Measurement and Data Processing

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Software Engineering

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:

Basic knowledge in mathematics and physics on the level of Bc courses in the 1st year at CTU in Prague and general knowledge of Fourier transform.

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:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Applications of Informatics in Natural Sciences (compulsory course in the program)