FEKT-LREMAcad. year: 2017/2018
Basic measurement error definition, statistical data evaluation. Measurement automation, principals of basic programs. Precise measurements, parameters stability. Measurement devices for high frequency range. Network and impedance analyzers.
Learning outcomes of the course unit
The graduate is able (1) to use basic measurement methods, he or she is able to (2) analyse measured data and he or she is able to design modern automation measurement system. He or she is able to realize the controlling program in Agilent VEE (4), Matlab (5) and LabView (6).
The subject knowledge on the Bachelor’s degree level is requested.
Recommended optional programme components
Recommended or required reading
HAASZ, V., ROZTOČIL, J., NOVÁK, J., Číslicové měřicí systémy, ČVUT Praha 2000 (CS)
ĎAĎO, S., VEDRAL, J., Číslicové měření, přístroje a metody, ČVUT Praha, 2006 (CS)
Planned learning activities and teaching methods
Teaching methods include lectures and practical laboratories.
Assesment methods and criteria linked to learning outcomes
Tests written during semester and laboratory exercises (30 points), final exam (70 points).
Language of instruction
1. Measurement error definition, error quantification. Correct measurement rules.
2. Automation measurements.
3. Agilent VEE.
4. Basic oscilloscope measurements.
5. Frequency measurements.
6. Spectrum analysers.
7. Scalar network analysers.
8. vector network analysers.
9. Impedance analysers.
10. PC digital cards.
11. Signal generators.
Presenting principles of basic measurement methods for high frequency range. Presenting automation techniques, programs. Automation measurement setup.
Specification of controlled education, way of implementation and compensation for absences
The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.
Classification of course in study plans
- Programme EEKR-ML1 Master's
branch ML1-EST , 1. year of study, summer semester, 6 credits, optional specialized
- Programme EEKR-ML Master's
- Programme EEKR-CZV lifelong learning
branch ET-CZV , 1. year of study, summer semester, 6 credits, optional specialized