EE431 Digital Signal Processing
EE431 Digital Signal Processing
Syllabus | International University of Sarajevo - Last Update on Sep 09, 2025
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Electrical and Electronics Engineering
TBA
Course Lecturer
Course Objectives
The course aims to introduce concepts and methods of DSP. It describe's discrete signals and systems and their applications. The course covers discrete-time convolution, difference equations, the z-transform and the discrete Fourier transform. Designing of both recursive and non-recursive digital filters. The use of MATLAB and Simulink for examples and reinforcement of comprehension is essential part of the course.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Text Book is: Digital Signal Processing: principles, algorithms and applications. 4th ed. Proakis and Manolakis. Pearson. 2014.
Additional Literature
Digital Signal Processing using MATLAB. Proakis and ingle 3rd edition, Cengage Learning, 2012. Discrete Time Signal Processing. Oppenheim and Schafer, 3rd edition, Pearson, 2009. Applied Digital Signal Processing. Manolakis and Ingle, Cambridge University Press, 2012. Essentials of Digital Signal Processing. Lathi and Green, Cambridge University Press, 2014.Teaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Homework assignments and Projects
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction, Class Mechanics, Introduction to DSP | Ch1 |
| 2 | Discrete-Time Signals and Systems. Characterization, Description and Testing HW1 | Ch2 |
| 3 | The Z-Transform And Its Application | Ch3 |
| 4 | The Z-Transform And Its Application HW2 (HW1 Due) (test 1) | Ch3 |
| 5 | Frequency Analysis Of Signals And Systems | Ch4 |
| 6 | Frequency Analysis Of Signals And Systems HW3 (HW2 Due) (test 2) | Ch5 |
| 7 | Discrete Fourier Transform | Ch7 |
| 8 | Review, class project overview and Midterm | |
| 9 | Discrete Fourier Transform | Ch7 |
| 10 | Implementation Of Discrete-Time Systems HW4 (HW3 Due) (test 3) | Ch9 |
| 11 | Implementation Of Discrete-Time Systems | Ch9 |
| 12 | Design Of Digital Filers HW5 (HW4 Due) (test 4) | Ch10 |
| 13 | Design Of Digital Filers | Ch10 |
| 14 | Design Of Digital Filers HW5 Due (test 5) | Ch10 |
| 15 | Final exam |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| EE431.1 | Course | Thursday 17:00 - 19:50 | A F1.11 | - | - |
| EE431.1 | Tutorial | Monday 14:00 - 15:50 | A F1.4 - Class/Laboratory | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : LO: 1 2 3 4
Midterm exam
AI: Not AllowedAlignment with Learning Outcomes : LO: 1 2 3
Assignments
AI: Not AllowedAlignment with Learning Outcomes : LO: 1 2 3 4
Project
AI: Not AllowedAlignment with Learning Outcomes : LO: 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 hourse
42 hours ⏳ (14 week × 3 h)
Assignments
25 hours ⏳ (5 week × 5 h)
Active tutorials
20 hours ⏳ (10 week × 2 h)
Home study
28 hours ⏳ (14 week × 2 h)
Midterm exam study
5 hours ⏳ (1 week × 5 h)
Final exam study
5 hours ⏳ (1 week × 5 h)
Project work
25 hours ⏳ (5 week × 5 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 [EE431] 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 | |||||||||
| EE431 | Digital Signal Processing | 3 | 2 | 6 | ||||||
| Prerequisite | Senior Standing | It is a prerequisite to | - | |||||||
| Lecturer | TBA | Office Hours / Room / Phone | ||||||||
| TBA | ||||||||||
| Assistant | Assistant E-mail | tnamas@ius.edu.ba | ||||||||
| Course Objectives | The course aims to introduce concepts and methods of DSP. It describe's discrete signals and systems and their applications. The course covers discrete-time convolution, difference equations, the z-transform and the discrete Fourier transform. Designing of both recursive and non-recursive digital filters. The use of MATLAB and Simulink for examples and reinforcement of comprehension is essential part of the course. | |||||||||
| Textbook | Text Book is: Digital Signal Processing: principles, algorithms and applications. 4th ed. Proakis and Manolakis. Pearson. 2014. | |||||||||
| 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. Homework assignments and Projects. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction, Class Mechanics, Introduction to DSP | Ch1 | ||||||||
| Week 2 | Discrete-Time Signals and Systems. Characterization, Description and Testing HW1 | Ch2 | ||||||||
| Week 3 | The Z-Transform And Its Application | Ch3 | ||||||||
| Week 4 | The Z-Transform And Its Application HW2 (HW1 Due) (test 1) | Ch3 | ||||||||
| Week 5 | Frequency Analysis Of Signals And Systems | Ch4 | ||||||||
| Week 6 | Frequency Analysis Of Signals And Systems HW3 (HW2 Due) (test 2) | Ch5 | ||||||||
| Week 7 | Discrete Fourier Transform | Ch7 | ||||||||
| Week 8 | Review, class project overview and Midterm | |||||||||
| Week 9 | Discrete Fourier Transform | Ch7 | ||||||||
| Week 10 | Implementation Of Discrete-Time Systems HW4 (HW3 Due) (test 3) | Ch9 | ||||||||
| Week 11 | Implementation Of Discrete-Time Systems | Ch9 | ||||||||
| Week 12 | Design Of Digital Filers HW5 (HW4 Due) (test 4) | Ch10 | ||||||||
| Week 13 | Design Of Digital Filers | Ch10 | ||||||||
| Week 14 | Design Of Digital Filers HW5 Due (test 5) | Ch10 | ||||||||
| Week 15 | Final exam | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 35 | LO: 1, 2, 3, 4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm exam | 1 | 20 | LO: 1, 2, 3 | Not Allowed | |
| Assignments | 5 | 15 | LO: 1, 2, 3, 4 | Not Allowed | |
| Project | 2 | 40 | LO: 1, 2, 3, 4, 5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hourse | 3 | 14 | 42 | Assignments | 5 | 5 | 25 | |||
| Active tutorials | 2 | 10 | 20 | Home study | 2 | 14 | 28 | |||
| Midterm exam study | 5 | 1 | 5 | Final exam study | 5 | 1 | 5 | |||
| Project work | 5 | 5 | 25 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 30/09/2025 | |||||||||