34984 - Digital Signal Processing (2nd cycle)

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Affordable and clean energy Industry, innovation and infrastructure

Academic Year 2021/2022

Learning outcomes

At the end of the course, students will have the skills to analyze and design digital signal processing algorithms for energy-efficient communications systems. In particular, students will be able to: analyze digital systems in time-domain, frequency-domain, Z-domain; design digital filters; design multi-rate digital signal processing systems; design adaptive filters. Finally, the student will know the methodologies for analyzing and designing efficient signal processing blocks taking into account the required energy resources.

Course contents

  • Discrete-time signals, spectral representation, convolution, and correlation operators.
  • Discrete-time linear systems, numerical filters, and filtering.
  • Sampling, quantization, and reconstruction.
  • Multi-rate systems, decimation, interpolation.
  • Discrete Fourier transform (DFT), fast Fourier transform (FFT), Z transform, discrete cosine transform (DCT).
  • Circular convolution and block filtering through FFT.
  • Design of FIR and IIR filters.
  • Spectral analysis.
  • Linear prediction, estimation, optimal filtering, and adaptive filtering.
  • Filter banks.
  • Image and video compression (JPEG and MPEG standard), digital audio processing.

Readings/Bibliography

  • A. V. Oppenheim, R. W. Schafer, Elaborazione Numerica dei Segnali, Franco Angeli, 1996.
  • J. G. Proakis, C.M.Rader, F. Ling, C. L. Nikias, et alii, Algorithms for Statistical Signal Processing, Prentice Hall, Upper Saddle River, NJ, 2002.
  • M. Bellanger, Digital Processing of Signals, Third Ed. John Wiley & Sons, 2000.
  • R. C. Gonzalez, R. E. Woods, Digital Image Processing, Second edition, Prentice Hall, NJ, 2002.

Teaching methods

The lectures are supported by some design examples to solve practical problems in digital signal processing by writing Matlab code: audio source localization, audio reverberation, and channel equalization. The code developed in the class is available to the students for further development.

Assessment methods

Pre- and post-tests will be used to assess skills in analyzing and designing digital signal processing algorithms in real-world applications.
All students will show substantial improvement in stated learning outcomes, as indicated by pre- and post-evaluation of real problems.

Teaching tools

Lectures and exercises are carried out with the help of a personal computer and the Matlab platform freely available to students. There are also tutorials on audio and video coding techniques (JPEG, MPEG, and MP3) and processing sensors' data.

Office hours

See the website of Andrea Giorgetti