Course detail

Technical Applications of Artificial Intelligence Methods

FSI-RUIAcad. year: 2023/2024

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

Mode of study

Not applicable.

Guarantor

prof. RNDr. Miloslav Druckmüller, CSc.

Department

Institute of Mathematics (ÚM)

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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

 Pratt, W. K.: Digital Image Processing (4th Edition), New York: Wiley 2007 (EN)

Recommended reading

Not applicable.

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme N-IMB-P Master's

    specialization BIO , 1. year of study, summer semester, compulsory-optional
    specialization IME , 1. year of study, summer semester, compulsory-optional

  • Programme N-MET-P Master's, 1. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. RNDr. Miloslav Druckmüller, CSc.

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

prof. RNDr. Miloslav Druckmüller, CSc.

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

eLearning

eLearning: currently opened course