Courses 2022-23


Ad hoc ITEE PhD courses

Some ITEE courses are organized in conjunction with other PhD programs hosted by Departments of the Polytechnic and Fundamental Sciences School of the Federico II University 


Academic year 2022-23

Course title Period Lecturer Course Description / Syllabus Venue / Organizer CREDITS

Academic Entreprenuership

29-31/05 -  05-15-20-22/06/2023 prof. Pierluigi Rippa, Silvia Cosimato, Nadia Di Paola - DIE Unina prof. Pierluigi Rippa - DIE, Unina 4

The Linear Parameter Varying approach: theory and application

25-29-30/05  -  01/06/2023

prof. Olivier Saname 

Grenoble Institute of Technology


This course is concerned with Linear Parameter Varying (LPV) systems. In the first part we will provide definitions of such systems, as well as some modelling methods to get LPV systems state space representations from physical systems or from non linear models. Then properties such as controllability, observability and stability will be defined and some characterizations will be presented and discussed. The second part deals with control design methods for LPV systems. We will mainly put the focus on the referred to as polytopic and grid-based approaches, for which state feedback and dynamical output feedback control design methods will be given. The synthesis of state observers will also be considered. The third part is dedicated to the application of LPV methods to automotive systems. Two main cases will be presented: the modelling and control of semi-active suspension systems, and the multivariable control of Vehicle Dynamics. The objective will be to see different ways to use the potential of LPV approaches in realistic cases. The last part will be dedicated to the training phase in Matlab/Simulink in order to be able to model, analyze, control and simulate LPV systems for some simple examples.
There will be a final assessment
prof. Luigi Glielmo - DIETI, Unina 4

I pilastri della trasformazione digitale

17-18/4 – 5-8-10/5/2023

dr. Francesco Tortorelli

La progettazione di sistemi informativi complessi sotto la responsabilità di più organizzazioni, in settori quali sanità, trasporti e agricoltura, richiede un approccio metodologico e una sensibilità agli aspetti regolatori. Il corso illustra il contesto metodologico, funzionale e regolatorio per iniziative di trasformazione digitale, obbligatorio quando almeno uno degli attori è una pubblica amministrazione, e in molti casi anche tra privati. Il corso tratta i principi e l’applicazione dell’interoperabilità, il quadro regolatorio europeo e nazionale del settore, con particolare riguardo ai servizi che garantiscono, con valore legale e probatorio, le transazioni digitali (identificazione di soggetti e organizzazioni, trasmissione di documenti, sottoscrizione di documenti e conservazione di documenti). Il corso prevede una prova di valutazione finale.
prof. Nicola Mazzocca - DIETI, Unina 3

Design of Extended Reality Software Systems

 Spring/Summer 2023

Dr. Domenico Amalfitano, prof. Anna Rita Fasolino

The course introduces Extended Reality (XR) Software Systems to the researchers working in ICT and health domains, and presents software engineering solutions for specifying the requirements, designing, implementing, and evaluating the quality of such software systems. At the same time, the module will show the new research trends in the field of Extended Reality Software Systems and its novel applications in industry.
ITEE - ICTH PhD programs 4
How to boost your PhD   11-18-25/01 - 1-8-15-22/02 - 1/03/2023  Prof. Antigone Marino 
Technical skills are the first ingredient for a successful career, but often the competition with others is played on other skills. 
This course covers training on soft skills, aiming to widen the PhD student spectrum.

 How to boost your PhD

 ITEE - ICTH - CQB PhD programs  4

Electronic Scan Antennas for Radar Signal Processing Applications



Dr. Enzo Carpentieri  

This course will discuss the applications of Electronic Scanning Antennas in the Radar field and will provide the students with a brief introduction and a review of the main techniques/algorithms that can be applied for target filtering, beamforming, and detection. After a theoretical part, the attention will be shifted to some specific examples with emphasis on solutions proposed in practice. At the end of each lecture, students are encouraged to start a discussion on possible alternative techniques or solutions.

 Electronic Scan Antennas

Prof. A. De Maio, Dr. V. Carotenuto - DIETI-ITEE    2

Using Deep Learning properly

10-12-17-19-24/01/2023 Dr. Andrea Apicella, DIETI

Designing and implementing a Deep Learning system is not an easy task.
The process requires several choices regarding model design, data engineering, parameter modification and testing. This process is easily subject to errors that are not easily identifiable and, in some cases, may lead to overestimating the performance of the proposed solution. This course aims to provide a general pipeline for designing and validating a machine learning system, avoiding the most common errors that can easily be made. To this end, it will be shown how to implement the experimental evaluation of simple classification tasks, highlighting their peculiarities and points to pay attention to. The practical part of the course is based on PyTorch, one of the best-known packages for neural networks. An introductory view of it is given. There will be a final assessment.

 Using Deep Learning properly




IoT Data Analysis

09-13-16-20-23-27/01/2023 Dr. Raffaele Della Corte, DIETI

This course will present advances on Data Analysis with emphasis on its adoption in the Internet of Things environments, where vast amounts of data are generated from multiple and heterogeneous data sources. The course will provide the students with the concepts and advanced techniques for analyzing field data (such as computer logs, event trace, system level metrics, IoT data) to understand the behavior of an IoT system from a dependability point of view. The course puts the basis for the development of analysis frameworks the students can leverage in their own research field. The final assessment will require students to prepare a good quality presentation about the potential application of the provided Data Analysis concepts and techniques to their research activities. Student’s presentations will take place in the last lesson. Details about the presentation format and schedule will be given during the course.





On the challenges and impact of Artificial Intelligence in the Insurance domain



Dr. Lorenzo Ricciardi Celsi, PhD, MBA

The course introduces how Artificial Intelligence (AI) is reshaping insurance, ranging from multimedia forensics to car crash detection based on telematics data. In addition to a general introduction to the field of AI applications in insurance, we discuss a few topics that are widely considered very effective and promising. We will also discuss the results obtained with respect to some relevant use cases. In particular, (i) the role of deep learning in multimedia forensics, and (ii) the concept and benefits of explainable AI will be discussed. There will be a final assessment

On the challenges


ITEE PhD   3

Virtualization technologies and their applications




Dr. Luigi De Simone, DIETI

The course presents advanced virtualization technologies used today for both research and industrial applications, including embedded systems, networking, and telecom equipments. The course will provide the students with the basis for developing experimental testbeds and novel systems with high-performance and reliability properties in their own research field. Every lesson consists of a first part on the overview of the specific virtualization technology, and a second part on a hands-on session to show how to use that technology in practice. At the end of the lesson, students are encouraged to start a discussion on why and how to adopt that virtualization approach in their research activities. The course foresees ten two-hours lectures split in four to five weeks, two to three days per week.
An additional eleventh lecture (of at least 3 hours) will be devoted to the final assessment.
To earn the credits, students need to provide a good quality presentation about the potential application of virtualization in the context of their research field, with the current state-of- the-art. 

Course syllabus

 Virtualization technologies





Statistical data analysis for science and engineering research


Prof. Roberto Pietrantuono, DIETI

The course provides an overview of the experimental design and data analysis and is intended to teach PhD the use of statistical methods and data analysis as part of their research.

More specifically, the course introduces the main elements required to plan robust experiments according to the Design of Experiment (DoE) methodology and the basic statistics required to properly analyse the resulting data depending on the experimental settings. The course will also treat data analysis under unplanned experiments with observational data. Common errors in experimental planning and misuse of statistics will be highlighted throughout the course.

The course will show the application of what explained on exemplifying science and engineering research problems. The course foresees six two-hours lectures split in three weeks, two days per week.

An additional seventh lecture will be devoted to the final assessment.

 Statistical Data Analysis




Matrix Analysis for Signal Processing with MATLAB Examples

Spring 2023

Proff. Antonio De Maio, Augusto Aubry, Dr. Vincenzo Carotenuto, DIETI

The course provides an overview on some topics in matrix theory together with their intrinsic interaction with and application to signa processing. The most important and "useful" tools, methods, and matrix structures are emphasized and complemented with MATLAB examples. The lectures cover basic matrix structures and operations, the concept of matrix norm, orthonormal matrices, singular value decomposition, positive (negative) semidefinite matrices and their eigenvalue characterization, Schur complement, matrix gradient, least square problems, Kronecker product. The course foresees four two-hours lectures split in two weeks, two days per week.

An additional fifth lecture will be devoted to the final assessment.



Big Data Architecture and Analytics Spring 2023  Dr. Giancarlo Sperlì, DIETI The course presents Big Data methodologies and architectures for supporting several data analytics tasks, with example applications in the e-health field, from different points of view. In particular, the course provides an introduction to Big Data and Data Analytics Lifecycle, with reference to the design of large and complex data systems. Then, it focuses on the processes of ingestion, modelling, analysis and visualization of Big Data. Possible applications of these methodologies and architectures for digital Health case studies will be discussed. There will be a final assessment.


ITEE - ICTH - CQB PhD programs


Cooperative and Non Cooperative Localization Systems

Spring 2023

Proff. Antonio De Maio, Augusto Aubry, Dr. Vincenzo Carotenuto, DIETI

The course provides an overview about radiofrequency cooperative and non- cooperative localization systems. The first part introduces basic concepts on radar systems and a variety of applications leveraging radar technology. The second part provides the working principles of diverse radiolocatization techniques and presents fundamental issues on the satellite navigation systems. The third and last part is focused on two important practical systems: the Secondary Surveillance Radar (SSR) for air traffic control and the Automatic Identification Systems (AIS) for maritime localization. There will be a final assessment.




Strategic Orientation for STEM Research & Writing

Summer 2023

Dr Chie Shin Fraser

In a professional environment where the effective public communication and dissemination of research findings are deemed at least as important as the research itself, researchers, academics, and experts especially in the STEM domains are increasingly expected to perform to and live by the oft-repeated motto “publish or perish”. This course instills participants with a strategic perspective as a prerequisite for sharpening research focus and developing the core competency of effective expert/professional writing and communication, particularly with a view to getting published. There will be a final assessment.




Operations Research: Mathematical Modelling, Optimization Methods and Software Tools

Autumn 2023 Dr Adriano Masone, DIETI   

The course teaches how to build mathematical models of optimization problems, to be able to classify models and to know the mathematical foundations of algorithmic techniques that allow them to be solved. Furthermore, the course includes a laboratory part with emphasis on modelling and the use of commercial optimization software. Finally, optimization problems arising from real case studies in different application fields, and the related solution approaches, will be discussed at the end of this course. There will be a final assessment.





Scientific writing

Summer 2023 Prof. Stefano Russo, DIETI

The course covers the process of peer- reviewing and the tasks involved in reading, reviewing and writing scientific articles and their parts: abstract, introduction, original contributions, research method, experimentation, discussion of results, threats to validity, conclusions. There will be a final assessment.




Machine Learning for Science and Engineering Research

Summer 2024

Proff. A. Corazza, F. Isgrò, R. Prevete, C. Sansone, G. Pezzulo

The course introduces the main topics in machine learning for both supervised and unsupervised approaches. In addition to a general introduction to the field, we discuss a few topics that are widely considered very effective and promising. In particular, the concept of explainable AI will be discussed, with special attention to the case of neural networks. There will be a final assessment.




Data Science for Patient Records Analysis

Spring - Summer 2023

Prof. Marcello Cinque, Prof. Carmela Bravaccio, UNINA

The course provides an overview of data science methods and tools, from data preparation to data modeling and validation, and it is intended for PhD students in science and engineering disciplines who need to use machine learning and data analysis as part of their research. The course focuses on the application of the analyzed methods for the analysis of patient records, in the context of a real case study. There will be a final assessment.


ICTH - ITEE PhD Programs


From observability to privacy and security in discrete event systems

Winter 2023 

(38th cycle, second year)

Prof. G. De Tommasi, DIETI - Prof. F. Basile, Univ. of Salerno - Prof. C. Sterle, DIETI

The course tackles several topics related to the state estimation of Discrete Event Systems (DES) in presence of events whose occurrence cannot be detected, although their effect on the system is assumed to be known, and hence modeled. Starting from the state estimation problem for non- deterministic (uncertain) DES, the notion of diagnosability for unobservable fault will be introduced. Both graph-based and optimization- based techniques to assess diagnosability and to perform fault detection will be presented. If the unobservable events are used to model secret behaviors, the techniques adopted for state estimation and fault diagnosis can be further extended to deal with security issues such as non- interference and opacity. All the aspects will be framed both in the context of finite state automata (i.e., when dealing with regular languages), and for Petri nets, being these modeling tools the most used ones in the context of control engineering and industrial automation. At the end of the course there will be a final assessment.




Advanced Modelling and Control of Energy Storage Systems, Power Converters and Electrical Drives


 Spring 2024

(38th cycle, second year)

Prof. Ciro Attaianese, Prof. Diego Iannuzzi, DIETI

The course provides an advanced modelling and control of electrical energy storage systems and drives using methods and analysis as part of their research.

More specifically, the course introduces the main elements required to model, design, and control the storage devices, power converters and motors considering the whole electrical system as smart actuator. The course will show the application of what explained on exemplifying science and engineering research problems. The course will be split into two modules where the first one is focused on Energy Storage Systems and integration with power converters, the second one is focused on smart electrical drives. There will be a final assessment. The course foresees twelve two-hours lectures split in six weeks, two days per week.




Imprenditorialità Accademica

Spring 2023 Prof. P. Rippa, DII 

Il corso di "IMPRENDITORIALITA' ACCADEMICA", della durata di 10 h e seguito da un laboratorio di Business Plan (vedere sez.: Altre attività didattiche), è impartito da docenti di Ingegneria Gestionale della Scuola Politecnica e delle Scienze di Base, ed è finalizzato alla valorizzazione dei risultati della ricerca.

Il corso fornisce una panoramica sui modelli di valorizzazione economica dei risultati della ricerca scientifica, nonché sugli strumenti di tutela della proprietà intellettuale di matrice accademica.

E' prevista una prova finale.

The course is in Italian language.

Scuola Politecnica e delle Scienze di Base 



PhD Courses shared with MSc curricula or with other PhD programs

List of courses offered to ITEE PhD students, provided they have not attended them in their past career. The list includes advanced courses shared with the 7 master degrees offered by the DIETI department (courses of student's own choice, typically in the last year of MSc curricula).

ITEE PhD students may attend courses from the non-exhaustive list below and claim the corresponding credits, typically in the first or second year of their PhD program.

Students interested in attending other courses offered in MSc programs of the Federico II University of Napoli (including in master degrees of UniNA departments different form DIETI), or in other Universities, need to inform the ITEE Coordinator before attending them and claiming corresponding credits.



ITEE students attending courses shared with master degrees of the Federico II University, HAVE TO ENROLL into the course contacting the lecturer and informing the ITEE Secretariat.

Code Course title Credits Lecturer SSD Semester
U3514  Machine Learning - Statistical learning 6 A. Corazza INF/01 I
15809 Social, ethical, and psychological issues in artificial intelligence 6 G. Tamburrini INF/01  I
U3525 Biometric systems 6 Daniel Riccio  INF/01 II
U3536 Human Robot Interaction 6 S. Rossi  INF/01 I
U3515 Neural networks and deep learning 6 R. Prevete  INF/01 II
30220 Dispositivi e sistemi fotovoltaici 9 S. Daliento ING-INF/01 I
30028  Misure a Microonde ed Onde Millimetriche 9 C. Curcio  ING-INF/02 II
U2253 Progettazione in sicurezza elettromagnetica dell'ambiente ospedaliero 9 G. Ruello ING-INF/02 II
U1777 Tomografia  9 A. Liseno ING-INF/02
U2758 Nanotechnologies for Electrical Engineering 6 C. Forestiere ING-IND/31 I
U2480 Electrodynamics of Continuous Media 9 C. Serpico ING-IND/31 II
U0991 Introduzione al Ferromagnetismo 3 C. Serpico ING-IND/31 II
U3483  Introduction to Quantum Circuits G. Miano ING-IND/31 I
15259  Modelli numerici per i campi  9 G. Rubinacci  ING-IND/31  I
U2738 Generatori, convertitori e dispositivi di accumulo 6 D. Iannuzzi  ING-IND/32 I
04247 Elaborazione Numerica dei Segnali 6 G. Scarpa ING-INF/03 II
U2265  Tecniche di elaborazione dei segnali per la bioingegneria 9 A. De Maio ING-INF/03 II
11497 Teoria dell’Informazione 6 M. Lops ING-INF/03 I
U1564 Radiolocalizzazione e Navigazione Satellitare 6 A. Aubry ING-INF/03 I
31687  Sistemi radar 9 A. De Maio ING-INF/03  I
U3532 Data Analytics 6 A. Tulino  ING-INF/03 I
U2246  Visione per Sistemi Robotici 9 A. Verdoliva ING-INF/03 II
U3584 Quantum Information 6 A.S. Cacciapuoti ING-INF/03 I
U2325  Robotics Lab 6 V. Lippiello ING-INF/04 II
U1954  Identificazione e Controllo Ottimo 6 F. Garofalo ING-INF/04 II
U2331 Field and service robotics 6 F. Ruggiero ING-INF/04 II
U0907 Analisi e Prestazioni di Internet 6 A. Pescapè ING-INF/05 II
U0603  Big Data Analytics and Business Intelligence 6 V. Moscato ING-INF/05 II
U2343 Cloud and datacenter networking 3 R. Canonico ING-INF/05 II
06649 Intelligenza artificiale 6 Flora Amato ING-INF/05 II
U2256  Machine Learning e Big Data per la Salute 9 V. Moscato ING-INF/05 I-II
33774 Metodi formali 3 V. Vittorini ING-INF/05 II
28552 Protocolli per Reti Mobili 6 S. Avallone ING-INF/05 II
U0604 Secure Systems Design 6 V. Casola ING-INF/05 I
U3548  Distributed Systems 6 S. Russo ING-INF/05 I
U2506  Software security per sistemi industriali 3 D. Cotroneo ING-INF/05 I
U3554 Software security 6 R. Natella ING-INF/05 II
U3573 Data Management  6 Carlo Sansone ING-INF/05 I
U2643 Hardware and Software Architectures for Big Data – Mod. A 6 E. Masciari ING-INF/05 I
U2644 Hardware and Software Architectures for Big Data – Mod. B 6 V. Moscato ING-INF/05 II
U1592 - U1593 Biomedical Imaging and Computer Interface for Biological Systems 12

M. Cesarelli, P. Bifulco

U2248 Strumentazione e Ingegneria Clinica 9 P. Bifulco ING-INF/06 II
U2653 Incertezza dei dati  6 L. Angrisani  ING-INF/07  II
U1881 Instrumentation and Measurements for Smart Industry  6 P. Arpaia ING-INF/07 II 
30040 Misure su sistemi wireless 9 L. Angrisani ING-INF/07 II
16227 Ottimizzazione Combinatoria 6 P. Festa  MAT/09 II
U2338 Statistical Learning and Data Mining 6 R. Siciliano SECS-S/01 I

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