Course detail

Data Processing

FSI-GSZ-KAcad. 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

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

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

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Frank Lamb: Industrial Automation: Hands-On, McGraw-Hill Education, 2013
Behzad Ehsani: Data Acquisition using LabVIEW, Packt Publishing, 2016
Handbook of Modern Sensors: Physics, Designs, and Applications 5th ed. 2016 Edition, Springer International Publishing, Switzerland 2016
LabVIEW Measurements Manual, National Instruments, April 2003 Edition, Part Number 322661B-01, dostupné z www.ni.com

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-KSB-K Master's 1 year of study, summer semester, compulsory

Type of course unit

 

Guided consultation in combined form of studies

9 hod., compulsory

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

9 hod., compulsory

Teacher / Lecturer

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

Guided consultation

34 hod., optionally

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