SylabUZ
| Course name | Experimental techniques I |
| Course ID | 11.9-WE-INFP-TechnEksper01-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | Computer Science |
| Education profile | academic |
| Level of studies | Erasmus programme |
| Beginning semester | winter term 2017/2018 |
| Semester | 1 |
| ECTS credits to win | 2 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Credit with grade |
| Laboratory | 15 | 1 | - | - | Credit with grade |
To familiarize with the stages of planning and conducting experiments.
To shape ability in conducting experiments and developing and documenting the results of experiments.
To make aware of the place and role of the experiment in the development of science and technology.
n/a
Information: acquisition and processing. Information as a basic factor for civilisation development of a contemporary society, information society. Experiment as a basic manner of collection information about an object, phenomenon or process. Basic concepts of the information theory.
Elements of the experiment theory. Designing experiments. General rules and procedures for carry out experiments. The significance of mathematical modelling in the experiment methodology. Measurement as a basic element of the experiment methodology.
General characteristics and basic elements of measurement information acquisition systems. The relations of information acquisition systems with data telecommunication systems of information processing and computer control systems.
Analysis and working out of experiment results. Measurement errors and uncertainties. Error sources. Measurement error classification. The calculation of systematic errors in direct and indirect measurements. Mathematical model and random error calculation. The elimination of redundancy errors. Analysing measurement result uncertainties. Shaping measurement results. Connecting measurement results. Documenting experiment results.
Lecture: conventional lecture, problem lecture, discussion
Laboratory: working with source document, group work, laboratory exercises
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – the credit is given for obtaining positive grades in written tests carried out at least once a semester.
Laboratory – to receive a final passing grade student has to receive positive grades in all laboratory exercises provided for in the laboratory syllabus.
Calculation of the final grade: lecture 50% + laboratory 50%
none
Modified by prof. dr hab. inż. Ryszard Rybski (last modification: 05-05-2017 13:27)
