Prerequisite/Recommended prerequisite for participation in the module

The module adds to the knowledge obtained in Basic Programming, Applied Statistics and Probability Theory.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Knowledge on the different sensors available and the fundamental measuring principles.
• Knowledge on the computer based data acquisition, accuracy and error handling.
• Understand methodology for design of experiments and test series and for reduction of ambiguity of experimental results, and for comparability with model predictions.
• Understand processing methods for analog and digital data (continuous vs. discrete).

Skills

• Isolate principal measurable parameters.
• Be able to plan experiments in order to get optimal information compared to the experimental effort.
• Be able to choose the right sensor technology for the problem at hand.
• Setting up the A/D and D/A converters with commercial programs or by own programs.
• Isolate principal measurable parameters.
• Basic knowledge on digital image analysis.

Competences

• Be able to plan a laboratory or field experiment and setup appropriate data acquisition.
• Be able to discuss validity of results and errors of the data acquired in relation to choice of sensor and analysis method.

Type of instruction

Lectures, etc. supplemented with project work, workshops, presentation seminars, lab tests.

Extent and expected workload

Since it is a 5 ECTS project module, the workload is expected to be 150 hours for the student.

Exam

Exams

Name of exam	Measurement Technology, Data Acquisition, Test and Validation
Type of exam	Written or oral exam Individual oral or written exam
ECTS	5
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures