Summer 2025

(first year of cycle XL)

dr. Adriano Masone, DIETI - Unina

Summer 2026

(second year of cycle XL)

prof.ssa Anna Corazza, DIETI - Unina

Summer 2025

(first year of cycle XL)

prof. Lucio Franzese, DIETI - Unina 

Summer 2026

(second year of cycle XL) 

prof. Mario Barbareschi, DIETI - Unina 

Innovation and Entrepreneurship

Spring-summer 2025

(first year of cycle XL)

prof. Pierluigi Rippa, DII - Unina

Spring-summer 2025

(first year of cycle XL)

prof.Giancarlo Sperlì, DIETI -  Unina

Spring-summer 2025

(first year of cycle XL)

The course provides an overview on some topics in matrix theory together with their intrinsic interaction with and application to signal 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, Householder transformations, Givens rotation, QR factorization, singular value decomposition, positive (negative) semidefinite matrices and their eigenvalue characterization, Schur complement, Cholesky factorization, matrix gradient, least square problems, Kronecker product.

Matrix_Analysis_2024 (Syllabus of a.y. 2023-24)

dr.Massimo Rosamilia, DIETI - Unina

Spring 2025

(first year of cycle XL)

La progettazione di sistemi informativi complessi sotto la responsabilità di diverse organizzazioni settoriali (come ad esempio la sanità, i trasporti o l’agricoltura) e intersettoriali, così come l’estensione di un progetto in tali ambiti, richiede un approccio metodologico e una sensibilità agli aspetti regolatori. Le lezioni di questo corso illustrano il contesto metodologico, funzionale e regolatorio - italiano ed europeo -, per iniziative di trasformazione digitale. Tale contesto è da ritenersi sempre obbligatorio quando almeno uno degli attori è una pubblica amministrazione e in molti casi anche tra privati. I macro argomenti trattati toccheranno, gli standard, i principi e l’applicazione dell’interoperabilità, il quadro regolatorio europeo e nazionale del settore, con particolare riguardo a quei servizi (trust services) 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). Verranno toccati i principi generali della contrattualistica pubblica e il regime degli acquisti. Gli strumenti appresi in questo ciclo di lezioni consentiranno di estendere i campi di applicazione della ricerca; in tal senso il project work finale, in data da definirsi, consisterà in un caso di applicazione di quanto appreso nelle lezioni nel contesto delle linee di ricerca del dottorato. Il corso prevede una prova di valutazione finale.

 I Pilastri Trasformazione Digitale (syllabus a.y. 2023-24)

prof. Nicola Mazzocca, DIETI - Unina

Winter 2024-25

(first year of cycle XL)

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 (syllabus of a.y. 2023-24)

DIETI - Unina

Winter 2025-26

(second year of cycle XL)

The 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

 IoT-DataAnalysis (syllabus of a.y. 2023-24)

prof. Raffaele Della Corte, DIETI - Unina 

Winter 2025-26 (second year of cycle XL)

Nowadays, the assessment of the dynamic temperature evolution of electronic systems – regardless of their final applications – is of utmost importance. A solid foundation in thermal analysis, modeling and simulations will be presented in this ad hoc course. Although electronic devices and circuits will be considered as practical case-studies, the expertise gained in the use of the presented tools can be easily adopted in alternative areas in both IT and industrial scenarios.

The primary target of this course is to guide students in correctly setting up thermal problems and extracting/exploiting key thermal metrics. The course includes a comprehensive schedule, featuring hands-on experience with commercial software for finite element method (FEM) simulations. Specifically, students will be provided with temporary licenses for COMSOL Multiphysics to conduct thermal simulations. In addition, the course covers the process of fitting thermal metrics in MATLAB and their modeling within a SPICE-like environment, offering practical insights and discussions. Examples of fully circuital electrothermal simulations in the SPICE-like software will also be presented.

There will be a final assessment, in which students will be required to present how they can effectively apply the course material to a case study falling within their own research areas.

  Numerical_Methods (syllabus of a.y. 2023-24)

 dr. Antonio Pio Catalano, DIETI - Unina

Winter 2025-26 (second year of cycle XL)

The course provides an overview of the experimental design and data analysis and is intended for PhD students in science and engineering disciplines who need to use 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. Common errors in experimental planning and misuse of statistics will be highlighted throughout the course. Finally, a brief introduction to analysis from observational data will be given. The course will show the application of what explained on exemplifying science and engineering research problems, possibly depending on the need of the participants. There will be a final assessment. The course foresees six two-hours lectures split in three weeks, two days per week.

 Statistical Data Analysis (Syllabus of a.y. 2023-24)

DIETI - ITEE PhD Board

Winter 2024-2025

(first year of cycle XL)

To earn the credits, at the end of the course 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. Students’ presentations will take place in the last lesson. Details about the presentation format and schedule of the presentations will be given during the course.

 Virtualization technologies (Syllabus of a.y. 2023-24)

Winter 2025-26

(second year of cycle XL)

The course covers topics regarding the design, realization, and evaluation of Network Intrusion

Detection Systems (NIDSs) used for protecting networks against attacks. Specifically, the course details how Machine Learning (ML) and Deep Learning (DL) approaches can be properly exploited to develop these detection systems. The course briefly provides the basics on the most common attacks against networks and on ML and DL models used to counteract them. The students will learn methodological guidelines to determine which are the most suitable models and input data based on, for instance, the problem to face (e.g., anomaly detection, attack classification), the information available (e.g., supervised vs. unsupervised), and the attacks to deal with (e.g., DDoS, BotNet). The course follows a “hands-on” approach that will guide the students toward the actual design, implementation, and performance evaluation of NIDSs, exploiting the tools provided by state-of-the-art (Python) frameworks (e.g., Scikit-learn, Keras, PyTorch). The course will make extensive use of actual case studies based on data from real network attacks (e.g., attacks against IoT or Android devices) to provide the operational scenarios for the detection systems. There will be a final assessment concerning the realization of a basic prototype and a report describing its design and evaluation.

  Hands-on Network (syllabus of a.y. 2023-24)

dr.  Antonio Montieri, DIETI - Unina

Winter 2025-26

(second year of cycle XL)

In a globally competitive environment where the value of research, and indeed one’s professional/academic success, is measured in terms of how impactfully research findings are communicated/disseminated (publication) and how many times they are cited (citation impact), researchers and academics - especially in STEM domains - are increasingly held to the motto “publish or perish!” Yet too many remain myopically focused on technical skills, failing to prioritise the strategic and communication skills needed to effectively showcase technical merit… until faced with imminent rejection of their submitted papers! Participants will learn to avoid such myopia with the value-driven orientation necessary to thrive amidst competition. Along with capacity-building attributes and core competencies including creative thinking and cross-disciplinary team work, participants will work to develop effective professional communication and writing skills, with a view to getting published. Designed to be interactive, this course emphasises active participation/discussion, and as with comparable offerings, has a normal course load incl: readings, in-class exercises/activities and at-home work (individual/team). Overall assessments are based on various learning components including collaborative research project, manuscript preparation and submission as applicable.

 Strategic Orientation STEM Research Writing (Syllabus of a.y. 2023-24)

Sig.ra Adriana D'Auria, DIETI -  Unina

Spring 2025

(first year of cycle XL)

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

Prof. Antonio De Maio, DIETI - ITEE PhD Board

DIETI

ITEE PhD

Winter 2025-2026

(second year of cycle XL)

Prof. Gianmaria De Tommasi, DIETI Unina 

 Spring 2026

(second year of cycle XL)

Prof. Diego Iannuzzi, DIETI - ITEE PhD Board

M. Cesarelli, P. Bifulco