ECTS - Computational Methods in Electrical and Electronics Engineering

Computational Methods in Electrical and Electronics Engineering (EE506) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Computational Methods in Electrical and Electronics Engineering EE506 3 0 0 3 5
Pre-requisite Course(s)
EE 350, MATH 276
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Question and Answer, Drill and Practice.
Course Coordinator
Course Lecturer(s)
  • Assoc. Prof. Dr. Kemal Efe Eseller
Course Assistants
Course Objectives The aim of this course is to review the basic numerical methods in engineering and to teach advanced computational methodologies which is to be beneficial in engineering research. The course is expected to make the graduate students able to solve the complex problems such as numerical solution of differential equation, optimization and statistical analysis which are frequently encountered in graduate level research in electrical and electronics engineering.
Course Learning Outcomes The students who succeeded in this course;
  • - Ability to use direct and iterative methods in the solution of system of linear equations - Ability to use and implement statistical methods - Ability to construct polynomial approximations to functions by interpolation and extrapolation - Ability to implement linear transforms - Ability to use MATLAB to implement numerical methods - Ability to use and implement optimization techniques
Course Content Root finding and numerical integration, fixed and floating point arithmetic and error standards, one and multidimensional interpolation and extrapolation, numerical optimization techniques, least squares, statistical methods (Monte Carlo), computational approaches to linear transformations (Karhunen-Loeve, discrete Fourier).

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to MATLAB and basic rules of the software -
2 Review of basic numerical methods (root finding, numerical integration, etc.) Review previous week's notes
3 Review of basic numerical methods (root finding, numerical integration, etc.) Review previous week's notes
4 Fixed and floating point arithmetic, number representations, IEEE floating-point standard, error propagation, forward error analysis of primitive operations Review previous week's notes
5 Interpolation and extrapolation (linear and polynomial interpolation in 1-D, 2-D and 3-D) Review previous week's notes
6 Solutions of linear algebraic equations with different methods Review previous week's notes
7 Solutions of linear algebraic equations with different methods Review previous week's notes
8 Midterm Examination (including a MATLAB test) Review previous week's notes
9 Numerical approaches to optimization (gradient methods, handling the constraints, Lagrange multipliers) Review previous week's notes
10 Numerical approaches to optimization (gradient methods, handling the constraints, Lagrange multipliers) Review previous week's notes
11 Modeling of data (review of least squares) Review previous week's notes
12 Statistical methods (Monte Carlo methods) Review previous week's notes
13 Linear transforms (Karhunen-Loeve transform, independent component analysis) Review previous week's notes
14 1-D and 2-D discrete Fourier transform (DFT) Review previous week's notes
15 Project Presentations Review of topics
16 Final Examination period Review of topics

Sources

Course Book 1. Steven Chapra, Raymond Canale, “Numerical Methods for Engineers”, McGraw-Hill, 6th Edition, 2009
2. F. B. Hildebrand , “Introduction to Numerical Analysis”, Dover, 2nd Edition, 1987
3. H. Mathews, K.D. Fink, “Numerical Methods Using Matlab”, Pearson, 4th Edition, 2004

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 10 20
Presentation - -
Project 1 20
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 30
Final Exam/Final Jury 1 30
Toplam 14 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems.
2 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems.
3 An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.)
4 An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively.
5 An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies.
6 An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines.
7 An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written.
8 An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation.
9 Be conscious on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself.
10 A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project.
11 Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results of engineering solutions.
12 Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions.
13 Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planing, improving or changing the norms with a criticism.
14 A competency on developing strategy, policy and application plans on the mechatronics engineering and evaluating the results in the context of qualitative processes.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 14 3 42
Presentation/Seminar Prepration
Project 1 20 20
Report
Homework Assignments 5 3 15
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury
Prepration of Final Exams/Final Jury
Total Workload 125