Prof. Mojtaba
Soltanalian, UIC
Course webpage: http://msol.people.uic.edu/ECE531
Popular science description: here and here!
Lectures are given Tuesdays and Thursdays, 2:003:15pm in 320 LH
Office hours: Thursdays 3:455:00pm, SEO 1031
* Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993, and (possibly)
* Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven M. Kay, Prentice Hall 1998,
available in hard copy at the UIC Bookstore.
The following online resources will be partly used in the class:
http://user.it.uu.se/~ps/sysidbook.pdf
http://user.it.uu.se/~ps/SASnew.pdf
Other useful references:
 Harry L. Van Trees, Detection, Estimation, and Modulation Theory,
 H. Vincent Poor, Introduction to Signal Detection and Estimation
 Louis L. Scharf and Cedric Demeure, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis
 Carl Helstrom,
Elements of Signal Detection and Estimation.
 The slides can be found here.
Date 
To be covered 
Further info. 
1/16 
Introduction to ECE 531 

1/18 
Basics A Refresher 

1/23 
Modeling for Estimation / Spectral Estimation 

1/25 
Minimum Variance Unbiased Estimator (MVUE) 

1/30 
CramerRao Lower Bound (CRLB) 
(P) Kenneth Palacio Baus: Image Change Detection Algorithms 
2/1 
CRLB / Linear Model 

2/6 
General MVUE 

2/8 
General MVUE 
(P) Amruthavarshini Ramesh: Shredded Signal Reconstruction via Sparsity Enhancement 
2/13 
Best Linear Unbiased Estimator (BLUE) 

2/15 
Review Class 

2/20 
Midterm 1 

2/22 
Maximum Likelihood (ML) Estimation 
(P) Patrick Dahl: CramerRao Bound for Distributed Positioning in Sensor Networks 
2/27 
ML Estimation 
(P) Kenneth Palacio Baus: The ExpectationMaximization Algorithm 
3/1 
Least Squares Estimation 

3/6 
Bayesian Estimation 
(P) Diaa Badawi: The Gaussian Data Assumption Leads to the Largest CramerRao Bound 
3/8 
Kalman Filtering 
(P) Tumin Wu: Estimation in Wireless Sensor Networks 
3/13 
Midterm 2 

3/15 
No Classes 

3/20 
Detection Theory Preliminaries 
(P) Zhizhen Shen: Mean Squared Error: Love It or Leave It? 
3/22 
Detection Theory Deterministic Signals 
(P) Zihao Gong: Minimum Variance in Biased Estimation 
3/27 
Spring Break, No Classes 

3/29 
Spring Break, No Classes 

4/3 
Detection Theory Random Signals 
Deadline to select project topic; (P) Yohannes Azeze: ML and MAP DirectionofArrival Estimation 
4/5 
Detection Theory Selected Topics 
(P) Yishu Bai: ML Estimation and CRB for Narrowband AR Signals 
4/10 
Optimization for Detection and Estimation 
(P) Yubo Fan: Distributed Least Squares Estimation Over Networks 
4/12 
Review Class 
(P) Yizhou Zhao: StateofCharge Estimation for Supercapacitors; (P) Qu Yongquan: CramerRao Bound Analog of Bayes' Rule; (P) Amruthavarshini Ramesh: Optimized Unbiased Sampling for Monte Carlo Simulations 
4/17 
No Classes 

4/19 
No Classes 

4/24 
Project Presentations 

4/26 
Project Presentations 

5/1 
Project Presentations 

5/3 
Project Presentations 

Final Exam Week 5/7  5/11 
Paper Presentations & Discussions
10%
Computer Project 15%
Homework 15%
Midterm exams 15% each
Final exam 30%
* An extra paper presentation for 10%
bonus point may be accommodated. Prior consent from the instructor is required.
* These weights are approximate and
may be subject to change.
*
Late
homework will not be accepted.
Homework (updated
422018)
HW1: out 2/7 due 2/15
 Estimation Book, problems 1.4, 2.9, 2.11, 3.10, 3.11, 3.12
HW2: out 2/7 due 2/20
 Estimation Book, problems 4.1, 4.2, 5.9, 6.6, 6.13
HW3: out 4/2 due 4/12  Detection Book, problems 3.8, 3.14, 4.1, 4.6
HW4: out 4/2 due 4/26  Detection Book, problems 4.10, 5.12, 5.13
Paper presentations
The presentations
should be around 2025 mins.
Some suggested papers can be found here.
Note
Students who wish to observe religious
holidays should notify the instructor by the tenth day of the semester of the
date when they will be absent unless the religious holiday is observed on or
before the tenth day of the semester. In such cases, the students should notify
the instructor at least five days in advance of the date when he/she will be
absent. Every reasonable effort will be made to honor the request.
Academic
dishonesty by students including plagiarism will result in appropriate
disciplinary action. Intentional use or attempt to use unauthorized assistance,
materials, information, or people in any examination, quiz, or assignment may
lead to penalties such as a failing grade. College of Engineering and
University guidelines will be followed.