Digital Signal Processing
Course: ESE531
Units: 1.0 CU
Term: Spring 2019
When: TR 4:30-6pm
Where: DRLB A2
Instructor: Tania Khanna (Moore 201Q) (seas: taniak) (office hours: W 2-4pm and by appointment)
TA: Taishan Li (seas: taishan) Office hours: WF 10am-11:30am in Towne 211
TA: Mingxuan Sun (seas: mingxuan) Office Hours: T 6-7:30pm and Th 3-4:30pm in Towne 211
Graders: Yulai Weng (seas: yulai); Zhefu Peng (zfpeng)
Prerequisites: ESE 324/224 or equivalent. Undergraduate students need permission of instructor. Roundup of topics you should be familiar with.
Quick Links:
[Course Objectives]
[Grading]
[Policies]
[Spring
2019 Calendar]
[Reading]
[Piazza]
Catalog Level Description:
This course covers the fundamentals of discrete-time signals and systems and digital filters.
Specific topics include discrete-time Fourier transform (DTFT); Z-transforms; frequency response of linear discrete-time systems; sampling of continuous time signals, analog to digital conversion, sampling-rate conversion; basic discrete-time filter structures and types; finite impulse response (FIR) and infinite impulse response (IIR) filters; linear phase conditions; design of FIR and IIR filters; discrete Fourier transform (DFT) and the fast Fourier transform (FFT) algorithm. Applications in filtering and spectrum estimation, image filtering, adaptive filters, equalization.
Role and Objectives
Students will:
- Learn the fundamentals of digital signal processing
- Provide an understanding of discrete-time signals and systems and digital filters
- Enable you to apply DSP concepts to a wide range of fields
- Gain the ability to read the technical literature on DSP
- Apply the techniques learned in a final project encompassing many different application types
Rough Syllabus (by weeks)
- Introduction
- Discrete-Time (DT) Signals
- Time-Domain Analysis of DT Systems
- Discrete Fourier Transform (DFT)
- Fast Fourier Transform (FFT)
- Discrete-Time Fourier Transform (DTFT)
- z-Transform
- Sampling of Continuous Time Signals
- Data Converters and Modulation
- Upsampling/Downsampling
- Discrete-Time Filter Design
See Spring
2019 course calendar for day-by-day calendar with assignments.
Main Text
- A. V. Oppenheim and R. W. Schafer (with J. R. Buck), Discrete-Time Signal Processing. 3rd. Edition, Prentice-Hall, 2010
Grading
Grading is based on homework assignments, final project, midterm, and final exam.
- Homework Assignments [20%]
- Final Project [25%]
- Midterm [25%]
- Final [30%]
Policies
Homework Turnin
Homework will be due on select days indicated on the course calendar at midnight and must be uploaded into Canvas as a single PDF. Handwritten assignments will be accepted, but when specified computer generated figures, graphs and results must be submitted and everything should be still combined into a single PDF and submitted in Canvas. Homeworks must be legible and all work should be shown. Illegible submissions will not be graded.
Late Assignments
Late assignments will not be accepted or graded. It is your responsibility to allow for enough time to submit your assignment online before the deadline cutoff and to make sure that you have turned in the correct document.
If assignments or exams fall due on a religious holiday or for extenuating circumstances, please make arrangements
with the instructor to accommodate before the posted due date.
Absentees
Use the Penn Course Absence Report (CAR) in Penn-in-Touch to report
absences.
Grade Adjustment
Regrade requests must be submitted to the TA no later than 1 week after the assignment grades are released to the students. A cover sheet detailing the disrepency should be submitted with a copy of the original homework, and the TA reserves the right to regrade the entire assignment. Students are responsible for checking posted grades in a timely manner.
Collaboration
You may help each other understand how to use the CETS computers and course tools.
Each student is expected to do his/her own work -- including developing the
details, writing code, performing simulations, and writing the
solutions. For the homeworks and projects, you are free to
discuss basic strategies and approaches with your fellow classmates or
others, but detail designs, implementations, analysis, and writeups should
always be the work of the individual. If you get advice or insights from
others that influenced your work in any way, please acknowledge this in
your writeups.
In general, you are expected to abide by Penn's
Code of Academic Integrity. If there is any uncertainty, please ask.
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