516-0843/01 – Signal processing (ZS)

Gurantor departmentInstitute of PhysicsCredits4
Subject guarantordoc. Dr. Ing. Michal LesňákSubject version guarantordoc. Dr. Ing. Michal Lesňák
Study levelundergraduate or graduate
Study languageCzech
Year of introduction2013/2014Year of cancellation
Intended for the facultiesHGF, USPIntended for study typesBachelor, Follow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
LES66 doc. Dr. Ing. Michal Lesňák
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2

Subject aims expressed by acquired skills and competences

The aim of the course is to familiarize students with basic knowledge in signal processing. The theoretical findings are practically trained on simple experiments.

Teaching methods

Lectures

Summary

Compulsory literature:

DLipsman, Ronald L., and Jonathan Rosenberg. A guide to MATLAB: for beginners and experienced users. Cambridge University Press, 2006. Mathworks Inc.: MATLAB R13 HELP, Mathworks Inc., 2002.

Recommended literature:

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Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

Careful preparation for lessons.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Course Content 1. Introductory lecture. Basic concepts of experimental data processing. 2. Uncertainties kinds of uncertainties, a division of uncertainties. 3. Introduction to probability, random variables, properties and frequency distribution function. 4. Statistical analysis of one-dimensional data. Point estimates of the parameters of the position, dispersion and shape. 5. Introduction to Matlab - user interface, command window, basic commands and functions. 6. Control of the program Matlab, data entry, use toolbox, statistical toolbox. 7. Graphic data processing assistance program Matlab. 8. Presentation of data in Matlab, data from other applications and other applications. 9. Simple linear regression, regression models, nonlinear regression. 10. Using Fourier transform, properties of Fourier transform, fast Fourier transform (FFT). 11. Ideal for natural and immediate sampling, Shannon - Kotělnikův theorem. 12. Introduction to the Statigraf. 13. Individual work, implementation of simple measurements and their processing.

Conditions for subject completion

Full-time form (validity from: 2013/2014 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded exercises evaluation Graded credit 100  51
Mandatory attendence parzicipation:

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Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2018/2019 (B1701) Physics (1702R001) Applied Physics P Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (B1701) Physics (1702R001) Applied Physics P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (B1701) Physics (1702R001) Applied physics P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (B1701) Physics (1702R001) Applied Physics P Czech Ostrava 2 Choice-compulsory study plan
2015/2016 (B1701) Physics (1702R001) Applied physics P Czech Ostrava 2 Choice-compulsory study plan
2014/2015 (B1701) Physics (1702R001) Applied physics P Czech Ostrava 2 Choice-compulsory study plan
2013/2014 (B1701) Physics (1702R001) Applied physics P Czech Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner