Electrical and Electronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15150001 | Signals and Systems | 2025 | Autumn | 5 | 3+0+0 | 3 | 5 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Asst. Prof. Saim ERVURAL |
| Instructor(s) | Asst. Prof. İbrahim ONARAN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. İbrahim ONARAN | A-125 | [email protected] | 7678 | Tuesday 17:00-18:00 |
Course Content
Understanding Signals and Systems foundational topics allows engineers to build everything from smartphones to medical devices:Signal Analysis: Signals are physical quantities that carry information over time (e.g., changing velocity or audio waves). The course breaks down these signals and translates them between the time domain and frequency domain.System Operators: Systems process input signals to generate output signals. Students learn to model these processes using linear time-invariant (LTI) systems and the mathematical operation of convolution.Core Mathematics: You will spend significant time mastering tools like the Fourier Transform (to analyze frequencies), the Laplace Transform (to evaluate system stability in continuous time), and the Z-Transform (for discrete-time/digital systems).
Objectives of the Course
The purpose of a Signals and Systems course is to provide the foundational mathematical framework needed to analyze, manipulate, and design technologies that process information. It teaches how physical data (like sound, voltage, or radio waves) behaves and how devices (like circuits, filters, or software) interact with and alter that data.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P2 | Ability to identify, describe, mathematically express, and solve challenging engineering problems; the capability to select and utilize appropriate analysis and modeling techniques for this purpose. | 5 |
| P5 | Ability to plan experiments, conduct them, collect data, analyze and interpret results regarding complex engineering problems or discipline-specific research topics. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Explaining signals mathematically and performing mathematical operations on signals | P.2.46 | 1 |
| O2 | Recognize basic signals such as sinusoidal signals, complex exponentials, delta and step functions, and classify signals as continuous-time or discrete-time, periodic or non-periodic, energy or power signal, even or odd symmetrical forms | P.2.47 | 1 |
| O3 | Understand various system properties such as causality, time invariance, linearity, and stability | P.2.48 | 1 |
| O4 | Understand the operations of convolution sum and convolution integral and their role in the analysis of linear time-invariant systems | P.5.18 | 1 |
| O5 | Computing the Fourier series (and inverse) of periodic continuous time and discrete time signals from the definition equations and using the properties of the Fourier series | P.5.19 | 1 |
| O6 | Calculating the Fourier transform (and inverse) of continuous time signals from the definition equations and using the properties of the Fourier transform | P.5.20 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Signals and Introduction to Systems |
| 2 | Continuous-Discrete Time Signals |
| 3 | Continuous-Discrete Time LTI Systems |
| 4 | Features of LTI Systems and Block Diagrams |
| 5 | Response of Continuous LTI Systems for Exponential Functions |
| 6 | Non-Periodic Signals Fourier Series |
| 7 | Fourier Series Fourier Analysis in Periodic Signals - I |
| 8 | Midterm Week |
| 9 | Periodic Signals and Fourier Transform in Continuous Time |
| 10 | For Exponential Functions -Response of Advanced LTI Systems |
| 11 | Non-Periodic Signals Fourier Series |
| 12 | Fourier Series in Periodic Signals |
| 13 | Z-transform |
| 14 | Final Week |
Textbook or Material
| Resources | signals and systems ders kitabı |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 40 | 40 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 42 | 42 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 166 | ||
| Total Work Load / 30 | 5,53 | ||
| Course ECTS Credits: | 6 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 | P5 |
|---|---|---|---|
| O1 | Explaining signals mathematically and performing mathematical operations on signals | 5 | - |
| O2 | Recognize basic signals such as sinusoidal signals, complex exponentials, delta and step functions, and classify signals as continuous-time or discrete-time, periodic or non-periodic, energy or power signal, even or odd symmetrical forms | 5 | - |
| O3 | Understand various system properties such as causality, time invariance, linearity, and stability | 4 | - |
| O4 | Understand the operations of convolution sum and convolution integral and their role in the analysis of linear time-invariant systems | - | 5 |
| O5 | Computing the Fourier series (and inverse) of periodic continuous time and discrete time signals from the definition equations and using the properties of the Fourier series | - | 4 |
| O6 | Calculating the Fourier transform (and inverse) of continuous time signals from the definition equations and using the properties of the Fourier transform | - | 5 |