480-2030/02 – Computer Processing of Experimental Data (PZED)

Gurantor departmentDepartment of PhysicsCredits3
Subject guarantordoc. RNDr. Dalibor Ciprian, Ph.D.Subject version guarantordoc. RNDr. Dalibor Ciprian, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageEnglish
Year of introduction2018/2019Year of cancellation
Intended for the facultiesFEI, USP, FMTIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
CIP10 doc. RNDr. Dalibor Ciprian, Ph.D.
NIK01 Ing. Marek Nikodým, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 1+2

Subject aims expressed by acquired skills and competences

The objective of the course is to teach the students how to apply the data processing and evaluation methods to the results obtained from various experimental techniques used in physics and chemistry.

Teaching methods



The course extends the knowledge in the field of data evaluation using computers. The emphasis is placed on practical lectures in computer laboratory. The data evaluation methods are presented in MATLAB programming language, and demonstrated using the results obtained either from computer models or from real expriments.

Compulsory literature:

Mathworks Inc.: MATLAB R13 HELP, Mathworks Inc., 2002. BEVINGTON, P., KEITH ROBINSON, D. Data Reduction and Error Analysis for the Physical Sciences 3rd Edition, McGraw-Hill, 2015, ISBN 978-0072472271

Recommended literature:

CHAPRA, S. C. Applied Numerical Methods with MATLAB for Engineers and Scientists, McGraw-Hill, 2012, ISBN 978-0-07-340110-2

Way of continuous check of knowledge in the course of semester

discussion with students during the lessons


no e-learning available

Další požadavky na studenta

There are no additional requests.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Introduction to MATLAB programming language, import of data 2. Script writting and debugging 3. MATLAB toolboxes and their applications 4. User functions writting 5. The ideal, natural and immediate sampling, Shannon - Kotelnik theorem. 6. Statistical analysis of univariate data. 7. Numeric smmothing and experimental data filtering. 8. Data convolution and deconvolution. 9. Nonparametric data regression, signal differentiation and integration. 10. Parametric regression - linear and nonlinear models. 11. Fourier analysis and its applications 12. Wavelet analysis and its applications

Conditions for subject completion

Full-time form (validity from: 2018/2019 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded credit Graded credit 100  51
Mandatory attendence parzicipation: compulsory seminars - max 3 absences with leave

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (B3942) Nanotechnology (3942R001) Nanotechnology P English Ostrava 2 Compulsory study plan
2018/2019 (B3942) Nanotechnology (3942R001) Nanotechnology P English Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner