FACULTY OF ENGINEERING

Department of Mechatronics Engineering

CE 462 | Course Introduction and Application Information

Course Name
Intoduction to Sparse Representations
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 462
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Service Course
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Application: Experiment / Laboratory / Workshop
Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course seeks a place on solid foundations to introduce the basic theoretical and numerical concepts of sparse representation algorithms, and to illustrate their practical applications.
Learning Outcomes The students who succeeded in this course;
  • explain the fundamentals of sparse representations,
  • implement greedy pursuit algorithms,
  • analyze underdetermined linear systems,
  • describe convex relaxation techniques and approximate solutions,
  • apply the theory of sparse representations in practical problems.
Course Description Provides introductory knowledge on the basics of sparse representations with theoretical and numerical aspects, and practical applications in real life.

 



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 Basic introduction to sparse and redundant representations
2 Underdetermined linear systems, regularization techniques, and convexity M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 1)
3 Pursuit algorithms in practice M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 3)
4 From exact to approximate solutions M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 5)
5 Iterative-shrinkage algorithms M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 6)
6 Sparsity-seeking methods in signal processing M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 9)
7 Dictionary learning algorithms M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 12)
8 MAP and MMSE estimation M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 11)
9 Applications – Image deblurring, image denoising M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 10, Ch.14)
10 Applications – Image compression, image super-resolution M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 13, Ch.15.4)
11 Applications – Image inpainting, image cartoon/texture separation M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 15.2, Ch. 15.3)
12 Project presentations
13 Project presentations
14 Project presentations
15 Review of the semester
16 Final Exam

 

Course Notes/Textbooks

Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Michael Elad, Springer 2010. ISBN 978-1-4419-7010-7

Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
30
Presentation / Jury
1
30
Project
1
40
Seminar / Workshop
Oral Exams
Midterm
Final Exam
Total

Weighting of Semester Activities on the Final Grade
3
100
Weighting of End-of-Semester Activities on the Final Grade
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
14
2
28
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
3
5
15
Presentation / Jury
1
24
24
Project
1
35
35
Seminar / Workshop
0
Oral Exam
0
Midterms
0
Final Exam
0
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

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

 


NEWS |ALL NEWS

Izmir University of Economics
is an establishment of
izto logo
Izmir Chamber of Commerce Health and Education Foundation.
ieu logo

Sakarya Street No:156
35330 Balçova - İzmir / Turkey

kampus izmir

Follow Us

İEU © All rights reserved.