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.
Supervisor
Department
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, optional
specialization NBIO , any year of study, winter semester, 5 credits, optional
specialization NGRI , any year of study, winter semester, 5 credits, optional
specialization NNET , any year of study, winter semester, 5 credits, optional
specialization NVIZ , any year of study, winter semester, 5 credits, optional
specialization NCPS , any year of study, winter semester, 5 credits, compulsory
specialization NSEC , any year of study, winter semester, 5 credits, optional
specialization NEMB , any year of study, winter semester, 5 credits, optional
specialization NHPC , any year of study, winter semester, 5 credits, optional
specialization NISD , any year of study, winter semester, 5 credits, optional
specialization NIDE , any year of study, winter semester, 5 credits, optional
specialization NISY , any year of study, winter semester, 5 credits, optional
specialization NMAL , any year of study, winter semester, 5 credits, optional
specialization NMAT , any year of study, winter semester, 5 credits, optional
specialization NSEN , any year of study, winter semester, 5 credits, optional
specialization NVER , any year of study, winter semester, 5 credits, optional
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
- Introduction to digital signal processing, sampling and quantization.
- Frequency analysis of digital signals, DTFT, DFT and FFT.
- 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 - correlation and power spectral density.
- State space representation.
- System identification.
- Wiener and Kalman filtering.
- Vector signal processing
Exercise
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.
Projects
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:
- Simple signal processing for a microphone array
- Estimation of transfer function of a mechanical system
- Changing the properties of sound using time-frequency processing.
- Sub-band audio coding.
eLearning
eLearning: currently opened course