Hours:
24 hours (6 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Largo Lucio Lazzarino 1, 6th floor, Pisa

To register to the course, click here

Short Abstract:

In the last years, information communication, computation and storage technologies are jointly reshaping the way we use technology, meeting the future needs of a wide range of big data and artificial intelligence applications and, paving the way for a full-customized autonomous user experience. In 2020 the 5G -Next Generation Communication Networks is expected to be operational and a global game changer from a technological, economic, societal and environmental perspective. 5G industry is intensively working today on designing, prototyping and testing fundamental technological advances to de-liver the promised performance in terms of latency, energy efficiency, wireless broadband capacity, elasticity, etc. Nevertheless, many experts say that the next big step for cellular networks is not 5G, it is the distributed support of the cloud and AI.

This set of lectures will cover the vision, the use cases, the architecture design and technical tools for understanding the key enabling technologies that will enable beyond 5G networks to meet its challenging performance targets and how ‘the cloud’ will play an operational role in future wireless networks. The lecture will also introduce and detail very innovative concepts freshly under investigation for future B5G/6G networks such as Reconfigurable Intelligent surfaces and the integration of non-terrestrial communication and edge intelligence with terrestrial communication systems. Moreover, dedicated lectures will promotes the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability. Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted packet, irrespective of the meaning conveyed by the packet. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment. Focusing on semantic and goal-oriented aspects and, possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal. Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks. 6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.

The lecture will offer a flora for interactive discussions on future research axes and open challenges on B5G/6G networks.

Course Contents in brief:

1. Lecture 1 (3h) - Introduction to evolution of Wireless Networks from 3G+ to 5G. Details on technologies enabling the revolution between 4G and 5G networks.
2. Lecture 2 (1.5h) – 6G, The next frontier: Visions, roadmaps, opportunities, issues, HW reality check
3. Lecture 3 (3h) – The quest for high spectrum bands: mmW, sub-THz and VLC communications. Issues and practical examples.
4. Lecture 4 (3h) – From the Mobile Edge Computing paradigm to network cloudification and Edge AI
5. Lecture 5 (1.5h) – Sustainable and energy efficient 6G Connect-Compute-Control Networks
6. Lecture 6 (1.5h) - 6G: The reconfigurable Intelligent Surfaces Opportunities for shaping the Environment as a Service. The RISE-6G case.
7. Lecture 7 (1.5h) - 6G: The non-terrestrial Communication integration and the on demand intelligent support for 3-dimensional services. The 5G-Allstar case.
8. Lecture 7 (3h) – Challenges in AI & Wireless Communications for 6G
9. Lecture 8 (3h) - 6G: The Semantic Communication opportunity and recent research results.
10. Lecture 9 (1.5h) - the Goal-Oriented Communications opportunity and recent research results.
11. Lecture 10 (1.5h) A strategy for future 6G research axes. Open discussions.

 Schedule:

• 13/06/2023: 8:30 - 13:30  Aula Riunioni del Piano 6 - Largo Lucio Lazzarino 
• 14/06/2023: 8:30 - 13:30  Aula Riunioni del Piano Terra - via Caruso
• 15/06/2023: 8:30 - 13:30  Aula Riunioni del Piano 6 - Largo Lucio Lazzarino 
• 17/07/2023: 8:30 - 12:30  Aula Riunioni del Piano Terra - via Caruso
• 19/07/2023: 8:30 - 11:00  Aula Riunioni del Piano Terra - via Caruso
• 20/07/2023: 8:30 - 11:00  Aula Riunioni del Piano Terra - via Caruso

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Empirical studies in software engineering provide a systematic way of evaluatingEmpirical studies in software engineering provide a systematic way of evaluatingtheories, languages, concepts, tools or methodologies, considering the industrial context in whichthey are applied [1]. The course will prepare students by examining how to plan, conduct andreport on empirical studies in software engineering. The course will cover all of the principalmethods applicable to software engineering (controlled experiments, case studies, surveys,systematic literature reviews, and ethnography) and will describe quantitative and qualitativemethods of analysis, including hypothesis testing and grounded theory. To showcase the differentmethods, the course will critically review representative examples of published work. At the endof the course, the students will be able to approach real-world research problems in a scientificallysound way, and contribute to theory building in software engineering research.

Two versions of this course were delivered: 1) For the MSc and Ph.D students of the University ofFlorence, School of Mathematical, Physical and Natural Sciences in 2020; 2) for the Ph. D. studentsin Smart Computing at the University of Florence, in 2021. The course will be also delivered in Fall2022, again at the University of Florence, and at the Ph. D. in “Ingegneria dell’Informazione” at theUniversity of Pisa. A web version of the course has been made available on YouTube [2]. Thereference book for the course is the handbook from Wohlin et al. [1].

Course Contents in brief:

1. Overview of Empirical Research Methods in Software Engineering Research
2. Formulating Research Questions
3. Data Types, Measurements, Scale
4. Data Collection Techniques
5. Building Theories in Software Engineering
6. Research Strategies: the ABC Framework for Software Engineering
7. Controlled Experiments
8. Hypothesis Testing and Statistical Tests
9. Qualitative Research Methods: Ethnography, Interviews, Grounded Theory
10. Survey Research in Software Engineering
11. Case Studies
12. Systematic Literature Reviews

 Schedule:

1. 13/09/2023: 9:30 - 12:30
2. 14/09/2023: 9:30 - 12:30
3. 15/09/2023: 9:30 - 12:30
4. 18/09/2023: 9:30 - 12:30
5. 19/09/2023: 9:30 - 12:30
6. 20/09/2023: 9:30 - 12:30
7. 21/09/2023: 9:30 - 11:30

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor
Aula Riunioni del Piano 6 del Dipartimento di Ingegneria dell’Informazione, Largo Lucio Lazzarino 1, Pisa

To register to the course, click here

Short Abstract:

Over the last decade, research has scaled up tremendously in complexity. The number of contributing institutions, collaborations, projects, and funding opportunities has grown exponentially, thus characterising research as a multifaceted, high-frequency, global-scale phenomenon deeply bonded to a delicate socio-economical and geopolitical context. Researchers nowadays produce millions of articles, data, software, patents, preprints, and grant proposals each year, leaving explicit digital fingerprints of their daily endeavour.

The ever-increasing availability of this data has catalysed the emergence of a new multidisciplinary field, called Science of Science, which, by helping us to comprehend the evolution of science and its dynamics quantitatively, has the potential to unlock enormous scientific, technological, and educational value. A blend of methodologies, tools and theoretical frameworks from multiple fields, such as data science, network science, artificial intelligence, and social science, offers novel opportunities to make sense of these millions of data points. Together, they unfold a complex yet compelling story on scientific career pathways, scientific collaborations, knowledge shaping and production, and the manifold, competing factors leading to scientific advancement.

Such opportunities – and the challenges here stemming – are feeding a growing community of researchers aiming at understanding scientific progress and its inner mechanisms, and providing insight on the factors that can generate successful science, allocate better the available resources, increase equality and fair access to opportunities, and therefore benefit science as a whole.

Course Contents in brief:

1. Science of science
2. Scientometrics
3. Bibliometrics
4. Data science
5. Open science
6. Scholarly communication

Schedule:

1. 19/06/2023: 9:00 - 13:00, Aula Riunioni del Piano 6 - Largo Lucio Lazzarino 
2. 20/06/2023: 9:00 - 13:00, Aula Riunioni del Piano 6 - Largo Lucio Lazzarino 
3. 21/06/2023: 9:00 - 13:00, Aula Riunioni del Piano Terra - via Caruso 
4. 22/06/2023: 9:00 - 13:00, Aula Riunioni del Piano Terra - via Caruso
5. 23/06/2023: 9:00 - 13:00, Aula Riunioni del Piano Terra - via Caruso

Hours:
16 hours (4 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Aula Virtuale Teams:

Aula

Materiale Didattico:

http://www.banterle.com/francesco/courses/2023/mc/

Short Abstract:

This course introduces students to Monte Carlo methods and sampling techniques with a focus on visual computing. These are crucial to accelerating the computations of a variety of computational simulations where we need to draw high-quality samples or to integrate a complex multi-dimensional function such as physically-based rendering for computing the radiance of buildings, estimating the price of options, or how epidemics spread out. At the end of this course, students will have both theoretical and practical tools that they can apply to a variety of problems to achieve high-quality solutions. During the course, students will see and study successful examples of this beautiful theory to visual computing; e.g., visual processing, computer vision, finance, etc.

Course Contents in brief:

1. Introduction.
2. Monte-Carlo Estimation.
3. Monte-Carlo Integration.
4. Uniform Random Numbers.
5. Non Uniform Random Numbers.
6. Variance Reduction techniques.
7. Quasi Monte-Carlo.
8. Monte-Carlo Applications

Schedule:

1. 23/05/2023: 9:00 - 13:00
2. 30/05/2023: 9:00 - 13:00
3. 01/06/2023: 9:00 - 13:00
4. 06/06/2023: 9:00 - 13:00
5. 13/06/2023: 9:00 - 13:00

Hours:
16 hours (4 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

The course will focus on modern image processing and computer vision problems with a strong computational flavor. We will start with image representations from a linear algebraic standpoint – from the classical Fourier to 2-D discrete cosine (DCT) and wavelet transforms, and finally sparse signal representations. Based on this foundation, two key areas will be emphasized: a.) the fundamental generative problem of image resolution enhancement, popularly known as image super-resolution. Both model based and machine-learning methods will be covered, culminating in their combination. b.) the discriminative problem of image classification and segmentation (pixel level classification). The goal will be to show how optimization principles help in the design of prior guided (or domain enriched) learning frameworks that can integrate the robustness merits of classical model based techniques with the superior modeling capacity of machine learning and artificial intelligence (AI) techniques such as modern deep learning architectures.

Course Contents in brief:

Day 1 (4 hours): Course Overview and Mathematical Preliminaries

1. The anatomy of an image: historical context and new challenges
2. Linear Algebra Review
3. Convex Optimization Review
4. 2-D Fourier Transform: Interpretation and Visualization

Day 2 (4 hours): Image Transforms 

1. 2-D Discrete Cosine Transform
   • Analytical derivation
   • Energy compaction principle
   • Connections to DFT
   • Compression demo
2. 1-D and 2-D Wavelet Transforms (the evolution from STFT)
   • Wavelets for joint time-frequency localization
   • Mallat pyramid algorithm for fast computation of 1-D/2-D DWT
3. Applications of image transforms in compression, denoising 

Day 3 (4 hours): Image Super-resolution

1. Introduction of image super-resolution
2. Model-based multi-frame image super-resolution methods
3. Sparsity based super-resolution methods
4. Deep learning-based image super-resolution

Day 4 (4 hours): Discriminative Problems – Image Classification and Segmentation

1. Sparsity based methods (use linear algebra and optimization review)
2. Dictionary Learning (use linear algebra and optimization review)
3. Deep Learning methods
4. Applications: face recognition, medical image analysis for diagnosis

 Schedule:

1. Day1 – 9 AM – 1 PM
2. Day2 – 9 AM – 1 PM
3. Day3 – 9 AM – 1 PM
4. Day4 – 9 AM – 1 PM
5. Day5 (Friday) – Project assigned to be completed and submitted by following Monday.

Hours:
16 hours (4 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Light plays a central role in our everyday lives. On the most fundamental level, throughLight plays a central role in our everyday lives. On the most fundamental level, throughphotosynthesis, light can be thought at the origin of the life itself. The study of light properties has ledto promising alternative energy sources, lifesaving medical advances in diagnostics technology andtreatments, extremely speed internet communications and many other discoveries that have changedthe society and shaped our understanding of the universe. These technologies were developed sinceNewton and Laplace fundamental researches on the properties of light and are now generally codifiedin the terms of Optics and Photonics. The European Commission recently defined Optics andPhotonics as parts of the six Key Enabling Technologies (KETs) of Europe. Indeed, Optics andPhotonics have a substantial leverage effect on the European economy and workforce: 20-30 % of theeconomy and 10 % of the workforce depend on Optics and Photonics. The course will provide a briefexcurtion on light from the beginnings to the most recent applications.

Course Contents in brief:

1. The nature of light: why is the sky blue? (3 hours)
2. The nature of light: why is the sky blue? (3 hours)
3. Light-matter interaction(s) (3 hours)
4. Optical resonant structures (3hours)
5. Transducing and (bio)sensing (3 hours)
6. Final remarks and test (2 hours+1 hour)

Schedule:

1. 27/03/2023, h. 9:00-13:00
2. 28/03/2023, h. 9:00-12:00
3. 29/03/2023, h. 9:00-12:00
4. 30/03/2023, h. 9:00-12:00
5. 31/03/2023, h. 9:00-12:00

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

The Artificial Intelligence domain is present in our everyday life, from speech command recognition to face detection, from fitness tracking to autonomous driving, etc. This course aims to provide participants with a tool to identify, build, and use the best machine learning model for their specific use cases. Throughout the course, we will use MATLAB for different Machine and Deep Learning workflows and hands-on examples for regression and classification of signals and images.

Course Contents in brief:

• Part 1: Data Pre-processing and Introduction to Machine Learning in MATLAB
  1. Import data into MATLAB. Automatic Code Generation for Importing Data - Filtering data and visualization. Using Datastores to import collection of files.
  2. Overview of Machine Learning techniques. Unsupervised and Supervised techniques (clustering, regression, classification).
  3. Create a custom data type with an object-oriented approach
  4. Hands-on Project
• Part 2: Introduction to Deep Learning in MATLAB
  1. Getting started with pretrained models
     • Choosing pretrained models for image classification
     • Import pretrained models from open-source frameworks (PyTorch, TensorFlow)
     • Using layer activations as features to train another machine learning model
  2. Transfer Learning
     • Using Deep Network Designer App
     • Application Examples with most relevant Neural Networks
  3. Building simple Neural Networks from scratch
     • Creating Network Architectures
     • Understanding Network Training and Monitoring Training Progress
     • Application Examples with CNN and LSTM
  4. (Optional) Advanced techniques for deep learning in MATLAB
     • Extended framework with custom training loops
     • Defining custom layers
     • Hands-on Project

Schedule:

• 14/03/2023: h. 13.00-18.00
• 15/03/2023: h. 9.00-14.00
• 23/03/2023: h. 13.00-18.00
• 24/03/2023: h. 9.00-14.00 

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Modern ICT applications are increasingly asking for systems with a relevant level of autonomy. Applications for Industry 4.0, for Internet of Things (IoT) or for service robotics, to mention a few, are gaining more and more attention nowadays. The ability to carry out complex tasks is critically related to the ability to retrieve meaningful information coming from the available sensors. To this end, it is mandatoryto understand how a measurement process can be analytically described and how the sensorial data can be manipulated to extract the quantities of interest with the highest possible precision, i.e., the best estimate. This problem becomes even more challenging in dynamic environments when multiple systems interact together, e.g., robotic systems. In this course, the notions needed to correctly model a measurement process will be firstly introduced. Then, an introduction to Bayesian and non-Bayesian classic estimators will be given. Two of the most popular estimators will be studied in details for linear and nonlinear systems: the Weighted Least Squares and the Kalman Filter. Examples of applications such as clock synchronisation, state estimation for Smart Grid as well as localisation for single or multiple robots will be presented. Finally, a discussion on other state-of-the-art solutions for localisation as well as on implications for closed-loop systems in the presence of uncertainty will be offered.

Course Contents in brief:

1. Background on Statistics: Probability, Random variables, Multivariate Pdfs, Conditional and Marginal pdfs, Propagation of error, stochastic processes
2. Data analysis and estimation algorithms (Maximum Likelihood (ML), Least Squares (LS),Maximum A Posteriori (MAP), Minimum Mean Squared Error (MMSE))
3. Linear and nonlinear Weighted Least Squares and Kalman filtering, with applications to automation and robotics
4. Distributed estimation for team of robots with distributed Kalman Filters

Schedule:

1. 06/03/2023: h. 9.00-13.00, 14.30-16.30
2. 07/03/2023: h. 9.00-13.00, 14.30-16.30
3. 08/03/2023: h. 9.00-13.00
4. 09/03/2023: h. 9.00-13.00 

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Artificial Intelligence (AI) is already happening today, and it is pervasive, often invisibly embedded in our day-to-day tools. As AI evolves, so do the many controversies that surround the use of this advanced technology. From military drones to shopping recommendations, AI is powering a wide array of smart products and services across nearly every industry—and with it, creating new ethical dilemmas for which there are no easy answers. As technology continues to develop at an unprecedented rate, those involved with AI often lack the tools and knowledge to expertly navigate ethical challenges. This course examines today’s most pressing ethical issues related to AI and explores ways to leverage technology to benefit mankind. It provides insights into how to achieve responsible innovation of technology, to contribute to the quality of human life, sustainability and fair allocation of risks and benefits.

Course Contents in brief:

1. Explore the foundations of Philosophy of Technology and Responsible Innovation for the benefit of mankind
2. Understand the technological basis of ethics in AI
3. Analyse machine bias and other ethical risks
4. Explore issues of AI in safety and progress, human rights, economics of happiness and deep ecology
5. Assess the individual and corporate responsibilities related to AI deployment
6. Examine the available frameworks for Derisking AI by design
7. Examine the state-of-the-art for regulatory frameworks on artificial intelligence
8. Exploit AI and Business Models Innovation in the space industry through the lenses of ethical challenges
9. Work in teams to resolve Case study assignments inspired by real-life

Schedule:

1. 13/02/2023 h. 8.30-13.30
2. 14/02/2023 h.13.30-18.30
3. 15/02/2023 h. 8.30-13.30
4. 16/02/2023 h. 8.30-13.30

Hours:
15 hours (4 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor
Aula Riunioni del Piano 6 del Dipartimento di Ingegneria dell’Informazione, Largo Lucio Lazzarino 1, Pisa

To register to the course, click here

Short Abstract:

What is "out there"? What reality awaits the engineer?

The real problems of efficiency and effectiveness in the industry that await the engineer require a knowledge that does not stop at simple specific technical competence but which inevitably concerns organizational concepts, process management techniques and a continuous development of improvement and problem solving projects.

To manage projects, to design/optimize/improve processes, to solve problems, moving in the environment and in the context that surrounds him, the company organizational structure; this is what the engineer does in the world of work.

Concepts such as Total Quality, 5S, TPM, Lean organization etc. are now in the vocabularies of any company; they too represent a technical skill (not at all obvious!) but their simple theoretical knowledge is not sufficient for their practical application.

Through a smart and practical approach, the course aims to "an immersive experience" in the real operational problems that the engineer encounters in the workplace and to provide concepts and practical tools to address the issue of organization.

Course Contents in brief:

1. “The process is the boat you drive and the organization is the sea you sail on”
2. Communication is the foundation of organization and problem solving
3. Visual language and visual design of processes and tools
4. Principles of relational dynamics for team building
5. Designing improvement: analysis and decision tools
6. Designing improvement: Planning
7. Artificial intelligence, simulation and IoT represent future developments in process design and management

Schedule:

15/02/2023: 14:00 - 18:00, Aula Riunioni del Piano Terra - via Caruso

16/02/2023: 14:00 - 18:00, Aula Riunioni del Piano Terra - via Caruso

23/02/2023: 15:00 - 18:00, Aula Riunioni del Piano 6 - Largo Lucio Lazzarino

24/02/2023: 14:00 - 18:00, Aula Riunioni del Piano 6 - Largo Lucio Lazzarino