Course Number: ECSE 4530

Course Title: Digital Signal Processing (Fall 2019-2020, Undergraduate)

Class schedule: Monday & Thursday, 10:00 am-11:20 am

Classroom: JONSSN 4104 (i.e., JEC-4104)

Credit hours: 3

Instructor: Derya Malak

Office: JEC 6038

Office Hours: Wednesday 4pm-6pm, Friday 1pm-2pm

Course Description:

The main objective of this course is to provide a comprehensive treatment of the theory, design, and implementation of digital signal processing algorithms. In the first half of the course, we will emphasize frequency-domain and Z-transform analysis. In the second half of the course, we will investigate advanced topics in signal processing, including multirate signal processing, filter design, adaptive filtering and quantizer design. The course is intended to be fairly application-independent, to provide a strong theoretical foundation for future study in communications, control, or image processing.

Learning Outcomes:

An understanding of

• discrete-time signals and input-output relationships in discrete-time, linear time invariant systems.
• the use of the Fourier transform, Fourier series, Discrete-Time Fourier Transform, Discrete Fourier Transform, Z-transform, and when to use them.
• the sampling theory and how to reconstruct signals.
• the design and implementation of FIR and IIR digital filters.
• adaptive digital filtering, signal estimation and prediction.

References:

J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th Edition, Prentice-Hall, 2006 (Textbook).

A. Oppenheim and R. Schafer, Discrete-Time Signal Processing, 3rd edition, Prentice-Hall, 2009.

M. Hayes, Schaum's Outline of Digital Signal Processing, 2nd Edition, McGraw Hill, 2011.

Prerequisites:

ECSE 2410 Signals and Systems and ECSE 2500 Engineering Probability. Also MATH 2010 Multivariable Calculus and Matrix Algebra or permission of instructor.

Grading Criteria:

The grade will be based on the average homework grade (worth 20%), two midterm exams in class (worth 25% each), and a final exam (worth 30%).

Homework will be assigned every 4-5 classes (about 6 homeworks total) and posted on Piazza. These homeworks will be a mixture of paper-and-pencil problems to hand in, and MATLAB problems to submit online using a system called MATLAB Grader. You may discuss problems with other students, but you must prepare your solution independently.

Homework is due at the start of class (defined as the first 10 minutes) on the date indicated and you will turn in your homework on Gradescope. To do so, you will need to create a PDF of your work and save it to your computer before submitting. Please see the guide for students which is available here: https://www.gradescope.com/get_started.

For each student, the lowest homework score will not count towards the total homework score. Late homework will not be accepted. The TA will be responsible for homework grading and any questions about grading should be directed to the TA.

All exams will be closed book. Dr. Malak will assist in grading the exams and handle any questions or appeals.

Course policies:

• If you require extra time on exams or another form of accommodation, please contact the Dean of Students Office or the Office of Disability Services for Students. Please do this early in the term so that we have plenty of time to plan.
• You are expected to approach the instructor with any issue that may affect your performance in class ahead of time. This includes absence from important class meetings, late assignments, inability to perform an assigned task, the need for extra time on assignments, etc. You should be prepared to provide sufficient proof of any circumstances based on which you are making a special request as outlined in the Rensselaer Handbook of Student Rights and Responsibilities.
• Grade appeals, on homework or exam, must be submitted in writing within 72 hours of its return to the class. No verbal complaints will be considered.

Academic Integrity:

Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts that violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities and The Graduate Student Supplement define various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. This is particularly important for the MATLAB-based homework problems in this class.

Submission of any assignment that is in violation of this policy may result in a penalty of an F in the class, and may be subject to further disciplinary action.