ECE 5200
Transcript Abbreviation:
Intro Dig Sig Proc
Course Description:
Sampling and reconstruction; discrete-time rate conversion; processing of discrete-time signals; design of discrete-time filters, selected topics in adaptive filtering, time-frequency analysis, and wavelets.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Distance Learning (100% online)
Prerequisites and Co-requisites:
Prereq: 3050, and Stat 3470 or Math 4530; or Grad standing.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Master undergrad-level signals & systems concepts (e.g., linearity, time-invariance, causality, stability, impulse response, convolution, Fourier series, CTFT, DTFT, Laplace transform, Z-transform), applying these concepts to new problems
Master the fundamentals of sampling and reconstruction, i.e., conversion between the continuous-time and discrete-time domains, as well as discrete-time rate conversion (e.g., upsampling, downsampling, interpolation, decimation)
Master filter design based on magnitude response and phase response; FIR filter design methods like window-based, weighted least-squares, & equiripple designs; IIR filter design methods based on bilinear transform & least-squares
Be competent with the fundamental concepts in the processing of finite-duration discrete-time signals, including windowing, DFT, circular convolution, spectral analysis, FFT, fast convolution, and overlap/save processing
Be familiar with one or more selected topics in multidimensional, multirate, multiresolution, or adaptive signal processing, possibly including time-frequency analysis, filterbanks, or wavelets
Be competent with programming discrete-time signal processing and analysis tasks in Matlab, Python, or similar high-level languages
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Signals and systems review: system properties (e.g., linearity, time invariance, causality, stability), impulse response, convolution, Fourier series, CTFT, Laplace transform, DTFT, Z-transform | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Sampling and conversion: sampling, aliasing, Nyquist rate, sinc reconstruction, ZOH reconstruction, upsampling, downsampling, interpolation, decimation, rate conversion | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Processing of finite-length discrete-time signals: DFT, circular convolution, windowing, spectral analysis, matrix/vector formulations, FFT, fast convolution, overlap-save | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Design of discrete-time filters: ideal magnitude responses, group delay, linear phase, FIR designs (e.g., window-based, frequency-sampled, weighted least-squares, equiripple), IIR designs (e.g., bilinear transform, Prony's method, Shank's method). | 8.0 | 0.0 | 0 | 0.0 | 0 |
| Selected topics in multidimensional, multirate, multiresolution, or adaptive signal processing, possibly including time-frequency analysis, filterbanks, or wavelets | 12.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 38 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 30% |
| Two midterm exams | 40% |
| Final exam | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Discrete-Time Signal Processing | Oppenheim and Schafer |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
Embedded Literacies Info: