Signals

The course offers a comprehensive insight into the field of signal processing, starting with the definition of the concept of a signal, a brief historical overview of its development, and its role in electrical engineering and science in general. Students become familiar with different types of signals, including energy and power signals, periodic and non-periodic signals, and deterministic and random signals.

Special emphasis is placed on the usefulness of representing signals with other signals, methods of representation, and quality criteria of representation, along with examples of fundamental functions used for this purpose.

The course further addresses the frequency analysis of deterministic signals, including the Fourier series and Fourier integral, and introduces the basic principles of processing random signals, such as the correlation and covariance function, stationarity of processes, sample and time average, and ergodicity. Within the lectures on correlation and convolution, students learn about the definitions and properties of these concepts on different types of signals, their use for estimating signal similarity, the spectrum of stationary random signals, convolution in linear stationary systems, determining the transfer function, and detecting periodic components in noisy signals.

The course also thoroughly examines signal sampling and quantization, together with the purpose and methods of sampling, Shannon's sampling theorem, signal reconstruction, types of quantization, properties of quantization error, and examples of quantization.

The conclusion of the course is dedicated to digital signal processing, with a focus on the discrete Fourier transform (DFT), enabling students to gain a deep understanding and practical skills for working with modern signal systems.

The aim of this course is to familiarize students with different types of signals and to present different approaches to signal description and processing. Students develop an understanding of the concepts used for signal representation and get acquainted with signal analysis techniques based on correlation and convolution. The course provides the theoretical basis for many practical problems in the fields of imaging technologies, speech technologies, pattern recognition, artificial intelligence and machine learning.  

Lectures

Interactive teaching

Practical assignements