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FTE-32806 Automation for Bioproduction.
Sensing is an important part of automation in agriculture. The focus of the course is on proper usage of sensors. Therefore this course presents briefly a number of different sensors to measure pressure, temperature, pH, velocity, acceleration, position, distance and angles etc. This course teaches you how to obtain information concerning accuracy and disturbances from datasheets and other documentation that comes with any sensor. Different types of sensor errors and ways to suppress them can be distinguished and are addressed in this course. This course also teaches different measurement principles such as compensation and Wheatstone bridges. These principles guide the engineer when designing a measurement set-up. The accuracy of sensors can often be improved by calibration that is also considered in this course.
Signal conditioning, analog to digital (A/D) conversion and sampling (frequency spectrum, Shannon' s theorem, spectral analysis of signals) are important issues treated in this course as well as related phenomena such as aliasing and filtering.
Considerable attention will be given to imaging sensors. Imaging sensors are a very special, sophisticated type of sensors that are being used to obtain 2 or 3 dimensional information of a system. These sensors become increasingly important in agricultural automation. The processing of the resulting images using computer vision techniques constitutes an important part of this course.
After successful completion of this course students are expected to be able to:
- know the various sensor types, and be able to choose the right type of sensor for a particular instrumentation problem;
- know and understand the different measurement principles as well as their pros and cons;
- be able to interpret datasheets and documentation that comes with a sensor;
- be able to determine the accuracy of a sensor and be able to decide whether a sensor is suitable to measure a signal with a certain accuracy;
- know and be able to use different types of signal conditioning, their pros and cons;
- be able to determine the sampling frequency and the anti-aliasing filter from the spectrum of the to-be-measured signal;
- know under what conditions and how to calibrate a sensor;
- be able to process data from image sensors;
- be able to use the LabVIEW computer program for data acquisition and processing;
- be able to apply Kalman filtering to measured data;
- know the pros and cons of Kalman filtering.
- observations during practicals. All practical exercises have to be completed;
- reports (25%).
- written exam with open questions (75%).
To be announced.
|Compulsory for:||BAT||Biosystems Engineering||BSc||6MO|