SIGNAL THEORY

Knowledge and understanding

The knowhow achieved through the course will allow students to go deeper, when needed, in different topics relevant to the course.

Examples include, probability theory and the analysis of random signals in both the time and frequency domain.

Applying knowledge and understanding

Applying the competences achieved through the course, students will be able to model practical problems exploiting the specific tools regarding the analysis of random variables and stochastic processes.

Objective of this course is to provide knowledge about: fundamentals of probability theory, random variables, periodic and aperiodic deterministic signals, random processes, filtering of deterministic signals and random signals.

At the end the student will be able to

- model random experiments and derive their characteristic parameters making use, if appropriate, of random variables

- analyze signals (both determined and random) in time and frequency domains

- derive characteristic features of signals (both deterministic and random) in output of linear time-invariant systems starting from the characterization of the signals and of the system itself

Lectures for theory and exercises.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Deterministic signals: fundamentals, properties, representation in the time and frequency domain, Fourier transforms, systems, linear and time invariant systems, signal filtering.

Probability theory: fundamentals, random experiments and probability laws, random variables, transformation of random variables, systems of random variables.

Random signals: fundamentals, properties, characterization in the time and frequency domains, random signal filtering, noise, Markov processes

M. Luise, G. Vitetta. Teoria dei segnali. McGraw Hill.