Sensors and Data Acquisition (5 cr)
Code: R504D72-3001
General information
Enrollment
03.10.2022 - 08.01.2023
Timing
09.01.2023 - 31.03.2023
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Degree programmes
- Machine Learning and Data Engineering
Teachers
- Ari Karjalainen
Responsible person
Ari Karjalainen
Student groups
-
R54D21SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2021
Objective
The student deepens knowledge of sensor technology, embedded systems and IoT bus technologies and data acquisition equipment. The student knows the basics of analog-to-digital conversion. The student knows the principles of digital signal processing and their effect on the performance and accuracy of measurements.
Content
- Basics of electronic measurement technology
- Analog-to-Digital and Digital-to-Analog transform
- Sample-and-Hold
- Data Transfer Techniques
- Analog and digital data acquisition,
- Sensor/Transducer interfacing, Instrument amplifier
- Interference, Grounding and Shielding
Materials
Study material will be given during lessons,no need to purchase of books.Most of the material will be in electric form and materials from Internet will be used.
Raspberry Pi or similar units will be used.
Teaching methods
Lessons and exercises in laboratory, working in small groups and returning report of given tasks.
Completion alternatives
Not possible.
Content scheduling
Four hour long lessons in every week.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student knows the basic principles of analog and digital sensors and the conditions affecting the accuracy of measurements.
Assessment criteria, good (3)
The student can connect sensors to a data acquisition system.
Assessment criteria, excellent (5)
The student can apply interference cancellation, filtering and error correction methods to improve the performance of the measurement system.
Assessment methods and criteria
All given exercises must be reported and reports will be evaluated by instructor.
10 - 20 points per report will be maximum, total of 100 points will be available.
Evaluation scale:
< 50p 0
50 -59 1
60 - 69 2
70 - 79 3
80 -89 4
90 - 5