Bachelor's Degree in Electrical Engineering and Information Technology

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Signals Theory

COURSE LANGUAGE: English

YEAR OF THE DEGREE PROGRAMME (I, II, III): II

SEMESTER (I, II, ANNUAL): II

CFU: 9

REQUIRED PRELIMINARY COURSES (IF MENTIONED IN THE COURSE STRUCTURE “REGOLAMENTO”) 
Calculus I.

PREREQUISITES (IF APPLICABLE) 
Elements of differential and integral calculus for functions of several variables. 

LEARNING GOALS 
The aim of the course is to provide the basic tools for the analysis of deterministic signals and for their processing using linear systems, both in the time and frequency domain. A further goal is to introduce the basic concepts of probability theory, estimation and decision theory. 

EXPECTED LEARNING OUTCOMES (DUBLIN DESCRIPTORS) 

Knowledge and understanding  
The student must demonstrate the ability to classify and describe the signals of interest for engineering, both in the time and frequency domain. The student must also demonstrate the ability to analyze and design simple signal processing schemes, especially those based on linear systems. Moreover, the student must demonstrate to understand the random nature of many phenomena of interest for engineering and the ability to use the tools of probability, estimation and decision theory to model and solve simple problems of a random nature. 

Applying knowledge and understanding  
The student must demonstrate the ability to recognize problems that involve the analysis and processing of signals, and choose models appropriate to their description and solution. The student must also demonstrate the ability to select the parameters of simple signal processing schemes, especially those based on linear systems. The student must also demonstrate the ability to model and solve simple problems of a random nature with the tools of probability, estimation and decision theory. 

COURSE CONTENT/SYLLABUS 
Continuous-time and discrete-time deterministic signals: power and energy of signals, Fourier series and Fourier transform, band of a signal. Classification of systems in terms of causality, stability, linearity, time-invariance. Linear time-invariant systems: filtering in the time and frequency domain, band of a system, linear and non-linear distortion. Analog-to-digital and digital-to-analog conversion. Elements of probability theory. Random variables: complete and synthetic characterization of a variable, of a couple of variables, of a vector of random variables. Random variables of common use. Parameter estimation. Hypothesis testing. 

READINGS/BIBLIOGRAPHY
(Slides will be made available through the course TEAMS channel.) 

TEACHING METHODS 
Teaching is 100% based on lectures, which include both theory and exercises. 

EXAMINATION/EVALUATION CRITERIA 

Exam type:

  • Written and oral.

In case of a written exam, questions refer to:

  • Numerical exercises.