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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 16:00--17:15 at LG 106

For details meeting schedule, see Schedule/Downloads Instructor: Professor Joon Ho Cho

Office) LG 409

Phone) +82-54-279-2377

E-mail) jcho (at) postech dot ac dot kr

Office Hours: by appointment. Homepage:

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 : Springer-Verlag, 1994.
L. L. Scharf, Statistical Signal Processing. Reading, MA: Addition-Wesley, 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
        - Non-Bayesian detection and estimation
    Sequence observation
        - Convergence of a random sequence
        - Bayesian detection and estimation
        - Non-Bayesian detection and estimation
    Waveform observation
        - KL expansion, Sampling theorem, etc.
        - Bayesian detection and estimation
        - Non-Bayesian detection and estimation

III. Non-statistical Inference
    Least squares
    Methods of moments
    Spectral estimation

IV. Recent Advances in Statistical Signal Processing Prerequisites:

Undergraduate Level

  1. Signals and Systems
  2. Digital Communications
  3. 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. Course Requirements:

1. 1st midterm exam (F 10/13, 7:30pm-12 pm, LG 106) 

15 %

2. 2nd midterm exam (F 11/17, 7:30pm-12 pm, LG 106

20 %

3. Final exam (F 12/15, 7:30pm-12pm, LG 106) 

25 %

4. Homework (Quiz)

30 %

5. Participation

10 %

100 %

In each exam, a student is allowed to bring an A4-size 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.
Only approximately 1/3 of homework problems will be randomly selected and graded. Final grade will be based on total score. Grading Policies for Homework 

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.      Re-grade 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 sub-problem will be graded according to the following scale: 100%, 60%, 20%, and 0%. Grading Policies for Exam and Quiz 

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 make-up exam for the absentee.

3.      Re-grade requests must be filed in writing within one week after the graded exam has been returned to students.

4.      Each sub-problem 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.