Measurement and Data Processing

Course guarantor:

Ivan Procházka

Lecturer:

Josef Blažej, Ivan Procházka

Tutor:

Josef Blažej, Ivan Procházka

Supervisor:

Department of Laser Physics and Photonics

Synopsis:

Basic knowledge for the measurements and data processing and result interpretation: errors, precision, accuracy, normal distribution and its propeties, data fitting, separation of the signal from the noise.

Requirements:

During the semester, the course guarantor will determine 3 dates for written control tests and one, only one, replacement date during the examination period. The tests are scored, the minimum requirement for successfully writing an individual test is to obtain half of the possible points.

The condition for obtaining ungraded assessment is the successful writing of 3 tests. The exam evaluation is determined from the points earned in 3 tests.

Syllabus of lectures:

1.Definition of terms

2.Type of measurements and related error sources

3.Normal errors distribution

4.Normal errors distribution consequences

5.Data fitting and smoothing: interpolation, fitting, least square algorithm, mini-max methods, weighting methods, test #1

6.Data fitting and smoothing: parameters estimate, fitting strategy, solution stability

7.Data fitting and smoothing: polynomial fitting, 'best fitting' polynomial, splines, demo

8.Data editing: normal data distribution, 3*sigma, relation to data fitting, deviations from normal distribution, tight editing criteria, test #2

9.Signal mining: noise properties, correlation, lock-in measurements

10.Signal mining methods: Correlation estimator, Fourier transform application

11.Signal mining methods - examples

12.Review, test

Syllabus of tutorials:

like lecture

Study Objective:

Knowledge: The course objektive is gaining of knowledge in the field of data processing and correct interpretation of results.

Skills: The student will be able to process and correctly interpret the measured data and observations

Study materials:

Key references:

[1] P. Hansen, V. Pereyra, G. Scherer, Least Squares Data Fitting with Applications, Baltimore, MD, USA:JHU Press, 2013, ISBN 978-1-4214-0786-9.

[2] J. Mandel, The Statistical Analysis of Experimental Data, Dover Publications 1984, ISBN: 978-0486646664.

Recommended references:

[3] http://kfe.fjfi.cvut.cz/~blazej/en/edu/index.html

Note:

english version available

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• Physical Engineering - Computational physics (PS)
• Physical Engineering - Plasma Physics and Thermonuclear Fusion (elective course)
• Physical Engineering - Laser Technology and Photonics (PS)