GRADUATE SCHOOL

M.SC. in Computer Engineering (With Thesis)

EEE 509 | Course Introduction and Application Information

Course Name
Selected Topics in Signal Processing
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
EEE 509
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course covers the fundamentals of discrete-time signals and systems, design and implementation of signal processing systems, and selected topics from the current state-of-the-art research and developments in signal processing.
Learning Outcomes The students who succeeded in this course;
  • learn how to use time-domain and transform-domain techniques for representing and analyzing discrete-time signals and systems,
  • know how to use discrete-time systems to process continuous-time signals,
  • know how to design and implement discrete-time filters,
  • know how to design optimal filters for estimating discrete-time random processes,
  • know how to develop adaptive filters capable of responding to varying processing requirements,
  • know how to design and implement various signal processing algorithms using Matlab and signal processing toolbox.
Course Description Techniques used in the time-frequency analysis of nonstationary signals: Empirical Time Decomposition, Ensemble Empirical Time Decomposition, Multivariate Emprirical Time Decomposition, Variational Mode Decomposition, Intrinsic Time-Scale Decomposition, Short-time Fourier Transform, Fourier Based Synchrosqueezing Transform

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Empirical Mode Decomposition Related research papers
2 Ensemble Empirical Mode Decomposition Related research papers
3 Multivariate Empirical Mode Decomposition Related research papers
4 Variational Mode Decomposition Related research papers
5 Multivariate Variational Mode Decomposition Related research papers
6 Intrinsic Time-Scale Decomposition Related research papers
7 Intrinsic Time-Scale Decomposition Applications Related research papers
8 Short-time Fourier Transform Related research papers
9 Fourier Based Synchrosqueezing Transform (FSST) Related research papers
10 Studen presentations Related research papers
11 Student presentations Related research papers
12 Student presentations Related research papers
13 Student presentations Related research papers
14 Student presentations Related research papers
15 Student presentations Related research papers
16 Review of the semester

 

Course Notes/Textbooks

1) Time-Frequency Signal Analysis and Processing, 2nd Edition, Editor: Boualem Boashash, Academic Press, ISBN: 9780123984999, Jan. 2016.

2) Time-Frequency Analysis, Leon Cohen, Prentice Hall, 1995. ISBN 0-13-594532-1

The textbook referenced above and course slides

Suggested Readings/Materials Related Research Papers

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
20
Presentation / Jury
1
20
Project
1
30
Seminar / Workshop
Oral Exams
Midterm
Final Exam
1
30
Total

Weighting of Semester Activities on the Final Grade
3
70
Weighting of End-of-Semester Activities on the Final Grade
1
30
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
15
4
60
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
47
47
Presentation / Jury
1
20
20
Project
1
40
40
Seminar / Workshop
0
Oral Exam
0
Midterms
0
Final Exam
1
0
    Total
215

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information.
2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.
4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs
7 Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.
8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.
9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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