Course detail

Technical Applications of Artificial Intelligence Methods

FSI-RUI Acad. year: 2023/2024 Summer semester

The course consists of two parts. The first part deals with many-valued logic, theory of fuzzy sets and their applications in artificial intelligence. The second part consists of image processing and pattern recognition for applications in technology and science.

Language of instruction

Czech

Number of ECTS credits

5

Entry knowledge

Basic knowledge of mathematical logic, set theory and mathematical analysis

Rules for evaluation and completion of the course

Course-unit credit based on written test.
The exam has a written and oral part.


Attendance at seminars is controlled. An absence can be compensated via solving additional problems.

Aims

The aim of the course is to provide students with information about usage of multi-valued logic in technical applications and with computer image analysis and pattern recognition.


Knowledge of multi-valued logic, fuzzy sets theory, linguistic models and expert systems used in technical applications. Knowledge of image processing, analysis and pattern recognition.

The study programmes with the given course

Programme N-MET-P: Mechatronics, Master's, compulsory

Programme N-IMB-P: Engineering Mechanics and Biomechanics, Master's
specialization BIO: Biomechanics, compulsory-optional

Programme N-IMB-P: Engineering Mechanics and Biomechanics, Master's
specialization IME: Engineering Mechanics, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Multi-valued logic, formulas, truth evaluation
2. T-norms, T-conorms, generalized implications
3. Fuzzy sets and operations with them
4. Linguistic variables, linguistic models, control systems
5. Expert systems based on multi-valued logic
6. Classical and digital photography
7. CCD a CMOS technology
8. Noise, classification, analysis, filtration
9. MTF a PSF, convolution, deconvolution
10. Fourier methods of image processing
11. Adaptive filters, image segmentation
12. Classification of objects and pattern recognition
13. Classification of objects and pattern recognition

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer

Syllabus

1. Multi-valued logic, formulas, truth evaluation
2. T-norms, T-conorms, generalized implications
3. Fuzzy sets and operations with them
4. Linguistic variables, linguistic models, control systems
5. Expert systems based on multi-valued logic
6. Classical and digital photography
7. CCD a CMOS technology
8. Noise, classification, analysis, filtration
9. MTF a PSF, convolution, deconvolution
10. Fourier methods of image processing
11. Adaptive filters, image segmentation
12. Classification of objects and pattern recognition
13. Classification of objects and pattern recognition