Course detail
Data Processing
FSI-GSZ-K Acad. year: 2025/2026 Summer semester
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
Czech
Number of ECTS credits
4
Supervisor
Entry knowledge
Theoretical knowledge of physics, fundamentals of electronics and algorithmization.
Rules for evaluation and completion of the course
The course consists of exercises and lectures. Exercise is completed by credit (awarded in the 13th week). To obtain it is required 100% participation in exercises and activity in exercises. Students will work out the individual work in the prescribed range and quality. Based on the quality of the work in the exercise, the student earns up to 30 points for the exam The work must be submitted in writing and checked and recognized by the teacher. The test is realized by written test, student can get up to 70 points from this test, where 30 points from exercises. Evaluation of the test result is given by the ECTS grading scale.
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
The aim of the course is to organize the knowledge and methods used in data processing in the production process.
Obtaining general principles in data collection and processing. Overview of modern methods in data processing with a focus on Industry 4.0
The study programmes with the given course
Programme N-KSB-K: Quality, Reliability and Safety, Master's, compulsory
Type of course unit
Guided consultation in combined form of studies
9 hours, compulsory
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
Guided consultation
34 hours, optionally
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
9 hours, compulsory
Syllabus
1. Data acquisition in Matlab environment, basic information
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