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Syllabus Course Title:

EECE 645 Statistical Signal Processing (Fall 2018) Class Meetings:

MW 09:30--10:45 at LG 104

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 TAs:


Office) LG 418

Phone) +82-54-279-8017

E-mail) wngml1308 (at) Homepage:

It is your responsibility to check every new announcement posted in the homepage. Text:  

No textbook. However, lecture notes and videos are available at Schedule/Downloads. 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 (10/17 Wed., 7:30pm-12 pm, LG 104) 

15 %

2. 2nd midterm exam (11/22 Th., 7:30pm-12 pm, LG 104

20 %

3. Final exam (12/17 Mon., 7:30pm-12pm, LG 104) 

25 %

4. 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. Grading Policies for Exam

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.