Course detail
Data Processing
FSI-GSZAcad. year: 2023/2024
The course acquaints students with the problems of data processing in the production process. The basic methods of data collection, industrial buses, methods of data transmission including security, data analysis and processing and last but not least the recording in the database system will be described. Emphasis is placed on current methods that meet the requirements for Industry 4.0.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Attendance at lectures is recommended, participation in laboratories is controlled. A maximum of two absences in the laboratories can be compensated by the independent elaboration of missing protocols.
Aims
Obtaining general principles in data collection and processing. Overview of modern methods in data processing with a focus on Industry 4.0
Study aids
Prerequisites and corequisites
Basic literature
BURDA, Michal, 2012. Databázové systémy. Hradec Králové: Gaudeamus. ISBN 978-80-7435-203-4. (CS)
DANIŠ, Stanislav, 2009. Základy programování v prostředí Octave a Matlab. Praha: Matfyzpress. ISBN 978-80-7378-082-1. (CS)
LabVIEW Measurements Manual, National Instruments, April 2003 Edition, Part Number 322661B-01, dostupné z www.ni.com (EN)
Recommended reading
Elearning
Classification of course in study plans
- Programme N-KSB-P Master's 1 year of study, summer semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
1. Information and data, basic concepts, types and methods of data acquisition.
2. Buses and sensors used in industry
3. Data transmission, protocols, compression, encryption
4. IoT and cloud systems
5. Fundamentals of data processing in Matlab/Simulink
6. Fundamentals of data processing in LabVIEW
7. Databases - SQL language - query creation, relational databases
8. Text editors, spreadsheets, graphics
9. Advanced data processing methods - data classification
10. Advanced data processing methods - evolutionary algorithms
11. Advanced data processing methods - fuzzy sets
12. Practical examples of the topics covered.
13. Credit
Laboratory exercise
Teacher / Lecturer
Syllabus
2. Data acquisition in Matlab environment, data acquisition from sensor
3. Data processing in Matlab (Octave)
4. Data collection in LabVIEW environment, basic information
5. Data acquisition in LabVIEW environment, data acquisition from sensor
6. Compression and encryption of acquired data
7. Spreadsheet processors, data processing
8. Spreadsheets, extended functions
9. MS Access, tables, search queries
10. MS Access, relational DB
11. SQL queries, relational DB
12. Inspection and completion of protocols
13. Credit
Elearning