Technical Applications of Artificial Intelligence Methods (FSI-RUI)

Academic year 2023/2024
Supervisor: prof. RNDr. Miloslav Druckmüller, CSc.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:

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.
Learning outcomes and competences:

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.
Prerequisites:
Basic knowledge of mathematical logic, set theory and mathematical analysis
Course contents:

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.
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:

Course-unit credit based on written test.
The exam has a written and oral part.
Controlled participation in lessons:

Attendance at seminars is controlled. An absence can be compensated via solving additional problems.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture

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

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
Literature - fundamental:
2.  Pratt, W. K.: Digital Image Processing (4th Edition), New York: Wiley 2007
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-MET-P full-time study --- no specialisation -- Cr,Ex 5 Compulsory 2 1 S
N-IMB-P full-time study IME Engineering Mechanics -- Cr,Ex 5 Compulsory-optional 2 1 S
N-IMB-P full-time study BIO Biomechanics -- Cr,Ex 5 Compulsory-optional 2 1 S