PhD courses
Alberto Tesi
Date: November 25, 27
25 Nov 14,15 room S12 Santa Marta
27 Nov 9:00 room “Caminetto” Santa Marta
Hours/CFU’s: 8/2
Curriculum: Control, Optimization and Complex Systems
more details in email
Carlo Carobbi
Date: 5/12/24; 9-13 am
Hours/CFU’s: 4/1
Room: 177 Santa Marta
on line: https://meet.google.com/rmm-uaoa-vph
Curriculum: Electronics, Electromagnetics and Electrical Systems
Abstract:
Technical standards frequently provide support to, if not the basis of, academic studies and research in all the fields of engineering. Many, probably the majority, of the thesis works, PhD dissertations and scientific papers in engineering take advantage, inspiration or are even devoted to the content of specific technical standards. Nonetheless, few of the authors of such works are aware about the process leading to the writing and publication of a technical standard, its maintenance, the context in which technical standards are used by industry and referred to by regulations issued by national and regional authorities. Therefore, generally the outcomes of these studies have no impact on standards’ development. There are many reasons for this, such as academic research results are not brought to the attention of standard organizations, or do not address the actual needs of users of technical standards. The scope of this presentation is to provide information about the work of standard organizations and the context in which technical standards are applied, particularly in the electrical and information engineering sector.
Laura Carnevali
Date: 09,11,16,18 December, Santa Marta
Hours/CFU’s: 12/3
Date/Room/Time:
09/12/24, Santa Marta, aula 049, 10:15-13:15
11/12/24, Santa Marta, aula 035, 10:15-13:15
16/12/24, Santa Marta, aula 173, 09:00-12:00
18/12/24, Santa Marta, aula 173, 09:00-12:00
Curriculum: Informatics
Abstract: This course introduces the fundamentals of Markov Decision Processes (MDPs), a powerful framework for modeling of sequential decision problems under uncertainty. Topics include (but are not limited to): syntax and semantics of MDPs, adversaries, probabilistic reachability of MDPs, applications of MDPs using the PRISM tool.
Simone Magistri
Date/room:
08/01/2025, 14:00 – 17:00, Aula 49, Santa marta.
10/01/2025, 14:00 – 17:00 , Aula 49, Santa Marta.
13/01/2025, 14:00 – 17:00, Aula 175, Santa marta.
17/01/2025, 14:00 – 17:00, Aula 49, Santa marta.
Hours/CFU’s: 12/3
Curriculum: Computer Engineering
Abstract: The widespread adoption of Artificial Intelligence in real-world applications has been driven by the ability of Deep Neural Networks (DNNs) to solve increasingly complex problems in various fields, such as computer vision and natural language processing. However, the current DNN learning paradigm is static: DNNs are typically trained for single tasks with fixed datasets, making them rigid and prone to becoming outdated as real-world data and tasks evolve, particularly in sectors like transportation, healthcare, and robotics. Updating models often requires costly re-training on both old and new data, which is computationally and environmentally expensive due to the large number of parameters and the demanding training procedures involved. Additionally, privacy concerns may prevent access to old data, making re-training unfeasible. Naively updating models solely on new tasks leads to catastrophic forgetting—where models lose prior knowledge when learning new information. Continual Learning, also known as Incremental Learning, is a field of machine learning aimed at developing adaptive learning paradigms for DNNs to continuously learn new tasks while preserving previous knowledge. This course explores the fundamentals of continual learning, including continual learning scenarios such as Domain Incremental, Class Incremental, and Task Incremental, as well as regularization techniques on state-of-the-art models like Convolutional Neural Networks (CNNs) and Transformers. We will also examine recent breakthroughs, open challenges, and research directions for advancing this field.
The goal of the course is to introduce the concept of Physics Informed Deep Neural Networks (PINN), discuss its implementation from scratch in PyTorch and use advanced ad-hoc developed open-source libraries such as Nvidia-modulus for addressing real-world problems in various fields (engineering, physics, oil). We discuss recent topics such as Mixture-of-Models, Neural Operators, Physics-Informed Kolmogorov-Arnold Networks and Physics-Informed Computer Vision.
– Duration of Course: 16 hrs/4CFU (for Smart Computing, Ingegneria
Where & When:
Plesso Didattico Morgagni – viale Morgagni, 44-48, 50134 Firenze (FI)
– aula 217, Tuesday 14/01, 9:30 – 11:30
– aula 219, Wednesday 15/01, 9:30 – 12:30
– aula 219, Thursday 16/01, 9:30 – 12:30
– aula 219, Monday 20/01, 9:30 – 12:30
– aula 219, Wednesday 22/01, 9:30 – 12:30
– aula 219, Friday 24/01, 9:30 – 11:30
The course will encompass:
1. An in-depth study of differential equations and their numerical resolution using PINN.
2. An exploration of the theory and applications of Physics-Informed Neural Networks to many fields (Engineering, Physics, Medicine, Oil Industry, etc).
3. Practical sessions involving real-world applications.
Each lecture will comprise a part about the theory and a half of hands-on practice over Python Scripts and notebooks.
Either the lecture slide and the lecture code will be shared, and something is already available on the course page. For more info about the course, see
https://androbomb.github.io/teaching/
https://forms.gle/waufiF6kEwTRsveK9
2. Join the Discord channel as soon as possible.
Should you require any further information or clarification, please do not hesitate to mail me at
bombini__AT__fi.infn.it
CV: INF
Hours/CFU: 12 hours /3 CFU
Time: 9.30 : 12.30
For other details see:
Lorenzo Mucchi
Stefano Caputo
Title: Fundamentals of Physical Layer Security
Curriculum: Telecommunications and Telematics
Hours: 8 / 2 CFU
Dates: 28-29 GEN 9.30-13.30
Room: 035 Santa Marta
Abstract: Physical layer security (PLS) is a novel paradigm that aims to achieve secure wireless communications by exploiting the physical properties of the transmission channels, such as noise, interference, and fading. Unlike conventional cryptographic methods, which rely on mathematical algorithms and secret keys, PLS uses signal design and processing techniques to degrade the signal quality of the eavesdroppers and realize keyless secure transmission. PLS offers several advantages, such as avoiding the difficulties in key distribution and management, providing flexible security levels through adaptive transmission design, and leveraging the features of next-generation wireless networks, such as spatial diversity, cooperation, and cognition. This PhD course provides an overview of the main techniques of PLS and illustrates the applications of PLS in 5G and 6G networks, highlighting the challenges and opportunities of physical layer security in the contemporary landscape.
Stefano Caputo
Date: 3, 5, 7 Feb 2025
Time/Room: 9:30 – 13:30, 046 Santa Marta
CV: TLC per 2 CFU; EEE per 1 CFU
CFU: 3
Abstract: The course will give to the students the fundamentals of visible light communications (VLC). VLC refers to a data communications medium using visible light between 400 THz (780 nm) and 800 THz (375 nm). Low-cost wireless communication network can be created using VLC, often known as Li-Fi systems. LiFi is a potential solution to the shortage of global wireless radio spectrum. The light can be used as a communication medium for ubiquitous computing, because light-producing devices (such as indoor/outdoor lamps, TVs, traffic signs, commercial displays, car headlights/taillights, etc.) are used everywhere. The course will provide a general background on LiFi technology, discussing the major advantages and existing challenges to technology integration in 6G mobile Networks. Recent key advancements in physical layer techniques, such as localization, ultra-high-speed indoor connection, ultra-low latency outdoor link, will be discussed.
Luigi Chisci
Note: students are invited to register Here
Date: 10-18 / 2 /2025
Hours/CFU’s: 20/5
Date/Time:
10/2/2025 Lunedi ore 9-13
11/2/2025 Martedi ore 9-13
13/2/2025 Giovedi ore 9-13
17/2/2025 Lunedi ore 9-13
18/2/2025 Martedi ore 9-13
Curriculum: Control, Optimization and Complex Systems
Abstract: This course aims to provide both theoretical and practical tools to tackle estimation problems encountered in several areas of engineering and science. In particular, it is shown how to formulate such estimation problems as instances of a general dynamical system state estimation problem and how to derive the mathematical solution of the latter problem. Then it is shown that, for a linear Gaussian system, such a solution yields the well known Kalman filter. Further, approximate techniques (e.g. extended and unscented Kalman filters, particle filter, etc.) are presented for the case of nonlinear and/or non-Gaussian systems, for which an exact closed-form solution cannot be found. To conclude the theoretical part, theoretical limitations (i.e. the Cramer-Rao lower bound) on the quality of estimation are discussed. In the second part of the course, we illustrate some applications of linear/nonlinear Kalman filtering (e.g., tracking, robotic navigation, environmental data assimilation).
Franco Bagnoli
Date: 12/2, 19/2, 26/2, 5/3
Hours/CFU’s: 8/2
Time: 9:30 – 11:30
On-line at link: https://meet.google.com/nhe-dsor-ynb
Material: https://teaching.complexworld.net/english/cellular-automata-2025
Registration: https://forms.gle/Szby6ppW62PKcRZq5
Curriculum: Control, Optimization and Complex Systems
Abstract: Cellular automata are fully discrete systems and are used as simple models in many context, from physics to biology to computer science. They can be defined in a deterministic way, anche thus be studied as dynamical systems, extending the notions of chaos, for instance, or in a probabilistic way furnishing many examples of phase transition. The two concepts can be mixed, for instance by studying the effect of a small noise. One of the recent fields of study concerns the problem of controlling such systems, and since they are highly non-linear, standard techniques cannot be used and custom ones have to be developed.
1) Introduction to discrete modeling and simulations
2) Probabilistic cellular automata and phase transitions
3) Deterministic cellular automata, attractors
4) Control, generalizations and extensions
Pierluigi Mansueto
Date/time/Room
- 13/02/2025 – Room 046 – 14:00/17:00
- 17/02/2025 – Room 031 – 14:00/17:00
- 19/02/2025 – Room 046 – 14:00/17:00
Santa Marta
Hours/CFU’s: 8/2
Curriculum: Control, Optimization and Complex Systems
Abstract: Clustering is one of the most extensively studied problems in unsupervised learning, as highlighted by various surveys and books. Its aim is to organize a collection of elements into coherent groups called clusters: similar elements should be assigned to the same cluster while different elements should be in different clusters. This course concerns the analysis of the main models and approaches for clustering problems, with a particular focus on one of the most recognized formulation, i.e., the Minimum Sum-of-Squares Clustering (MSSC). In addition to the well-known K-MEANS, we will also examine: methodologies that do not require the number of clusters (k) a priori; novel meta-heuristics to improve the K-MEANS performance; one of the (relatively) new frontiers for this topic, that is, semi-supervised clustering.
Andrea Tani
Date:
20 Feb. 2025 9.30 – 13.30 room 171 SM
21 Feb. 2025 14.00 – 18:00 room 171 SM
CFU: 2
CV: TLC
room: 171
Abstract:
The ever-increasing demand for bandwidth in wireless communication systems, driven by multimedia and Internet of Things (IoT) applications, has led to a significant shortage of available spectrum resources as reported by the U.S. Federal Communications Commission, the underutilization of licensed spectrum due to regulated access has proven to be “a more significant problem than the physical scarcity of spectrum. This observation has highlighted the need to move away from static resource allocation strategies and has driven intense research into dynamic spectrum sharing among systems with heterogeneous radio access technologies and different priorities for access to licensed and unlicensed bands. These research efforts have culminated in the development of cognitive radio (CR) technology, which seeks to exploit underutilized spectral resources in the frequency, time, and space domains by reusing them opportunistically. Notably, dynamic spectrum sharing and management have been recognized as potential enablers for 6G, falling under the category of “new spectrum.” At the core of cognitive radio lies spectrum sensing, tasked with determining whether a particular portion of the spectrum is “available” or not. In other words, the goal is to discriminate between two mutually exclusive hypotheses. Following a brief introduction to the application scenarios of the CR paradigm, the first part of the course provides the theoretical foundation of spectrum sensing within the framework of detection theory, with a particular focus on blind techniques. The second part of the course addresses the challenges associated with applying blind spectrum sensing techniques in the context of in-band full-duplex technology, as well as their operation at millimeter-wave and terahertz frequencies, both of which are envisioned as potential 6G enablers. Finally, we present the application of spectrum sensing in the integration of unmanned aerial vehicles UAVs into 5G/6G networks, addressing challenges such as dynamic spectrum access in multi-UAV scenarios
Carlo Odoardi, Lorenzo Capineri
Date: 9,23 Apr 2025
Room/Time:
– 9/4 10.15-13.15, room 48 (or sala saminetto), Santa Marta
– 23/4 14.30-17.30, room 48 (or sala saminetto), Santa Marta
Hours/CFU’s: 6/1
Curriculum: Soft Skills
Abstract:
Part 1. Strategic Human Resource Management for Innovation Prof. Carlo Odoardi
The strategic management of the human capital present in an organization falls within the framework defined as “organizational innovation” which can be defined as the connection of people to organizational objectives and goals in order to improve performance and develop a culture aimed at enhancing skills and professionalism to support innovation, flexibility and competitive advantage in continuous collective or organizational learning. An organization focused on the enhancement of people must necessarily equip itself with a series of new systems and measurement methods to verify and monitor the innovative variables necessary to determine specific professional behaviours at an individual, team and organizational or managerial level in an integrated vision. Today, research and experimentation carried out over the last twenty years in work contexts has highlighted the strategic importance of the interaction and integration between “organizational innovation and technological innovation”. The field of intervention research has produced a series of models, methods and tools for the development of organizations in an innovative way and above all to outline new organizational models (agile organizations or smart organizations) and management models (new managerial and leadership models for innovation) where the system of professional relational networks are central to dealing with continuous metamorphoses.
Part 2. The complexity of the role of the future engineer Prof. Lorenzo Capineri
This course focuses on the complexity of STEM education, especially for engineering courses, considering the present and future role of these professionals in society. The high specialization of technologies has required vertical education in engineering topics, while the role of engineers of the future will be broader to solve societal problems at a global level. The ability to fill this role can be improved by stimulating the education of engineers with humanistic and social topics, multicultural teamwork and preparation to stimulate creativity and innovation within a laboratory community. In this path we find the relevance of Adriano Olivetti training process. This course reports some experiences and methodologies of innovative teaching that can be useful for the design of university teaching courses in STEM subjects”. Contributions from large industry managers is foreseen.
Ultrasound medical imaging: an engineer’s perspective
Proff. Ramalli/Meacci
- 6 May 08:30 – 12:30
- 9 May 13:30 – 17:30
Room: 033 Santa Marta
CV: Electronics, Electromagnetics and Electrical Systems
Hours/CFU: 8/2
abstract:
Ultrasound imaging techniques are a widely used diagnostic tool in medicine. They owe their success to a series of features that make them ideal for medical applications. Indeed, they use a form of energy that does not entail harmful effects on biological tissues. Moreover, these techniques can be implemented in relatively low-cost and low-size systems working in real-time, which can be useful to perform exams directly at the bedside or in the operating room.
The course will provide the basics of the physics of ultrasound, starting from the ultrasound wave generation to the concepts of reflection and backscattering. Similarities between ultrasound echograpic and radar systems will be highlighted.
Then, the course will focus on the signal and image processing techniques used for morphological and motion (tissue and blood-flow) imaging. Technical requirements, limitations, and solutions, which impact the final image quality, will be discussed.
Finally, the course will give an overview of advanced, state-of-the-art equipment and techniques for biomedical applications, including ultrafast imaging systems and 3-D imaging.
The course includes a demo session with an ultrasound scanner fully developed by the Department of Information Engineering of the University of Florence.
Franco Bagnoli
Date: 7/5, 14/5, 21/5, 28/5, 4/6, 11/6
Hours/CFU’s: 12/2
Time: 14:30-16:30
on line at link: https://meet.google.com/eox-upfq-pxf
Material: https://teaching.complexworld.net/english/from-babbage-to-chatgpt-2025
Registration: https://forms.gle/gZb2Sovzkc5dYmue7
Curriculum: Soft Skills
Abstract: We shall review the evolution of computers and their applications, from the first mainframes dedicated to computing, to the evolution in the business world, the birth of the Internet, the switch to personal computers, and finally to the internet & microprocessor world of today. In parallel, we shall examine the development of operating systems and computer languages, the social impact and its driving force, the connections with literature and science fiction.
Complex-valued neural network: theoretical aspects and applications for failure prevention in electrical systems.
Marco Bindi
Room / Time TBD
CV: EEE
CFU:1
DATE: 30 May 09:00-13:00
Room: Sala Caminetto Santa Marta
This course is structured into two main parts and will be held in a single day, lasting four hours. The first part provides a theoretical introduction to Multi-Layer Neural Networks with Multi-Valued Neurons (MLMVN), highlighting their key properties and differences from conventional real-valued feedforward neural networks. The second part focuses on practical applications of MLMVN in classification problems related to fault diagnosis in medium-voltage electrical cables, DC/DC power converters, and Power Quality assessment. Additionally, live demonstrations of the training process using a MATLAB application may be presented during the session.
Electrical load forecasting for applications in the Smart Grid
M. Intravia
CV: EEE
CFU:1
DATE: 26 May 09:00-13:00
Room: Sala Caminetto Santa Marta
The course begins by highlighting the importance of electrical load forecasting in Smart Grids, particularly in relation to energy management systems. Forecasting plays a crucial role: by predicting energy demand in advance, utilities and operators can enhance efficiency, integrate renewable energy sources more effectively, and prevent overloads or shortages.
The course then consists of a first theoretical part, where the fundamental concepts of forecasting are introduced, along with the most commonly used techniques in this field. In particular, both classical (or statistical) approaches and more advanced approaches based on neural networks, such as LSTM networks, will be considered.
In the second part, practical examples of these algorithms applied to electrical load forecasting will be presented. Additionally, the implementation of these algorithms in Python code will be demonstrated.
UAV Communications
Andrea Tani
11-12 June 9.30 am
10/06 9:30-13:30 Room 171 Santa Marta
12/06 9:30-13;30 Room 173 Santa Marta
Hours/CFU: 8/2
CV: TLC
abstract:
Nowadays, Unmanned Aerial Vehicles (UAVs), also commonly known as drones have discovered a wide range of applications in various domains, including aerial inspections, photography, precision agriculture, traffic management, search and rescue operations, package delivery, and telecommunications, among others.To achieve ubiquitous and high-data-rate services in challenging scenarios, new-generation wireless networks must embrace innovative technologies. The integration of terrestrial and aerial nodes to create a vertical heterogeneous network offers flexible and reliable mobile communication infrastructure. Deploying UAVs as aerial base stations in wireless communication systems promises cost-effective connectivity, particularly in areas without existing infrastructure coverage. Low-altitude UAVs outperform terrestrial communications in terms of deployment speed, flexibility, and quality of service, thanks to short-range line-of-sight links.The first part of the course focuses on examining empirical models that characterize both Air-to- Air (AA) and Air-to-Ground (AG) propagation channels, considering various scenarios (Rural, Sub- Urban, Urban). It also provides an overview of UAV-aided wireless communications, addressing typical use cases like UAV-aided ubiquitous coverage and UAV-aided relaying. Special emphasis is placed on addressing the challenges related to trajectory planning and the deployment of UAV networks to achieve optimal wireless coverage.The second part of the course deals with the UAV-to-UAV communications, UAV-Cellular spectrum sharing, and provides insights into techniques for identifying and countering malicious jamming attacks.
Enrico Collini, Luciano Alessandro Ipsaro Palesi
AI for time-series and recommendation systems
CV:INF
19/06/25 14-17 Room 32 Santa Marta
20/06/25 09-12 Room 55 Santa Marta
The growing relevance of artificial intelligence (AI) in addressing complex, real-world challenges has led to advancements in two key areas: time-series prediction and recommendation systems.
In this course, AI for Time-Series and Recommendation Systems, such as Deep Learning and Ensemble Learning techniques will be presented and discussed, along with clustering techniques.
Initially, concepts related to time-series prediction will be introduced, emphasizing the importance of data quality. The program will continue by explaining how machine learning models can provide both short-term and long-term predictions. A key aspect of these systems is the integration of explainable AI (XAI) methodologies, which enhance the understanding of model outputs and improve decision-making.
Building on the foundational concepts of time-series prediction the course will also explore the role of clustering techniques for data processing and knowledge discovery for recommendation systems.
During the course, theoretical concepts will be reinforced with practical application, including mobility-related metrics (e.g., bike-sharing systems), rainfall-induced landslide forecasting, and fashion retail recommendation systems to personalize customer experiences and improve sales.
Architectures and Protocols Design Towards Quantum Internet
Roberto Picchi
room: 171 Santa Marta
Time: 9-13
Date 25-26 June
CV: TLC CFU: 2
Abstract: Quantum mechanics revolutionized the understanding of the physical world, yet the realization of quantum computing remained theoretical for decades. Richard Feynman introduced the concept of quantum computing, demonstrating its advantages over classical Turing machines, which was later formalized by David Deutsch in 1985. Subsequent research further refined quantum computing models, highlighting their fundamental differences from classical computation. The rapid advancement of telecommunications has driven increasing interest in quantum communication networks. While terrestrial quantum communication via Optical Fibers (OFs) suffers from significant signal loss, necessitating costly repeaters, Quantum Satellite Networks (QSNs) present a promising alternative. These networks facilitate long-distance entanglement distribution and enable intercontinental quantum communication. Research on Low Earth Orbit (LEO) quantum satellite backbones aims to interconnect quantum servers, enhancing computational capabilities. A critical challenge is optimizing End-to-End (E2E) entanglement generation. By leveraging Software-Defined Networking (SDN), proposed architectures minimize the number of hops while maximizing network capacity, striking a balance between performance and cost across centralized and distributed models in various satellite constellations. Studies in the literature highlight the benefits of embedding a Control Plane (CP) within the satellite constellation. A two-tier CP model has been proposed, with a Master Control Station (MCS) on the ground managing the network, while satellite-integrated CPs handle entanglement generation. Additionally, protocols for E2E entanglement generation have been introduced and evaluated. Furthermore, research on drone-based Metropolitan Quantum Networks suggests they offer a flexible, cost-effective solution. SDN has been shown to play a crucial role in managing Quantum Drone Networks (QDNs), enabling efficient entangled pair distribution. An SDN-based architecture for Metropolitan Quantum Drone Networks (MQDNs) has been proposed, incorporating an entanglement generation protocol and an optimization framework. Performance evaluations in literature assess fidelity, entanglement rate, and overhead, demonstrating the feasibility of QDNs for distributed quantum computing and Quantum Key Distribution (QKD).
Angeli
CV: Control, Optimization and Complex Systems
CFU: 1
Time: 23 july 2025, time 10:00 – 12:00 and 14:00 – 16:00
Room 048 Santa Marta
or https://meet.google.com/gzb-mvpw-shm
Abstract:
Chemical reaction networks are a modelling tool for the complex molecular interactions underpinning life at the cellular level. They bear close resemblance to Petri Nets and are characterized, at the dynamical level, by the presence of strong nonlinearity.
As such they have attracted the attention of both biologists and engineers, trying to understand how their structure can affect their functionality and their dynamical behaviors.
The course will introduce the basic models of chemical reaction networks and illustrate some robust tools for the study of their dynamics using ordinary differential equations.
Network Simulation Meets AI
Pecorella Imputato
– 11/9 room 029 9-17
– 12/9 room 046 9-17
Hours/CFU: 12/3
This course explores how simulation and artificial intelligence (AI) can be combined to design and evaluate computer networks. Given the growing complexity of modern networks, effective resource planning and evaluation of optimization strategies are essential to meet dynamic traffic demands. The course begins with a review of computer networking fundamentals, then introduces the principles of discrete-event simulation, with a focus on ns-3 as a flexible and powerful network simulator. We then explore how AI tools can be integrated with ns-3 to enhance network design and performance evaluation. The course covers simulation execution, data analysis, and concludes with insights into network performance and opportunities for optimization.
Filtering devices
see: https://drive.google.com/file/d/1E_Ct1N7fXUt5CPl3Br5KtVnpPGoQNr2r/view?usp=sharing
Giacomo Giannetti
CV: EEE
Date: 15,16,17,18 September 10.30-12.30
Room: Meeting room DINFO
Filtering devices are key components in communication systems as they route signals according to their frequency spectrum. In this PhD course, a broad overview on filtering devices of interest in electrical engineering is provided. First, filters are introduced with a focus on microwave filters. The different filter responses and features are then presented. Second, the many different realization techniques are shown, discussed, and compared. Third, the integration of filtering devices with other components is outlined with a focus on the pros and cons of such configurations. Fourth, a design example for a case study of practical relevance is described. Then, microwave measurements are introduced and a hands-on experience about the tuning and measurement of microwave filters and diplexers with nano-VNAs is proposed.
Statistical Model Checking
G.Gori
CV:INF:
Hours/CFU: 8/2
Date: 30/9/25, meeting room DINFO SM 14-18
Date: 03/10/25, meeting room DINFO SM9-13
Statistical Model Checking (SMC) has emerged as a crucial technique to automatically verify the correctness of complex systems, particularly in safety-critical applications where it can be also useful to quantify the probability of event occurrence (e.g., system failure) to provide reliable forecasts of systems’correct behaviour.
SMC is a probabilistic verification method that assesses the correctness of system models by employing statistical techniques. Unlike traditional formal verification methods, SMC leverages statistical sampling to analyze the behavior of systems under uncertainty. By generating random or guided samples from the system’s state space, SMC can efficiently evaluate the likelihood of critical events and quantify the system’s performance.
In safety-critical applications such as railways, autonomous vehicles and aerospace systems, ensuring the reliability and safety of the system is paramount. Traditional verification techniques may struggle to handle the complexity and uncertainty inherent in these systems. SMC offers a practical solution by providing probabilistic guarantees of system behavior, allowing designers to identify potential issues and mitigate risks early in the development process.
The course will first recall the basic principles of model checking (algorithms and temporal logics) and of its use as a formal verification technique. It will then introduce the SMC technique and its capability to statistically address the state space exploration.
One of the key tools for that implements SMC is UPPAAL SMC, an extension of the UPPAAL model checker tailored for statistical analysis. UPPAAL SMC enables users to model complex systems using timed automata, define probabilistic properties of interest, and perform statistical analysis to assess the system’s reliability.
Through hands-on exercises and practical examples, participants in this course will learn how to utilize UPPAAL SMC effectively for modeling and verifying safety-critical systems.
Digital Watermarking: From Classical Models to Machine Learning Approaches
D.Baracchi
CV: TLC
Hours, CFU 12/3
– Lunedì 20 ore 10:15 – 13:15: Aula 032 Santa Marta
– Mercoledì 22 ore 14:00 – 17:00: Aula 175 Santa Marta
– Lunedì 27 ore 10:15 – 13:15: Aula 032 Santa Marta
– Mercoledì 29 ore 14:00 – 17:00: Aula 175 Santa Marta
With the rapid growth of digital media, ensuring the ability to trace a content’s origin and verify its authenticity has become a pressing challenge. This course introduces digital watermarking, a set of techniques for embedding hidden, imperceptible information into digital content to enable provenance tracking and authenticity verification. Participants will first explore classical model-based approaches before moving on to recent advances in machine-learning-based watermarking. These include techniques for watermarking AI-generated content as well as methods for embedding watermarks into neural networks themselves. Through a combination of lectures and practical examples, attendees will gain the skills needed to apply watermarking techniques effectively for real-world content authentication tasks.
Video Forensics: tools, techniques, and current challenges
D.Shullani,
Hours/CFU: 12/3
CV:TLC
– 21/10, room 035, 14:00 – 17:00
– 24/10, room 171, 16:00 – 19:00
– 28/10, room 035, 14:00 – 17:00
– 30/10, room 046, 09:30 – 12:30
Video forensics is a rapidly growing field, particularly due to the increasing prevalence of artificially generated content. How can we distinguish what is real from what is not? To address these questions, the course will explore key aspects of video forensics, beginning with codecs (i.e. H.264/AVC and H.265/HEVC ) which are essential for understanding encoding artifacts and their forensic significance. Students will develop a deeper understanding of multimedia forensic tools that operate on both video content and container structures, including signal-based and data-driven algorithms. Finally, the course will address practical issues, such as constructing datasets and ensuring they meet required technical standards.
*: PhD course proposal, still to be approved by the PhD committee
PhD Seminars
Normally 1 CFU
Giacomo Bucci – 15 ottobre 2025 alle ore 18.30
“The history of computer technology in 100 cards”
Anche quest’anno IEEE propone all’inizio di ottobre, in
coincidenza con il tradizionale IEEE Day, la IEEE History Week.
Nella settimana dal 6 al 10 ottobre IEEE invita a riflettere
sull’importanza di conservare memoria della storia
dell’Associazione così come dell’evoluzione delle tecnologie che
formano il tessuto nel quale l’Associazione opera.
Per l’occasione il Gruppo dei Life Members della Sezione italiana
intende presentare una recente iniziativa editoriale che illustra, in
maniera sintetica, passato e presente degli strumenti di calcolo
digitale. Coautore dell’opera dal titolo “Computer technology in
100 cards” è il professor Giacomo Bucci insieme al collega Enrico
Vicario.
Il prof. Bucci è professore emerito all’Università di Firenze. Nel corso
degli anni ha raccolto reperti per documentare la storia del calcolo
elettronico. Obiettivo principale è stato quello di far conoscere
meglio, anche all’interno, gli strumenti di calcolo, molti dei quali
sono stati, o sono ancora, sulla nostra scrivania.
Un primo catalogo della collezione è stato
oggetto di una Nota storica pubblicata dalla
Sezione italiana di IEEE per iniziativa
dell’History Activity Committee. Una versione
in inglese arricchita è diventata un volumetto
pubblicato da McGraw Hill Italia dal titolo
quanto mai appropriato “Computer technology
in 100 cards”. Malgrado il formato non proprio
tascabile, il volumetto di cento schede può essere considerato un
vademecum per riconoscere e comprendere gli strumenti di calcolo da
anni entrati via via nella vita quotidiana.
Il Life Members Affinity Group della Sezione italiana di IEEE propone un incontro del prof. Bucci con il Gruppo stesso per presentare, attraverso il volume, la storia dell’educazione informatica nel nostro paese.
Alexander Julian GALLO
Detecting cyber attacks in cyber-physical systems
Time: 11:00 Meeting room DINFO
Alexander Julian Gallo (Politecnico di Milano)
Detecting cyber attacks in cyber-physical systems
15/10/25, 15.00, meeting room DINFO
Abstract: Recent years have seen the onset of cyber threats against a number of cyber-physical systems, including safety-critical infrastructure, such as power distribution grids and water networks. Secure control has arisen as a counterpart to traditional IT security as a means to diagnose the presence of cyber attacks, as well as to accommodate their effects. In this talk, I will start by addressing the problem of cyber-attack detection in cyber-physical systems, highlighting known structural properties which must hold for attacks to remain undetectable. I will then show how in large-scale systems locally secure information can be leveraged to ensure attacks remain detectable, and how this information can be further exploited to reconstruct secure local state estimates. Finally, I will show how active techniques can be used to thwart these structural conditions, by altering the system’s behavior compared to the attacker’s internal model.
Alexander J. Gallo received the M.Eng. and his Ph.D. degree in Control Engineering from Imperial College London, London, UK, in 2016 and 2021. From 2021 to 2024 he was a postdoctoral researcher at the Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands. Since Sep 2024, he is a postdoctoral researcher at the Politecnico di Milano, Milan, Italy. His main research interests include distributed cyber-security and fault tolerant control for large-scale interconnected systems, with a particular focus on energy distribution networks, as well as health-aware and fault tolerant control of wind turbines. More recently, he has been working on providing guarantees to data-driven decision and control problems with the scenario approach.
Prof. Philippe Toint (Université de Namur),
29/92025
time 11:30
room: 012 (CD Morgagni)
Abstract: A very simple first-order algorithm is proposed for solving potentially stochastic nonlinear optimization problems with deterministic nonlinear equality constraints. This algorithm adaptively selects steps in the plane tangent to the constraints orsteps that reduce infeasibility, without using a merit function or filter.
The tangent steps are based on the AdaGrad method for unconstrained
minimization. The objective function is never evaluated by the algorithm, making it suitable for noisy problems. Its worst-case evaluation complexity is analyzed, and, remarkably, global convergence rates in O(1/k) are obtained which match the best known rates for unconstrained problems. Numerical experiments are presented suggesting that the performance of the algorithm is comparable to that of first-order methods for unconstrained problems, and that its reliability is remarkably stable for increasing noise levels.
Under such conditions, it is crucial to monitor the state and function of these ecosystems. Satellite remote sensing provides an invaluable tool to do so. In the EFEO group at the Max Planck Institute for Biogeochemistry (MPI-BGC) we try to develop data-driven methodologies based on satellite data to better understand the functioning of ecosystems and thereby monitoring them in a changing climate. A key aspect in our approach is to recognise that land ecosystem and landscape are intrinsically complex, based on both the natural diversity within the landscape and on the effect humankind has on it. Instead of ignoring this complexity we instead
try to embrace it and consider it as a feature of the system. In this talk I will present a general overview of the research direction of the EFEO group and I will provide some examples of the work are currently doing including: (1) trying to catch tropical phenology by
combining multi scale remote sensing; (2) characterising the effect of biodiversity on ecosystem functional properties; and (3) determining the effects of tree cover heterogeneity on cloud
formation.
The Seminar is free and open,
for further information please contact
Prof.Giovanni Forzieri (giovanni.forzieri@unifi.it )
“Caminetto” Room Santa Marta time: 10.00
Dependency management and software reuse have historically focused on deterministic, modular code components with well-defined interfaces, typically maintained within structured software ecosystems. However, the emergence of foundation models (FMs) introduces a fundamental shift. Reuse is no longer constrained to code libraries but extends to heterogeneous artifacts such as data, models, prompts, and agentic architectures, for which behaviour is often non-deterministic and interfaces are context-sensitive. At the same time, software development practices are being reshaped by increasing reliance on FM-based tools, with reuse being mediated by the developer’s ability to articulate intent and the model’s capability to generate appropriate responses. This thought-provoking presentation suggests that this transition challenges foundational dependency management practices such as versioning and backward compatibility, requiring an accommodation of the dynamics of FM-centred ecosystems. It also suggests a change from code-centric to interaction-centric reuse, rethinking dependency management to adapt to new development paradigms.
About the Speaker Dr. Filipe R. Cogo is a Staff Researcher at the Centre for Software Excellence, Huawei Technologies Co. in Kingston, Ontario, Canada. His research focuses on applying machine learning and mining software repositories to address technical and social challenges in software engineering. Filipe holds a Ph.D. in Computer Science from Queen’s University (2020), where they conducted research at the Software Analysis and Intelligence Lab (SAIL) under the supervision of Prof. Ahmed E. Hassan. His work has been published in leading software engineering venues, including TSE, EMSE, TOSEM, ICSE, and FSE.
Luis Felipe Bueno (Federal University of São Paulo).
A Jacobi-type Newton method for Nash equilibrium problems with descent guarantees
room 119 (CD Morgagni)
Abstract: A common strategy for solving an unconstrained two-player Nash equilibrium problem with continuous variables is applying Newton’s method to the system obtained by the corresponding first-order necessary optimality conditions. However, when taking into account the game dynamics, it is not clear what is the goal of each player when considering they are taking their current decision following Newton’s iterates. In this talk we provide an interpretation for Newton’s iterate as follows: instead of minimizing the quadratic approximation of the objective functions parameterized by the other player current decision (the Jacobi-type strategy), we show that the Newton iterate follows this approach but with the objective function parameterized by a prediction of the other player action. This interpretation allows us to present a new Newtonian algorithm where a backtracking procedure is introduced in order to guarantee that the computed Newtonian directions, for each player, are descent directions for the corresponding parameterized functions. Thus, besides favoring global convergence, our algorithm also favors true minimizers instead of maximizers or saddle points, unlike the standard Newton method, which does not consider the minimization structure of the problem in the non-convex case. Thus, our method is more robust compared to other Jacobi-type strategies or the pure Newtonian approach, which is corroborated by our numerical experiments. We also present a proof of the well-definiteness of the algorithm under some standard assumptions, together with a preliminary analysis of its convergence properties taking into account the game dynamics.
Come realizzare interfacce grafiche utilizzando le librerie gratuite LVGL con GUI Guider di NXP su piattaforme microcontrollore i.MXRT
Laboratorio ex-forno aula 85 SAnta Marta
17/6/2025 ore 900:1700
L’evento è a numero chiuso.
pre-registrazione qui:
https://forms.office.com/pages/responsepage.aspx?id=NQzrC7uc60-Z5VieQVx5RANIh9iy37VAuT0ulq6AW4lUQzZaRFFEM01XRE1RT0xFUEI1MDlJVjJKUiQlQCNjPTEu&route=shorturl
La Sua partecipazione verrà confermata una settimana prima dell’evento.
Durante questo workshop saranno introdotte e utilizzate le librerie grafiche LVGL, integrate nel tool GUI Guider per realizzare facilmente interfacce grafiche. Si vedranno in dettaglio tutti gli step necessari partendo dalla simulazione su PC fino al debug su evaluation board dedicata con display grafico.
Le sessioni pratiche permetteranno di apprendere come impostare un progetto e sfruttare a pieno le funzionalità offerte delle librerie e dagli strumenti supportati da NXP. La parte finale dell’evento sarà dedicata a mostrare come le librerie LVGL rappresentino una possibile soluzione anche per architetture basate su microprocessore. Attraverso una breve dimostrazione sarà possibile valutare l’output di LVGL in azione su dispositivi basati su core Cortex-A e in ambiente Linux. I link al materiale da scaricare sul proprio pc verranno comunicati agli iscritti prima dell’evento.
Agenda
9:30- 9:45
Collaborazione tra Università e Industria: chiave per l’innovazione
9:45 – 10:30
Introduzione LVGL: come questa libreria è trasversale nel portfolio NXP da MCX fino a i.MX
10:30 – 11:30
GUI Guider e il suo utilizzo: potenzialità dello strumento e «dipendenze nei progetti custom»
11:30 – 11:45 Coffee Break
11:45 – 13:00
Sessioni pratiche 1: dalla creazione di un progetto GUI Guider per i.MX RT alla simulazione su PC
13:00 – 14:00 Lunch
14:00 – 15:15
Sessioni pratiche 2: dall’export del progetto al test/debug su target
15:15 – 15:30 Coffee Break
15:30 – 16:00
Sessione pratica – suggerimenti e best practice, utilizzo widget, lab e molto altro
16:00 – 16:45
Demo: LVGL su architettura i.MX 91, MPU entry level basato su single core Cortex-A55. Portabilità del codice e performance
16:45 Q&A / Closure
Development of physio-driven machine Learning algorithms to assess cardio-respiratory function and diagnose of sleep apnea
tenuto dalla prof. Azadeh Yadollahi recentemente nominata Distinguished Lecturer per il 2025 dalla IEEE Engineering Medicine and Biology Society.
Sala Caminetto Santa Marta 28/5/2025 11.00
Prof. Gabriele Eichfelder dell’Università tecnica di Ilmenau (Germania)
Multiobjective mixed-integer convex quadratic programming
Time: 11:30
Room: caminetto (S.Marta)
Sala Caminetto Santa Marta,
17/4/2025 ore 11
Prof Joao Magalhães (Universidade NOVA de Lisboa)
11 April – 11:30 – Room 008 Centro Didattico Morgagni, V.le Morgagni 40-44
Title
Contrastive Beam Diffusion Models for Decoding Visual Sequences
Abstract
While diffusion models excel at generating high-quality images from text prompts, they struggle with visual consistency in image sequences. Existing methods generate each image independently, leading to disjointed narratives – a challenge further exacerbated in non-linear storytelling, where scenes must connect beyond adjacent frames. We introduce a novel beam search strategy for latent space exploration, enabling conditional generation of full image sequences with beam search decoding. Unlike prior approaches that use fixed latent priors, our method dynamically searches for an optimal sequence of latent representations, ensuring coherent visual transitions. To address beam search’s quadratic complexity, we integrate a contrastive mechanism that efficiently scores search paths and enables pruning, prioritizing alignment with both textual prompts and visual context. Human evaluations confirm that our approach outperforms baseline methods, producing full sequences with superior coherence, visual continuity, and textual alignment. By bridging advances in search optimization and latent space refinement, this work sets a new standard for structured image sequence generation.
Bio
João Magalhães is a Full Professor at the Department of Computer Science, Universidade NOVA de Lisboa, is national co-Director of the CMU-Portugal partnership and leads the Multimodal Systems Group at NOVA LINCS. He holds a PhD from Imperial College London (2008) and conducts research at the intersection of AI, vision, and language, focusing on generative models, controllable LLMs, multimedia search, multimodal conversational AI, and temporal models. João has coordinated and contributed to numerous international projects with partners like BBC, Amazon and Google. He has held key organizational roles in top-tier conferences, including General Chair of ACM Multimedia 2022 and PC Chair of ACM Multimedia 2026. His work has earned multiple awards, including first and second place in the Amazon Alexa Taskbot Challenge. He also contributed to MPEG-7 and MPEG-21 standards during his time in industry. João is currently a member of the ACM Multimedia Steering Committee.
Time 14.30
Room 175 Santa Marta
2 seminars:
Martina Cerulli
Approaches to Solving Convex Semi-Infinite Programs with a Non-Convex Inner Problem
Semi-Infinite Programs (SIPs) are optimization problems characterized by a finite number of decision variables but an infinite number of constraints, typically parameterized over a continuous domain. These problems arise in various applications, including robust optimization, control theory, and game theory, where constraints must be satisfied over an entire range of parameters. Due to their complexity, solving SIPs requires specialized techniques that efficiently handle the infinite constraint set. We discuss a solution approach for SIPs based on the dualization of the inner problem, i.e., the problem of finding the constraint that is the most violated by a given point. After a brief introduction to SIPs and the classical solution techniques for these optimization programs, we will will present the results of the paper “Convergent algorithms for a class of convex semi-infinite programs” by M. Cerulli, A. Oustry, C. D’Ambrosio, and L. Liberti (SIAM Journal on Optimization, 2022). This paper focuses on convex SIPs with an infinite number of quadratically parametrized constraints, not necessarily convex with respect to the parameter. A new convergent approach to solve these SIPs is proposed, leveraging the dualization technique. Based on the Lagrangian dual of the lower-level problem, a convex and tractable restriction of the considered SIP is derived. We state sufficient conditions for the optimality of this restriction. If these conditions are not met, the restriction is enlarged through an Inner-Outer Approximation Algorithm, and its value converges to the value of the original semi-infinite problem. This new algorithmic approach is compared with the classical Cutting Plane algorithm on two applications: constrained quadratic regression and a zero-sum game with cubic payoff. To conclude the talk, we will give some hints on how to deal with the main challenge in addressing these problems: solving the separation problem, namely, finding the most violated constrain. In “Convex semi-infinite programming algorithms with inexact separation oracles” by A. Oustry and M. Cerulli (Optimization Letters, 2024) we propose to tackle this difficulty by solving the separation problem approximately, using an inexact oracle.
———————————————
Mahsa Yousefi
Fully stochastic trust-region methods with Barzilai-Borwein steplengths
We discuss stochastic gradient methods using stochastic adaptations of Barzilai-Borwein (BB) steplengths for finite-sum minimization problems. Our approach builds on the Trust-Region-ish (TRish) framework, a first-order stochastic trust-region method based on careful step normalization. Our framework, TRishBB, is designed to enhance the performance of TRish while reducing the computational cost of its second-order variant. In this talk, we introduce TRishBB in three variants, each leveraging BB steplengths in a stochastic setting. We will highlight the theoretical foundations of TRishBB, key insights, and properties from the convergence analysis, and discuss its practical impact on machine learning applications with numerical results.
Limited memory gradient methods for unconstrained optimization
Dr. Giulia Ferrandi e il Dr. Michiel Hochstenbach
Room 327 Morgagni
Time:11:30
Abstract : The limited memory steepest descent method proposed by Fletcher (LMSD, [1]) for unconstrained optimization problems stores a few past gradients in a matrix, to compute multiple stepsizes at once. In the quadratic case, the matrix of gradients forms a basis for a Krylov subspace, and the stepsizes are the reciprocals of a few Ritz values of the Hessian matrix. LMSD for general nonlinear functions is derived from the quadratic case, but needs some adaptations since the Hessian matrix is no longer constant. We review LMSD as presented by Fletcher, and propose some new variants. For strictly convex quadratic objective functions, we study the numerical behavior of different techniques to decompose the matrix of gradients and compute new stepsizes. In particular, we introduce a method to improve the use of harmonic Ritz values. We also show the existence of a secant condition associated with LMSD, where the approximating Hessian is projected onto a low-dimensional space. In the general nonlinear case, we propose two new alternatives to Fletcher’s method: first, the addition of symmetry constraints to the secant condition valid for the quadratic case; second, a perturbation of the last differences between consecutive gradients, to satisfy multiple secant equations simultaneously. We show that Fletcher’s method can also be interpreted from this viewpoint.
Adaptive Randomized Pivoting for low-rank approximation
room 119 Morgagni
Time 11:30
Alice Cortinovis
Abstract : We consider the problem of finding a low-rank approximation of a given matrix in an efficient way. We focus on approximations that are built from rows and columns of the matrix, starting with the column subset selection problem. We propose a randomized strategy to select suitable rows and columns, called Adaptive Randomized Pivoting. The algorithm is simple and it guarantees, in expectation, an approximation error that matches the optimal existence result in the Frobenius norm. To show the versatility of Adaptive Randomized Pivoting, we apply it to select indices in the Discrete Empirical Interpolation Method, in cross approximation of general matrices, and in the Nyström approximation of symmetric positive semidefinite matrices. In all these cases, the resulting randomized algorithms enjoy bounds on the expected error that match – or improve – the best known deterministic results.
14 Marzo 2025, ore 11:00 – 12:30
Scuola di Ingegneria, Università di Firenze
Aula Caminetto – Salone di Villa Cristina
PROF. CRISTIANO TOMASSONI
UNIVERSITÀ DI PERUGIA
IEEE DISTINGUISHED LECTURE
ABSTRACT: The Additive Manufacturing (AM) technology, also known as 3D-printing technology, offers several interesting and attractive features, including fast prototyping, geometry flexibility, easily customizable products, and low cost (in some cases). However, using such technologies for microwave devices is not straightforward as AM has not been specifically developed for microwave components, and in most cases, some adaptation and post-processing is necessary. Furthermore, there are many AM technologies available, and it is important to understand their characteristics before selecting one.
In the presentation, an overview of the different AM technologies available will be provided. Additionally, an analysis of some of the most common AM technologies used for the manufacturing of microwave components will be conducted in more detail, with the help of several examples. Several microwave components manufactured with some of the most popular AM technologies will be shown, along with a detailed description of the manufacturing process, post-processing, and all actions necessary to make the component perform well. Furthermore, it will be shown how the flexibility of this technology allows the development of new classes of components with non-conventional geometries that can be exploited to obtain high-performing components in terms of compactness, weight, losses, etc.12
Register at: https://events.vtools.ieee.org/m/469088
Mercoledì 12 Marzo ore 10, Aula Caminetto – Santa Marta
Prof. Jerzy Sawicki, Ph.D.
Center for Rotating Machinery Dynamics and Control
Cleveland State University
Control Driven Advances in Smart Rotating Machinery
Over the past three decades, significant advancements have been made in the design of rotating machinery equipped with smart components and embedded functions. These advancements have been driven by developments in actuators, sensors, and power electronics technologies, along with improvements in data acquisition, signal processing, and control theory. This presentation will provide an overview of the current state-of-the-art and showcase examples of smart technologies applied to rotating machines. Several technologies, either most recently developed or under development will be presented, all of which involve active control and techniques that are relevant to smart solutions applied to rotating machinery. Key developments will be described, and case studies from the speaker’s research will be highlighted.
The presentation will commence with an introduction to Cleveland State University and the Washkewicz College of Engineering, followed by an overview of the research activities at the Center for Rotating Machinery Dynamics and Control.
Bio
Jerzy Sawicki earned his Ph.D. in Mechanical Engineering from Case Western Reserve University, USA. He also holds a B.S. and M.S. in Mechanical Engineering and Applied Mathematics from Gdansk University of Technology and the University of Gdansk, Poland, respectively. Currently, he serves as the Department Chair and holds the D.E. Bently and A. Muszynska Endowed Chair and Professorship in Mechanical Engineering at Cleveland State University (CSU). He founded and directs the Center for Rotating Machinery Dynamics and Control (RoMaDyC) at CSU’s Washkewicz College of Engineering. From 2010 to 2020, he served as the Vice President for Research at CSU. Since 2017, he has been the Editor-in-Chief of the ASME Journal of Engineering for Gas Turbines and Power.
Dr. Jannis Kurtz Amsterdam University
Date 6 Feb. 2025
Time 14:30
room 175 Santa Marta
Abstract: In recent years, there has been a rising demand for transparent and explainable machine learning (ML) models. A large stream of works focuses on algorithmic methods to derive so called counterfactual explanations (CE). Although significant progress has been made in generating CEs for ML models, this topic has received minimal attention in the Operations Research (OR) community. However, algorithmic decisions in OR are made by complex algorithms which cannot be considered to be explainable or transparent. In this work we argue that there exist many OR applications where counterfactual explanations are needed and useful. In the first part of the talk, we translate the concept of CEs into the world of linear optimization problems and define three different classes of CEs: strong, weak and relative counterfactual explanations. For all three types we derive problem formulations and analyze the structure. We show that the weak and strong CE formulations have some undesirable properties while relative CEs can be derived by solving a tractable convex optimization problem. We test all concepts on a real-world diet problem and we show that relative CEs can be calculated efficiently on NETLIB instances. In the second part of the talk, we analyze CEs for binary knapsack problems, making a first step towards general integer problems. For special cases of the problem, we present solution methods which perform well in preliminary computational experiments.
Soft Skill
CFU 1
Profili dei ricercatori e Valutazione della ricerca
Partecipate al nostro webinar informativo su “Profili dei Ricercatori e Valutazione della Ricerca,” dove esploreremo gli strumenti e le strategie essenziali per costruire e migliorare i profili dei ricercatori. Questa sessione mira a fornire ai partecipanti le conoscenze necessarie per dimostrare e descrivere efficacemente l’impatto del lavoro di un autore.
Obiettivi Formativi:
-Costruire il proprio profilo ricercatore
-Dimostrare e descrivere i dati dell’impatto di un autore
Questo webinar è progettato per ricercatori, accademici e professionisti interessati a comprendere come sfruttare Web Of Science e InCites per la valutazione della ricerca e la costruzione del profilo.
Non perdere questa opportunità per migliorare le competenze e conoscenze nella valutazione dell’impatto della ricerca.
Data / Ora: 16 Gennaio 2025 11:00-12:00
Link di registrazione
https://clarivate.com/academia-government/events/profili-dei-ricercatori-e-valutazione-della-ricerca/
13 Dicembre 2024, ore 9:00 – 16:30
Scuola di Ingegneria, Università di Firenze
Aula Caminetto – Salone di Villa Cristina
La Giornata affronta la continuità tra didattica, ricerca e trasferimento tecnologico, incardinati nella Scuola di Ingegneria e nel Dipartimento di Ingegneria dell’Informazione, con l’industria elettronica dell’area fiorentina. Dai contributi industriali emergerà la figura professionale dei futuri ingegneri elettronici. Alla giornata parteciperanno le seguenti aziende:
Leonardo SpA; Eldes Srl; Microtest SpA; Saitec Srl; Powersoft SpA; Promel Srl; Pasquali Microwave Systems Srl; Advance Microwave Engineering Srl; Ampatech Srl; Pramatech Srl; SSE Srl; ABB e-mobility SpA.
E’ necessaria l’iscrizione: https://forms.gle/zL74NT9SEPEpJznq8
In chiusura verranno consegnati i premi di laurea attribuiti agli studenti della laurea magistrale in Ingegneria dei Sistemi Elettronici.
Hervè A. Corti, Introduction to Quantum Computatione Title
Room 051 Santa Marta
Time 10.15
Date: 11/12/24
Lavinia Amorosi
Title: A Mathematical Programming Approach to Hierarchical Clustering
date/place: December 3rd 2024, 11:30 a.m., Room 049 Santa Marta
abstract: Hierarchical clustering is a statistical technique to study the occurring groups (clusters) within a dataset creating a hierarchy of clusters. This is represented by a rooted tree (dendrogram) whose leaves correspond to the data points, and each internal node represents the cluster containing its descendant leaves. Among methods to perform hierarchical clustering, the agglomerative ones are based on greedy procedures that return a sequence of nested partitions where each level up joins two clusters of the lower partition relying on a local criterion.
In this talk, motivated by the lack of exact approaches that guarantee global optimality, we focus on a unified mathematical programming formalization for single and complete linkage procedures. We evaluate, according to different measures commonly used in this context, the dendrograms obtained from the exact resolution of the formulations and those produced by the greedy approach.
Furthermore, by exploiting the mathematical formulation, we also present a scalable matheuristic algorithm capable of generating better quality dendrograms than those produced by the greedy approach even for large size datasets.
Date/time: 26 Nov. 16:00
Room: 035 Santa Marta
Title: Image matching: vecchie glorie e nuovi orizzonti
Abstract. L’image matching (calcolo di corrispondenze) è alla base della maggior parte dei metodi di ricostruzione 3D e di stima di posa in computer vision, con applicazioni notevoli quali SfM (Structure from Motion), Visual SLAM (Simultaneous Localization and Mapping) e più recentemente NeRF (Neural Radiance Fields) and GS (Gaussian Splatting). Dopo aver introdotto le fasi principali del processo di image matching, verrà presentata l’evoluzione di tali approcci fino allo stato dell’arte attuale, mostrando i pregi e i difetti delle tecniche più rilevanti (alcune della quali basate su deep learning) e le criticità connesse ai metodi di valutazione e ai dataset impiegati. Infine verranno discusse semplici strategie generali basate sull’utilizzo di piani virtuali per il miglioramento di metodi di image matching.
E. Planas
2024, 13th Dic, 9:00 a.m., On line at link:
https://us02web.zoom.us/j/87052041160?pwd=WvmfWSNWSI48NWGhXXjfaboCZ88GOB.1
Meeting ID: 870 5204 1160
Passcode: 0uARAZ
Abstract:
This seminar introduces the National Institute of Informatics (NII) andopportunities for international cooperation with NII. NII is the Inter-University Research Institute (ROIS) for advanced ComputerScience Research and Global Science Data Infrastructure support, situated inthe heart of Tokyo, Japan. NII is ranked top 2 for publications in ComputerScience in Japan. Research activity is divided in four Departments:
1.Principles of Informatics;
2.Information Systems Architecture Science;
3.Digital Content and Media Sciences;
4.Information and Society.
The Global Liaison Offi ce (GLO) at NII has established partnerships with more than 100 MOU partners. GLO supports international exchanges via the“MOU Grant Program” under which a NII researcher can invite a colleaguefrom overseas for a stay, or send some member of his team for a researchvisit (up to 2 months, around 40 researches a year supported). GLO alsooff ers the “NII International Internship Program” in which we welcome andfund MOU partners Master’s and PhD. students for a research stay in one ofour teams (3 to 6 months, around 140 students per year).
A smart city represents an improvement of today’s cities both functionally and structurally, that strategically utilizes many smart factors, such as information and communications technology (ICT), to increase the city’s sustainable growth and strengthen city functions, while ensuring citizens’ quality of life and health. Cities can be viewed as a microcosm of “objects” with which citizens interact daily: street furniture, public buildings, transportation, monuments, public lighting and much more. Moreover, a continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure. Industrial sites represent similar scenarios, where data collected from distributed objects
allow to actuate powerful control strategies.
The Internet of Things (IoT) concept imagines all these objects being “smart”, connected to the Internet, and able to communicate with each other and with the external environment, interacting and sharing data and information. Each object in the IoT can be both the collector and distributor of information regarding
mobility, energy consumption, air pollution, as well as potentially offering cultural and tourist information.
As a consequence, cyber and real worlds are strongly linked in a smart city, such as in industrial site. New services can be deployed when needed and evaluation mechanisms will be set up to assess the health and success of the system under control. This talk will present some innovative developments in areas related to smart cities and smart industries, leveraging on the features supported by network intelligence at the edge of the network.
Soft Skills
Courses and seminars for Soft Skills organised out from Department of Information Engineering
1 CFU per 6 hours of course
BiblioTech, Bibliographic Search for Engineering PhD Students
Dispensed from Technology area Library
Courses list from “Student Learning” Florence University
Dispensed from Florence University
Courses list for Ph.D. students from Florence University
Dispensed from Florence University