Course detail

Data Processing

FSI-GSZ-KAcad. year: 2020/2021

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.

Learning outcomes of the course unit

Obtaining general principles in data collection and processing. Overview of modern methods in data processing with a focus on Industry 4.0

Prerequisites

Theoretical knowledge of physics, fundamentals of electronics and algorithmization.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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

Planned learning activities and teaching methods

The course is taught in the form of lectures that have the character of explanation of basic principles and theory of the given discipline. Teaching is complemented by laboratory exercises.

Assesment methods and criteria linked to learning outcomes

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.

Language of instruction

Czech

Work placements

Not applicable.

Aims

The aim of the course is to organize the knowledge and methods used in data processing in the production process.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme N-KSB-K Master's, 1. year of study, summer semester, 4 credits, compulsory

  • Programme M2I-K Master's

    branch M-KSB , 2. year of study, summer semester, 4 credits, compulsory

Type of course unit

 

Guided consultation in combined form of studies

9 hours, 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 acquisition systems, basic properties
4. Data transfer, protocols, compression, encryption
5. Data recording, recording media, storage methods
6. Text editors, spreadsheets, graphics
7. Basics of data processing in Matlab / Simulink
8. Basics of data processing in LabVIEW
9. SQL language - query creation, relational database
10. SQL language - relational database
11. Systems for IoT and cloud systems
12. Advanced methods of data processing
13. Practical examples of the topics covered.

Guided consultation

34 hours, 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 acquisition systems, basic properties
4. Data transfer, protocols, compression, encryption
5. Data recording, recording media, storage methods
6. Text editors, spreadsheets, graphics
7. Basics of data processing in Matlab / Simulink
8. Basics of data processing in LabVIEW
9. SQL language - query creation, relational database
10. SQL language - relational database
11. Systems for IoT and cloud systems
12. Advanced methods of data processing
13. Practical examples of the topics covered.

Laboratory exercise

9 hours, 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

eLearning