Electronic noise theory is an important topic in sensors and telecommunication systems. Noise measurements are often tricky and require application specific set-up and techniques. In the first part of the course, starting from physical principles, we will review the fundamentals of electronic noise with a particular focus to microwave noise. In parallel, important system parameters will be recalled such as the equivalent noise temperature, the noise figure, the minimum noise figure and the optimum impedance for noise. The second part of the course will be devoted to noise measurements and, as a case of study, we will consider a radio receiver. Finally, in the third part, we will explore the application of microwave noise measurements to radioastronomy and the potential of these technologies for the realization of a new class of sensors: when a radio-telescope looks at the universe it simply measure the faint microwave noise produced by the stars.
Syllabus. Electronic noise fundamentals. Studying a circuit with noise. System and circuit noise parameters. Measuring the electronic noise. Electronic noise as an opportunity: radioastronomy and sensors.

Measurement by image processing are today largely used in science and technology in order to obtain 2D and 3D models of objects. In this course the fundamental technology to obtain good analogic images and correct digitalization of it in order to have significative images will be illustrated, together with the main data processing techniques to get single measurements and 3D surfaces.
Also the main beam scanning techniques to get shape information and internal structures of object will be given.

PROGRAM:
Recap on field and waves, image generation, image digitalization. 2D and 3D measurements by image processing: stereo vision, multi stereo vision, structured light applications for 3D measurements 2D and 3D measurements by beam scanning: LIDAR, RADAR, Ultrasonic, X-Ray.

Modern machines are now equipped with sophisticated System Control and Data Acquisition (SCADA) systems capable of recording information about actual operation. The resulting database contains a complete knowledge of the machine's operation and performance, but is often under-utilised in normal industrial practice. The main objective of the module is to explore the real potential of new data mining methods and signal processing approaches for performance monitoring and fault detection, with a specific focus on rotating machinery. The topic is at the intersection of mechanical, industrial and information engineering and will be explored in the module through the analysis of real test cases using machine learning algorithms.

PROGRAM:

• SCADA infrastructures and database management
• Data filtering and pre-processing
• Analyzing the actual operational performance on a short- and long-term time horizon
• Supporting maintenance and the fault diagnosis through machine learning
• Real test case analysis and practical assignment

The growing complexity of modern control systems and autonomous technologies demands advanced techniques that integrate control theory with machine learning. This course covers two interconnected modules: Model-Based Optimization Techniques for Control and Advanced Deep Learning and Reinforcement Learning Paradigms.
The first module will focus on model-based optimization methods widely used in control systems. Methodologies such as Calculus of variations for optimal control, Linear Quadratic Controller (LQR) and Dynamic Programming (DP) will be explored. Theoretical foundations will be paired with practical examples to ensure a clear understanding of how these techniques can be applied to real-world control problems.
The second module will address the most recent advances in deep learning and reinforcement learning. Paradigms such as meta-learning, meta reinforcement learning, imitation learning, curriculum learning, and behavioral cloning will be discussed. Additionally, the course will delve into the architecture of attention mechanisms and transformer networks. Practical case studies using Python and Pytorch will be presented.

PROGRAM:
Module 1: Model-Based Optimization Techniques for Control

• Introduction to model-based control strategies
• Calculus of variations for optimal control
• Linear Quadratic Controller (LQR)
• Dynamic Programming (DP)
• Practical applications in control systems

Module 2: Advanced Deep Learning and Deep Reinforcement Learning Technologies

• Recap of basic deep learning concepts (DNNs, CNNs)
• Meta-learning and meta reinforcement learning
• Imitation learning and behavioral cloning
• Curriculum learning for adaptive policy optimization
• Attention mechanisms and transformer networks in control
• Practical examples and code implementation using Pytorch

This course will provide students with both the theoretical background and practical skills necessary to apply modern...

Information about the position of users, structures, and systems is crucial in many engineering applications. This course presents an overview of the main characteristics and requirements of location-aware applications in several operating scenarios, together with the fundamental electronic measurement techniques. Methods and algorithms for static position estimation and dynamic tracking are also described.

PROGRAM:

1. Electronic systems for short-range distance measurement and positioning:
   Characteristics and requirements of location-aware applications.
   Performance of available solutions: radio-frequency systems (Ultra-wideband, wireless personal area network), ultrasound systems, magnetic-field-based systems, integration with satellite positioning and navigation systems.
2. Position measurement techniques:
   Time-of-flight measurement: Time of Arrival, Time Difference of Arrival, Round-Trip Time.
   Power measurement: Received Signal Strength. Direction measurement: Angle of Arrival.
   Processing techniques: trilateration, triangulation, fingerprinting, dead reckoning.
3. Methods and algorithms for position estimation: Tracking, sensor fusion, linear and non-linear least squares methods, maximum likelihood method, Cramer-Rao lower bound on the variance of estimators, Kalman filter, extended and unscented Kalman filter.

Quality control is essential in many industrial applications. Numerous measurement methodologies are available to characterize a product or a process. Among these, contactless methods certainly offer several advantages, particularly in terms of measurement speed and the non-intrusive nature of the technique. Therefore, an in-depth knowledge of these systems is crucial for designing and developing new measurement solutions that can serve as the foundation for industrial quality control and model validation.
There are many industrial applications in fields such as fluid dynamics, leak detection in tanks, verification of electrical circuits, detection of thermal losses, optimization of heat exchangers, biomedical engineering, material fatigue studies, FEM model validation, and more.
During the course, different methodologies will be presented, along with their operating principles, the challenges related to their implementation, and numerous practical examples will be provided.

PROGRAM:
Study of non-contact investigation methods, including infrared thermography, Digital Image Correlation (DIC), and 3D scanning technologies. Detailed analysis of signal-to-noise ratio, optical system selection, and measurement uncertainty evaluation.
Guidelines for optimal system usage and a series of practical application examples will also be provided.

Nowadays, access to green, reliable, sustainable, and affordable energy source is a critical necessity for our social and economic prosperity. An energy mix based on nuclear reactors, both fission and fusion, and renewable energy sources represents one of the most promising options for decarbonisation. Nuclear power from fission reaction is already available but the possibility to switch to a fusion based alternative is the current challenge. In this context,Europe recognises the fission and fusion options as a potential long-term answer to energy sustainability.
The technological challenges for the development of innovative fission and fusion reactors are numerous, but the issue of materials is certainly of great importance. There is an ongoing need to validate and develop materials that will be able to withstand high radiation loads. The combination of high radiation density and high thermo-mechanical stresses makes the reactor environment highly aggressive for materials. These conditions cause changes in the microstructure and micro-composition of metal alloys, resulting in dimensional instability and degradation of mechanical properties. For structural materials, physical properties such as low coefficient of thermal expansion, high thermal conductivity and low elastic modulus are extremely important as they help to minimise temperature and stress gradients. To ensure high component performance, it is required to consider materials that retain high ductility to accommodate thermal and mechanical expansion, high temperature tensile strength and the ability to withstand long-term deformation.

TEACHING CONTENT:
Introduction of sustainable nuclear power plants: Fission and Fusion Plants
Resistance behaviour of metals and alloys subjected to neutron irradiation: microstructural damage and irradiation effects Structural alloys for fission and fusion power plants

Both Big Data processing systems and blockchain technologies have a distributed nature. Big Data encompasses large volumes of complex and dynamic data, which are often characterized by a multimodal nature and by a high generation rate. As a consequence processing and managing Big Data pose challenges that go beyond the capabilities of conventional software and systems. This course presents some of the main distributed computing paradigms and technologies that can be exploited to process Big Data. Blockchain technologies also rely on distributed systems, but this is required to improve data security and to avoid trusted central entities, rather than to expand data processing capabilities. A blockchain is a chain of immutable transaction blocks that are memorized across multiple networked data stores - the blockchain network - each of which can be owned and handled by autonomous organizations with conflicting interests. Blockchains make strong use of cryptography and of (distributed) trustless consensus mechanisms to guarantee irreversibility, authenticity and integrity of their content. This module gives an overview of the main types of blockchain technologies, including programmable blockchains and smart contracts.
Syllabus. Introduction to Big Data Processing. The MapReduce data processing model, the Apache Hadoop platform, and the Spark processing engine. Fundamental concepts of information security and cryptography. Introduction to blockchain technologies, blockchain components, and types of blockchains. From Bitcoin's blockchain to programmable blockchains, the Ethereum blockchain, and the Solidity language for smart contracts.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

L'insegnamento si pone l'obiettivo di fornire agli studenti una conoscenza completa dei concetti fondamentali della proprietà intellettuale (PI), inclusi brevetti, marchi, diritti d'autore e segreti commerciali.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

L'insegnamento si pone l'obiettivo di fornire agli studenti le conoscenze e gli strumenti statistici avanzati per la progettazione di esperimenti e l'analisi di dati complessi in vari campi di applicazione.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

L'insegnamento si pone l'obiettivo di fornire agli studenti le conoscenze e gli strumenti necessari per realizzare presentazioni efficaci a convegni. Insegnare agli studenti come strutturare un discorso chiaro e conciso. Sviluppare le capacità di public speaking degli studenti, con particolare attenzione alla comunicazione verbale e non verbale.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

L'insegnamento si pone l'obiettivo di fornire agli studenti le conoscenze e gli strumenti necessari per scrivere progetti di ricerca UE di alta qualità che siano competitivi per il finanziamento.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

Il corso tratta aspetti di carattere trasversale, toccando temi fra cui: Fondamenti di etica, Etica della ricerca in specifici campi di ricerca, Metodologie di ricerca etica.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

Il corso tratta aspetti di programmazione dei calcolatori elettronici di carattere generale, fornendo sia gli erudimenti sintattici del liguaggio Python sia gli strumenti per l'utilizzo di Python nell'ambito del calcolo numerico.

Il modulo fa parte di una serie di insegnamenti messi a disposizione di tutti i dottorandi del XLI ciclo che afferiscono ai vari collegi di dottorato dell'Ateneo (vd. https://www.unipg.it/files/pagine/1998/didattica_tipc.pdf)

Il modulo si propone di fornire agli studenti le conoscenze e gli strumenti necessari per scrivere una tesi di dottorato di alta qualità. Discutere le diverse fasi della stesura di una tesi di dottorato, dalla scelta dell'argomento alla revisione finale. Insegnare agli studenti come condurre ricerche bibliografiche efficaci e come citare correttamente le fonti.
Sviluppare le capacità di scrittura accademica degli studenti, con particolare attenzione alla chiarezza, alla concisione e all'argomentazione.

This teaching module is a primer on numerical algorithms to integrate ordinary differential equations (ODE), and the practical applications in modeling of electric circuits for power devices with Matlab/Simulink.

PROGRAM:

• Introduction to ordinary differential equations (ODE) and their use in modeling of electric circuits.
• Numerical computations in the ODE solving: stability and stiffness.
• Introduction to Matlab/Simulink environment for electrical circuits and systems. Main features for the simulation of power electronic components.
• Simulations of electric circuits and power converters commonly used in industrial applications.

The role of engineers is rapidly changing in the new industry 4.0 framework and requires cross-functional and multidisciplinary knowledge and skills.
Mechanisms are used to transmit forces and torques and to move objects. In this scenario, mechanisms have been and continue to be essential components of mechanical systems for industrial applications. Since the knowledge of kinematics is crucial for the design and control of kinematic chains, the course aims at presenting kinematics of mechanisms introducing students to a proper design of mechanisms.

PROGRAM:
Rigid body mechanisms. Recalls on mechanisms: joints, architectures and topologies, analysis and synthesis.
Recalls on position analysis: generalities and examples of planar serial and parallel mechanisms. Constraint analysis: joints, spatial serial chains and examples, planar single loop chains.
Velocity analysis: joints, spatial serial chains and examples, planar single loop chains.
The underactuation concept
Design of underactuated mechanism Compliant mechanisms.
Example project.

The current Industrial scenario is rapidly evolving to enhance productivity, reduce resource consumption (energy and material) and prioritize human-centric values. This transformation, often referred to as the digital and green transition, began with Industry 4.0 and is now evolving towards Industry 5.0.
With a strong focus on additive manufacturing technologies, thorough the investigation of multi-sensing technologies, applied AI and digital twinning for cyber-physical systems, this module explores paradigm shifts and fundamental open challenges underlying the ongoing industrial revolution, with the goal of providing a comprehensive overview of how advanced manufacturing is being reshaped through the digital and green transitions.
The module begins with an overview of advanced manufacturing technologies and how they fit within the Industry 4.0 paradigm. Following an illustration of how state-of-the-art technologies of multi-sensing, AI and digital shadowing/ twinning are supporting the digital transition in manufacturing and inspection, the module introduces the next revolution: the green transition in manufacturing. Through the illustration of the three dimensions of sustainability (environmental, social and economic) the module discusses how manufacturing machines and systems are evolving to embrace the new paradigms of Industry 5.0.

Multiphase flows are widely used in a large variety of practical applications, such as spray, coatings, cavitation, phase change problems, cooling, microfluidics, porous media, in industrial and biomedical Engineering. The course aims at the introduction of mathematical methods and experimental tools for studying some classes of turbulent multiphase flows. Applications will be discussed and numerical algorithms for the solution of the governing equations will be presented.

PROGRAM:
Syllabus:

Basics of kinematics of fluid flows: Lagrangian and Eulerian descriptions of motion. Balance equations for mass, momentum, and energy. Constitutive theory of fluids. Derivation of the governing equations for multiphase flows in fluids.

Classifications: separate vs. dispersed flows.
Dispersed flows: two-fluid and single-fluid Eulerian formulations, Lagrangian formulation.
Models for unclosed terms.
Separate flows: Eulerian formulation for interface capturing/tracking schemes. Models for unclosed terms.

Numerical methods for computational fluid dynamics (brief overview)
Case studies on both Eulerian and Lagrangian models using open-source CFD software (openFoam).

Case studies and applications, with an overview on high performance computing methods.

Vehicle electrification for e-mobility demands a deep analysis of the thermal problems in order to increase vehicle efficiency and battery life and performance. An efficient thermal management of an electrified vehicle has to involve every system of the vehicle.
In the first part of the course, starting from physical and theoretical principles, we will review the fundamentals of these components. In parallel, the theory of CFD models used for the simulation of these components will be analyzed.
In the second part of the course, two case studies (a battery and an electric motor) will be analyzed with the definition of the initial and boundary conditions. Then, CFD simulations of the chosen case studies will be performed.

PROGRAM:

• Electric motors and batteries fundamentals
• CFD models for e-mobility
• Analysis of initial and boundary conditions
• CFD simulation of a battery case study
• CFD simulation of an electric motor case study

Introduction
1.1 Introduction to data Compression
1.1.1 Source and Channel Coding
1.2 Compression Techniques
1.2.1 Lossless Compression
1.2.2 Lossy Compression
1.2.3 Measures of Performance
1.3 Modeling and Coding

Mathematical Preliminaries for Lossless Compression
2.1 Overview
2.2 A Brief Introduction to Information Theory
2.2.1 Derivation of Average Information
2.3 Models
2.3.1 Physical Models
2.3.2 Probability Models
2.3.3 Markov Models
2.3.4 Composite Source Model
2.4 Coding
2.4.1 Uniquely Decodable Codes
2.4.2 Prefix Codes
2.4.3 The Kraft-McMillan Inequality
2.5 Algorithmic Information Theory
2.6 Minimum Description Length Principle

Huffman Coding
3.1 Overview
3.2 The Huffman Coding Algorithm
3.2.1 Minimum Variance Huffman Codes
3.2.2 Optimality of Huffman Codes
3.2.3 Length of Huffman Codes
3.2.4 Extended Huffman Codes
3.3 Non-Binary Huffman Codes
3.4 Adaptive Huffman Coding
3.4.1 Update Procedure
3.4.2 Encoding Procedure
3.4.3 Decoding Procedure
3.5 Golomb Codes
3.6 Rice Codes
3.6.1 CCSDS Recommendation for Lossless Compression
3.7 Applications of Huffman Coding
3.7.1 Lossless Image Compression
3.7.2 Text Compression
3.7.3 Audio Compression

Arithmetic Coding
4.1 Overview
4.2 Introduction
4.3 Coding a Sequence
4.3.1 Generating a Tag
4.3.2 Deciphering the Tag
4.4 Generating a Binary Code
4.4.1 Uniqueness and Efficiency of the Arithmetic Code
4.4.2 Algorithm Implementation
4.4.3 Integer Implementation
4.5 Comparison of Huffman and Arithmetic Coding
4.6 Adaptive Arithmetic Coding
4.7 Applications

Dictionary Techniques
5.1 Overview
5.2 Introduction
5.3 Static Dictionary
5.3.1 Digram Coding
5.4 Adaptive Dictionary
5.4.1 The LZ77 Approach
5.4.2 The LZ78 Approach
5.5 Applications 133
5.5.1 File Compression—UNIX compress
5.5.2 Image Compression—The Graphics Interchange Format (GIF)
5.5.3 Image Compression—Portable Network Graphics (PNG)