Angelo COLUCCIA

Angelo COLUCCIA

Professore II Fascia (Associato)

Settore Scientifico Disciplinare ING-INF/03: TELECOMUNICAZIONI.

Dipartimento di Ingegneria dell'Innovazione

Edificio La Stecca - S.P. 6, Lecce - Monteroni - LECCE (LE)

Ufficio 2° Piano - Edificio La Stecca, Piano 2°

Telefono +39 0832 297206

Associate Professor (Professore Associato, II fascia)

Area di competenza:

My research interests are in the area of multi-channel and multi-agent signal processing, including cooperative sensing and estimation in wireless networks, detection, and localization. Relevant application fields are radar detection and signal processing, wireless communications (including 5G and beyond), and emerging network contexts (including intelligent cyber-physical systems, smart devices, and social networks).

Orario di ricevimento

By appointment; contact the instructor by email or at the end of class meetings. Available every day except for periods officially out of office.

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Curriculum Vitae

Angelo Coluccia received the PhD degree in Information Engineering in 2011, and is currently an Associate Professor of Telecommunications at the Department of Engineering, University of Salento (Lecce, Italy). He has been a research fellow at Forschungszentrum Telekommunikation Wien (Vienna, Austria), and has held a visiting position at the Department of Electronics, Optronics, and Signals of the Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-Supaero, Toulouse, France). His research interests are in the area of multi-channel, multi-sensor, and multi-agent statistical signal processing for detection, estimation, localization, and learning problems. Relevant application fields are radar, wireless networks (including 5G and beyond), and emerging network contexts (including intelligent cyber-physical systems, smart devices, and social networks). He is Senior Member of IEEE, Member of the Sensor Array and Multichannel Technical Committee for the IEEE Signal Processing Society, and Member of the Technical Area Committee in Signal Processing for Multisensor Systems of EURASIP (European Association for Signal Processing).

Didattica

A.A. 2023/2024

ELEMENTI DI STATISTICAL LEARNING

Corso di laurea INGEGNERIA INFORMATICA

Tipo corso di studio Laurea Magistrale

Lingua ITALIANO

Crediti 9.0

Ripartizione oraria Ore totali di attività frontale: 81.0

Anno accademico di erogazione 2023/2024

Per immatricolati nel 2022/2023

Anno di corso 2

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso Intelligenza artificiale

Sede Lecce

Introduzione all’elaborazione dei dati e Machine Learning

Corso di laurea DATA SCIENCE PER LE SCIENZE UMANE E SOCIALI

Tipo corso di studio Laurea Magistrale

Lingua ITALIANO

Crediti 10.0

Ripartizione oraria Ore totali di attività frontale: 60.0

Anno accademico di erogazione 2023/2024

Per immatricolati nel 2023/2024

Anno di corso 1

Struttura DIPARTIMENTO DI SCIENZE UMANE E SOCIALI

Percorso Human and Social Data

Introduzione all’elaborazione dei dati e Machine Learning

Corso di laurea DATA SCIENCE PER LE SCIENZE UMANE E SOCIALI

Tipo corso di studio Laurea Magistrale

Lingua ITALIANO

Crediti 9.0

Ripartizione oraria Ore totali di attività frontale: 54.0

Anno accademico di erogazione 2023/2024

Per immatricolati nel 2023/2024

Anno di corso 1

Struttura DIPARTIMENTO DI SCIENZE UMANE E SOCIALI

Percorso Data Analytics

WIRELESS SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2023/2024

For matriculated on 2022/2023

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2022/2023

ELEMENTS OF STATISTICAL LEARNING

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2022/2023

For matriculated on 2021/2022

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter ARTIFICIAL INTELLIGENCE

Location Lecce

WIRELESS SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2022/2023

For matriculated on 2021/2022

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2021/2022

FONDAMENTI DI COMUNICAZIONI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 9.0

Docente titolare Francesco BANDIERA

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Anno accademico di erogazione 2021/2022

Per immatricolati nel 2019/2020

Anno di corso 3

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

SEGNALI E SISTEMI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 9.0

Docente titolare Giuseppe RICCI

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Anno accademico di erogazione 2021/2022

Per immatricolati nel 2020/2021

Anno di corso 2

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2021/2022

For matriculated on 2020/2021

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2020/2021

FONDAMENTI DI COMUNICAZIONI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 9.0

Docente titolare Francesco BANDIERA

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Anno accademico di erogazione 2020/2021

Per immatricolati nel 2018/2019

Anno di corso 3

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

SEGNALI E SISTEMI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 9.0

Docente titolare Giuseppe RICCI

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Anno accademico di erogazione 2020/2021

Per immatricolati nel 2019/2020

Anno di corso 2

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2020/2021

For matriculated on 2019/2020

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2019/2020

FONDAMENTI DI COMUNICAZIONI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 9.0

Docente titolare Francesco BANDIERA

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Anno accademico di erogazione 2019/2020

Per immatricolati nel 2017/2018

Anno di corso 3

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

SEGNALI E SISTEMI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 8.0

Docente titolare Giuseppe RICCI

Ripartizione oraria Ore totali di attività frontale: 72.0

  Ore erogate dal docente ANGELO COLUCCIA: 18.0

Anno accademico di erogazione 2019/2020

Per immatricolati nel 2018/2019

Anno di corso 2

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2019/2020

For matriculated on 2018/2019

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2018/2019

TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2018/2019

For matriculated on 2017/2018

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

Torna all'elenco
ELEMENTI DI STATISTICAL LEARNING

Corso di laurea INGEGNERIA INFORMATICA

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore totali di attività frontale: 81.0

Per immatricolati nel 2022/2023

Anno accademico di erogazione 2023/2024

Anno di corso 2

Semestre Primo Semestre (dal 18/09/2023 al 22/12/2023)

Lingua ITALIANO

Percorso Intelligenza artificiale (A202)

Sede Lecce

Basics of Probability and Statistics, Mathematics

The course provides a broad coverage of the essential elements of statistical learning as well as concepts, methodologies and tools that find application in machine learning, data science, and related data-driven fields. 

Knowledge and understanding. Students must have a solid background of statistical techniques, including probability and stochastic processes, that can be applied to solve problems in engineering with a data-driven approach. They should be able to:

  • Describe the characteristics of advanced statistical learning techniques and discuss the principles of data science and machine learning design;
  • Understand the different types of techniques that can be exploited to solve regression, classification, and other learning problems;
  • Describe how traditional machine learning algorithms and (deep) neural networks can be suited to different types of problems.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, classification, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of machine learning, and how advanced statistical techniques can be adopted to efficiently cope with them;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs;
  • Discuss the evolution of the data-driven paradigm, the related ongoing trends and risks.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches, while having a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics of a variety of statistical and machine learning concepts and techniques; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs; ability to communicate in a rigorous way backed by statistical reasoning and data science knowledge.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects of statistical (machine) learning and, in general, cultural issues linked to data science especially in the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are asked to refer to and compare different sources and textbooks, possibly by also autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large).

Teaching Methods. The course aims at enabling students to understand statistical learning theory and data-driven methods, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every concept or technique will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The course consists of frontal lessons with slides and blackboard, together with class exercises. There will be theoretical lessons, qualitative discussions, and examples about how knowledge is put into practice in real cases. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Introduction to Machine Learning, recapitulation of Probability and Stochastic Processes

 

Learning theory for parametric models 

(linear regression,statistical decision theory and classification, bias, MSE, trade-off, model complexity, Maximum Likelihood, Bayesian inference, curse of dimensionality, cross-validation, MSE linear estimation and applications, stochastic gradient descent, least-squares approach)

 

Overview of Supervised Learning

(Least Squares and Nearest Neighbors,local methods in high dimensions, statistical models, supervised learning and function approximation, model selection and the bias-variance trade-off)

 

Classification methods

(Bayesian classification, the Nearest Neighbor rule, logistic regression, Fisher’s linear discriminant, classification trees and bagging, the boosting approach, random forests)

 

Sparse signal representation and learning

(LASSO, compressed sensing, embeddings, ensemble learning)

 

Learning in Reproducing Kernel Hilbert Spaces

(Kernel smoothers and regression, representer theorem, kernel ridge regression, support vector machines)

 

Unsupervised learning

(clustering, principal components and dimensionality reduction)

 

Bayesian learning

(regression: a Bayesian perspective, Occam’s razor rule, the exponential family and the Maximum Entropy principle, latent variables and the EM algorithm, Gaussian mixture models and clustering, Gaussian processes)

 

Monte Carlo methods

(random number generation and sampling, Monte Carlo methods and the EM algorithm, Markov chain Monte Carlo methods and the Metropolis method, Gibbs sampling, hidden Markov models, particle filtering and Kalman filtering)

 

Neural Networks and Deep Learning

(perceptron, the backpropagation algorithm, universal approximation, neural network architectures, deep autoencoder)

 

Ongoing trends and risks in data-driven approaches

 

 

Textbooks (other specific references are provided during the course)

 

S. Theodoridis, "Machine Learning: A Bayesian and Optimization Perspective", 2nd edition, Academic Press, 2020

T. Hastie, R. Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", 2nd edition, Springer, 2009

O. Simeone, "A Brief Introduction to Machine Learning for Engineers", Foundations and Trends in Signal processing, Now publishing, 2021

ELEMENTI DI STATISTICAL LEARNING (ING-INF/03)
Introduzione all’elaborazione dei dati e Machine Learning

Corso di laurea DATA SCIENCE PER LE SCIENZE UMANE E SOCIALI

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea Magistrale

Crediti 10.0

Ripartizione oraria Ore totali di attività frontale: 60.0

Per immatricolati nel 2023/2024

Anno accademico di erogazione 2023/2024

Anno di corso 1

Semestre Primo Semestre (dal 18/09/2023 al 12/01/2024)

Lingua ITALIANO

Percorso Human and Social Data (A235)

Il corso è un corso di base per studenti di area umanistica/sociale, non si assumono prerequisiti tecnici specifici; sono utili conoscenze pregresse di informatica e statistica.

  • Introduzione all’elaborazione dei dati e suoi strumenti
  • Elementi base di programmazione, con particolare riferimento alle finalità di elaborazione/processamento digitale dei dati
  • Il linguaggio Python
  • Introduzione all’intelligenza artificiale e machine learning
  • Strumenti matematici e statistici essenziali per il machine learning
  • Utilizzo di Python per elaborazione dati e machine learning
  • Introduzione alle problematiche sociali ed etiche delle tecnologie ICT e del machine learning

Conoscenze e comprensione.

Al termine del corso gli studenti avranno acquisito le seguenti conoscenze: conoscenza degli elementi della programmazione strutturata e ad oggetti; conoscenza degli aspetti fondamentali dell’acquisizione e elaborazione digitale di dati; conoscenza dei principali modelli di learning, con particolare attenzione a quelli più usati nelle applicazioni di data science; conoscenza dei principali strumenti e approcci di statistical (machine) learning utilizzati in ambito data science.

 

Capacità di applicare conoscenze e comprensione.

Al termine del corso gli studenti avranno acquisito gli strumenti essenziali per la scrittura di piccoli programmi in Python che eseguano semplici elaborazioni dati e richiamino alcune funzioni principali di interesse per il machine learning. Sapranno definire ed interpretare gli strumenti idonei all’elaborazione di diverse tipologie di dati, in funzione dell’obiettivo applicativo che si intende raggiungere, e individuare quali problemi si prestano ad essere affrontati con approcci di machine learning.

 

Autonomia di giudizio.

Gli studenti saranno guidati all’apprendimento critico di quanto insegnato in classe, confrontando diversi approcci possibili per uno stesso problema e valutandone pro e contro.

 

Abilità comunicative.

È essenziale che gli studenti siano in grado di comunicare con interlocutori di vario tipo in modo chiaro, logico ed efficace, utilizzando gli strumenti appresi, le conoscenze e il gergo specialistico. Il corso promuove lo sviluppo di skills quali l’abilità di identificare ed esporre in termini precisi le caratteristiche dei diversi strumenti di elaborazione dei dati e machine learning, nonché l’abilità di descrivere ed analizzare le opzioni disponibili, comunicano in modo rigoroso supportato da argomentazioni tecniche e scientifiche.

 

Capacità di apprendimento.

Gli studenti dovranno acquisire l’abilità critica di discutere, con originalità ed autonomia, gli aspetti più importanti legati all’elaborazione dei dati ed al machine learning, inclusi aspetti culturali legati alla data science e tecnologie ICT. Essi dovranno essere in grado di sviluppare e applicare le conoscenze acquisite nel prosieguo dei propri studi e in generale nella più ampia prospettiva della propria vita professionale e di lifelong learning.

Lezione frontale con ausilio di materiali digitali, utilizzo di strumenti software.

Esame scritto seguito da eventuale discussione orale.

Introduzione all’elaborazione dei dati

  • Glossario minimo di concetti e tecnologie ICT
  • Evoluzione tecnologica, hardware e software
  • Il paradigma digitale, dall’acquisizione dei segnali alla digitalizzazione
  • Rappresentazione delle informazioni, codifica e compressione
  • Elaborazione e trasmissione dei dati
  • Uno sguardo all’ecosistema dell’ingegneria dell’informazione (ICT) e alla sua evoluzione: elettronica, automatica/robotica, telecomunicazioni, informatica, cibernetica, algoritmi di machine learning e intelligenza artificiale

 

Elementi base di programmazione

  • Perché la programmazione
  • Tipologie di approcci alla programmazione e linguaggi
  • Elaborazione/processamento digitale dei dati
  • Introduzione al linguaggio Python
  • Variabili e tipi di dati
  • Operatori aritmetici e logici
  • Costrutti fondamentali
  • Funzioni
  • Esempi di algoritmi fondamentali
  • Debugging

 

Introduzione all’intelligenza artificiale e machine learning

  • Evoluzione dell’intelligenza artificiale
  • La scienza dei dati
  • Tipologie di apprendimento automatico
  • Il ruolo dei modelli e dell’elaborazione statistica
  • Strumenti matematici e statistici essenziali per il machine learning
  • Modelli e algoritmi fondamentali per l’apprendimento statistico
  • Utilizzo di Python per elaborazione dati e machine learning
  • Problematiche sociali ed etiche delle tecnologie ICT e del machine learning

· Materiale didattico del corso (slides), fornito dal docente

· Allen Downey, “Pensare in Python”, Seconda Edizione, Green Tea Press Needham, MA, disponibile gratuitamente al link https://github.com/AllenDowney/ThinkPythonItalian/blob/master/thinkpython_italian.pdf

· Tutorial ufficiale di Python, https://pytutorial-it.readthedocs.io/it/python3.10/

· Chris Mattmann, “Machine Learning with TensorFlow”,Second Edition, Manning Publication, Shelter Island, 2020

Introduzione all’elaborazione dei dati e Machine Learning (ING-INF/03)
Introduzione all’elaborazione dei dati e Machine Learning

Corso di laurea DATA SCIENCE PER LE SCIENZE UMANE E SOCIALI

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore totali di attività frontale: 54.0

Per immatricolati nel 2023/2024

Anno accademico di erogazione 2023/2024

Anno di corso 1

Semestre Primo Semestre (dal 18/09/2023 al 12/01/2024)

Lingua ITALIANO

Percorso Data Analytics (A236)

Il corso è un corso di base per studenti di area umanistica/sociale, non si assumono prerequisiti tecnici specifici; sono utili conoscenze pregresse di informatica e statistica.

  • Introduzione all’elaborazione dei dati e suoi strumenti
  • Elementi base di programmazione, con particolare riferimento alle finalità di elaborazione/processamento digitale dei dati
  • Il linguaggio Python
  • Introduzione all’intelligenza artificiale e machine learning
  • Strumenti matematici e statistici essenziali per il machine learning
  • Utilizzo di Python per elaborazione dati e machine learning
  • Introduzione alle problematiche sociali ed etiche delle tecnologie ICT e del machine learning

Conoscenze e comprensione.

Al termine del corso gli studenti avranno acquisito le seguenti conoscenze: conoscenza degli elementi della programmazione strutturata e ad oggetti; conoscenza degli aspetti fondamentali dell’acquisizione e elaborazione digitale di dati; conoscenza dei principali modelli di learning, con particolare attenzione a quelli più usati nelle applicazioni di data science; conoscenza dei principali strumenti e approcci di statistical (machine) learning utilizzati in ambito data science.

 

Capacità di applicare conoscenze e comprensione.

Al termine del corso gli studenti avranno acquisito gli strumenti essenziali per la scrittura di piccoli programmi in Python che eseguano semplici elaborazioni dati e richiamino alcune funzioni principali di interesse per il machine learning. Sapranno definire ed interpretare gli strumenti idonei all’elaborazione di diverse tipologie di dati, in funzione dell’obiettivo applicativo che si intende raggiungere, e individuare quali problemi si prestano ad essere affrontati con approcci di machine learning.

 

Autonomia di giudizio.

Gli studenti saranno guidati all’apprendimento critico di quanto insegnato in classe, confrontando diversi approcci possibili per uno stesso problema e valutandone pro e contro.

 

Abilità comunicative.

È essenziale che gli studenti siano in grado di comunicare con interlocutori di vario tipo in modo chiaro, logico ed efficace, utilizzando gli strumenti appresi, le conoscenze e il gergo specialistico. Il corso promuove lo sviluppo di skills quali l’abilità di identificare ed esporre in termini precisi le caratteristiche dei diversi strumenti di elaborazione dei dati e machine learning, nonché l’abilità di descrivere ed analizzare le opzioni disponibili, comunicano in modo rigoroso supportato da argomentazioni tecniche e scientifiche.

 

Capacità di apprendimento.

Gli studenti dovranno acquisire l’abilità critica di discutere, con originalità ed autonomia, gli aspetti più importanti legati all’elaborazione dei dati ed al machine learning, inclusi aspetti culturali legati alla data science e tecnologie ICT. Essi dovranno essere in grado di sviluppare e applicare le conoscenze acquisite nel prosieguo dei propri studi e in generale nella più ampia prospettiva della propria vita professionale e di lifelong learning.

Lezione frontale con ausilio di materiali digitali, utilizzo di strumenti software.

Esame scritto seguito da eventuale discussione orale.

Introduzione all’elaborazione dei dati

  • Glossario minimo di concetti e tecnologie ICT
  • Evoluzione tecnologica, hardware e software
  • Il paradigma digitale, dall’acquisizione dei segnali alla digitalizzazione
  • Rappresentazione delle informazioni, codifica e compressione
  • Elaborazione e trasmissione dei dati
  • Uno sguardo all’ecosistema dell’ingegneria dell’informazione (ICT) e alla sua evoluzione: elettronica, automatica/robotica, telecomunicazioni, informatica, cibernetica, algoritmi di machine learning e intelligenza artificiale

 

Elementi base di programmazione

  • Perché la programmazione
  • Tipologie di approcci alla programmazione e linguaggi
  • Elaborazione/processamento digitale dei dati
  • Introduzione al linguaggio Python
  • Variabili e tipi di dati
  • Operatori aritmetici e logici
  • Costrutti fondamentali
  • Funzioni
  • Esempi di algoritmi fondamentali
  • Debugging

 

Introduzione all’intelligenza artificiale e machine learning

  • Evoluzione dell’intelligenza artificiale
  • La scienza dei dati
  • Tipologie di apprendimento automatico
  • Il ruolo dei modelli e dell’elaborazione statistica
  • Strumenti matematici e statistici essenziali per il machine learning
  • Modelli e algoritmi fondamentali per l’apprendimento statistico
  • Utilizzo di Python per elaborazione dati e machine learning
  • Problematiche sociali ed etiche delle tecnologie ICT e del machine learning

· Materiale didattico del corso (slides), fornito dal docente

· Allen Downey, “Pensare in Python”, Seconda Edizione, Green Tea Press Needham, MA, disponibile gratuitamente al link https://github.com/AllenDowney/ThinkPythonItalian/blob/master/thinkpython_italian.pdf

· Tutorial ufficiale di Python, https://pytutorial-it.readthedocs.io/it/python3.10/

· Chris Mattmann, “Machine Learning with TensorFlow”,Second Edition, Manning Publication, Shelter Island, 2020

Introduzione all’elaborazione dei dati e Machine Learning (ING-INF/03)
WIRELESS SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2022/2023

Year taught 2023/2024

Course year 2

Semestre Secondo Semestre (dal 04/03/2024 al 14/06/2024)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Communications, Networks, Statistical Signal Processing.

The course provides an overview of modern communication principles and techniques, and how they are composed into "systems". The focus is on multiuser wireless systems, in particular mobile cellular networks from 2G (GSM) to 4G (LTE) and 5G, satellite and localization systems.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of digital communications and systems:

  • Describe the characteristics of advanced digital communication techniques and discuss the principles of modern system design;
  • Understand the different types of diversity that can be exploited to improve the performance of a communication system;
  • Illustrate data-aided and non-data-aided synchronization techniques for timing recovery in baseband and passband;
  • Describe how surveillance and (geo)localization can be performed via radio signals, and illustrate satellite-based navigation system.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, detection, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of (mobile) multiuser systems, and how advanced digital communication techniques can be adopted to efficiently cope with them;
  • Discuss the evolution of cellular networks from a system perspective, state-of-the-art technologies and security, and the ongoing trends;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs.
  • Recognize and understand the tendencies and innovations in the ICT field, with awareness of related privacy, security, and ethical issues.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches to address modern telecommunication needs, and to have a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics or a variety of telecommunication systems, identifying their salient features without getting lost into protocol/standard details; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects in the design of telecommunication systems and, in general, cultural issues linked to related areas within the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are explicitly asked to refer to and compare different sources and textbooks, also by autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large), summarizing them for an effective study.

Teaching Methods. The course aims at enabling students to understand and be able to solve design issues in telecommunications systems, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every system will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The discussion will be organized into four parts: 1. Description of the main characteristics of the system. 2. Comparison with previous technology addressing the same communication needs, and analysis of the additional requirements. 3. Derivation of selected algorithms and optmization/detection/estimation techniques relevant to the addressed system. 4. Analysis of the implications in terms of user experience, applications to contemporary/future contexts, and security. The course consists of frontal lessons with slides and blackboard, together with class exercises and labs using MATLAB and software-defined radio equipment. There will be theoretical lessons, qualitative discussion on system aspects, and examples about how knowledge is put into practice in real systems. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of five open questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Advanced digital communication techniques and modern systems (hours: 24 + 2 lab/seminar)

Recapitulation of fundamental principles of digital communications; diversity, combining techniques and MIMO systems; multiuser systems: multiplexing, multiple access, optimality and fairness in resource allocation, link adaptation functions (power control, Adaptive Modulation and Coding, tradeoffs), error recovery (ARQ, FEC and Hybrid-ARQ); overview on spread-spectrum and multi-carrier systems (CDMA, OFDM), multiuser detection.

 

Telecommunication networks and mobile cellular systems (hours: 27 + 8 lab/seminar)

Historical development of data and voice networks, PSTN; general principles of cellular networks. The GSM system: architecture, burst structure, overview on signaling and mobility procedure. Evolution towards GPRS/EDGE. 3G: UMTS overview and evolution towards HSPA. 4G technologies and next generation systems: LTE, main ideas towards 5G (cooperation, smart antennas, cognitive radio). The 5G ecosystem and its main innovations (mmWave, massive MIMO, fronthaul-backhaul, virtualisation). Introduction to Network Security and intrusion detection (scanning, attacks, DDoS).

 

Satellite systems (hours: 4 + 2 lab/seminar)

Overview on satellite and deep space communications systems. High-throughput (broadband) satellite communications.

 

Localization and positioning systems(hours: 12 + 4 lab/seminar)

Introduction to surveillance through radio signals. Recapitulation of synchronisation techniques and relationship with ranging and position estimation. Overview on radar systems. (Geo)localization and satellite-based positioning systems. GPS: principles, signal structure, augmentation, modernization. Current trends and topics in localization.

Textbooks (other specific references are provided during the course)

 

A. Goldsmith: "Wireless Communications", Cambridge University Press, 2005

J.G. Proakis: "Digital Communications" (4th ed.), McGraw Hill, 2000

T.S. Rappaport: "Wireless Communications: principles and practice" (2nd ed.), Prentice Hall, 2002

S. Sesia, I. Toufik, M. Baker: "LTE: The UMTS Long Term Evolution - from theory to practice", Wiley, 2009

U. Mengali, A.N. D'Andrea: "Synchronization techniques for digital receivers", Springer, 2007

J. Bao-Yen Tsui: "Fundamentals of Global Positioning System Receivers: A Software Approach", Wiley, 2000

WIRELESS SYSTEMS (ING-INF/03)
ELEMENTS OF STATISTICAL LEARNING

Degree course COMPUTER ENGINEERING

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2021/2022

Year taught 2022/2023

Course year 2

Semestre Primo Semestre (dal 19/09/2022 al 16/12/2022)

Language INGLESE

Subject matter ARTIFICIAL INTELLIGENCE (A182)

Location Lecce

Basics of Probability and Statistics, Mathematics

The course provides a broad coverage of the essential elements of statistical learning as well as concepts, methodologies and tools that find application in machine learning, data science, and related data-driven fields. 

Knowledge and understanding. Students must have a solid background of statistical techniques, including probability and stochastic processes, that can be applied to solve problems in engineering with a data-driven approach. They should be able to:

  • Describe the characteristics of advanced statistical learning techniques and discuss the principles of data science and machine learning design;
  • Understand the different types of techniques that can be exploited to solve regression, classification, and other learning problems;
  • Describe how traditional machine learning algorithms and (deep) neural networks can be suited to different types of problems.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, classification, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of machine learning, and how advanced statistical techniques can be adopted to efficiently cope with them;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs;
  • Discuss the evolution of the data-driven paradigm, the related ongoing trends and risks.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches, while having a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics of a variety of statistical and machine learning concepts and techniques; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs; ability to communicate in a rigorous way backed by statistical reasoning and data science knowledge.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects of statistical (machine) learning and, in general, cultural issues linked to data science especially in the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are asked to refer to and compare different sources and textbooks, possibly by also autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large).

Teaching Methods. The course aims at enabling students to understand statistical learning theory and data-driven methods, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every concept or technique will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The course consists of frontal lessons with slides and blackboard, together with class exercises. There will be theoretical lessons, qualitative discussions, and examples about how knowledge is put into practice in real cases. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Introduction to Machine Learning, recapitulation of Probability and Stochastic Processes

 

Learning theory for parametric models 

(linear regression,statistical decision theory and classification, bias, MSE, trade-off, model complexity, Maximum Likelihood, Bayesian inference, curse of dimensionality, cross-validation, MSE linear estimation and applications, stochastic gradient descent, least-squares approach)

 

Overview of Supervised Learning

(Least Squares and Nearest Neighbors,local methods in high dimensions, statistical models, supervised learning and function approximation, model selection and the bias-variance trade-off)

 

Classification methods

(Bayesian classification, the Nearest Neighbor rule, logistic regression, Fisher’s linear discriminant, classification trees and bagging, the boosting approach, random forests)

 

Sparse signal representation and learning

(LASSO, compressed sensing, embeddings, ensemble learning)

 

Learning in Reproducing Kernel Hilbert Spaces

(Kernel smoothers and regression, representer theorem, kernel ridge regression, support vector machines)

 

Unsupervised learning

(clustering, principal components and dimensionality reduction)

 

Bayesian learning

(regression: a Bayesian perspective, Occam’s razor rule, the exponential family and the Maximum Entropy principle, latent variables and the EM algorithm, Gaussian mixture models and clustering, Gaussian processes)

 

Monte Carlo methods

(random number generation and sampling, Monte Carlo methods and the EM algorithm, Markov chain Monte Carlo methods and the Metropolis method, Gibbs sampling, hidden Markov models, particle filtering and Kalman filtering)

 

Neural Networks and Deep Learning

(perceptron, the backpropagation algorithm, universal approximation, neural network architectures, deep autoencoder)

 

Ongoing trends and risks in data-driven approaches

 

 

Textbooks (other specific references are provided during the course)

 

S. Theodoridis, "Machine Learning: A Bayesian and Optimization Perspective", 2nd edition, Academic Press, 2020

T. Hastie, R. Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", 2nd edition, Springer, 2009

O. Simeone, "A Brief Introduction to Machine Learning for Engineers", Foundations and Trends in Signal processing, Now publishing, 2021

ELEMENTS OF STATISTICAL LEARNING (ING-INF/03)
WIRELESS SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2021/2022

Year taught 2022/2023

Course year 2

Semestre Secondo Semestre (dal 01/03/2023 al 09/06/2023)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Communications, Networks, Statistical Signal Processing.

The course provides an overview of modern communication principles and techniques, and how they are composed into "systems". The focus is on multiuser wireless systems, in particular mobile cellular networks from 2G (GSM) to 4G (LTE) and 5G, satellite and localization systems.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of digital communications and systems:

  • Describe the characteristics of advanced digital communication techniques and discuss the principles of modern system design;
  • Understand the different types of diversity that can be exploited to improve the performance of a communication system;
  • Illustrate data-aided and non-data-aided synchronization techniques for timing recovery in baseband and passband;
  • Describe how surveillance and (geo)localization can be performed via radio signals, and illustrate satellite-based navigation system.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, detection, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of (mobile) multiuser systems, and how advanced digital communication techniques can be adopted to efficiently cope with them;
  • Discuss the evolution of cellular networks from a system perspective, state-of-the-art technologies and security, and the ongoing trends;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs.
  • Recognize and understand the tendencies and innovations in the ICT field, with awareness of related privacy, security, and ethical issues.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches to address modern telecommunication needs, and to have a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics or a variety of telecommunication systems, identifying their salient features without getting lost into protocol/standard details; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects in the design of telecommunication systems and, in general, cultural issues linked to related areas within the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are explicitly asked to refer to and compare different sources and textbooks, also by autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large), summarizing them for an effective study.

Teaching Methods. The course aims at enabling students to understand and be able to solve design issues in telecommunications systems, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every system will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The discussion will be organized into four parts: 1. Description of the main characteristics of the system. 2. Comparison with previous technology addressing the same communication needs, and analysis of the additional requirements. 3. Derivation of selected algorithms and optmization/detection/estimation techniques relevant to the addressed system. 4. Analysis of the implications in terms of user experience, applications to contemporary/future contexts, and security. The course consists of frontal lessons with slides and blackboard, together with class exercises and labs using MATLAB and software-defined radio equipment. There will be theoretical lessons, qualitative discussion on system aspects, and examples about how knowledge is put into practice in real systems. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of five open questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Advanced digital communication techniques and modern systems (hours: 24 + 2 lab/seminar)

Recapitulation of fundamental principles of digital communications; diversity, combining techniques and MIMO systems; multiuser systems: multiplexing, multiple access, optimality and fairness in resource allocation, link adaptation functions (power control, Adaptive Modulation and Coding, tradeoffs), error recovery (ARQ, FEC and Hybrid-ARQ); overview on spread-spectrum and multi-carrier systems (CDMA, OFDM), multiuser detection.

 

Telecommunication networks and mobile cellular systems (hours: 27 + 8 lab/seminar)

Historical development of data and voice networks, PSTN; general principles of cellular networks. The GSM system: architecture, burst structure, overview on signaling and mobility procedure. Evolution towards GPRS/EDGE. 3G: UMTS overview and evolution towards HSPA. 4G technologies and next generation systems: LTE, main ideas towards 5G (cooperation, smart antennas, cognitive radio). The 5G ecosystem and its main innovations (mmWave, massive MIMO, fronthaul-backhaul, virtualisation). Introduction to Network Security and intrusion detection (scanning, attacks, DDoS).

 

Satellite systems (hours: 4 + 2 lab/seminar)

Overview on satellite and deep space communications systems. High-throughput (broadband) satellite communications.

 

Localization and positioning systems(hours: 12 + 4 lab/seminar)

Introduction to surveillance through radio signals. Recapitulation of synchronisation techniques and relationship with ranging and position estimation. Overview on radar systems. (Geo)localization and satellite-based positioning systems. GPS: principles, signal structure, augmentation, modernization. Current trends and topics in localization.

Textbooks (other specific references are provided during the course)

 

A. Goldsmith: "Wireless Communications", Cambridge University Press, 2005

J.G. Proakis: "Digital Communications" (4th ed.), McGraw Hill, 2000

T.S. Rappaport: "Wireless Communications: principles and practice" (2nd ed.), Prentice Hall, 2002

S. Sesia, I. Toufik, M. Baker: "LTE: The UMTS Long Term Evolution - from theory to practice", Wiley, 2009

U. Mengali, A.N. D'Andrea: "Synchronization techniques for digital receivers", Springer, 2007

J. Bao-Yen Tsui: "Fundamentals of Global Positioning System Receivers: A Software Approach", Wiley, 2000

WIRELESS SYSTEMS (ING-INF/03)
FONDAMENTI DI COMUNICAZIONI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea

Crediti 9.0

Docente titolare Francesco BANDIERA

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Per immatricolati nel 2019/2020

Anno accademico di erogazione 2021/2022

Anno di corso 3

Semestre Primo Semestre (dal 20/09/2021 al 17/01/2022)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Analisi 1, Segnali e Sistemi, Calcolo delle Probabilità e Statistica

Teoria dell'Informazione, codifica di sorgente, codifica di canale, applicazioni. Legge di Shannon. Introduzione al software-defined radio con applicazioni.

si veda la parte generale

lezione frontale, esercitazioni

scritto con orale opzionale

come da calendario

Introduzione alla Teoria dell'Informazione

Codifica di sorgente

Canali di comunicazione e capacità di canale

Codifica di canale e teorema di Shannon

Codici a rivelazione e correzione di errori

Approfondimenti di Teoria dell'Informazione e applicazioni

Comunicazioni analogiche in MATLAB

Software-defined radio

Comunicazioni digitali in MATLAB

Benedetto-Biglieri-Castellani: "Teoria della trasmissione numerica"

Cover-Thomas: "Elements of Information Theory"

FONDAMENTI DI COMUNICAZIONI (ING-INF/03)
SEGNALI E SISTEMI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea

Crediti 9.0

Docente titolare Giuseppe RICCI

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Per immatricolati nel 2020/2021

Anno accademico di erogazione 2021/2022

Anno di corso 2

Semestre Secondo Semestre (dal 01/03/2022 al 10/06/2022)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Analisi 1 e Analisi 2

si veda la parte generale

si veda la parte generale

lezione frontale, esercitazioni, attività al calcolatore

scritto con orale opzionale

come da calendario

Esercizi numerici e approfondimenti sul programma del corso

Introduzione al linguaggio MATLAB

Principi di elaborazione numerica dei segnali e applicazioni

Ricci-Valcher: "Segnali e Sistemi"

SEGNALI E SISTEMI (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2020/2021

Year taught 2021/2022

Course year 2

Semestre Secondo Semestre (dal 01/03/2022 al 10/06/2022)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Communications, Networks, Statistical Signal Processing.

The course provides an overview of modern communication principles and techniques, and how they are composed into "systems". The focus is on multiuser wireless systems, in particular mobile cellular networks from 2G (GSM) to 4G (LTE) and 5G, satellite and localization systems.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of digital communications and systems:

  • Describe the characteristics of advanced digital communication techniques and discuss the principles of modern system design;
  • Understand the different types of diversity that can be exploited to improve the performance of a communication system;
  • Illustrate data-aided and non-data-aided synchronization techniques for timing recovery in baseband and passband;
  • Describe how surveillance and (geo)localization can be performed via radio signals, and illustrate satellite-based navigation system.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, detection, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of (mobile) multiuser systems, and how advanced digital communication techniques can be adopted to efficiently cope with them;
  • Discuss the evolution of cellular networks from a system perspective, state-of-the-art technologies and security, and the ongoing trends;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs.
  • Recognize and understand the tendencies and innovations in the ICT field, with awareness of related privacy, security, and ethical issues.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches to address modern telecommunication needs, and to have a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics or a variety of telecommunication systems, identifying their salient features without getting lost into protocol/standard details; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects in the design of telecommunication systems and, in general, cultural issues linked to related areas within the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are explicitly asked to refer to and compare different sources and textbooks, also by autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large), summarizing them for an effective study.

Teaching Methods. The course aims at enabling students to understand and be able to solve design issues in telecommunications systems, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every system will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The discussion will be organized into four parts: 1. Description of the main characteristics of the system. 2. Comparison with previous technology addressing the same communication needs, and analysis of the additional requirements. 3. Derivation of selected algorithms and optmization/detection/estimation techniques relevant to the addressed system. 4. Analysis of the implications in terms of user experience, applications to contemporary/future contexts, and security. The course consists of frontal lessons with slides and blackboard, together with class exercises and labs using MATLAB and software-defined radio equipment. There will be theoretical lessons, qualitative discussion on system aspects, and examples about how knowledge is put into practice in real systems. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of five open questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Advanced digital communication techniques and modern systems (hours: 24 + 2 lab/seminar)

Recapitulation of fundamental principles of digital communications; diversity, combining techniques and MIMO systems; multiuser systems: multiplexing, multiple access, optimality and fairness in resource allocation, link adaptation functions (power control, Adaptive Modulation and Coding, tradeoffs), error recovery (ARQ, FEC and Hybrid-ARQ); overview on spread-spectrum and multi-carrier systems (CDMA, OFDM), multiuser detection.

 

Telecommunication networks and mobile cellular systems (hours: 27 + 8 lab/seminar)

Historical development of data and voice networks, PSTN; general principles of cellular networks. The GSM system: architecture, burst structure, overview on signaling and mobility procedure. Evolution towards GPRS/EDGE. 3G: UMTS overview and evolution towards HSPA. 4G technologies and next generation systems: LTE, main ideas towards 5G (cooperation, smart antennas, cognitive radio). The 5G ecosystem and its main innovations (mmWave, massive MIMO, fronthaul-backhaul, virtualisation). Introduction to Network Security and intrusion detection (scanning, attacks, DDoS).

 

Satellite systems (hours: 4 + 2 lab/seminar)

Overview on satellite and deep space communications systems. High-throughput (broadband) satellite communications.

 

Localization and positioning systems(hours: 12 + 4 lab/seminar)

Introduction to surveillance through radio signals. Recapitulation of synchronisation techniques and relationship with ranging and position estimation. Overview on radar systems. (Geo)localization and satellite-based positioning systems. GPS: principles, signal structure, augmentation, modernization. Current trends and topics in localization.

Textbooks (other specific references are provided during the course)

 

A. Goldsmith: "Wireless Communications", Cambridge University Press, 2005

J.G. Proakis: "Digital Communications" (4th ed.), McGraw Hill, 2000

T.S. Rappaport: "Wireless Communications: principles and practice" (2nd ed.), Prentice Hall, 2002

S. Sesia, I. Toufik, M. Baker: "LTE: The UMTS Long Term Evolution - from theory to practice", Wiley, 2009

U. Mengali, A.N. D'Andrea: "Synchronization techniques for digital receivers", Springer, 2007

J. Bao-Yen Tsui: "Fundamentals of Global Positioning System Receivers: A Software Approach", Wiley, 2000

TELECOMMUNICATION SYSTEMS (ING-INF/03)
FONDAMENTI DI COMUNICAZIONI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea

Crediti 9.0

Docente titolare Francesco BANDIERA

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Per immatricolati nel 2018/2019

Anno accademico di erogazione 2020/2021

Anno di corso 3

Semestre Primo Semestre (dal 29/09/2020 al 18/12/2020)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Analisi 1, Segnali e Sistemi, Calcolo delle Probabilità e Statistica

Teoria dell'Informazione, codifica di sorgente, codifica di canale, applicazioni. Legge di Shannon. Introduzione al software-defined radio con applicazioni.

si veda la parte generale

lezione frontale, esercitazioni

scritto con orale opzionale

come da calendario

Introduzione alla Teoria dell'Informazione

Codifica di sorgente

Canali di comunicazione e capacità di canale

Codifica di canale e teorema di Shannon

Codici a rivelazione e correzione di errori

Approfondimenti di Teoria dell'Informazione e applicazioni

Comunicazioni analogiche in MATLAB

Software-defined radio

Comunicazioni digitali in MATLAB

Benedetto-Biglieri-Castellani: "Teoria della trasmissione numerica"

Cover-Thomas: "Elements of Information Theory"

FONDAMENTI DI COMUNICAZIONI (ING-INF/03)
SEGNALI E SISTEMI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea

Crediti 9.0

Docente titolare Giuseppe RICCI

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Per immatricolati nel 2019/2020

Anno accademico di erogazione 2020/2021

Anno di corso 2

Semestre Secondo Semestre (dal 01/03/2021 al 11/06/2021)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Analisi 1 e Analisi 2

si veda la parte generale

si veda la parte generale

lezione frontale, esercitazioni, attività al calcolatore

scritto con orale opzionale

come da calendario

Esercizi numerici e approfondimenti sul programma del corso

Introduzione al linguaggio MATLAB

Principi di elaborazione numerica dei segnali e applicazioni

Ricci-Valcher: "Segnali e Sistemi"

SEGNALI E SISTEMI (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2019/2020

Year taught 2020/2021

Course year 2

Semestre Secondo Semestre (dal 02/03/2021 al 05/06/2021)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Communications, Networks, Statistical Signal Processing.

The course provides an overview of modern communication principles and techniques, and how they are composed into "systems". The focus is on multiuser wireless systems, in particular mobile cellular networks from 2G (GSM) to 4G (LTE) and 5G, satellite and localization systems.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of digital communications and systems:

  • Describe the characteristics of advanced digital communication techniques and discuss the principles of modern system design;
  • Understand the different types of diversity that can be exploited to improve the performance of a communication system;
  • Illustrate data-aided and non-data-aided synchronization techniques for timing recovery in baseband and passband;
  • Describe how surveillance and (geo)localization can be performed via radio signals, and illustrate satellite-based navigation system.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, detection, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of (mobile) multiuser systems, and how advanced digital communication techniques can be adopted to efficiently cope with them;
  • Discuss the evolution of cellular networks from a system perspective, state-of-the-art technologies and security, and the ongoing trends;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs.
  • Recognize and understand the tendencies and innovations in the ICT field, with awareness of related privacy, security, and ethical issues.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches to address modern telecommunication needs, and to have a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics or a variety of telecommunication systems, identifying their salient features without getting lost into protocol/standard details; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects in the design of telecommunication systems and, in general, cultural issues linked to related areas within the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are explicitly asked to refer to and compare different sources and textbooks, also by autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large), summarizing them for an effective study.

Teaching Methods. The course aims at enabling students to understand and be able to solve design issues in telecommunications systems, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every system will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The discussion will be organized into four parts: 1. Description of the main characteristics of the system. 2. Comparison with previous technology addressing the same communication needs, and analysis of the additional requirements. 3. Derivation of selected algorithms and optmization/detection/estimation techniques relevant to the addressed system. 4. Analysis of the implications in terms of user experience, applications to contemporary/future contexts, and security. The course consists of frontal lessons with slides and blackboard, together with class exercises and labs using MATLAB and software-defined radio equipment. There will be theoretical lessons, qualitative discussion on system aspects, and examples about how knowledge is put into practice in real systems. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of five open questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Advanced digital communication techniques and modern systems (hours: 24 + 2 lab/seminar)

Recapitulation of fundamental principles of digital communications; diversity, combining techniques and MIMO systems; multiuser systems: multiplexing, multiple access, optimality and fairness in resource allocation, link adaptation functions (power control, Adaptive Modulation and Coding, tradeoffs), error recovery (ARQ, FEC and Hybrid-ARQ); overview on spread-spectrum and multi-carrier systems (CDMA, OFDM), multiuser detection.

 

Telecommunication networks and mobile cellular systems (hours: 27 + 8 lab/seminar)

Historical development of data and voice networks, PSTN; general principles of cellular networks. The GSM system: architecture, burst structure, overview on signaling and mobility procedure. Evolution towards GPRS/EDGE. 3G: UMTS overview and evolution towards HSPA. 4G technologies and next generation systems: LTE, main ideas towards 5G (cooperation, smart antennas, cognitive radio). The 5G ecosystem and its main innovations (mmWave, massive MIMO, fronthaul-backhaul, virtualisation). Introduction to Network Security and intrusion detection (scanning, attacks, DDoS).

 

Satellite systems (hours: 4 + 2 lab/seminar)

Overview on satellite and deep space communications systems. High-throughput (broadband) satellite communications.

 

Localization and positioning systems(hours: 12 + 4 lab/seminar)

Introduction to surveillance through radio signals. Recapitulation of synchronisation techniques and relationship with ranging and position estimation. Overview on radar systems. (Geo)localization and satellite-based positioning systems. GPS: principles, signal structure, augmentation, modernization. Current trends and topics in localization.

Textbooks (other specific references are provided during the course)

 

A. Goldsmith: "Wireless Communications", Cambridge University Press, 2005

J.G. Proakis: "Digital Communications" (4th ed.), McGraw Hill, 2000

T.S. Rappaport: "Wireless Communications: principles and practice" (2nd ed.), Prentice Hall, 2002

S. Sesia, I. Toufik, M. Baker: "LTE: The UMTS Long Term Evolution - from theory to practice", Wiley, 2009

U. Mengali, A.N. D'Andrea: "Synchronization techniques for digital receivers", Springer, 2007

J. Bao-Yen Tsui: "Fundamentals of Global Positioning System Receivers: A Software Approach", Wiley, 2000

TELECOMMUNICATION SYSTEMS (ING-INF/03)
FONDAMENTI DI COMUNICAZIONI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea

Crediti 9.0

Docente titolare Francesco BANDIERA

Ripartizione oraria Ore totali di attività frontale: 81.0

  Ore erogate dal docente ANGELO COLUCCIA: 27.0

Per immatricolati nel 2017/2018

Anno accademico di erogazione 2019/2020

Anno di corso 3

Semestre Primo Semestre (dal 23/09/2019 al 20/12/2019)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Analisi 1, Segnali e Sistemi, Calcolo delle Probabilità e Statistica

Teoria dell'Informazione, codifica di sorgente, codifica di canale, applicazioni. Legge di Shannon. Introduzione al software-defined radio con applicazioni.

si veda la parte generale

lezione frontale, esercitazioni

scritto con orale opzionale

come da calendario

Introduzione alla Teoria dell'Informazione

Codifica di sorgente

Canali di comunicazione e capacità di canale

Codifica di canale e teorema di Shannon

Codici a rivelazione e correzione di errori

Approfondimenti di Teoria dell'Informazione e applicazioni

Comunicazioni analogiche in MATLAB

Software-defined radio

Comunicazioni digitali in MATLAB

Benedetto-Biglieri-Castellani: "Teoria della trasmissione numerica"

Cover-Thomas: "Elements of Information Theory"

FONDAMENTI DI COMUNICAZIONI (ING-INF/03)
SEGNALI E SISTEMI

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea

Crediti 8.0

Docente titolare Giuseppe RICCI

Ripartizione oraria Ore totali di attività frontale: 72.0

  Ore erogate dal docente ANGELO COLUCCIA: 18.0

Per immatricolati nel 2018/2019

Anno accademico di erogazione 2019/2020

Anno di corso 2

Semestre Secondo Semestre (dal 02/03/2020 al 05/06/2020)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Analisi 1 e Analisi 2

si veda la parte generale

si veda la parte generale

lezione frontale, esercitazioni, attività al calcolatore

scritto con orale opzionale

come da calendario

Esercizi numerici e approfondimenti sul programma del corso

Introduzione al linguaggio MATLAB

Principi di elaborazione numerica dei segnali e applicazioni

Ricci-Valcher: "Segnali e Sistemi"

SEGNALI E SISTEMI (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2018/2019

Year taught 2019/2020

Course year 2

Semestre Secondo Semestre (dal 02/03/2020 al 05/06/2020)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Communications, Networks, Statistical Signal Processing.

The course provides an overview of modern communication principles and techniques, and how they are composed into "systems". The focus is on multiuser wireless systems, in particular mobile cellular networks from 2G (GSM) to 4G (LTE) and next-generation (5G), and (geo)localization systems.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of digital communications and systems:

  • Describe the characteristics of advanced digital communication techniques and discuss the principles of modern system design;
  • Understand the different types of diversity that can be exploited to improve the performance of a communication system;
  • Illustrate data-aided and non-data-aided synchronization techniques for timing recovery in baseband and passband;
  • Describe how surveillance and (geo)localization can be performed via radio signals, and illustrate satellite-based navigation system.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, detection, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of (mobile) multiuser systems, and how advanced digital communication techniques can be adopted to efficiently cope with them;
  • Discuss the evolution of cellular networks from a system perspective, state-of-the-art technologies and security, and the ongoing trends;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs.
  • Recognize and understand the tendencies and innovations in the ICT field, with awareness of related privacy, security, and ethical issues.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches to address modern telecommunication needs, and to have a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics or a variety of telecommunication systems, identifying their salient features without getting lost into protocol/standard details; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects in the design of telecommunication systems and, in general, cultural issues linked to related areas within the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are explicitly asked to refer to and compare different sources and textbooks, also by autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large), summarizing them for an effective study.

Teaching Methods. The course aims at enabling students to understand and be able to solve design issues in telecommunications systems, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every system will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The discussion will be organized into four parts: 1. Description of the main characteristics of the system. 2. Comparison with previous technology addressing the same communication needs, and analysis of the additional requirements. 3. Derivation of selected algorithms and optmization/detection/estimation techniques relevant to the addressed system. 4. Analysis of the implications in terms of user experience, applications to contemporary/future contexts, and security. The course consists of frontal lessons with slides and blackboard, together with class exercises and labs using MATLAB and software-defined radio equipment. There will be theoretical lessons, qualitative discussion on system aspects, and examples about how knowledge is put into practice in real systems. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of five open questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Advanced digital communication techniques and modern systems (hours: 28 + 4 lab/exercise)

Recapitulation of fundamental principles of digital communications; diversity, combining techniques and MIMO systems; multiuser systems: multiplexing, multiple access, optimality and fairness in resource allocation, link adaptation functions (power control, Adaptive Modulation and Coding, tradeoffs), error recovery (ARQ, FEC and Hybrid-ARQ); overview on spread-spectrum and multi-carrier systems (CDMA, OFDM), multiuser detection.

 

Telecommunication networks and mobile cellular systems (hours: 20 + 4 lab/exercise)

Historical development of data and voice networks, PSTN; general principles of cellular networks. The GSM system: architecture, burst structure, overview on signaling and mobility procedure. Evolution towards GPRS/EDGE. 3G: UMTS overview and evolution towards HSPA. 4G technologies and next generation systems: LTE, main ideas towards 5G (cooperation, smart antennas, cognitive radio). Introduction to Network Security and intrusion detection. Security in GSM/3G (scanning, attacks, DDoS).

 

Synchronization techniques (hours: 9 + 2 lab/exercise)

Maximum Likelihood, data-aided and non-data aided techniques for timing recovery; joint phase and time recovery; synchronization in flat fading channels, low-complexity (ad-hoc) schemes.

 

Localization and positioning systems (hours: 10 + 4 lab/exercise)

Introduction to surveillance through radio signals; (geo)localization and satellite-based positioning systems. GPS: principles, signal structure, augmentation, modernization. Current trends and topics in localization.

Textbooks (other specific references are provided during the course)

 

A. Goldsmith: "Wireless Communications", Cambridge University Press, 2005

J.G. Proakis: "Digital Communications" (4th ed.), McGraw Hill, 2000

T.S. Rappaport: "Wireless Communications: principles and practice" (2nd ed.), Prentice Hall, 2002

S. Sesia, I. Toufik, M. Baker: "LTE: The UMTS Long Term Evolution - from theory to practice", Wiley, 2009

U. Mengali, A.N. D'Andrea: "Synchronization techniques for digital receivers", Springer, 2007

J. Bao-Yen Tsui: "Fundamentals of Global Positioning System Receivers: A Software Approach", Wiley, 2000

TELECOMMUNICATION SYSTEMS (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2017/2018

Year taught 2018/2019

Course year 2

Semestre Secondo Semestre (dal 04/03/2019 al 04/06/2019)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Communications, Networks, Statistical Signal Processing.

The course provides an overview of modern communication principles and techniques, and how they are composed into "systems". The focus is on multiuser wireless systems, in particular mobile cellular networks from 2G (GSM) to 4G (LTE) and next-generation (5G), and (geo)localization systems.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of digital communications and systems:

  • Describe the characteristics of advanced digital communication techniques and discuss the principles of modern system design;
  • Understand the different types of diversity that can be exploited to improve the performance of a communication system;
  • Illustrate data-aided and non-data-aided synchronization techniques for timing recovery in baseband and passband;
  • Describe how surveillance and (geo)localization can be performed via radio signals, and illustrate satellite-based navigation system.

 

Applying knowledge and understanding. After the course the student should be able to:

  • Work with analytical models and solve optimization, detection, and estimation problems related to the course topics;
  • Describe the peculiar aspects and main challenges of (mobile) multiuser systems, and how advanced digital communication techniques can be adopted to efficiently cope with them;
  • Discuss the evolution of cellular networks from a system perspective, state-of-the-art technologies and security, and the ongoing trends;
  • Understand the differences among several techniques addressing the same problem and recognize the main trade-offs.
  • Recognize and understand the tendencies and innovations in the ICT field, with awareness of related privacy, security, and ethical issues.

 

Making judgements. Students are guided to learn critically what is taught during classes, comparing different approaches to address modern telecommunication needs, and to have a clear view of the big picture.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired, their scientific knowledge, and the specialty vocabulary. The course promotes the development of the following skills: ability to highlight and expose in precise terms the characteristics or a variety of telecommunication systems, identifying their salient features without getting lost into protocol/standard details; ability to describe and analyze the different options available for a given application scenario or use case, and illustrate the main trade-offs.

 

Learning skills. Students must acquire the critical ability to discuss, with originality and autonomy, the most important aspects in the design of telecommunication systems and, in general, cultural issues linked to related areas within the ICT domain. They should be able to develop and apply the knowledge learned in the continuation of their studies and in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students are explicitly asked to refer to and compare different sources and textbooks, also by autonomously selecting authoritative materials from the vast amount of information available (libraries, online repositories, and the Web at large), summarizing them for an effective study.

Teaching Methods. The course aims at enabling students to understand and be able to solve design issues in telecommunications systems, keeping an unified view and being able to navigate the complexity of modern scenarios. This will be done using the following teaching method. Every system will be introduced in terms of motivations, technical peculiarities, and application scope. The presentation of each topic will be linked to the background studied in previous courses, and continuously connected to the preceding and subsequent topics within the present course. The discussion will be organized into four parts: 1. Description of the main characteristics of the system. 2. Comparison with previous technology addressing the same communication needs, and analysis of the additional requirements. 3. Derivation of selected algorithms and optmization/detection/estimation techniques relevant to the addressed system. 4. Analysis of the implications in terms of user experience, applications to contemporary/future contexts, and security. The course consists of frontal lessons with slides and blackboard, together with class exercises and labs using MATLAB and software-defined radio equipment. There will be theoretical lessons, qualitative discussion on system aspects, and examples about how knowledge is put into practice in real systems. A part of the lessons will be also devoted to illustrate related ongoing research directions in the field.

Written and/or oral. The final (typically written) exam consists of five open questions aimed at verifying to what extent the student 1) has gained knowledge and understanding of the selected topics of the course, 2) is able to discuss complex aspects in a synthetic way, and 3) has gained adequate degree of maturity in linking concepts within a system view. Small exercises may be included in the questions so that the student can demonstrate his/her ability to 1) correctly adopt formal techniques for solving well-defined problems, and 2) integrate different concepts and tools.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Textbooks (other specific references are provided during the course)

 

A. Goldsmith: "Wireless Communications", Cambridge University Press, 2005

J.G. Proakis: "Digital Communications" (4th ed.), McGraw Hill, 2000

T.S. Rappaport: "Wireless Communications: principles and practice" (2nd ed.), Prentice Hall, 2002

S. Sesia, I. Toufik, M. Baker: "LTE: The UMTS Long Term Evolution - from theory to practice", Wiley, 2009

U. Mengali, A.N. D'Andrea: "Synchronization techniques for digital receivers", Springer, 2007

J. Bao-Yen Tsui: "Fundamentals of Global Positioning System Receivers: A Software Approach", Wiley, 2000

TELECOMMUNICATION SYSTEMS (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING AND ELECTRONIC TECHNOLOGIES

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2016/2017

Year taught 2017/2018

Course year 2

Semestre Secondo Semestre (dal 01/03/2018 al 01/06/2018)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

TELECOMMUNICATION SYSTEMS (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Degree course COMMUNICATION ENGINEERING

Subject area ING-INF/03

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2015/2016

Year taught 2016/2017

Course year 2

Semestre Secondo Semestre (dal 01/03/2017 al 02/06/2017)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

TELECOMMUNICATION SYSTEMS (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Corso di laurea COMMUNICATION ENGINEERING

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore totali di attività frontale: 0.0

Per immatricolati nel 2014/2015

Anno accademico di erogazione 2015/2016

Anno di corso 2

Semestre Secondo Semestre (dal 29/02/2016 al 03/06/2016)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

TELECOMMUNICATION SYSTEMS (ING-INF/03)
TELECOMMUNICATION SYSTEMS

Corso di laurea COMMUNICATION ENGINEERING

Settore Scientifico Disciplinare ING-INF/03

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore totali di attività frontale: 0.0

Per immatricolati nel 2013/2014

Anno accademico di erogazione 2014/2015

Anno di corso 2

Semestre Secondo Semestre (dal 02/03/2015 al 06/06/2015)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

TELECOMMUNICATION SYSTEMS (ING-INF/03)

Pubblicazioni

International journals

[J46] Angelo Coluccia, Giuseppe Ricci, and Christ D. Richmond: “Adaptive radar detection without secondary data for uncooperative spectrum sharing scenarios”, IEEE Transactions on Signal Processing, vol. 69, 25 May 2021

[J45] Angelo Coluccia, Alessio Fascista, Arne Schumann, Lars Sommer, Anastasios Dimou, Dimitrios Zarpalas, Miguel Méndez, David de la Iglesia, Iago González, Jean-Philippe Mercier, Guillaume Gagné, Arka Mitra, Shobha Rajashekar: “Drone-vs-Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge”, Sensors, 2021

[J44] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “A KNN-Based Radar Detector for Coherent Targets in Non-Gaussian Noise”, IEEE Signal Processing Letters, vol. 28, 2021

[J43] Alessio Fascista , Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados: “Downlink Single-Snapshot Localization and Mapping With a Single-Antenna Receiver”, IEEE Transactions on Wireless Communications, 2021

[J42] Alessio Fascista, Angelo Coluccia, Giuseppe Ricci: “A Pseudo Maximum Likelihood Approach to Position Estimation in Dynamic Multipath Environments”, Signal Processing (Elsevier), 2020

[J41] Angelo Coluccia, Gianluca Parisi, Alessio Fascista: “Detection and Classification of Multirotor Drones in Radar Sensor Networks: a review”, Sensors, 2020

[J40] Angelo Coluccia: “On the probabilistic modeling of fake news (hoax) persistency in online social networks and the role of debunking and filtering”, Internet Technology Letters, Vol. XX, No. XX, 2020

[J39] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “CFAR Feature Plane: a Novel Framework for the Analysis and Design of Radar Detectors”, IEEE Transactions on Signal Processing, vol. 68, 2020

[J38] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “A k-nearest neighbors approach to the design of radar detectors”, Signal Processing (Elsevier), vol. 174, Sep. 2020

[J37] Angelo Coluccia, Giuseppe Ricci, Olivier Besson: “Design of robust radar detectors through random perturbation of the target signature”, IEEE Transactions on Signal Processing, vol. 67 , no. 19, Oct.1, 2019

[J36] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “A novel approach to robust radar detection of range-spread targets”, Signal Processing (Elsevier), 2019

[J35] Alessio Fascista, Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados: “Millimeter-Wave Downlink Positioning with a Single-Antenna Receiver”, IEEE Transactions on Wireless Communications, vol. 18, no. 9, Sep. 2019

[J34] Angelo Coluccia, Alessio Fascista: “Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks”, Journal of Sensor and Actuator Networks, 8(2):31, 2019

[J33] Francesco Sasso, Angelo Coluccia, Giuseppe Notarstefano: “Interaction-Based Distributed Learning in Cyber-Physical and Social Networks”, IEEE Transactions on Automatic Control, vol. xx, no. xx, 2019

[J32] Luca Carlino, Francesco Bandiera, Angelo Coluccia, Giuseppe Ricci: “Improving localization by testing mobility”, IEEE Transactions on Signal Processing, vol. 67, no. 13, 1 July 2019

[J31] Angelo Coluccia: “Crowdsensing networks in the IoT age”, Transactions on Emerging Telecommunications Technologies (Wiley), vol. 30, no. 4, Apr. 2019 e3621

[J30] Angelo Coluccia and Alessio Fascista: “A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks”, Sensors, vol. 19, no. 988, Feb. 2019

[J29] Angelo Coluccia and Fabio Ricciato: “On the estimation of link delay distributions by cumulant-based moment matching”, Internet Technology Letters, Vol. 1, No. 1, Jan./Feb. 2018

[J28] Alessio Fascista, Giovanni Ciccarese, Angelo Coluccia, Giuseppe Ricci: “Angle-of-Arrival based Cooperative Positioning for Smart Vehicles“, IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 9, Sep. 2018, pp. 2880-2892

[J27] Angelo Coluccia and Alessio Fascista: “On the Hybrid TOA/RSS Range Estimation in Wireless Sensor Networks”, IEEE Transactions on Wireless Communications, Vol. 17, No. 1, Jan. 2018

[J26] Angelo Coluccia, Alessio Fascista: “An alternative procedure to CUSUM for cyber-physical attack detection”, Internet Technology Letters (Wiley), 2017

[J25] Angelo Coluccia, Giuseppe Ricci: “Adaptive radar detectors for point-like Gaussian targets in Gaussian noise”, IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, Jun. 2017

[J24] Alessio Fascista, Giovanni Ciccarese, Angelo Coluccia, Giuseppe Ricci: “A localization algorithm based on V2I communications and AOA estimation”, IEEE Signal Processing Letters, vol. 24, no. 1, Jan. 2017

[J23] Olivier Besson, Angelo Coluccia, Eric Chaumette, Giuseppe Ricci, François Vincent: “Generalized likelihood ratio test for detection of Gaussian rank-one signals in Gaussian noise with unknown statistics”, IEEE Transactions on Signal Processing, vol. 65, no. 4, 15 Feb. 2017

[J22] Francesco Bandiera, Angelo Coluccia, Vincenzo Dodde, Antonio Masciullo, Giuseppe Ricci: “CRLB for I/Q imbalance estimation in FMCW radar receivers”, IEEE Signal Processing Letters, vo. 23, no. 12, Dec. 2016

[J21] Angelo Coluccia: “Robust opportunistic inference from non-homogeneous distribution-free measurements”, IEEE Transactions on Signal Processing, vol. 64, no. 15, Aug. 2016

[J20] Angelo Coluccia, Giuseppe Notarstefano: “A Bayesian framework for distributed estimation of arrival rates in asynchronous networks”, IEEE Transactions on Signal Processing, vol. 64, no. 15, Aug. 2016

[J19] Francesco Bandiera, Olivier Besson, Angelo Coluccia, Giuseppe Ricci: "ABORT-like detectors: a Bayesian approach", IEEE Transactions on Signal Processing, vol. 63, no. 19, 1 Oct. 2015

[J18] Angelo Coluccia: "Regularized Covariance Matrix Estimation Via Empirical Bayes", IEEE Signal Processing Letters, vol. 22, no. 11, Nov. 2015

[J17] Angelo Coluccia, “A Low-Complexity Approach for Improving the Accuracy of Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 521948, 12 pages

[J16] Catia Canoci, Ignazio Ciufolini, Angelo Coluccia, Claudio Paris, Giuseppe Ricci, Gianfausto Salvadori, Giampiero Sindoni: "On the statistics of the orbital residuals of the LAGEOS satellites", Modern Physics Letters A, vol. 30, no. 19, 21 June 2015.

[J15] Angelo Coluccia, Giuseppe Ricci: "ABORT-like detection strategies to combat possible deceptive ECM signals in a network of radars", IEEE Transactions on Signal Processing, vol. 63, no. 11, Jun. 2015

[J14] Angelo Coluccia, Giuseppe Ricci: "A tunable W-ABORT-like detector with improved detection vs rejection capabilities trade-off", IEEE Signal Processing Letters, vol. 22, issue 6, Jun. 2015, pp. 713-717

[J13] Francesco Bandiera, Angelo Coluccia, Giuseppe Ricci: "A Cognitive Algorithm for Received Signal Strength Based Localization", IEEE Transactions on Signal Processing, vol. 63, no. 7, Apr. 2015

[J12] Angelo Coluccia, Fabio Ricciato, Peter Romirer-Meierhofer: "Robust Estimation of Mean Failure Probability in Access Networks", Computer Networks, Vol. 73, n. 14, November 2014, pp. 282–301

[J11] Angelo Coluccia, Vincenzo Chironi, Stefano D'Amico: "Non-idealities compensation in full-digital receivers with application to Ultra-Wide Band", Wireless Personal Communications (Springer), Vol. 78, Issue 1, September 2014, pp 671-686

[J10] Angelo Coluccia, Fabio Ricciato: "RSS-based localization via Bayesian ranging and Iterative Least Squares positioning", IEEE Communications Letters, Vol. 18, n. 5, May 2014, pp. 873 - 876

[J9] Angelo Coluccia, Fabio Ricciato, Giuseppe Ricci: "Positioning based on signals of opportunity", IEEE Communications Letters, vol. 18, no. 2, Feb. 2014

[J8] Angelo Coluccia: "On the expected value and higher-order moments of the Euclidean norm for elliptical normal variates", IEEE Communications Letters, vol. 17, no. 12, Dec. 2013

[J7] Angelo Coluccia, Alessandro D’Alconzo, Fabio Ricciato: "Distribution-based anomaly detection via generalized likelihood ratio test: A general Maximum Entropy approach", Computer Networks, vol. 57, n. 17, (9 December) 2013, pp. 3446-3462

[J6] Angelo Coluccia: "Rethinking Stream Ciphers: can extracting be better than expanding?", Wireless Personal Communications, Springer, vol. 73, n. 1, 2013, pp. 77-94

[J5] Angelo Coluccia, Fabio Ricciato: "A Software-Defined Radio tool for experimenting with RSS measurements in IEEE 802.15.4: implementation and applications", International Journal of Sensor Networks, vol. 14, n. 3, 2013

[J4] Angelo Coluccia: "Reduced-Bias ML-Based Estimators with Low Complexity for Self-Calibrating RSS Ranging", IEEE Transactions on Wireless Communications, Vol. 12, issue 3, pp. 1220--1230, March 2013

[J3] Angelo Coluccia, Alessandro D’Alconzo, Fabio Ricciato: "On the optimality of max-min fairness in Resource Allocation", Annals of Telecommunications, Volume 67, Issue 1 (2012), Page 15-26, http://dx.doi.org/10.1007/s12243-011-0246-y

[J2] Alessandro D'Alconzo, Angelo Coluccia, Peter Romirer-Maierhofer: "Distribution-Based Anomaly Detection in 3G Mobile Networks: From Theory to Practice", International Journal on Network Management, Special Issue in “Traffic Monitoring and Network Measurements: from Theory to Practice”, John Wiley and Sons, September/October 2010, Volume 20, Issue 5, pp. 245--269

[J1] Fabio Ricciato, Angelo Coluccia, Alessandro D’Alconzo: "A review of DoS attack models for 3G cellular networks from a system-design perspective", Computer Communications, Volume 33, Issue 5, 15 March 2010, Pages 551-558

 

Book chapters

[B2] Angelo Coluccia, Alessandro D’Alconzo, Fabio Ricciato: "Distribution-Based Anomaly Detection in Network Traffic", in "Data Traffic Monitoring and Analysis: from Measurement, Classification, and Anomaly Detection to Quality of Experience", Editors: Ernst Biersack, Christian Callegari, Maja Matijasevic, Lecture Notes in Computer Science, Volume 7754, pp. 202-216, Springer Berlin Heidelberg, 2013/1/1

[B1] Christian Callegari, Angelo Coluccia, Alessandro D’Alconzo, Wendy Ellens, Stefano Giordano, Michel Mandjes, Michele Pagano, Teresa Pepe, Fabio Ricciato, Piotr Z̊uraniewski: "A Methodological Overview on Anomaly Detection", in "Data Traffic Monitoring and Analysis: from Measurement, Classification, and Anomaly Detection to Quality of Experience", Editors: Ernst Biersack, Christian Callegari, Maja Matijasevic, Lecture Notes in Computer Science, Volume 7754, pp. 148-183, Springer Berlin Heidelberg, 2013/1/1

 

Conference proceedings

[C47] Alessio Fascista, Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados: “RIS-aided Joint  Localization and Synchronization with a Single-Antenna mmWave Receiver”, IEEE ICASSP 2021

[C46] Philip Gertzell, Jacob Landelius, Hanna Nyqvist, Alessio Fascista, Angelo Coluccia, Gonzalo Seco-Granados, Nil Garcia, Henk Wymeersch: “5G multi-BS Positioning with a Single-Antenna Receiver”, IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London (UK), 31 Aug.-3 Sep. 2020

[C45] Alessio Fascista, Angelo Coluccia, Giuseppe Ricci: “Direct position estimation of a mobile receiver in multipath environments via adaptive beamforming”, 28th European Signal Processing Conference (EUSIPCO), Amsterdam (The Netherlands), 18-22 Jan. 2021

[C44] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “Robust CFAR radar detection using a k-nearest neighbors rule”, 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona (Spain), 4-8 May 2020

[C43] Alessio Fascista, Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados: “Low-complexity accurate mmWave positioning for single-antenna users based on angle-of-departure and adaptive beamforming”, 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona (Spain), 4-8 May 2020

[C42] Angelo Coluccia, Alessio Fascista, Arne Schumann, Lars Sommer, Marian Ghenescu, Tomas Piatrik, Geert De Cubber, Mrunalini Nalamati, Ankit Kapoor, Muhammad Saqib, Nabin Sharma, Michael Blumenstein, Vasileios Magoulianitis, Dimitrios Ataloglou, Anastasios Dimou, Dimitrios Zarpalas, Petros Daras, Celine Craye, Salem Ardjoune, David de la Iglesia, Miguel Méndez, Raquel Dosil, Iago González: “Drone-vs-Bird Detection Challenge at IEEE AVSS2019’’, 16th IEEE International Conference on Video and Signal based Surveillance (AVSS), Taipei (Taiwan), 18-21 Sep. 2019

[C41] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “Spectrum sensing by higher-order SVM-based detection”, 27th European Signal Processing Conference (EUSIPCO), A Coruna (Spain), 2-6 Sep. 2019

[C40] Angelo Coluccia, Fabrizio Dabbene, Chiara Ravazzi: “Bayesian Identification of Distributed Vector AutoRegressive Processes”, European Control Conference, Naples, Italy, 25-28 Jun. 2019

[C39] Angelo Coluccia, Giuseppe Ricci, Olivier Besson: “A novel approach to the design of robust detectors”, Italian Workshop on Radar and Remote Sensing, Rome, 30-31 May 2019

[C38] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci: “Online estimation and smoothing of a target trajectory in mixed stationary/moving conditions”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, 12-17 May 2019

[C37] Angelo Coluccia, Giuseppe Ricci: “Radar Detection in K-Distributed Clutter Plus Thermal Noise Based on KNN Methods”, IEEE Radar Conference, Boston, MA, USA, 22-26 Apr. 2019

[C36] Francesco Sasso, Angelo Coluccia, and Giuseppe Notarstefano: “An Empirical Bayes Approach for Distributed Estimation of Spatial Fields”, European Control Conference, Limassol, Cyprus, 12-15 Jun. 2018

[C35] Francesco Sasso, Angelo Coluccia, and Giuseppe Notarstefano: “Distributed Learning from Interactions in Social Networks”, European Control Conference, Limassol, Cyprus, 12-15 Jun. 2018

[C34] Angelo Coluccia, and Giuseppe Ricci: “A random-signal approach to robust radar detection”, 52nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 21-23 Mar. 2018

[C33] Alessio Fascista, Giovanni Ciccarese, Angelo Coluccia, and Giuseppe Ricci: “A change-detection approach to mobile node localization in bounded domains”, 52nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 21-23 Mar. 2018

[C32] Angelo Coluccia, Marian Ghenescu, Tomas Piatrik, Geert De Cubber, Arne Schumann, Lars Sommer, Johannes Klatte, Tobias Schuchert, Juergen Beyerer, Mohammad Farhadi, Ruhallah Amandi, Cemal Aker, Sinan Kalkan, Muhammad Saqib, Nabin Sharma, Sultan Daud Khan Makkah, Michael Blumenstein: “Drone-vs-bird detection challenge at IEEE AVSS2017”, 14th IEEE International Conference on Video and Signal based Surveillance (AVSS), Lecce (Italy), 29 Aug. - 1 Sep. 2017

[C31] Angelo Coluccia, Giuseppe Notarstefano: “Distributed estimation in uncalibrated heterogeneous networks”, 20th World Congress of the International Federation of Automatic Control (IFAC), Toulouse (France), 9-14 July 2017

[C30] G. De Cubber, R. Shalom, A. Coluccia, O. Borcan, R. Chamrád, T. Radulescu, E. Izquierdo, Z. Gagov: “The SafeShore system for the detection of threat agents in a maritime border environment”, IARP Workshop on Risky Interventions and Environmental Surveillance, Les Bons Villers (Belgium), May 2017

[C29] Francesco Bandiera, Luca Carlino, Angelo Coluccia, Giuseppe Ricci: “A cognitive algorithm for RSS-based localization of possibly moving nodes“, 19th Conference on Information Fusion, Heidelberg (Germany), 5-8 July 2016

[C28] Angelo Coluccia, Vincenzo Dodde, Antonio Masciullo, Giuseppe Ricci: “Estimation and compensation of I/Q imbalance for FMCW radar receivers”, IEEE Statistical Signal Processing Workshop (SSP), Palma de Mallorca (Spain), 26-29 June 2016

[C27] Francesco Bandiera, Luca Carlino, Angelo Coluccia, Giuseppe Ricci: "RSS-based localization of a moving node in homogeneous environments", IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun (Mexico), 13-16 Dec. 2015

[C26] Angelo Coluccia, Gianni Ciccarese, Giuseppe Ricci: "A simple localization strategy based on TDOA measurements", IET Intelligent Signal Processing Conference (ISP), London, 1-2 Dec. 2015

[C25] Francesco Bandiera, Olivier Besson, Angelo Coluccia, Giuseppe Ricci: "A Bayesian Approach to Orthogonal Rejection Tests", IEEE International Radar Conference, Arlington, Virginia, 11-15 May 2015

[C24] Francesco Bandiera, Angelo Coluccia, Giuseppe Ricci, Fabio Ricciato, Danilo Spano: "TDOA localization in asynchronous WSNs", The 12th IEEE International Conference on Embedded and Ubiquitous Computing (EuC), Milan (Italy), 26-28 Aug. 2014

[C23] Angelo Coluccia, Fabio Ricciato: "Improved Estimation of Instantaneous Arrival Rates via Empirical Bayes", The 13th IEEE IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), Piran (Slovenia), 2-4 Jun. 2014

[C22] Angelo Coluccia, Giuseppe Ricci: "A radar network based W-ABORT approach to counteract deceptive ECM signals", IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Alberobello (Italy), 23-25 Jun. 2014

[C21] Francesco Bandiera, Angelo Coluccia, Giuseppe Ricci: "A Test of Homogeneity for RSS measurements within a Wireless Sensor Network", IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Alberobello (Italy), 23-25 Jun. 2014

[C20] Angelo Coluccia, Giuseppe Notarstefano: "A Hierarchical Bayes Approach for Distributed Binary Classification in Cyber-Physical and Social Networks", 19th World Congress of the International Federation of Automatic Control (IFAC), Cape Town (South Africa), 24-29 Aug. 2014

[C19] Francesco Bandiera, Angelo Coluccia, Giuseppe Ricci, Andrea Toma: "RSS-based localization in non-homogeneous environments", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence (Italy), 5-9 May 2014

[C18] Angelo Coluccia, Giuseppe Notarstefano: "Distributed Bayesian estimation of arrival rates in asynchronous monitoring networks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence (Italy), 5-9 May 2014

[C17] Angelo Coluccia, Giuseppe Notarstefano: "Distributed estimation of binary event probabilities via hierarchical Bayes and dual decomposition", 52nd IEEE Conference on Decision and Control (CDC), Florence (Italy), 10-13 Dec. 2013

[C16] Angelo Coluccia: "Robust estimation of the mean probability of binary events: a low-complexity minimax approach", 18th IEEE International Conference on Digital Signal Processing (DSP'13), Santorini (Greece), 1-3 July, 2013

[C15] Angelo Coluccia, Vincenzo Chironi, Stefano D'Amico: "Non-idealities compensation in full-digital receivers with application to Ultra-Wide Band", IEEE International Conference on Computer, Information and Telecommunication Systems (CITS), Athens (Greece), 7-8 May 2013

[C14] Angelo Coluccia: "Rethinking Stream Ciphers: can extracting be better than expanding?", 2nd IEEE Workshop on Privacy, Security and Trust in Mobile and Wireless Systems (MobiPST), Munich, 30 Jul. 2012

[C13] Angelo Coluccia, Fabio Ricciato: "A Software-Defined Radio tool for experimenting with RSS measurements in IEEE 802.15.4: implementation and applications", Fifth IEEE International Workshop on Sensor Networks (SN), Munich, 30 Jul. 2012

[C12] Angelo Coluccia, Fabio Ricciato: "Maximum Likelihood trajectory estimation of a mobile node from RSS measurements", 9th IEEE/IFIP Annual Conference on Wireless On-Demand Network Systems and Services (WONS), Courmayeur, 11--13 Jan. 2012

[C11] Angelo Coluccia, Eitan Altman: "SINR Base Station Placement and Mobile Association Games under Cooperation", 9th IEEE/IFIP Annual Conference on Wireless On-Demand Network Systems and Services (WONS), Courmayeur, 11--13 Jan. 2012

[C10] F. Ricciato, F. Strohmeier, P. Dorfinger, A. Coluccia: "One-way Loss Measurements From IPFIX Records", IEEE International Workshop (M&N 2011), Anacapri (Italy), 10-11 October 2011

[C9] Angelo Coluccia, Peter Romirer-Maierhofer, Fabio Ricciato: "Bayesian Estimation of Network-wide Mean Failure Probability in 3G Cellular Networks", Performance Evaluation of Computer and Communication Systems (PERFORM) Workshop, Vienna, Oct. 14-16 2010, Springer LNCS, 2011, Volume 6821/2011, 167-178, DOI: 10.1007/978-3-642-25575-5_14

[C8] Danilo De Donno, Fabio Ricciato, Luca Catarinucci, Angelo Coluccia, Luciano Tarricone: "Challenge: Towards Distributed RFID Sensing with Software-Defined Radio", ACM MobiCom '10, Chicago, September 20-24 2010

[C7] Angelo Coluccia, Fabio Ricciato: "On ML estimation for automatic RSS-based indoor localization", IEEE International Symposium on Wireless Pervasive Computing (ISWPC'10), Modena (Italy), May 5-7 2010

[C6] Peter Romirer-Maierhofer, Angelo Coluccia, Tobias Witek: "On the Use of TCP Passive Measurements for Anomaly Detection: A Case Study from an Operational 3G Network", 2nd COST TMA Workshop, 7 Apr. 2010, Zurich

[C5] Angelo Coluccia, Fabio Ricciato, Peter Romirer-Maierhofer: "On Robust Estimation of Network-wide Packet Loss in 3G Cellular Networks", 5th IEEE Broadband Wireless Access (BWA) Workshop, colocated with IEEE GLOBECOM '09

[C4] Fabio Ricciato, Angelo Coluccia, Alessandro D’Alconzo, Darryl Veitch, Pierre Borgnat, Patrice Abry: "On the role of flows and sessions in Internet traffic modeling: an explorative toy-model", IEEE GLOBECOM '09, 30-4 Dec. '09, Honolulu (Hawaii)

[C3] A. D'Alconzo, A. Coluccia, F. Ricciato, P. Romirer-Maierhofer: “A Distribution-Based Approach to Anomaly Detection and application to 3G mobile traffic”, IEEE GLOBECOM'09, 30-4 Dec. '09, Honolulu (Hawaii)

[C2] P. Romirer-Maierhofer, F. Ricciato, A. Coluccia: "Explorative Analysis of One-way Delays in a Mobile 3G Network", IEEE LANMAN '08, 3-6 Sep. '08, Cluj-Napoca, Transilvania (Romania)

[C1] L. Catarinucci, A. Coluccia, L. Tarricone: "Towards a standardization of SAR numerical evaluation", 4th Int. Work. on Biol. Eff. of EM fields, 16-20 Oct '06, Crete (Greece)

Temi di ricerca

• Statistical signal processing, in particular fo radar detection, multichannel, and array processing

• Localization and sensing via wireless communications

• Hybrid model-based and data-driven (machine learning) approaches

• Anomaly detection

• Internet traffic monitoring and analysis in cellular networks 

• Sensor networks and cooperative systems

• Distributed estimation and learning over networks/graphs with applications to cyber-physical and social networks

 

Projects

  • 2016-2018
    SafeShore "System for Detection of Threat Agents in Maritime Border Environment", funded under European Commission's Horizon2020 programme, http://safeshore.eu
  • 2015-2020
    OPT4SMART "Distributed Optimization Methods for Smart Cyber-Physical Networks", funded under European Research Council's Starting Grant programme (PI: prof. G. Notarstefano), http://opt4smart.eu
  • 2012-2013
    SOCIAL-ROBOTS "Self-Organizing and Cooperative Intelligent Autonomous mobiLe ROBOTS" (distributed control, optimization and estimation methods) supported by Italian research programme
  • 2012-2013
    RENDEZ-VOUS "Distributed Sensor Networks with high energy efficiency for industrial and avionic monitoring via Impulse Radio UWB", supported by PUGLIA FESR 2007-2013, Line 1.2, Action 1.2.4
  • 2010-2011
    RALLWiN "Robust Automatic Localization for Low-cost WSNs", in collaboration with ST Microelectronics
  • 2008-2012
    COST TMA Action IC0703 "Data Traffic Monitoring and Analysis", supported by European Science Foundation under the COST program (ICT domain)
  • 2005-2012
    DARWIN "Data Analysis and Reporting for WIreless Networks" at Forschungszentrum Telekommunikation Wien in collaboration with Mobilkom Austria and Technical University of Vienna (INTHF)