Announcements Syllabus Schedule/Downloads
Syllabus
Course Title:
EECE 645 Statistical Signal Processing (Fall 2017)
offered by Dept. of Electronic and Electrical Eng, Pohang University of Science and Technology.
Class Meetings:
T 16:0017:15 at LG 106
For details meeting schedule, see Schedule/Downloads
Instructor: Professor Joon Ho Cho
Office) LG 409
Phone) +82542792377
Email) jcho (at) postech dot ac dot kr
Office Hours: by appointment.
Homepage: http://cisl.postech.ac.kr/class/eece645/index.htm
It is your responsibility to check every new announcement posted in the homepage. Class notes will be available at Schedule/Downloads.
Text:
Lecture notes and videos on the course web. No textbook. However, reading the references is strongly recommended.
References:
H. V. Poor, An introduction to signal detection and estimation 2nd ed, New York : SpringerVerlag, 1994.
L. L. Scharf, Statistical Signal Processing. Reading, MA: AdditionWesley, 1991.
H. L. Van Trees, Detection, Estimation, and Modulation Theory, Wiley, 1971.
M.D. Srinath, R.K. Rajasekaran, and R. Viswanathan, Introduction to Statistical Signal Processing with Applications, Prentice Hall, 1996.
S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, 1993.
S. M. Kay, Modern Spectral Estimation: Theory and Application, Prentice Hall, 1988.
R. M. Gray and L. D. Davisson, An Introduction to Statistical Signal Processing, Cambridge Univ. Press, 2004.
Course Objectives:
The course objective is to let the student obtain
1) the capability of identifying the engineering problems that can be put into the frame of statistical signal processing,
2) the capability of solving the identified problems using the standard techniques learned through this course, and
3) the fundamental understanding of statistical signal processing that may help them study advanced topics and consequently make significant contributions to the theory and the practice of statistical signal processing.
Course Outline
I. Introduction
II. Statistical Inference
Vector observation
 Bayesian detection and estimation
 NonBayesian detection and estimation
Sequence observation
 Convergence of a random sequence
 Bayesian detection and estimation
 NonBayesian detection and estimation
Waveform observation
 KL expansion, Sampling theorem, etc.
 Bayesian detection and estimation
 NonBayesian detection and estimationIII. Nonstatistical Inference
Least squares
Methods of moments
Spectral estimationIV. Recent Advances in Statistical Signal Processing
Prerequisites:
Undergraduate Level
 Signals and Systems
 Digital Communications
 Probability, Random Variables, and Random Processes
Graduate Level (Optional): Digital Communications, Optimization Theory
Computer programming skills (MATLAB or C).
Contact the instructor for questions about prerequisites.
1. 1st midterm exam (F 10/13, 7:30pm12 pm, LG 106)
15 %
2. 2nd midterm exam (F 11/17, 7:30pm12 pm, LG 106)
20 %
3. Final exam (F 12/15, 7:30pm12pm, LG 106)
25 %
4. Homework (Quiz)
30 %
5. Participation
10 %
100 %
In
each exam, a student is allowed to bring an A4size crib sheet written on
both sides. 
Course
participation is graded subjectively by the instructor based on the class
attendance rate, active participation in discussions, office hour visiting,
voluntary projects, etc. 

1. Homework will be assigned approximately every week.
2. I allow collaboration on the homework. However, students must submit his/her own solutions and provide the names of collaborators.
3. Regrade requests must be filed in writing within one week after the graded homework has been returned to students.
4. Only 1/3 of assigned problems per each assignment will be graded.
5. Each subproblem will be graded according to the following scale: 100%, 60%, 20%, and 0%.
1. Students may take exams earlier than the original schedule if he/she requests at least one week earlier.
2. Excused class absence on an exam day may lead to a makeup exam for the absentee.
3. Regrade requests must be filed in writing within one week after the graded exam has been returned to students.
4. Each subproblem will be graded according to the following scale: 100%, 60%, 20%, and 0%.
Policy on Academic Dishonesty
If the instructor suspects academic dishonesty, the instructor will notify the student(s) and follow the procedure to report to the Graduate School without any exception. Students have the responsibility to be knowledgeable about the consequences of dishonesty.
09/07/17