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Majid Nabi :: DSP Course

Digital Signal Processing (DSP) Course

The course aims to provide fundamental knowledge about digital signals and processing systems. It starts with elementary signals, sinusoids, that can be composed to synthesize more complex signals. Frequency spectra of signals are introduced as an alternative representation of a signal that emphasizes its frequency components. Fourier series for analysis and synthesis of periodic signals is presented as well as the Fourier Transformation. We discuss converting signals from continuous time and value domains to a digital form and vice versa. Linear time-invariant systems are studied and their characteristic impulse response. Finite impulse response filters are studied. We also briefly introduce stochastic signal models and local frequency representations, such as spectrograms and wavelet analysis. The lectures are accompanied by instructions providing practical exercises using Matlab, to familiarize oneself with the theoretical concepts. The examination will include questions on paper as well as Matlab exercises.

Learning objectives

This course introduces the fundamentals of mathematical and digital representations of signals and of digital signal processing systems aimed at signal synthesis, transformation or analysis. At the end of the course the students will
  • be familiar with different kinds of signals, their characteristic properties, their mathematical and digital representations and conversion
  • have conceptual understanding of signal transformation (filtering), signal synthesis and analysis techniques

  • Course material

    In the first part of the course, we follow the following text books about Digital signal processing:
  • Signal Processing First, James McClellan, Ronald Schafer, and Mark Yoder. Pearson Prentice Hall, 2003.


  • Handouts, assignments, and other reading materials will be provided via OASE.