Signals

Introduction: Basic definitions, short history of the signal processing theory, position of the signal processing theory in electrotechnical and other sciences.

Signals classification: Signals with finite energy and finite average power, periodical a-periodical, deterministic and random signals.

Signals representations: Use of the signals representations, types of representations and representations quality measures, examples of basic functions sequences.

Frequency analysis: Fourier series and Fourier transform.

Random signals: Approaches to the random signal processing, stationary random process, correlation and covariance functions, sampling and time averages, ergodicity.

Signals correlation and convolution: Correlation and convolution definitions and properties for different types of signals.

Applications with correlation and convolution transformations: similarity measures, random signals spectrum evaluation, convolution and linear stationary systems, detection of periodic components in combination of signals.

Sampling and quantization: Purpose of the sampling and quantization, sampling theorem, representation of sampling and reconstruction, types of quantization, quantization error signal and his properties, examples for quantization.

Digital signal processing: Discrete Fourier Transform (DFT).

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