ECE 7001
Transcript Abbreviation:
Sto Proc Det Est
Course Description:
Stochastic processes; detection and decision theory; hypothesis testing, parameter estimation; and applications to communications and signal processing.
Course Levels:
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:
3.00
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)
Prerequisites and Co-requisites:
Prereq: 6001 (804).
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 806.
Course Goals / Objectives:
Learn mathematical properties of stochastic processes
Learn the theory of likelihood-ratio based hypothesis testing and signal detection in noise
Learn the theory of optimal parameter estimation; properties of estimators; and learn the tools for analysis of the efficacy of parameter estimators
Learn to design and analyze optimal and sub-optimal detection and estimation algorithms under realistic conditions
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 |
|---|---|---|---|---|---|
| Detection theory, decision theory, and hypothesis testing | 8.0 | 0.0 | 0 | 0.0 | 0 |
| Parameter estimation | 8.0 | 0.0 | 0 | 0.0 | 0 |
| Stochastic processes, characterization, white noise and Brownian motion | 5.0 | 0.0 | 0 | 0.0 | 0 |
| Autocovariance, crosscovariance and power spectral density | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Stochastic processes through linear systems | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Karhunen-Loeve and sampled signal expansions | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Detection and estimation from waveform observations, Wiener filters | 8.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 39 | 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 Assignments | 15% |
| Midterm Exam | 35% |
| Final Exam | 50% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Signal Detection and Estimation | Poor |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: