2nd year

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.

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

 

 

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.

Adaptive Systems (block C-opt) –> Identification and Neural Networks (24-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, Block C, c-choice2, II semester, Subjects , , ,
Adaptive Systems (block C-opt) –> Identification and Neural Networks (24-25)
2 YEAR II semester  6 CFU
Patrizio Tomei (4cfu)
Eugenio Martinelli (2cfu)		 A.Y. 2023-24
SANTOSUOSSO Giovanni Luca A.Y. 2024-25 (new name “Identification and Neural Networks”
Didatticaweb
Code: 80300088
SSD: ING-INF/04

Pre-requirement: The basics of systems theory and control are required.

LEARNING OUTCOMES: The course aims to provide the basic techniques for the design of predictors, filters, and adaptive controllers.

KNOWLEDGE AND UNDERSTANDING: Students must obtain a detailed understanding of design techniques with the help of MATLAB-SIMULINK to solve industrial problems of adaptive filtering, adaptive prediction, and adaptive control.

APPLYING KNOWLEDGE AND UNDERSTANDING: Students must be able to apply the project techniques learned in the course even in different industrial situations than those examined in the various phases of the course.

MAKING JUDGEMENTS: Students must be able to apply the appropriate design technique to the specific cases examined, choosing the most effective algorithms.

COMMUNICATION SKILLS: Students must be able to communicate using the terminology used for filtering, prediction, and adaptive control. They must also be able to provide logical and progressive exposures starting from the basics, from structural properties, from modeling to the design of algorithms, without requiring particular prerequisites. Students are believed to be able to understand the main results of a technical publication on the course topics. Guided individual projects (which include the use of Matlab-Simulink) require assiduous participation and exchange of ideas.

LEARNING SKILLS: Students must be able to identify the appropriate techniques and algorithms in real cases that arise in industrial applications. Furthermore, it is believed that students have the ability to modify the algorithms learned during the course in order to adapt them to particular situations under consideration.

Texts

Adaptive Filtering Prediction and Control, Graham C. Goodwin, Kwai Sang Sin, Dover Publications, 2009.

Control of Electrical Machines (B-C-E) –> CONTROL OF ELECTRICAL MOTORS AND VEHICLES (B-C1-C2-E) (25-26)

Simona Ranieri 2nd year, a.a.2022-2023, b-choice2B, Block B, Block C, Block C1, Block C2, Block E, II semester, Subjects , , , , ,
CEM
2 YEAR II semester 6 CFU
Cristiano M. Verrelli A.Y. 2021-22 to A.Y. 2024-25
 

 

 A.Y. 2025-26 (new name CONTROL OF ELECTRICAL MOTORS AND VEHICLES )
Code:8039782
SSD: ING-INF/04

 

LEARNING OUTCOMES: The course aims to provide a unified exposition of the most important steps and concerns in mathematical modeling and design of estimation and control algorithms for electrical machines such as:
– permanent magnet synchronous motors
– permanent magnet stepper motors
– synchronous motors with damping windings
– induction (asynchronous) motors
– synchronous generators.

KNOWLEDGE AND UNDERSTANDING: Students should be able to gain profound insight into the fundamental mathematical modeling and control design techniques for electrical machines, which are of interest and value not only to engineers engaged in the control of electric machines but also to a broader audience interested in (nonlinear) control design.

APPLYING KNOWLEDGE AND UNDERSTANDING: Students should be able to deeply understand mathematical modeling through nonlinear differential equations, stability and nonlinear control theory concepts, and design of (nonlinear) adaptive controls containing parameter estimation algorithms (important for applications). Students should be able to apply the related knowledge to learning control of robotic manipulators and cruise/yaw rate control of electric vehicles.

MAKING JUDGEMENTS: Students should be able to identify the specific design scenario and apply the most suitable techniques. Students should be able to compare the effectiveness of different controls while analyzing theoretical/experimental advantages and drawbacks.

COMMUNICATION SKILLS: Students should be able to use a single notation and modern (nonlinear) control terminology. Students should be able to exhibit a logical and progressive exposition starting from basic assumptions, structural properties, modeling, control, and estimation algorithms. Students are also expected to be able to read and capture the main results of a technical paper concerning the topics of the course, as well as to effectively communicate in a precise and clear way the content of the course. Tutor-guided individual projects (including Maple and Matlab-Simulink computer simulations and lab visits) invite intensive participation and exchanging ideas.

LEARNING SKILLS: Being enough skilled in the specific field to undertake the following studies characterized by a high degree of autonomy.

TEXTS

R. Marino, P. Tomei, C.M. Verrelli, Induction Motor Control Design, Springer, 2010.
Latest journal papers.

VERIFICATION OF THE KNOWLEDGE

Verify the knowledge and skills acquired by the student on the topics covered by the program. The intermediate exams, the final written tests, and the oral exam will consist of questions related to the topics covered by the program of the course. The questions are aimed at ascertaining the student’s knowledge and his/her reasoning skills in making logical connections between the different topics.

The final vote of the exam is expressed out of thirty and will be obtained through the following graduation system:

Not pass: important deficiencies in the knowledge and in the understanding of the topics; limited capacity for analysis and synthesis, frequent mistakes and limited critical and judgmental capacity, inconsistent reasoning, inappropriate language.

18-21: the student has acquired the basic concepts of the discipline and has an analytical capacity that comes out only with the help of the teacher. The way of speaking and the language used are almost correct, though not precise.

22-25: the student has acquired the basic concepts of the discipline in a discrete way; he/she knows how to discuss the various topics; he/she has an autonomous analysis capacity while adopting a correct language.

26-29: the student has a well-structured knowledge base. He/She is able to independently adopt a correct logical reasoning;  notations and technical language are correct.

30 and 30 cum laude: the student has a complete and in-depth knowledge base. The cultural references are rich and up-to-date while being expressed by means of brilliant technical language.

MEASUREMENT SYSTEMS FOR MECHATRONICS (2023-24) –> COMPUTER VISION (2024-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2023-2024, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, II semester, Subjects , ,
MEASUREMENT SYSTEMS FOR MECHATRONICS (2023-24) –> COMPUTER VISION (2024-25)
2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2021-22

A.Y. 2022-23
A.Y. 2023-24

Computer Vision A.Y. 24-25
Code: 8039787
SSD: ING/INF/07

LEARNING OUTCOMES: Learning 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 image medical fields.

KNOWLEDGE AND UNDERSTANDING: The student acquires 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 with 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 basic metrological definitions will be provided in support of background knowledge.

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 a group to demonstrate working group capabilities.

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