Intelligent Systems in Automation

The introductory part focuses on familiarizing with the fundamental concepts of artificial intelligence and automatic pattern recognition. Visual pattern recognition s focused on basic methods for their capture and analysis, including the segmentation of images into uniform areas, determining the characteristics of their shape and surface composition, as well as the methods for machine learning of visual  object recognition. 

Auditory pattern recognition is focused on methods for analyzing speech  signals, with an emphasis on short-term dynamic, speech feature extraction methods and on automatic machine learning and recognition of separately pronounced commands. 

In artificial speech, generation, basic methods for creating artificial speech, signals are discussed. In the area of human-machine speech communication: system, it focuses on the key components of such systems, relating to speech recognizers and synthesizers, as well as dialogue management systems, enabling effective interaction between humans and machines.

The objective of the course is to acquaint the student with the basic concepts and components of artificial intelligent systems in automation. In particular, the student is acquainted with the basics of machine vision, automatic speech recognition and speech synthesis, as well as with the basics of the modern methods of human-machine communication. The student is also acquainted with the basic concepts of intelligent systems and examples of the implementations of such systems. The acquired knowledge provides an insight into the possibilities and limitations in the use of image and speech recognition systems for the development of intelligent systems in automation.

The lectures provide a theoretical background of all the considered models and methods together with simple computational examples that illustrate the key characteristics of all the presented methods. A textbook and other study material, such as lecture notes with solved example problems and lecture slides, are available to the students. As part of the lectures, the students receive optional homework assignments including theoretical questions as well as computational exercises that enable the students to promptly verify the acquired knowledge. Practical work is carried out as part of the laboratory exercises, where the students mostly solve given programming problems.