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:

Smart sensors are ubiquitous in nowdays society and will become more and more important in the next future, their applications ranging over many aspects of daily life, such as: (i) healthcare: wearable devices for monitoring of physiological parameters in both hospitals or home (e.g., elderly); (ii) monitoring of athlete's perfomance; (iii) continuous monitoring of industrial plaforms for automatic diagnosis (selfmonitoring) or optimization in the production line (Industry 4.0); (iv) human-computer interfaces; (v) automotive. These sensors often give a continuous time monitoring and, thus, a large dataset of complex signals that need to be processed, both in real time or offline, depending on the specific application. Many monitoring systems are developed as a wireless sensor network, i.e., a network with a complex topology of the links, where each link is a communication channel between sensors.
Thus, a proper processing of these large datasets of time signals is crucial, not only regarding computational efficiency, but also for the extraction of informative parameters (information retrieval).
This course will cover the basic concepts and techniques used in the processing of complex time signals, also including feature extraction in multivariate signals with particular attention to connectivity features of time-varying networks.
In the first part, we will give an overview of the main results of classical signal processing. Some practical examples will be given during the lectures, in order to make the students more confident with signal processing tools.
In the second part, we will focus on connectivity measures defined on time-varying networks.
Finally, in the third part, we will give a few insights on recent developments in the processing of signals with complex intermittency, showing some applications in the field of neurophysiology.

Course Contents in brief:

1. Statistical signal processing
2. Multivariate signals
3. Time-varying networks
4. Network analysis and measures: connectivity, clustering, segregation vs. integration.
5. Complex systems and networks
6. Signals with complex intermittency

Schedule:

1. Day 1: 15/04/2024, 9:00 - 13:00
2. Day 2: 16/04/2024, 9:00 - 13:00
3. Day 3: 17/04/2024, 9:00 - 13:00
4. Day 4: 18/04/2024, 9:00 - 13:00
5. Day 5: 19/04/2024, 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
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:

Transformers have brought a significant shift in the AI field through the introduction of a novel learning and information processing paradigm. Their attention mechanisms, including self-attention, empower them to grasp intricate patterns and relationships in data, rendering them highly adaptable in addressing a variety of complex tasks. Transformers are capable of executing an extensive spectrum of AI tasks: Machine Translation, Text Generation, Sentiment Analysis, Named Entity Recognition, Text Classification, Image Generation and Processing, Image Captioning, Image Generation, Style Transfer, Object Detection, Multimodal AI, Multimodal Translation, Visual Question Answering, Text-to-Image Synthesis, Recommendation Systems, Time Series Analysis and Prediction, Speech Recognition and Synthesis, Graph-based Tasks, Molecular Structure Prediction, Conversational AI, Summarization, Question Answering, Formulation of Robot Instructions .
First introduced by Google in 2017, Transformers are today the core of revolutionary technologies, such as such as ChatGPT, Google Search, Dall-E, and Microsoft Copilot, overtaking the most commonly employed Deep Learning neural network architectures across various applications, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
In this series of lectures, participants will achieve professional experience with the industrial frameworks and languages for building transformer-based architectures in the domains of sequence and image modeling. The lectures focus on decoder/encoder architectures, fine-tuning techniques, alignment problems, vision models, multimodal models, text-to-image models, and large-scale inference.

Course Contents in brief:

• Introduction
  • The transformer architecture and its applications (BERT, GPT, ViT, DeTR, etc.) [1] [2] [3] [4]
  • Python and the huggingface framework (transfomers, tokenizers, datasets, diffusers, etc.)
• Decoder-only architectures
  • GPT family [3]
  • Few Shot Learning [1] [5]
• Encoder-only architectures
  • BERT family [4]
  • Clustering and Classification
• Encoder-decoder architectures
  • T5 familty [6]
  • Summarization, transalation, paraphrasing
• LLM Fine Tuning
  • Parameter-efficent fine-tuning (PEFT, LoRA family, etc.) [7]
  • Quantization methods (GPTQ, GGML, etc.)
• Aligment problem
  • ChatGPT [8]
  • Instruction Following fine-tuning
  • Reinforcement Learing from Human Feedback (RLHF)
• Vision Models
  • Vision Transfomers (ViT) [9]
  • Detection Transfomers (DeTR) [10]
  • Bootstrapping Language-Image Pre-training (BLIP)
• Multimodal Models
  • Contrastive Language-Image Pretraining (CLIP) [11]
  • Large Language-and-Vision Assistant (LLaVA)
  • Visual Question Answering (VQA)
  • Document Question Answering (DQA)
• Text to Image Models
  • Image Synthesis history (GANs)
  • Diffusion family [12]
• Large Scale Inference
  • FastAPI
  • vLLM
  • Text Generation Inference

Schedule:

1. 11/03/2024: 10.00 – 13.00, Lecture 1 (3h) - Via Caruso
2. 12/03/2024: 15.00 – 18.00, Lecture 2 (3h) - Via Caruso
3. 13/03/2024: 15.00 – 18.00, Lecture 3 (3h) - Largo L. Lazzarino
4. 14/03/2024: 15.00 – 18.00, Lecture 4 (3h) - Largo L. Lazzarino
5. 15/03/2024: 15.00 – 18.00, Lecture 5 (3h) - Via Caruso
6. 18/03/2024: 15.00 – 17.00, Lecture 6 (2h) - Via Caruso
7. 22/03/2024: 15.00 – 18.00, Lecture 7 (3h) - 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

Short Abstract:

In the last decades, cybersecurity has emerged as a critical and pressing concern. With the proliferation of technology and the interconnectedness of our world, the protection of computer systems, networks, and data has become a fundamental aspect that may impact everyday life and the safety of people. Indeed, security attacks can threaten the most variegates fields and applications, and, if not properly counteracted, the consequences can be severe, causing injuries or even death. One of the most immediate and straightforward examples can be a hacker that takes the remote control over brake and/or steering system of a vehicle, or an attacker that manipulates the information about the state of charge of a battery causing its explosion.

This course aims to give the basic principles of cybersecurity, providing knowledge on the main security threats common to almost all application contexts and the main techniques to counteract them. All the fundamental aspects concerning the implementation of security modules (both hardware and software) are presented, including the references and the validation methodologies to evaluate the security properties according to the desired level of security. Finally, a focus on the importance of cybersecurity in some application fields and some examples on the future trends of security applications are provided. In addition, some highlights on the role of hardware in security are given.

During the lectures some exercitations will be held to get more familiarity with the illustrated concepts and to make some practical experiments.

After the participation to this course, the attendee will have a basic but comprehensive knowledge of which are the main security threats and the main techniques to counteract or mitigate them. The matured knowledge will constitute a useful instrument that can be used to evaluate also other aspects of its research activities and improve them by integrating security mechanisms or developing solutions that are more suitable for later integration of security mechanisms.

Course Contents in brief:

1. Principles of Cybersecurity. [7.5 hours]
   • Overview of the security threats and attacks.
   • Overview of the fundamental security services to protect data and assets.
   • Overview of cryptographic primitives and algorithms to implement security services.
   • Ad-hoc solutions to implement security services without cryptography.
   • Exercitation(s).
2. Basic guidelines for the development of HW/SW security modules: security services, interface security policies and physical implementation. [4 hours]
   • Concept of security strength, long-term security protection and introduction to Post-Quantum Cryptography (PQC).
   • Focus on verification/validation systems for the developed modules.
   • Focus on interface security policies.
   • Focus on physical implementation: Side-Channel Attacks (SCAs) – Principles and examples.
   • Exercitation(s).
3. On the importance of cybersecurity in automotive, space, Battery Management Systems (BMSs), and server applications. [1.5 hours]
   • Examples of attacks and consequences.
   • Future trends: assets encryption in general-purpose processors for servers and battery passport.
4. The Role of Hardware in Security. [2 hours]
   • Focus on the concepts of Hardware Secure Module (HSM), Root-of-Trust and Chain-of-Trust.
   • Physically Unclonable Functions (PUFs).
   • Focus on Secure Boot routines.

Final Exam (multiple choice questions). [1 hour]

Schedule:

1. Day1 – 26 February 2023 – 9:30 /13:00, Lecture #1 – first part (2.5 h) + Exercitation (1 h)
2. Day2 – 27 February 2023 – 9:30 /13:00, Lecture #1 – second part (2.5 h) + Exercitation (1 h)
3. Day3 – 28 February 2023 – 9:30 /13:00, Lecture #2 (2.5 h) + Exercitation (1 h)
4. Day4 – 29 February 2023 – 9:30 /13:00, Lecture #3 (1.5 h) + Lecture # 4 – first part (1 h) + Exercitation (1 h)
5. Day5 – 1 March 2023 – 15:00 /17:00, Lecture #4 – second part (1 h) + Final Exam (1 h)

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 combination of the Internet of Things and Artificial Intelligence has made it possible to introduce numerous automations in our daily environments. Many new interesting possibilities and opportunities have been enabled, but there are also risks and problems. Often these problems are originated from approaches that have not been able to consider the users’ viewpoint sufficiently. We need to empower people in order to actually understand the automations in their surroundings environments, modify them, and create new ones, even if they have no programming knowledge.

The course discusses these problems and some possible solutions to provide people with the possibility to control and create their daily automations. It aims to allow attendees to gain knowledge and skills in addressing possible user problems such as managing complex or conflicting automations or being aware of privacy or security risks, and obtaining solutions enabling end-user understanding, creation, control, monitoring, and debugging automations that can be deployed in their daily environments (home, office, shops, industry, …). It will provide a discussion of the possible design space in terms of concepts, techniques, and tools, with particular attention to those supporting the trigger-action paradigm for representing the automations.

Course Contents in brief:

1. Introduction Course
2. The technological trends (IoT + AI) and their impact on daily automations
3. Understanding Users and their Tasks
4. Design criteria for transparency of intelligent environments
5. Trigger-action Programming (TAP)
6. Visual tools for TAP: Data-Flow, Wizards, Block-based
7. Real-world deployment, execution, monitoring
8. Security and Privacy in TAP
9. Configuring smart environments with multiple active automations
10. Explainable automations
11. Conversational Agents for end-user creation of automations
12. Humanoid Robots and automations
13. Augmented reality-based support for automation control
14. Intelligent automation recommandations
15. Usability evaluation: methods, metrics, user tests
16. Accessibility Evaluation
17. Final Exercise Presentations and Discussion

Schedule:

1. Day1:  05/02/2024 – time 9.00 – 13.00
2. Day2: 07/02/2024 – time 9.00 – 13.00
3. Day3: 13/02/2024 – time 9.00 – 13.00
4. Day4: 15/02/2024 – time 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:

Ph.D. course on the theory and practice of virtual reality applications, focused on the design of VR systems and on the software development of VR apps. These are some of the topics that will be discussed: history of VR, VR technology, game engines, HMDs and CAVEs, stereographics, motion tracking, VR interactions and haptics, VR locomotion, VR comfort and safety. Each course session will be a mix of theory (VR principles) and practice (VR coding examples).

Course Contents in brief:

Schedule:

1. Day 1 – Friday, 2 February 2024, 9:00-13:00 – “Introduction to VR, and Unity”
2. Day 2 – Thursday, 8 February 2024, 9:00-13:00 – “VR System Design and Unity Scripting”
3. Day 3 – Friday, 16 February 2024, 9:00-13:00 – “Stereographics, HMDs, and VR in Unity”
4. Day 4 – Friday, 23 February 2024, 9:00-13:00 – “VR Interactions, Inputs and UIs in Unity”
5. Day 5 – Friday, 1 March 2024, 9:00-13:00 – “VR Locomotion and Artificial Movements in Unity”

Hours:
24 hours (6 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:

Additive manufacturing (AM) is a series of technologies that aims at fabricating solid objects starting from the three-dimensional (3D) model, through the deposition of thin layers of material [1]. The ability to print objects of any shape and size on any type of substrate is an extremely important feature for applications where high in-situ precision is required, such as the fabrication of electronic [2] and microfluidic components [3] or the fabrication of biological tissues in in-situ bioprinting techniques [4]. Thanks to the generation of geometries directly on the required site, the almost total absence of dimensional mismatch between the receiving substrate, and the object to be printed, can be achieved. To correctly plan the printing trajectory onto a non-planar and complex surface a specific operating workflow can be followed starting from the acquisition of the geometry of the printing substrate [5]. Different technologies can be used for this step, obtaining the reconstructed surface where the printing path can be planned. Once the trajectory has been computed, it has to be registered in the operating workspace for proceeding with the material deposition through the desired AM technology [6]. The ability to print onto non-planar surfaces following the curvature of the geometry not only allows to obtain a perfect adhesion on the substrate but also to enhance the mechanical and electrical properties of the printed structure thanks to the deposition of a continuous filament as well as a better aesthetic result.

Course Contents in brief:

1. Introduction on additive manufacturing
2. Overview of slicing algorithms
3. Scanning approaches and algorithms for surface reconstruction
4. Path planning for 3D printing onto non planar surfaces
5. Registration/localization in the operating workspace
6. Applications of the additive manufacturing operating workflow

Schedule:

1. Day1 – 23 January 2024 – 14:00-18:00 (4 hours)
2. Day2 – 25 January 2023 – 14:00-18:00 (4 hours)
3. Day3 – 30 January 2023 – 14:00-18:00 (4 hours)
4. Day4 – 6 February 2023 – 14:00-18:00 (4 hours)
5. Day5 – 12 February 2023 – 14:00-18:00 (4 hours)
6. Day6 – 19 February 2023 – 14:00-18:00 (4 hours)

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:

Nanofabrication techniques have been the keystone for the scaling down of electronic devices in the semiconductor industry, but have also revolutionized other fields, being the means for the integration of nanodevices into various systems and contexts. Core components of nanodevices are low-dimensional semiconductive materials, which have allowed to boost the integration densities, but have also enabled the study and comprehension of various physical phenomena relying on their characteristic properties. This course is meant to give an overview of the methods and tools required for the fabrication of devices based on one-dimensional (1D) and two-dimensional (2D) materials, which are the result of a body of knowledge in electronics, physics and material science. Particular focus will be given to hybrid processes that combine standard CMOS techniques, e.g. lithography, thin film deposition, chemical vapor deposition, wet and dry etchings, with alternative techniques for fast prototyping of electronic devices, such as ink-jet printing. Through these processes, 1D&2D materials can be integrated together with bulk materials allowing the complete definition of electronic devices, which can be further collected in large-area networks. Case studies related to emergent fields of application, such as energy harvesting and flexible electronics will be discussed and supported by practical examples of fabrication workflows.

Course Contents in brief:

1. Overview of standard fabrication methods for integrated devices
2. Nanofabrication tools for micro and nanoscale devices
3. Beyond the standards: alternative materials and technologies on flexible substrates
4. Dive into practice: integration workflow from lithography to metal contacts and 2D-materials printi

Schedule:

1. Day 1 – Thursday, 18 January 2024, from 14:00 to 18:00 (4 hours)
2. Day 2 – Friday, 19 January 2024, from 14:00 to 18:00 (4 hours)
3. Day 3 – Monday, 22 January 2024, from 14:00 to 18:00 (4 hours)
4. Day 4 – Wednesday, 24 January 2024, from 14:00 to 18:00 (4 hours)

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:

This course will focus on the complexity of data collected in engineering experimental research. Therefore, the course will focus on topics like experiment design, sample size estimation, and multi-factorial methods. First, basic designs of experimental studies will be discussed, then a review of some well-known and widely used parametric and non-parametric methods will be provided. After that, the course will describe the concept of statistical power and its practical application in the choices to be made in experimental research. The last part of the course will focus on the differences between statistical analysis and Machine Learning approaches, to make PhD students aware of the most appropriate tool to be used in their own research.

The aim of the course is to make PhD students able to write a full statistical analysis plan, analyze at least part of their data and write a preliminary result section for documentation of any statistical procedures they have used.

 Course Contents in brief:

1. Experimental design: observational, case-control, retrospective, prospective.
2. Brief recap on descriptive and inferential statistics.
3. The ability to understand the assumptions and perform the following statistical tests: Multifactorial ANOVA, Repeated measures ANOVA, Multiple and non-linear regression, Survival Analysis.
4. Understand power and sample-size calculation (sample-size considerations) and perform them in the specific context of own studies.
5. Increase statistical power through surrogate data analysis.
6. When to choose between statistical analysis and Machine Learning approaches. 

Schedule:

1. 11/12/2023: 14:00 - 18:00, Aula Riunioni del Piano Terra - via Caruso 
2. 12/12/2023: 14:00 - 18:00, Aula Riunioni del Piano Terra - via Caruso
3. 13/12/2023: 14:00 - 18:00, Aula Riunioni del Piano Terra - via Caruso 
4. 15/12/2023: 14:00 - 18:00, Aula Riunioni del Piano 6 - Largo Lucio Lazzarino 
5. 18/12/2023: 14:00 - 18:00, 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:

Immersive course focussed on the synthesis, properties, and applications of silicon-based nanomaterials. Among the applications discussed there are, medical applications of silicon nanostructures, including targeted drug delivery and in vivo systems, metal and polymeric composites with porous silicon, energy related materials including Li-ion anodes and thermoelectrics, optical biosensing and chemical sensing.

Course Contents in brief:

Schedule:

1. 20/11/2023: 9:00-11:00
2. 21/11/2023: 9:00-11:00
3. 22/11/2023: 14:30-16:30
4. 23/11/2023: 9:00-11:00
5. 24/11/2023: 9:00-11:00
6. 27/11/2023: 9:00-11:00
7. 28/11/2023: 9:00-11:00
8. 29/11/2023: 14:30-16:30
9. 30/11/2023: 9:00-11:00
10. 01/12/2023: 9:00-11:00

Hours:
22 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 course aims to give an overview of the measurement techniques and instrumentation that are specific for the characterization of devices and antennas at RF and microwave frequency bands. After an introduction on the main characteristic parameters of typical devices and antennas used in high-frequency wireless systems, basic RF instrumentation will be presented together with related measurement procedures. Experimental sessions will be included to let the students to practice their measurement skills.
Antenna measurement techniques refers to the testing of antennas to ensure that the antenna meets specifications or simply to characterize it. Typical parameters of antennas are gain, bandwidth, radiation pattern, beamwidth, polarization, and impedance. The antenna pattern is the relative power density of the wave transmitted by the antenna in a given direction. A multitude of antenna pattern measurement techniques have been developed. The first technique developed was the far-field range, where the antenna under test (AUT) is placed in the far-field of a range antenna.
Due to the size required to create a far-field range for large antennas, near-field techniques were developed, which allow the measurement of the field on a surface close to the antenna (typically 3 to 10 times its wavelength). This measurement is then predicted to be the same at infinity. A third common method is the compact range, which uses a reflector to create a field near the AUT that looks approximately like a plane-wave.

Course Contents in brief:

1. Introduction to Scattering Parameters (S) and microwave devices.
2. Microwave/millimetric measurements. VNA and Instrumentation
3. Measurements of microwave devices parameters:
   • Scattering Parameters (S) of single-port passive devices
   • Scattering Parameters (S) of multi-port passive devices
4. Introduction to antenna measurements. What to measure? General setup and elements
5. Measurement of far field radiation patterns
   • Distance requirements for far field antenna measurements
   • Radiation pattern and directivity. Gain
   • Polarization, Co-polar and cross-polar patterns
6. Antenna measurement ranges classification:
   • Indoor ranges. Absorbing material
   • Far field ranges. Compact ranges. Near Field ranges
   • Positioners, probes, rotatory joints. RCS ranges
7. Near field measurements
   • Spherical, Cylindrical, Planar.
   • NF-to-FF Transformation and Diagnosis
8. Experimental practice with spherical range scale-model

 Schedule:

1. Day1 – time 25 sept. 2023 9:00-13:00 (4 hours)
2. Day2 – time 26 sept. 2023 9:00-13:00 (4 hours)
3. Day3 – time 27 sept. 2023 9:00-13:00 (4 hours)
4. Day4 – time 28 sept. 2023 9:00-13:00, 15:00-17:00 (6 hours)
5. Day5 – time 29 sept. 2023 9:00-13:00 (4 hours)