SGN-11007 Introduction to Signal Processing, 5 cr
Person responsible
Sari Peltonen, Heikki Huttunen
Lessons
| Implementation | Period | Person responsible | Requirements |
| SGN-11007 2019-01 | 1 |
Heikki Huttunen Sari Peltonen |
Weekly exercises and final exam. |
Learning Outcomes
After completing the course, the student is able to - discuss the fundamental concepts of signal processing and solve problems related to them, - use Matlab and computational methods to solve basic signal processing problems, - solve the properties of a linear filter through its transfer function and can design an FIR filter both by Matlab and manually using window design method, - implement the Fourier transform of a sequence both directly and using the FFT algorithm, - desing a digital system to convert the sampling rate, - design simple pattern recognition systems that use the signal processing tools for feature extraction.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Basics of Matlab for signal processing | ||
| 2. | Basics of digital signal processing: sampling theorem, discrete signals and systems, and the convolution operator. | ||
| 3. | Analysis of discrete signals and systems: discrete Fourier transform, FFT algorithm, z-transform, transfer function and frequency response. | Fourier transform, Fourier series and Discrete-time Fourier transform | |
| 4. | Design of linear systems using the window desing method | Parks-McClellan algorithm | |
| 5. | Multirate DSP: Decimation and Interpolation | ||
| 6. | Fundamentals of machine learning. Applications of signal processing algorithms in pattern recognition. | ||
| 7. | Applications. Visiting lectures from university or the industry. |
Instructions for students on how to achieve the learning outcomes
Measures of learning: Exam and mandatory exercises with bonus points. The grade is determined by the exam and activity in weekly exercises. The weekly exercises contain exercises both on usage of tools (Matlab) and pen and paper tasks. The exam measures how well the student has learned the core content.
Assessment scale:
Numerical evaluation scale (0-5)
Prerequisites
| Course | Mandatory/Advisable | Description |
| ELT-10017 Signals and Measurements | Advisable |
Additional information about prerequisites
The student should have a good knowledge of BSc level engineering mathematics.
Correspondence of content
| Course | Corresponds course | Description |
| SGN-11007 Introduction to Signal Processing, 5 cr | SGN-11006 Basic Course in Signal Processing, 5 cr | |
| SGN-11007 Introduction to Signal Processing, 5 cr | SGN-11000 Basic Course in Signal Processing, 5 cr |