EE570 Industrial Automation
EE570 Industrial Automation
Syllabus | International University of Sarajevo - Last Update on Sep 09, 2025
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Electrical and Electronics Engineering
Asif Šabanović
Course Lecturer
Course Objectives
This course is research-oriented, only Ph.D. and M.Sc. students can take this Course. The course aims to equip students with an understanding of R&D Processes. Specific industrial applications from Rail industry, Automotive, Robotics, Motion, Renewable Energy, Aviation or Space & Military defence are essentially considered. Each student has to work on a specific topic experimentally or by simulation which is based on several IEEE papers (minimum 2 papers). The topic is mainly related to the current Ph.D. or M.Sc. projects, and totally or partially cover the system target. Structure and definition of the topic will be given by the course holder. Students who successfully complete this course are able to - Understanding of the R&D process of the Automation Engineering for the industrial applications. - Programming a specific embedded System using C-Embedded (Softcore Microprocessor or Hardcore Microcontroller using C-Embedded). - An advanced level of Matlab simulation programming which is used to control an Electrical Drive System.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Research Papers: IEEE
Additional Literature
--Teaching Methods
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction | |
| 2 | Groupe Work: Lab/Simulation/ Programming | |
| 3 | Groupe Work: Lab/Simulation/ Programming | |
| 4 | Groupe Work: Lab/Simulation/ Programming | |
| 5 | Groupe Work: Lab/Simulation/ Programming | |
| 6 | Groupe Work: Lab/Simulation/ Programming | |
| 7 | Groupe Work: Lab/Simulation/ Programming | |
| 8 | Mid-Report Submission | |
| 9 | Groupe Work: Lab/Simulation/ Programming | |
| 10 | Groupe Work: Lab/Simulation/ Programming | |
| 11 | Groupe Work: Lab/Simulation/ Programming | |
| 12 | Groupe Work: Lab/Simulation/ Programming | |
| 13 | Groupe Work: Lab/Simulation/ Programming | |
| 14 | Groupe Work: Lab/Simulation/ Programming | |
| 15 | Final-Report Submission |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| EE570.1 | Course | Tuesday 12:00 - 14:50 | B F2.14 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 09:00 - 16:00 | A G.5 | |
| Wednesday | 09:00 - 14:00 | A G.5 |
Assessment Methods and Criteria
Assessment Components
Final Report
AI: Not AllowedAlignment with Learning Outcomes :
Mid Report
AI: Not AllowedAlignment with Learning Outcomes :
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture Hours
30 hours ⏳ (15 week × 2 h)
Home Works
20 hours ⏳ (10 week × 2 h)
Lab Work
60 hours ⏳ (15 week × 4 h)
Mid-Report Exam
20 hours ⏳ (2 week × 10 h)
Final-Report-Exam
20 hours ⏳ (2 week × 10 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [EE570] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Sep 09, 2025 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| EE570 | Industrial Automation | 3 | 0 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Asif Šabanović | Office Hours / Room / Phone | Tuesday: 9:00-16:00 Wednesday: 9:00-14:00 |
|||||||
| asabanovic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course is research-oriented, only Ph.D. and M.Sc. students can take this Course. The course aims to equip students with an understanding of R&D Processes. Specific industrial applications from Rail industry, Automotive, Robotics, Motion, Renewable Energy, Aviation or Space & Military defence are essentially considered. Each student has to work on a specific topic experimentally or by simulation which is based on several IEEE papers (minimum 2 papers). The topic is mainly related to the current Ph.D. or M.Sc. projects, and totally or partially cover the system target. Structure and definition of the topic will be given by the course holder. Students who successfully complete this course are able to - Understanding of the R&D process of the Automation Engineering for the industrial applications. - Programming a specific embedded System using C-Embedded (Softcore Microprocessor or Hardcore Microcontroller using C-Embedded). - An advanced level of Matlab simulation programming which is used to control an Electrical Drive System. |
|||||||||
| Textbook | Research Papers: IEEE | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
| Teaching Methods | ||||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction | |||||||||
| Week 2 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 3 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 4 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 5 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 6 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 7 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 8 | Mid-Report Submission | |||||||||
| Week 9 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 10 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 11 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 12 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 13 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 14 | Groupe Work: Lab/Simulation/ Programming | |||||||||
| Week 15 | Final-Report Submission | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Report | 1 | 50 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Mid Report | 1 | 50 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 2 | 15 | 30 | Home Works | 2 | 10 | 20 | |||
| Lab Work | 4 | 15 | 60 | Mid-Report Exam | 10 | 2 | 20 | |||
| Final-Report-Exam | 10 | 2 | 20 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 19/09/2025 | |||||||||