II semester

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

 

 

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).

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.

Machine Design (block A)

Simona Ranieri 2nd year, 6 CFU, a.a.2022-2023, Block A, II semester, Subjects ,
Machine Design (block A)
2 YEAR II semester  6 CFU
Luciano CANTONE A.Y. 2023-24
(by Engineering Sciences)
Code: 80300065
SSD:

LEARNING OUTCOMES: Designing mechanical components considering the need to save weight, material and energy while respecting safety, to promote the usefulness and social impact of the designed product.
KNOWLEDGE AND UNDERSTANDING: The design of mechanical systems, in particular, basic knowledge of the design methodologies of important machine components.
APPLYING KNOWLEDGE AND UNDERSTANDING: Know how to recognise, distinguish, and use the main techniques and tools to design mechanical components.
MAKING JUDGEMENTS: Students must assume the missing data of a problem and be able to independently formulate basic hypotheses (such as that on safety coefficients) based on the operational and functional context of the system/component they have to design.
COMMUNICATION SKILLS: Transfer information, ideas and solutions to specialist and non-specialist interlocutors through intensive use of English terminology.
LEARNING SKILLS: Students, by learning the basics of design, acquire the tools to learn the necessary design techniques of systems/components not directly addressed during the course.

VERIFICATION CRITERIA

Solution of the assignments given during the teaching. The written exam consists of 4 exercises, similar to those solved during the lessons. After passing the written test, there is an oral examination with two questions. The student’s evaluation is related to the understanding and mastery of the principles and methods of design for both written and oral exams.
The oral exam questions aim to ascertain the student’s knowledge and reasoning skills in connecting the different topics covered within the course.
The final vote of the exam is expressed out of thirty and follows the next graduation system:
Not pass, essential deficiencies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgmental capacity, the topics are set out inconsistently and with inappropriate language
18-21, the student has acquired the basic concepts of the discipline and has an analytical capacity that emerges only with the teacher’s help. The way of speaking and the language used are, on the whole, correct.
22-25, the student has acquired the basic concepts of the discipline discreetly, knows how to orient himself or herself among the various topics covered and has an autonomous analysis capacity that knows how to express with the correct language.
26-29, the student has a well-structured knowledge base. He/She can independently rework the knowledge acquired in the context of the choice of conventional and unconventional materials according to the application; the way of speaking and the 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, expressed with brilliance and properties of technical language.

The midterm tests are optional and allow you to avoid the oral examination with an overall mark of at least 18/30.

Syllabus

Consolidation of basic knowledge to put the student in the right conditions to face a generic machine design problem: Mechanical Engineering design in Broad, Perspective, Load Analysis, Materials, Static Body Stresses, Elastic strain, Deflection, Stability (Eulerian buckling), Vibrations (beam Eigen-modes), Failure Theories,Safety Factors, Reliability, High cycles Fatigue, Low cycles Fatigue, Surface Damage, Contact and impact problems. During the course, several design activities will be demonstrated by exercises and by real life applications.

TEXTS

Machine Component Design, 7th Edition International Student Version Robert C. Juvinall – (University of Michigan), Kurt M. Marshek (University of Texas at Austin)
Teacher’s slides

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY (ex Internal Combustion Engines)

Simona Ranieri 1st year, 2nd year, 9 CFU, a.a. 2023-2024, a.a. 2024-25, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, Block D, Block E, II semester, Subjects , , , ,
POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY (ex Internal Combustion Engines)
1 YEAR (Block C)

2 YEAR (Blocks A|B|D|E)

 II semester  9 CFU
Stefano CORDINER (6/9 cfu)
Lorenzo BARTOLUCCI (3/9 cfu)
 A.Y. 2021-22

Internal Combustion Engines
Since A.Y. 2022-23

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY
Code: 80300079
SSD: ING/IND/08
(by Mechanical Engineering)

PREREQUISITES: Technical Physics, Fluid Machinery

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:

The course aims to provide students with in-depth scientific training to correctly address the problems of designing, choosing and managing new propulsion systems for sustainable mobility starting from current solutions with internal combustion engines as well as creating the conditions for the development of innovative and low environmental impact solutions. To this end, students will develop in-depth knowledge of the operating principles of propulsion systems for transport and will learn simulation procedures for their verification and sizing. Finally, particular attention is dedicated to the most recent technological development of internal combustion engine technology aimed at overcoming current limits in terms of emissions and efficiency and defining innovative scenarios for sustainable mobility.

KNOWLEDGE AND UNDERSTANDING:
Course aim is to provide the students with tools for the analysis of the performances and the evaluation of proper design solution for internal combustion engines and their core components. At the end of the course, the student will be able to independently understand the functional link between design variables and the performance of internal combustion engines also in case of innovative design,

APPLYING KNOWLEDGE AND UNDERSTANDING:
The course, through the analysis of specific problems and quantitative data, is aimed at providing the tools for analysis and evaluation of the effects of different design choices. The theme of energy efficiency and pollution reduction are at the heart of the teaching organization. The student will be able to interpret and propose design solutions, even innovative ones, adapted to the specificity of the problems that are presented to him.

MAKING JUDGEMENTS:
By studying theoretical and practical aspects of engine design and critically assessing the influence of different design variables, the student will be able to improve his judgment and proposal in relation to design. and the management of internal combustion engines.

COMMUNICATION SKILLS:
The presentation of the theoretical and application profiles underlying the operation of internal combustion engines will be carried out to allow the knowledge of the technical language of the appropriate specialist terminology; The development of communication skills, both oral and written will also be stimulated through classroom discussion, participation in seminary activities and through final tests.

LEARNING SKILLS:
The learning capacity, even individual, will be stimulated through numerical exercises, the drafting of papers on specialized topics, the discussion in the classroom, also aimed at verifying the actual understanding of the topics treated. The learning capacity will also be stimulated by integrative educational aids (journal articles and economic newspapers) in order to develop autonomous application capabilities.

SYLLABUS:

Legislation evolution on Internal Combustin Engines. Definition of the performance of the propulsion systems and their operating characteristics in relation to the mission, driving cycles. Generalities on reciprocating internal combustion engines: Characteristics and classification, thermodynamic and performance analysis of reciprocating internal combustion engines.
Air supply for 4-stroke engines: volumetric efficiency and its evaluation; Design elements of intake systems: quasi-stationary effects; valve sizing; influence of other engine parameters; Variable Valve Actuation systems. 2-stroke engines: construction schemes; Non-stationary phenomena in intake and exhaust ducts: inertia and wave propagation; variable geometry systems; calculation models; Supercharging.
In cylinder charge Motion: Turbulence; swirl, squish, tumble; stratified charge engines.
Traditional and alternative fuels; Properties of motor fuels. Generalities: combustibles; stoichiometric air; calorific value Gaseous fuels: natural gas, hydrogen and mixtures. bio-ethanol, bio-diesel and DME. Characteristics and their use in engines: technical solutions, performance and emissions.
Fuel supply Premixed combustion engines; Non-pre-mixed combustion engines.
Combustion : Analytical elements of combustion; thermodynamics of combustion processes; calculation of the chemical composition and of the adiabatic equilibrium temperature ; transport phenomena ; chemical kinetics.
Pollutant emissions and abatement systems; Emissions: formation mechanisms, effects on health and the environment, measurement of emissions; influence of engine parameters; Innovative combustion solutions, Advanced Thermodynamic Cycles. Sustainable mobility. Operating principles of hybrid vehicles: series and parallel solution; motors a.c. and electrical employees; regenerative braking; lithium batteries, performance and prospects. Plug-in hybrid vehicles, i.c. engines “range extender”. Innovative control logics for optimal powersplitting between the different energy sources. Electric vehicles, characteristics and prospects. Numerical simulation tools will be presented for all course topics

ATTENDANCE

Course attendance is strongly recommended. During the course, students are invited to interact with the Professor during the class or office hours for any clarification or insight in specific topics related to the program.