Applied Signal Processing (ue)
Mechatronics Engineering
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Applied Signal Processing (ue) | MKT529 | Elective | Master's degree | 1 | Spring | 8 |
Name of Lecturer(s)
Assistant Prof. Dr. Suat KARAKAYA
Learning Outcomes of the Course Unit
1) Analyzing digital signals
2) Design of discrete-time systems
3) Effective use of transformation methods in system analysis.
4) Calculation of system outputs
5) Application of digital signal processing methods in a software environment
6) Application of basic estimation methods on mobile robot systems
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||
| 1 | 2 | 3 | 4 | ||
| Learning Outcomes | |||||
| 1 | Middle | High | No relation | High | |
| 2 | High | High | High | High | |
| 3 | High | High | High | High | |
| 4 | Middle | High | High | Middle | |
| 5 | High | High | No relation | No relation | |
| 6 | High | High | High | High | |
Mode of Delivery
e-course
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Intr. to Digital Signal Processing
Course Contents
This course covers; fundamentals of digital signal processing, continuous and discrete-time signals and systems, discrete-time systems, z-transform, sampling, Laplace transform, continuous and discrete-time convolution, FIR filter design, IIR filter design, Overview of estimation methods, Parametric estimation, non-parametric estimation, maximum likelihood estimation (MLE), least squares, particle filters (PF), mobile robotics applications.
Weekly Schedule
1) Continuous and discrete-time signals2) Discrete-time systems
3) z-transform
4) Laplace transform
5) Continuous and discrete-time convolution
6) FIR filter design, IIR filter design
7) Overview of estimation methods
8) Midterm Exam
9) Parametric estimation
10) Non-parametric estimation
11) Maximum likelihood estimation (MLE)
12) Least squares
13) Particle filters (PF)
14) Mobile robotics applications
15) Particle based localization
16) Final Exam
Recommended or Required Reading
1- Sebastian Thrun, Wolfram Burgard, Dieter Fox, Probalilistic Robotics, MIT Press
2- Bernard Widrow, Adaptive Signal Processing, Pearson
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
40% |
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Contribution of Final Examination to Course Grade |
60% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required