pdf), |
| April 1, 2026 | Special Topics:Alpha stable processes and Fractional Lower Order moment analysis
Lecture 12 (pdf),
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| April 8, 2026 | Presentation of projects |
| April 20, 2026 | Deadline for project reports |
Academic Integrity policies
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| Land Acknowledgement
I (we) wish to acknowledge this land on which the University of Toronto operates. For thousands of years it has been the traditional land of the Huron-Wendat, the Seneca, and most recently, the Mississaugas of the Credit River. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land. |
| Statements
Syllabus Statements on Inclusivity, Accommodations & Mental Health Support Inclusivity Statement: All students and faculty at the University of Toronto have a right to learn, work and create in a welcoming, respectful, inclusive and safe environment. In this class we are all responsible for our language, action and interactions. Discriminatory comments or actions of any kind will not be permitted. This includes but is not limited to acts of racism, sexism, Islamophobia, anti-Semitism, homophobia, transphobia, and ableism. As a class we will work together to create an inclusive learning environment and support each other�s learning. If you experience or witness any form of discrimination, please reach out to the Engineering Equity Diversity & Inclusion Action Group online, an academic advisor, a U of T Equity Office, or any U of T Engineering faculty or staff member that you feel comfortable approaching. Accommodations: If you have a learning need requiring an accommodation the University of Toronto recommends that students immediately register at Accessibility Services at www.studentlife.utoronto.ca/as. Location: 4th floor of 455 Spadina Avenue, Suite 400 Voice: 416-978-8060 Fax: 416-978-5729 Email: accessibility.services@utoronto.ca The University of Toronto supports accommodations of students with special learning needs, which may be associated with learning disabilities, mobility impairments, functional/fine motor disabilities, acquired brain injuries, blindness and low vision, chronic health conditions, addictions, deafness and hearing loss, psychiatric disabilities, communication disorders and/or temporary disabilities, such as fractures and severe sprains, recovery from an operation, serious infections or pregnancy complications. Mental Health: As a university student, you may experience a range of health and/or mental health issues that may result in significant barriers to achieving your per sonal and academic goals. The University of Toronto offers a wide range of free and confidential services and programs that may be able to assist you. We encourage you to seek out these resources early and often. If, at some point during the year, you find yourself feeling distressed and in need of more immediate support, visit the Feeling Distressed Webpage: www.studentlife.utoronto.ca/feeling-distressed, for more campus resources. � Off campus, immediate help is available 24/7 through Good2Talk, a post-secondary student helpline at 1-866-925-5454. � |