ENS211 Signals and Systems
ENS211 Signals and Systems
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
Syllabus Quick Jump
Search and navigate to any syllabus instantly
Faculty of Engineering and Natural Sciences
Tarik Namas
Course Lecturer
Course Objectives
Introduce continuous and discrete time signals, and the analysis of both in time and frequency domains. Demonstrate an understanding of the fundamental properties of linear systems, by explaining the properties to others. Use linear systems tools, especially transform analysis and convolution, to analyze and predict the behavior of signals and linear systems as a whole C17.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Fundamentals of Signals and Systems using the Web and Matlab, Edward W. Kamen, Bonnie S. Heck., Prentice Hall, 2007
Additional Literature
------Teaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Assigments
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Signals and Systems. Overview. | Lecture notes |
| 2 | Classification of signals. Various types of signals used in signal processing. | Chapter 1 |
| 3 | System properties | Chapter 1 |
| 4 | System Identity and Convolution | Chapter 2 |
| 5 | Fourier series: trigonometric and complex | Chapter 3 |
| 6 | Fourier series: trigonometric and complex | Chapter 3 |
| 7 | Fourier transform (FT) and Inverse FT. FT properties. | Chapter 3 |
| 8 | MIDTERM EXAM | |
| 9 | Discrete- time Signals and Systems | Chapter 4 |
| 10 | Fourier Analysis of Discrete-time signals | Chapter 4 |
| 11 | Discrete-time Convolution | Chapter 5 |
| 12 | Laplace Transform | Chapter 6 |
| 13 | System Analysis using Frequency Response | Chapter 5 |
| 14 | Analysis of ideal filters | Chapter 5 |
| 15 | Review |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| ENS211.1 | Course | Tuesday 17:00 - 19:50 | B F1.16 | - | - |
| ENS211.1 | Tutorial | Monday 17:00 - 18:50 | A F1.3 - Computer Lab | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Test
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Labs
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
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
45 hours ⏳ (15 week × 3 h)
Test
4 hours ⏳ (2 week × 2 h)
Home study
39 hours ⏳ (13 week × 3 h)
Lab reports
20 hours ⏳ (10 week × 2 h)
Midterm exam study
11 hours ⏳ (1 week × 11 h)
Final Exam Study
15 hours ⏳ (1 week × 15 h)
Labs
16 hours ⏳ (8 week × 2 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 [ENS211] 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 Feb 02, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| ENS211 | Signals and Systems | 3 | 2 | 6 | ||||||
| Prerequisite | MATH102 | It is a prerequisite to | EE331, EE334 | |||||||
| Lecturer | Tarik Namas | Office Hours / Room / Phone | ||||||||
| tnamas@ius.edu.ba | ||||||||||
| Assistant | Šejla Džakmić | Assistant E-mail | sdzakmic@ius.edu.ba | |||||||
| Course Objectives | Introduce continuous and discrete time signals, and the analysis of both in time and frequency domains. Demonstrate an understanding of the fundamental properties of linear systems, by explaining the properties to others. Use linear systems tools, especially transform analysis and convolution, to analyze and predict the behavior of signals and linear systems as a whole C17. |
|||||||||
| Textbook | Fundamentals of Signals and Systems using the Web and Matlab, Edward W. Kamen, Bonnie S. Heck., Prentice Hall, 2007 | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Assigments | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Signals and Systems. Overview. | Lecture notes | ||||||||
| Week 2 | Classification of signals. Various types of signals used in signal processing. | Chapter 1 | ||||||||
| Week 3 | System properties | Chapter 1 | ||||||||
| Week 4 | System Identity and Convolution | Chapter 2 | ||||||||
| Week 5 | Fourier series: trigonometric and complex | Chapter 3 | ||||||||
| Week 6 | Fourier series: trigonometric and complex | Chapter 3 | ||||||||
| Week 7 | Fourier transform (FT) and Inverse FT. FT properties. | Chapter 3 | ||||||||
| Week 8 | MIDTERM EXAM | |||||||||
| Week 9 | Discrete- time Signals and Systems | Chapter 4 | ||||||||
| Week 10 | Fourier Analysis of Discrete-time signals | Chapter 4 | ||||||||
| Week 11 | Discrete-time Convolution | Chapter 5 | ||||||||
| Week 12 | Laplace Transform | Chapter 6 | ||||||||
| Week 13 | System Analysis using Frequency Response | Chapter 5 | ||||||||
| Week 14 | Analysis of ideal filters | Chapter 5 | ||||||||
| Week 15 | Review | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm exam | 1 | 30 | 1,2,3,4,5 | Not Allowed | |
| Test | 2 | 20 | 1,2,3,4,5 | Not Allowed | |
| Labs | 8 | 10 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Test | 2 | 2 | 4 | |||
| Home study | 3 | 13 | 39 | Lab reports | 2 | 10 | 20 | |||
| Midterm exam study | 11 | 1 | 11 | Final Exam Study | 15 | 1 | 15 | |||
| Labs | 2 | 8 | 16 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 17/02/2026 | |||||||||