Innovation and Entrepreneurship

prof. P. Rippa, DII- Unina

prof. Mario Di Bernardo, DIETI 

prof.Giancarlo Sperlì - DIETI, Unina

30/04/24 - 02-07-09-14-21-28/05/24

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

dr.Massimo Rosmilia - DIETI, Unina

20, 22, 25 e 26 marzo e 4 e 5 aprile

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 

prof. Nicola Mazzocca - DIETI, Unina

23-25-30/01/2024  01-06-08/02/2024

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

DIETI-ITEE PhD

10-17-24-31/01/2024

7-14-21-28/02/2024

06/03/2024

Prof. Antigone Marino 

  How to boost

Prof. Antigone Marino 

Physics Department

08-09-15-16-22-23/02/2024

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

 IoT-DataAnalysis

prof. Raffaele Della Corte, DIETI 

26-30/01/2024  02-06-09/02/2024

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

 dr. Antonio Pio Catalano, DIETI

15-19-21-23-27-29/02/2024 

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

DIETI-ITEE PhD

08-10-15-19-24-25-29-31/01/2024  05-07-26/02/2024

This course will present advanced virtualization technologies used today for both research and industrial applications, including embedded systems, networking, cloud computing, and telecom devices. 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 with an overview of the specific virtualization technology, and the second part of 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.

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

DIETI

ITEE PhD

09-11-16-17-18/01/2024

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

dr.  Antonio Montieri - DIETI-Unina

07-15/12/2023 – 12-19/01 - 09-23/02/2024

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

Sig.ra Adriana D'Auria – DIETI- Unina

The course introduces the new vision of Natural Language Processing (NLP) considering the recent revolutions in the field. The advent of Large Language Models like ChatGPT has brought huge changes in the way we all see AI but it has also increased expectations and “noise” in people’s minds. At the same time, we observed different reactions from experts in the field, who were forced to specify the pros and cons of this revolution, and from non-experts who suddenly became proficient in their own view. In this perspective, we decided to offer to the doctoral school, and anyone else interested, a series of lectures on the foundations of modern NLP and aspects related to AI.

There will be a final assessment.

 Artificial Intelligence and Natural Language Processing (syllabus 2023)

prof. Francesco Cutugno  - DIETI - Unina

Dr. Domenico Amalfitano, prof. Anna Rita Fasolino

DIETI

ITEE - ICTH PhD programs

 How to boost your PhD (syllabus 2023)

 DIETI

ITEE - ICTH - CQB PhD programs

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 (syllabus 2023)

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

DIETI

ITEE PhD

DIETI

ITEE PhD

DIETI

ITEE PhD

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

DIETI

ITEE PhD

Winter 2023 

(38th cycle, second year)

DIETI

ITEE PhD

 Spring 2024

(38th cycle, second year)

DIETI

ITEE PhD 

M. Cesarelli, P. Bifulco