Course detail

Elements of Digital Signal and Image Processing

FEKT-MEDSAcad. year: 2019/2020

The course is intended as an introduction to signal and image processing and analysis in the English language.

Learning outcomes of the course unit

After completing the course, the student is capable of:
- interpreting the fundamental knowledge, concepts and their relationships in the field of signal and image processing,
- describing the basic methods in this area,
- using English terminology in the area, and reading the respective literature in English with understanding.

Prerequisites

successful completion of Bachelor study in the respective study branch, particularly: - basic university mathematics, including the complex integral transforms - introduction to continuous-time system theory

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

J.Jan: Digital Signal Filtering, Analysis and Restoration, IEE London (UK) 2000, ISBN 0 85296 760 8
J.Jan: Číslicová filtrace, analýza a restaurace signálů, VUTIUM 2002
J.Jan: Medical Image Processing, Reconstruction and Restoration. CRC 2006

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations. Techning methods include lectures. Course is taking advantage of e-learning (Moodle) system.

Assesment methods and criteria linked to learning outcomes

credits based on attending the lectures and on results of a written test (use of English terminology)

Language of instruction

English, Czech

Work placements

Not applicable.

Course curriculum

1. Fundamental concepts of signal theory and signal processing systems.
2. Digital signals - sampling and reconstruction, discrete spectra.
3. Principles and properties of digital linear filtering - FIR filters.
4. Principles and properties of digital linear filtering - IIR filters.
5. Noise suppression and signal restoration – averaging methods, optimal filtering.
6. Discrete correlation analysis
7. Discrete spectral analysis (deterministic signals)
8. Discrete spectral analysis (stochastic signals)
9. Basics of analogue image representation, two-dimensional signals and systems.
10. Discrete and digital images, 2D discrete transforms.
11. Basic 2D image processing operators, contrast and colour transforms.
12. Image enhancement - sharpening, noise suppression.
13. Introduction to reconstruction of images from tomographic projections.

Aims

Providing basic knowledge of English terminology in the area of signal and image processing and analysis.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Classification of course in study plans

  • Programme EEKR-MN Master's

    branch MN-BEI , 1. year of study, summer semester, 3 credits, general knowledge

  • Programme EEKR-M1 Master's

    branch M1-BEI , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-TIT , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-KAM , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-EVM , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-EST , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-MEL , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-SVE , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-EEN , 1. year of study, summer semester, 3 credits, general knowledge
    branch M1-BEI , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-TIT , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-KAM , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-EVM , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-EST , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-MEL , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-SVE , 2. year of study, summer semester, 3 credits, general knowledge
    branch M1-EEN , 2. year of study, summer semester, 3 credits, general knowledge

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer