Learning outcomes of the course
- The student learns the fundamental concepts of signal processing, can discuss them and solve problems related to them.
- The student learns the basic command of Matlab, and has learned the computational methods required in the subsequent courses.
- The student can solve the properties of a linear filter through its transfer function, and can design an FIR filter.
- The student can also implement the Fourier transform of a sequence both directly and using the FFT algorithm.
- The student can design simple pattern recognition systems that use the signal processing tools for feature extraction.
Contents of the course
- Basics of digital signal processing: sampling theorem, discrete signals and systems; the convolution operator.
- Analysis of discrete signals and systems: Fourier transform and the FFT algorithm; z-transform and the transfer function.
- Design of linear systems using the window method
- Signal analysis
- Fundamentals of machine learning. Applications of signal processing algorithms in pattern recognition.
- Applications. Visiting lectures from university and the industry.
- Teacher: Sari Peltonen