22MDML - Digital Measurement Laboratory
| Course specification | ||||
|---|---|---|---|---|
| Course title | Digital Measurement Laboratory | |||
| Acronym | 22MDML | |||
| Study programme | ||||
| Module | ||||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 4.0 | Status | ||
| Condition | Облик условљености | |||
| The goal | The goal of this course is introduction with design of experiments, especially design of software for computer integrated measurement systems. Using and developing programs in the LabVIEW/Python software for data acquisition and command of measuring instruments. Measurement uncertainty analysis based on the Guide to the Expression of Uncertainty in Measurement (GUM). | |||
| The outcome | Students gain knowledge which would alow them to select and specify measurement devices, acquisition systems and appropriate sensors, as well as to design software for the integration and management of measurement instruments and acquisition systems. They are able to analyze and evaluate the uncertainty of measurement results in accordance with the ISO standards (International Organization for Standardization) and the "bottom-up" approach to the GUM measurement uncertainty guide. | |||
| Contents | ||||
| Contents of lectures | What is LabVIEW/Python and what is it used for. Using LabVIEW/Python software platforms in a real-world environment. Data collection. Connecting measuring instruments and computers. Instrument management by application of software developed in LabVIEW/Python. Platform Basics and Environment. Projects. Virtual instruments. Block diagrams and panels. Basic commands and indicators. Data structures and graphical presentation. Main concept and mathematical tools for estimating measurement uncertainty by the ISO standards and GUM ("bottom up") approach for estimation of measurement uncertainty. Definitions of true and measured value, error, uncertainty and probability. Where does the uncertainty of measurement come from. Main sources of uncertainty: repeatability, calibration, temperature effects. Normal distribution - mean and standard deviation. Concept of standard uncertainty, combined standard uncertainty and extended measurement uncertainty. | |||
| Contents of exercises | Computer simulations and seminar papers based on theoretical lessons. | |||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 2 | 1 | 1 | ||
| Methods of teaching | Lectures, calculation exercises, computer work, consultations. | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | Test paper | |||
| Practical lessons | 30 | Oral examination | 30 | |
| Projects | ||||
| Colloquia | ||||
| Seminars | 40 | |||