Università degli Studi di Napoli "Parthenope"

Teaching schedule

Academic year: 
2017/2018
Belonging course: 
Course of Bachelor's Degree Programme on NAUTICAL, AERONAUTICAL AND METEO-OCEANOGRAPHIC SCIENCES
Disciplinary sector: 
TELECOMMUNICATIONS (ING-INF/03)
Language: 
Italian
Credits: 
9
Year of study: 
2
Teachers: 
Cycle: 
First Semester
Hours of front activity: 
72

Language

Lectures in Italian

Course description

Knowledge and understanding:
Thanks to the strict methodology of the scientific subjects
the student matures skills and understanding skills
necessary for subsequent studies

Ability to apply the knowledge and
comprehension ability:
The didactic setting contemplates that theoretical training is
by exercises that solicit the active participation, proactive attitude and the ability to autonomous processing.

Autonomy of judgment:
The proposed topics allow the development of the ability to understand and adapt learned approaches to cases other than those proposed in the classroom.

Communicative Skills:
The course setting is such that the student will develop proper language properties and will use it to use unambiguous terminology, proper to signal theory

Ability to learn independently
The proposed exercises are designed to develop the ability to identify important aspects before dealing with the exercise itself

Prerequisites

Knowledge of basic tools of mathematical analysis

Syllabus

Definition of Signal, Signal Classification, Synthetic Characterization of Signals, Time Duration, Periodic Signals, Examples of Continuous (CT) Time Signals, Area and Time Media of CT Signals, Energy and Power of a CT Signal, Basic Operations on CT Signals, Definition of Discrete Time (DT) Signals, Examples of DT Signals, Operation between DT Signals, Media, Power and Energy of DT Signals, Autocorrelation Function of CT and DT, Properties of the Autocorrelation Function, Recalls on Phasors, Fourier Series, Synthesis Formula and Analysis Formula, Spectrum and Phase Spectrum, Parseval Relationship, Examples, Fourier Transform, Fourier Transform Properties, Examples, Convolution, System Definition, LTI Systems, Probability Theory, Law of Probability, Axioms and Corollaries of the Law of Probability, Conditional Probability, Total Probability Law, Statistical Independence, Bayes Theorem, Random Variables, Cumulative Distribution Function(CDF), Property of the CDF, Classification of a random variable, Probability Density Function (pdf), Examples of pdf of known random variables, Mass Density Function (pmf), Full and Synthetic Statistical Description, Median and Percentiles, Expectation, Mean Square Value, Variance, Repeated Experiments, Transformation of random variables, The Expectation Theorem

Signal Analysis in Time Domain (24h)
Definition of Signal, Signal Classification, Synthetic Characterization of Signals, Time Duration, Periodic Signals, Examples of Continuous (CT) Time Signals, Area and Time Media of CT Signals, Energy and Power of a CT Signal, Basic Operations on CT Signals, Definition of Discrete Time (DT) Signals, Examples of DT Signals, Operation between DT Signals, Media, Power and Energy of DT Signals, Autocorrelation Function of CT and DT, Properties of the Autocorrelation Function

Signal Analysis in Frequency Domain (12h)
Recalls on Phasors, Fourier Series, Synthesis Formula and Analysis Formula, Spectrum and Phase Spectrum, Parseval Relationship, Examples, Fourier Transform, Fourier Transform Properties, Examples
Systems (6h)
Convolution, System Definition, LTI Systems

Probability Theory (8h)
Law of Probability, Axioms and Corollaries of the Law of Probability, Conditional Probability, Total Probability Law, Statistical Independence, Bayes Theorem,

Random Variables (22h)
Definition of Random Variables, Cumulative Distribution Function(CDF), Property of the CDF, Classification of a random variable, Probability Density Function (pdf), Examples of pdf of known random variables, Mass Density Function (pmf), Full and Synthetic Statistical Description, Median and Percentiles, Expectation, Mean Square Value, Variance, Repeated Experiments, Transformation of random variables, The Expectation Theorem

Teaching Methods

Lectures (in Italian) and Exercises

Textbooks

- G. Gelli, “Probabilità e informazione”
- G. Gelli, F. Verde, “Segnali e Sistemi”
- S. M. Ross "Introduction to Probability and Statistics for Engineers and Scientists"
- Power point documents provided by the teacher
- Exercises provided by the teacher

Learning assessment

The aim of the final exam is to check the level of achievement of the goals previously indicated.
The exam is divided into 2 parts:
- a written part that consists in the resolution of some exercises. The proposed exercises are of the same type as those presented and solved in classroom.
- an oral part on all the thematic presented in the course; The aim of the test is to evaluate the ability in studying course topics and in understanding basic arguments.
The final mark is given by the weighted average (1/3 and 2/3) of the scores of the 2 parts.

More information

Office Hours:
Monday 14.30:16:30

Teaching Material is available at the following link:
http://e-scienzeetecnologie.uniparthenope.it/

Lectures are in Italian. The professor is fluent in English and is available to interact with students in English, also during the examination