Matrix Analysis for Signal Processing with MATLAB Examples

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

dr. Massimo Rosamilia - DIETI - Unina

The Linear Parameter Varying approach: theory and application

This course is based on the recent book entitled Linear Parameter-Varying Control: Theory and Application to Automotive Systems, John Wiley & Sons, Inc., Hoboken, New Jersey, 2025 ISBN: 978-1-394-28595-2. It 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. Illustration several examples wll be shown. 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.

LPV

Design and control of robotic prostheses 

The course deals with anthropomorphic hands and robotic prostheses. Five teachers will deal with design, control, bio-interfaces for control and learning techniques for grasping and manipulation. 

Design and control of robotic prostheses

prof. F. Ficuciello - DIETI- Unina

Methodologies and Tools for conducting Systematic Literature Reviews and Systematic Mapping Studies

The objective of this course is to provide PhD students with a structured and rigorous approach to conducting Systematic Literature Reviews (SLRs) and Systematic Mapping Studies (SMSs). These methodologies are widely used across disciplines to analyze existing knowledge systematically, identify research gaps, and establish a strong foundation for future investigations.
The course is designed to introduce not only theoretical foundations but also hands-on practical techniques, ensuring that participants can effectively apply what they learn. Throughout the sessions, students will engage in interactive exercises, real-world case studies, and guided applications of systematic review methodologies, using relevant tools and frameworks.
SLRs focus on synthesizing evidence to answer specific research questions, while SMSs provide a broader overview of a research field by categorizing studies and identifying trends. Mastering these processes is crucial for doctoral students across various disciplines, as it enhances their ability to synthesize complex information, construct well-supported arguments, and contribute meaningful advancements to their respective research fields.

 SLRSMS

prof. Domenico Amalfitano - DIETI - UNINA

Big Data Architecture and Analytics

The course aims to investigate Big Data methodologies and architectures for supporting analytics in several application domains from different points of view. Specifically, the course provides an analysis of Big Data Management and Data Analytics Lifecycle, concerning the design of large and complex data systems. Furthermore, the course focuses on the processes of ingestion, modeling, analysis, and visualization of Big Data. We further investigate both batch and streaming processing architectures for supporting different case studies (i.e., social network analysis, health, and Industry 4.0), also discussing their deployment on cloud infrastructures and the integration of recent Artificial Intelligence models. There will be a final assessment.

Big Data

prof. Giancarlo Sperlì,
DIETI - Unina

Fiber optic sensing and optoelectronic circuits: design and application

Fiber optic sensors (FOS) have emerged as a crucial technology for real-time monitoring in different fields, including structural health monitoring, biomedical applications, and industrial automation. Their immunity to electromagnetic interference, high sensitivity, and ability to operate in extreme environments make them superior to traditional electronic sensors. State-of-the-art applications leverage Fiber Bragg Gratings (FBG) for strain and temperature sensing, interferometric techniques for precision measurements, and distributed sensing for large-scale infrastructure monitoring.
The design of an optoelectronic readout circuit is essential for accurately extracting and processing data from FOS. This involves a different kind of optical source (laser or LED) for signal transmission, a photodetector (PIN or APD) for optical-to-electrical conversion, and signal conditioning circuitry, including amplification, filtering, and analog-to-digital conversion. Advanced implementations integrate digital signal processors (DSPs) or microcontrollers to enhance resolution and enable real-time data analysis. Additionally, noise reduction techniques and wavelength-division multiplexing (WDM) are employed for multi-sensor networks.
This course will provide a comprehensive background on FOS technology, highlighting some cutting-edge applications and the sensing approaches that underlie their operation. An additional aspect explored in this course will be the design of optoelectronic readout circuits. Special attention will be given to the component selection phase, the integration strategies of optical and electronic components, signal processing techniques; all aimed at the design of high-performance sensing systems. Ultimately, through theoretical study and laboratory practice, students will acquire skills in the design and optimization of monitoring solutions based on fiber optic sensors for next-generation engineering applications.

Fiber Optic

 dr. Vincenzo Romano Marrazzo, DIETI - Unina

Electronic Scan Antennas for Radar Signal Processing Applications

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. A lecture on recently developed estimation techniques based on the compressed-sensing framework is also provided. 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. 

There will be a final assessment.

 Electronic scan antennas

prof. Antonio De Maio, dr. Massimo Rosamilia, DIETI- Unina

I pilastri della trasformazione digitale

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.

 Pilastri Trasformazione Digitale

 prof. Nicola Mazzocca, DIETI - Unina

IoT Data Analysis

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-Data Analysis

prof. Andrea Della Corte, DIETI - Unina

Using Deep Learning properly

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

DIETI - Unina

Design methodologies for digital circuits and systems oriented to FPGA

In the past few decades, digital integrated circuits have occupied a central role in the realization of digital signal processing algorithms, contributing to the design of a wide range of applications in the field of Internet of Things, measurements, biomedical, and telecommunication. An efficient realization of these circuits is pivotal to meet desired hardware performance, mainly expressed in terms of power consumption. At the same time, the adoption of suitable strategies, able to lead to a cost-effective product in a short time, is also desirable to alleviate the overall effort during the design. In this context, the adoption of Field Programmable Gate Arrays (FPGA) for the implementation of digital circuits constitutes a valuable design strategy, allowing to realize circuits of medium/high complexity with reduced costs in your own lab.

The aim of the course is to give an overview of the most important strategies used to program FPGAs, with special focus to techniques for efficient complex digital systems implementation. To this end, first of all basic information will be given about the structure of FPGAs and the hardware description languages, focusing the attention on Verilog HDL. Then, the attention will be focused on the High Level Synthesis (HLS) design methodology, which exploits C-based programming languages for the design. The steps used to program the FPGA will also be presented in Vivado tool. At the end of the course, the design of hardware accelerators for practical applications will also be shown to demonstrate the potentiality of HLS design flow for FPGA.

FPGA

dr. Gennaro Di Meo, DIETI- Unina

SOLAR CELLS: MODELLING AND APPLICATIONS

Global energy increasing demand places significant strains on current energy infrastructure. This emerging challenge, coupled with depleting traditional fossil-fuel based energy sources and the threat of climate change, requires the development of renewable energy technologies. Among the possible renewable energy approaches, photovoltaics (PV) represents a promising route. A solid foundation in solar cells working principles and typical behavior will be presented in this course. A better understanding of this topic will be given using PC1D software. Although its first part focuses on solar cells operational modelling, the expertise gained in the use of the presented topics can be easily adopted in alternative areas and scenarios. The primary target of this course is to guide students in correctly setting up electrical problems and extracting/exploiting key electrical parameters related on photovoltaic world. The course includes a comprehensive schedule, featuring practical experience with commercial software for electrical simulations within a SPICE environment. Methods and characterization techniques suitable for fault detection and malfunctioning of solar cells will also be explained, ranging from conventional current-voltage characteristics to impedance spectra analysis.

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.

Solar cell

 dr. Ilaria Matacena, DIETI - Unina

02-05-09-12/12/2024

prof. Lucio Franzese, DIETI - Unina

08-15-22-29/01/2025 05-12/02/2025

Prof. Antigone Marino, CNR

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

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

R.Natella

R.Pietrantuono

E. Andreozzi