Description:

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.

Contents:

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

Seminar contents:

like lecture

Recommended literature:

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

Keywords:

Measurement, observation, precision, accuracy, fitting, signal, noise

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar