Intelligent Systems in Automation

Higher education teachers: Dobrišek Simon
Credits: 6
Semester: winter
Subject code: 64669

Subject description


  • Elementary knowledge of high school mathematics and computer programming.

Content (Syllabus outline):

  • Introduction to pattern recognition and artificial intelligence: basic concepts and terminology.
  • Processing and recognition of visual patterns: image acquisition, image segmentation, shape and texture features, automatic learning, and object recognition.
  • Automatic visual detection and recognition of persons in surveilled areas. Methods of visual detection and recognition of faces and gaits in images.
  • Processing and recognition of auditory patterns: speech signal acquisition and segmentation, speech features (energy, cepstral coefficients and dynamic features), and automatic learning for isolated commands recognition.
  • Speech synthesis: acoustical modelling of speech, main methods of speech synthesis, learning speech synthesis from speech recordings.
  • Speech-based man-machine communication: system components for speech-based man-machine communication, speech recognition system, speech synthesis system, dialog system.

Objectives and competences:

The aim of this course is to acquaint the student with the basic concepts and components of artificial intelligent systems in automation: computer vision, speech recognition and synthesis, and modern modes of man-machine communication.

Intended learning outcomes:

  • After completing this course the student will be able to demonstrate knowledge and understanding of: building intelligent systems based on the use of the methods of visual and auditory patterns recognition; general usage of computer methods of image and speech processing in automation; and building intelligent user interfaces supporting natural man-machine communication.
  • During the course the student will gain and improve transferable skills such as: searching for and using professional information sources in the field of artificial intelligence, computer vision, and speech technologies; use of information technology: the use of open source development tools (OpenCV,WEKA), programming environments (GCC, Netbeans), programming languages (C++,Java); problem solving: problem analysis, algorithm design, implementation and testing of a program; and group work: the organisation and management of groups, active participation in groups.

Learning and teaching methods:

  • Lectures,
  • seminar exercises,
  • laboratory exercises,
  • coursework.

Study materials


  1. R. C. Gonzalez, R. E. Woods, S. L. Eddins: Digital Image Processing Using MATLAB , 2. izdaja, Gatesmark Publishing, 2009.
  2. J. C. Russ: The Image Processing Handbook, 6. izdaja, CRC, 2011.
  3. R. Pieraccini: The Voice in the Machine: Building Computers That Understand Speech, MIT Press , 2012.
  4. P. Taylor: Text-to-Speech Synthesis, Cambridge University Press, 2009.

Study in which the course is carried out

  • 3 year - 1st cycle - Applied Electrical Engineering - Control Engineering