* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code | 460-4103/02 | |||||
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Number of ECTS Credits Allocated | 4 ECTS credits | |||||

Type of Course Unit * | Optional | |||||

Level of Course Unit * | Second Cycle | |||||

Year of Study * | ||||||

Semester when the Course Unit is delivered | Summer Semester | |||||

Mode of Delivery | Face-to-face | |||||

Language of Instruction | Czech, English | |||||

Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester | |||||

Name of Lecturer(s) | Personal ID | Name | ||||

VAS218 | Ing. Michal Vašinek, Ph.D. | |||||

Summary | ||||||

This course is focused on basic and advanced methods for signal analysis and data compression. Lectures will be focused on the theoretical description of the algorithms such as Markov models, Entropy, Statistical models, Fourier and Wavelet transforms, and all aspects of the data compression methods. The Exercises will enable the student to experimentally test described methods on the artificial as well as the real-world data. The task will guide students through the application of the algorithms and allow them to understand all topics discussed in this course. | ||||||

Learning Outcomes of the Course Unit | ||||||

The goal of this course is to introduce the problematics of the signal processing and data compression. The students will be able to analyze any signal from the statistical and probability point of view. Moreover, the student will be able to choose the proper compression algorithm that is most efficient. The methods will be explained from theoretical as well as from a practical point of view. | ||||||

Course Contents | ||||||

Lectures:
1. Theory of Information 2. Theory of Probability 3. Markov models in Data Compression 4. Statistical methods of Compression 5. Dictionary methods of Compression 6. Transformations for Data Compression 7. Vector Quantization 8. Efficient data Structures for Data Compression 9. Similarity using Data Compression 10. Application of Data Compression algorithms Exercise: Follows the lecture content. | ||||||

Recommended or Required Reading | ||||||

Required Reading: | ||||||

Robert M. Gray and Lee Davisson;An Introduction to Statistical Signal Processing , Cambridge University Press, 2004
David Salomon and Giovanni Motta; Handbook of Data Compression, 5th Edition, Springer (Nov 2009). ISBN 978-1-84882-902-2 | ||||||

Sylaby k předmětu Zpracování signálu.
Robert M. Gray and Lee Davisson;An Introduction to Statistical Signal Processing, Cambridge University Press, 2004 David Salomon and Giovanni Motta; Handbook of Data Compression, 5th Edition, Springer (Nov 2009). ISBN 978-1-84882-902-2 | ||||||

Recommended Reading: | ||||||

Robert M. Gray and Lee Davisson;An Introduction to Statistical Signal Processing, Cambridge University Press, 2004
David Salomon and Giovanni Motta; Handbook of Data Compression, 5th Edition, Springer (Nov 2009). ISBN 978-1-84882-902-2 | ||||||

Robert M. Gray and Lee Davisson;An Introduction to Statistical Signal Processing, Cambridge University Press, 2004
David Salomon and Giovanni Motta; Handbook of Data Compression, 5th Edition, Springer (Nov 2009). ISBN 978-1-84882-902-2 | ||||||

Planned learning activities and teaching methods | ||||||

Lectures, Tutorials | ||||||

Assesment methods and criteria | ||||||

Tasks are not Defined |