Course detail

Digital Signal Processing (in English)

FIT-CZSaAcad. year: 2019/2020

Introduction to digital signal processing, sampling and quantization, Frequency analysis of digital signals, Principles of digital filters, Digital filter design, Practical implementation of digital filters. Processing in frequency domain, Sub-band signal processing, changing the sampling frequency, Wavelet analysis and synthesis, Random signals, State space representation, System identification, Wiener and Kalman filtering, Vector signal processing.

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Oppenheim A.V., Wilski A.S.: Signals and systems, Prentice Hall, 1997.
Jan J., Číslicová filtrace, analýza a restaurace signálů, VUT v Brně, VUTIUM, 2002, ISBN 80-214-1558-4.
Mallat S, A Wavelet Tour of Signal Processing (Third Edition), Academic Press, 2009, ISBN 9780123743701

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • Solving and submitting solution of two home-works during the semester (7pts each, total 14pts) 
  • Half-semestral exam (15pts) 
  • Submission and presentation of project (20pts)
  • Semestral exam, 51pts, requirement of min. 17pts.

Language of instruction

English

Work placements

Not applicable.

Aims

To refresh basic knowledge of signals and systems and to make students familiar with more advanced topics linked to artificial intelligence, cyber-physical systems, speech and sound processing and other related domains. To provide students with sufficient mathematical background allowing to understand conference and journal papers dealing with signal processing topics, and allowing for own independent work in signal processing. To provide students with sufficient practical knowledge for implementing and integrating signal processing algorithms.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MGMe , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme IT-MGR-2 Master's

    branch MIN , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, 5 credits, elective
    specialization NBIO , any year of study, winter semester, 5 credits, elective
    specialization NGRI , any year of study, winter semester, 5 credits, elective
    specialization NNET , any year of study, winter semester, 5 credits, elective
    specialization NVIZ , any year of study, winter semester, 5 credits, elective
    specialization NCPS , any year of study, winter semester, 5 credits, compulsory
    specialization NSEC , any year of study, winter semester, 5 credits, elective
    specialization NEMB , any year of study, winter semester, 5 credits, elective
    specialization NHPC , any year of study, winter semester, 5 credits, elective
    specialization NISD , any year of study, winter semester, 5 credits, elective
    specialization NIDE , any year of study, winter semester, 5 credits, elective
    specialization NISY , any year of study, winter semester, 5 credits, elective
    specialization NMAL , any year of study, winter semester, 5 credits, elective
    specialization NMAT , any year of study, winter semester, 5 credits, elective
    specialization NSEN , any year of study, winter semester, 5 credits, elective
    specialization NVER , any year of study, winter semester, 5 credits, elective
    specialization NSPE , any year of study, winter semester, 5 credits, compulsory

  • Programme IT-MGR-1H Master's

    branch MGH , any year of study, winter semester, 5 credits, recommended

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  1. Introduction to digital signal processing, sampling and quantization.
  2. Frequency analysis of digital signals, DTFT, DFT and FFT. 
  3. Principles of digital filters. 
  4. Digital filter design. 
  5. Practical implementation of digital filters.
  6. Processing in frequency domain
  7. Sub-band signal processing, changing the sampling frequency.
  8. Wavelet analysis and synthesis.
  9. Random signals - correlation and power spectral density.
  10. State space representation. 
  11. System identification.
  12. Wiener and Kalman filtering.
  13. Vector signal processing

Fundamentals seminar

13 hours, compulsory

Teacher / Lecturer

Syllabus

Demonstration exercises (1h per week) immediately follow the lectures and demonstrate the taught techniques to the students based on real code, mostly in python and Matlab/Octave. All codes will be available to the students. Two homeworks (to be solved during the semester) are based on these exercises.

Project

13 hours, compulsory

Teacher / Lecturer

Syllabus

The project is assigned in combination with another master course based on students specialization (for example in speech processing, or cyber-physical systems). It is solved in teams of up to 5 students, a report and short presentation are required. The data for projects will be provided, or acquired by the students. Examples of projects: 

  1. Simple signal processing for a microphone array  
  2. Estimation of transfer function of a mechanical system 
  3. Changing the properties of sound using time-frequency processing. 
  4. Sub-band audio coding.

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