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.