6 CFU

COMPUTER VISION (2024-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, II semester, Subjects , ,
COMPUTER VISION (2024-25)
2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2023-24 (ex MEASUREMENT SYSTEMS FOR MECHATRONICS)
A.Y. 24-25
Code: 8039787
SSD: ING/INF/07

LEARNING OUTCOMES:
Learning of basic concepts in digital image processing and analysis as a novel measurement system in biomedical fields. The main algorithms will be illustrated, particularly devoted to the mechatronics fields.

KNOWLEDGE AND UNDERSTANDING:
The student acquires the knowledge related to the possibility to use an image analysis platform to monitor the dynamics of a given phenomenon and to extract quantitative information from digital images such as object localization and tracking in digital videos.

APPLYING KNOWLEDGE AND UNDERSTANDING:
The student acquires the capability to implement the algorithms in Matlab through dedicated lessons during the course to the aim of being able to autonomously develop new codes for the solution of specific problems in different application fields.

MAKING JUDGEMENTS:
The student must be able to integrate the basic knowledge provided with those deriving from the other courses such as probability, signal theory, and pattern recognition. some fundamentals of measurement systems as well as of basic metrological definitions will be provided in support of background knwoledge.

COMMUNICATION SKILLS:
The student solves a written test and develops a project in Matlab that illustrates during the oral exam. The project can be done in group to demonstrate working group capabilities.

LEARNING SKILLS:
Students will be able to read and understand scientific papers and books in English also to deepen some topics. In some cases, students will develop also experimental tests with time-lapse microscopy acquisition in department laboratory.

SYLLABUS

Fundamentals of metrology. Basic definitions: resolution, accuracy, precision, reproducibility and their impact over an image based measurement system . Image processing introduction. Image representation. Spatial and pixel resolution. Image restoration. Deconvolution. Deblurring. Image quality assessment. Image enhancement. Image filtering for smoothing and sharpening. Image segmentation: pixel based (otsu method), edge based, region based (region growing), model based (active contour, Hough transform), semantic segmentation. Morphological operators. Object recognition and image classification. Case study: defects detection, object tracking in biology, computer assisted diagnosis, facial expression in human computer interface.
Matlab exercises.

TEXTS

Digital image processing, Gonzalez and Woods, Prentice Hall, New York, 2002.

BiPM, I. E. C., IFCC, I., IUPAC, I., & ISO, O. (2012). The international vocabulary of metrology— basic and general concepts and associated terms (VIM). JCGM, 200, 2012.

ATTENDANCE

Although attendance is optional, it is strongly recommended to follow the lessons. The professor recommends the students to subscribe the course on the Delphi website. The teams platform will be used as a consequence to communicate with the Professor, ask for doubts, and download the materials used for the lessons.

On Board Energy Generation and Storage (C2 opt)

Simona Ranieri 1st year, 6 CFU, a.a. 2024-25, a.a. 2025-26, Block C2, c2-choice3, I sem, Subjects , , ,
On Board Energy Generation and Storage (C2 opt)
1 YEAR (Block C2)
 1 semester 6 CFU
Prof. Fabio Matteocci
 A.Y. 2024-25 (new)
Code: 80300150
SSD: ING-INF/01

 

The course requires a basic knowledge of nanotechnologies applied to the generation and storage of electric power, as well as a basic understanding of the functioning of solar cells and batteries.

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:

The main objectives of the course are the study of electric power generation and storage systems that can be implemented on vehicles. The lessons therefore focus on next-generation photovoltaics, thin-film deposition techniques, storage systems, supercapacitors, and thermoelectricity. The generation and storage technologies will then be studied from an application perspective through case studies.

KNOWLEDGE AND UNDERSTANDING:

Students will be able to:

a) To learn the working principles for energy generation and storage (EGS);
b) To understand and explain the solutions for EGS when applied in vehicles;
c) To solve simple problems concerning the use of design of integrated EGS systems;
d) To know how to design, develop and release a simple EGS system for vehicle integration.

APPLYING KNOWLEDGE AND UNDERSTANDING:

The student will be able to recognize the applicability areas for the various EGS systems. She/He will also be able to apply the knowledge and understanding developed during the course to study and understand recent literature.

MAKING JUDGEMENTS:

Students should be capable of identifying specific design scenarios and applying the most appropriate techniques for EGS. Additionally, they should be able to compare the effectiveness of various EGS system while evaluating their advantages and disadvantages.

COMMUNICATION SKILLS:

The student will be able to clearly and unequivocally communicate the course content to specialized interlocutors. He will also be able to communicate the main approches to the development of EGS systems. The student will also have a sufficient background to undertake a thesis/research work in EGS applications.

LEARNING SKILLS:

Being sufficiently skilled in the specific field to undertake subsequent studies characterized by a high degree of autonomy.

SYLLABUS

1. Introduction on Nanotechnology: Top Down and Bottom Up Approaches2. Physical, Chemical Deposition, Solution Processing (Working Principle and Applications) 3. Energy Generation: Conventional and Emergent Photovoltaics (Working Principle and Applications).
4. Case of Study: Perovskite solar Cells (Working Principle, Deposition Techniques and applications)
5. Storage: Conventional and Emergent technologies for Batteries
6. Electrical and Chemical Properties of Batteries (Working Principle)
7. System Integration of Energy Generation and Storage solutions
8. Oppurtinities and Limitations of vehicle-integrated solutions for Generation and Storage 9. Beyond Batteries: Supercapacitors and thermoelectricity

The lecture will be held in the classroom with the projection of slides that will be released to the students at the end of the lecture.

The student will only be admitted to the final exam if they have attended 80% of the course hours.

 

 

Deep Learnig and applications (block C1-opt)

Simona Ranieri 2nd year, 6 CFU, a.a. 2025-26, Block C1, c1-choice31, II semester, Subjects , ,
2 YEAR II semester  6 CFU

Eugenio Martinelli
 A.Y. 2024-25 new
Didatticaweb
Code:
SSD: ING-INF/01

PREREQUISITES

Basic knowledge of probability theory, signal theory, and pattern recognition.

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:
Learning the basic concepts of deep learning algorithms. The main Machine Learning algorithms will be covered, followed by a focus on those related to deep learning, with particular emphasis on their application in the field of mechatronics.

KNOWLEDGE AND UNDERSTANDING:
The student acquires knowledge related to the field of Machine Learning, with particular reference to the ability to extract quantitative and qualitative information from images and videos and multivariate data and their subsequent processing for regression and classification tasks.

APPLYING KNOWLEDGE AND UNDERSTANDING:
The student acquires the capability to implement the algorithms in Matlab through dedicated lessons during the course to the aim of being able to autonomously develop new codes for the solution of specific problems in different application fields.

MAKING JUDGEMENTS:
The student must be able to integrate the basic knowledge provided with those deriving from the other courses, such as probability, signal theory, and pattern recognition.

COMMUNICATION SKILLS:
The student develops a project in Matlab that illustrates during the oral exam. The project can be done in groups to demonstrate working group capabilities.

LEARNING SKILLS:
Students will need to be able to read and understand scientific texts and articles in English for in-depth exploration of the topics covered. They should also independently expand their knowledge of the subject to include topics not directly addressed in the course, particularly those connected with the rapid technological developments in the field of Deep Learning and, more generally, in machine learning.

SYLLABUS

Today, deep neural networks surpass traditional hand-crafted algorithms and match human performance in various complex tasks, including image recognition, natural language processing, and prediction models. This course offers a comprehensive introduction to neural networks (NNs), covering traditional feedforward (FFNN) and recurrent (RNN) neural networks, as well as the most advanced deep-learning models like convolutional neural networks (CNN), Variational Autoencoders, and Diffusion models.

The primary objective of the course is to equip students with the theoretical knowledge and practical skills needed to understand and utilize neural networks (NN), while also familiarizing them with deep learning techniques for solving complex engineering challenges.
This goal is pursued in the course by:
• Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
• Illustrating the best practices for successful training and using these models (e.g., dropout, data augmentation, etc.) in a practical session using a phyton environment.
• Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, autoencoder for embedding, diffusion models, etc.)
• Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.

TEXTS

Pattern recognition and machine learning, Christopher Bishop.

Deep Learning, Ian Goodfellow et al.

– slides of the professor

 

 

Electronic Interfaces (block B-opt – E) (since 2022-23)

Simona Ranieri 1st year, 6 CFU, a.a.2022-2023, b-choice2A, Block B, Block E, II semester, Subjects , ,
Electronic Interfaces (block B-opt – E) (since 2022-23)
1 YEAR II semester  6 CFU
Christian Falconi A.Y. 2022-23 (since)
Code: 80300103
SSD: ING-INF/01

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:
The goal is to teach the fundamental principles and tools for designing electronic interfaces.
The contents of the course have general validity, but the focus will be on electronic interfaces for mechatronics.
The course is oriented toward design.

KNOWLEDGE AND UNDERSTANDING:
Students will need to know and understand the fundamental principles and tools for the analysis and design of electronic interfaces.

APPLYING KNOWLEDGE AND UNDERSTANDING:
Students will have to demonstrate that they are able to design electronic interfaces.

MAKING JUDGEMENTS:
Students will be able to evaluate the design of electronic interfaces.

COMMUNICATION SKILLS:
The students, in addition to illustrating the fundamental principles and tools for the design of electronic interfaces, must be able to explain each design choice.

LEARNING SKILLS:
Students must be able to read and understand scientific texts and articles (also in English) concerning electronic interfaces.

PREREQUISITES

Thévenin equivalent circuit.
Norton equivalent circuit.
Laplace transform
Fourier transform

Syllabus:

Fundamentals on electronic devices.
Equivalent circuits (mechanic systems, thermal systems,…).
Diode circuits.
Transistor circuits.
Nullors.
Operational amplifiers (op amps).
Universal active devices.
Non-idealities of op-amps and other universal active devices.
Op-amp circuits.
Simulations of electronic circuits (SPICE).
Electronic interfaces.
Circuits for mechatronics (design examples).

Multimedia Processing and Communication (block C-opt)

Simona Ranieri 2nd year, 6 CFU, a.a.2022-2023, Block C, Block C1, c-choice3, c1-choice32, I sem, Subjects , , ,
Multimedia Processing and Communication (block C-opt)
2 YEAR I semester  6 CFU
Tommaso Rossi

Cesare Roseti

 ICT and Internet Engineering
A.Y. 2023-24
Code:
SSD: ING-INF/03

FORMATIVE OBJECTIVES

The course module provides an overview of the technologies involved in the multimedia application evolution from analogue to digital, from linear television to video on demand. To this aim, the module addresses the main TV standards, the TCP/IP protocols involved in modern streaming services, the network architectures and the different service modes.

PREREQUISITES: A good background in TCP/IP protocols.

SYLLABUS:

PARTE I – Digital TV standards, MPEG-2  and  Transport Stream, IP encapsulation over  DVB.

PARTE II – IP multicast, IGMP, IP multicast routing

PARTE III –  Transport protocols for IP multimedia applications; Video streaming applications and CDN, the multimedia protocol stack, RTP and RTCP, multimedia signalling protocols: RTSP, SDP and SIP, Key Performance Indicators.

PARTE IV -Adaptive Streaming over HTTP, MPEG-DASH, Support to multimedia applications over 5G.