a.a. 2025-26

Digital Signal Processing – 9 CFU (optC1.b/optC2.b)

Simona Ranieri 1st year, 9 CFU, a.a. 2025-26, Block C, Block C1, Block C2, c1-choice31, c2-choice2, II semester, optC1.b, optC2.b, Subjects , , , , ,
Digital Signal Processing – 9 CFU (optC1.b/optC2.b)
1 YEAR II semester  9 CFU
ICT and Internet Engineering
Marina RUGGIERI (cfu)

Tommaso ROSSI (cfu)
A.Y. 2025-26 – program 📑
Code: 80300072
SSD: ING-INF/03

The Digital Signal Processing teaching modules offer students the opportunity to become designer providing a solid theoretical basis, multiple design techniques and Matlab script development skills.

DSP is offered to Mechatronics students with the option of 6 credits and 9 credits format. Students who select the 6-credit option might be interested in adding a +3 credits of formative activities, with a focus on pre-assigned additional topics of the DSP realm.

 

CONTROL OF ELECTRICAL MOTORS AND VEHICLES – 6 CFU (C1-C2-optE)

Simona Ranieri 2nd year, a.a. 2025-26, Block C1, Block C2, Block E, II semester, optE, Subjects , , , , , ,
CEM
2 YEAR II semester 6 CFU
Cristiano M. Verrelli  
 

 

 A.Y. 2025-26 (ex Control of Electrical Machines (B-C-E)
program 📑
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.

Mechanics of Systems for Simulations – 6 CFU (block A-B) (since 2025-26)

Simona Ranieri 1st year, 6 CFU, a.a. 2025-26, Block A, Block B, I sem, Subjects ,
Mechanics of Systems for Simulations – 6 CFU (block A-B) (since 2025-26)
1 YEAR
 1 semester 6 CFU
Marco Ceccarelli A.Y. 2025-26 program 📑
Code: 80300216 
SSD: ING-IND-13
(by Engineering Sciences)

OBJECTIVES

LEARNING OUTCOMES: The course aims to teach students the knowledge and tools that are needed to address the issues that are related to the identification, modeling, analysis, and design of multi-body planar systems in English language and terminology

KNOWLEDGE AND UNDERSTANDING: modeling and procedures to recognize the structure and characteristics of mechanisms and machines

APPLYING KNOWLEDGE AND UNDERSTANDING: acquisition of analysis procedures for the understanding of kinematic and dynamic characteristics of mechanisms and machines

MAKING JUDGEMENTS: possibility of judging the functionality of mechanisms and machines with their own qualitative and quantitative assessments

COMMUNICATION SKILLS: learning technical terminology and procedures for presenting the performance of mechanisms

LEARNING SKILLS: learning technical terminology and procedures for the presentation of the performance of mechanisms

PREREQUISITES: knowledge of basic mechanics of rigid bodies and computation skills

SYLLABUS

Structure and classification of planar mechanical systems, kinematic modeling, mobility analysis, graphical approaches of kinematics analysis, kinematic analysis with computer-oriented algorithms; dynamics and statics modeling, graphical approaches of dynamics analysis, dynamic analysis with computer-oriented algorithms, performance evaluation; elements of mechanical transmissions.

BOOKS:

Lopez-Cajùn C., Ceccarelli M., Mecanismos, Trillas, Città del Messico
Shigley J.E., Pennock G.R., Uicker J.J., “Theory of Machines and Mechanisms”, McGraw-Hill, New York
Handnotes and papers by the teachers

Radar and Localization – 6 CFU (optC2.a)

Simona Ranieri 2nd year, 6 CFU, a.a. 2025-26, Block C2, II semester, optC2.a, Subjects , ,
Radar and Localization – 6 CFU (optC2.a)
2 YEAR II semester 6 CFU
Prof. Mauro Leonardi A.Y. 2025-26
 

 

 (By ICT)
Code: 80300159
SSD: ING-INF/03

LEARNING OUTCOMES: Knowledge of the main applications and operations of radar systems with the necessary basic elements (both theoretical and technical-operational).

KNOWLEDGE AND UNDERSTANDING: Being aware, at the system level, performance in terms of scope, discrimination, ambiguity, Doppler filtering

APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to deal with new problems with the methods learned

MAKING JUDGEMENTS: the ability to choose among the various methods learned the proper one to face new problems and radar design.

Syllabus – Radar Systems

1. Fundamentals

  • General information on radar.

  • Spectrum usage.

  • Radar measurements:

    • Distance.

    • Radial velocity.

    • Angular location.

2. Radar Equation and Propagation

  • Fundamental radar equation.

  • Receiver and antenna noise.

  • Propagation: attenuation and reflections.

  • Losses.

3. Radar Cross Section and Target Models

  • Radar Cross Section (RCS).

  • Target fluctuation models:

    • Slow fluctuation.

    • Rapid fluctuation.

4. Target Detection

  • Detection of fixed targets.

  • Detection of moving targets.

  • Pulse integration.

5. Decision Theory and Radar Detection

  • Decision criteria.

  • Detection with a single pulse.

  • Detection with N pulses.

6. Radar Types

  • Pulsed radar.

  • Continuous Wave (CW) radar.

  • Frequency Modulated Continuous Wave (FMCW) radar.

  • Automotive radar.

MACHINE LEARNING METHODS FOR PHYSICS – 8 CFU (block D)

Simona Ranieri 2nd year, 8 CFU, a.a. 2025-26, Block D, II semester, Subjects , ,
Mathematical-Methods
2 YEAR (Block D)
 2 semester 8 CFU
(from Physics LM-17 )
Prof. Michele BUZZICOTTI A.Y. 2025-26 program 📑
Code: 80300140
SSD: FIS/01
https://www.master-mass.eu/

 

  • PREREQUISITES: Basic concepts of Linear Algebra, Mathematical Analysis and Python Programming
  • OBJECTIVE: The lectures are thought to give a solid knowledge of the theoretical Machine Learning (ML) background. A special focus is given to the ML application for data analysis of physical systems. The students will also learn how to implement a typical ML model using the standard libraries in a Python environment.

 

COMPUTER VISION – 6 CFU (since 2024-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, a.a. 2025-26, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, II semester, Subjects , ,
COMPUTER VISION – 6 CFU (since 2024-25)
2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2023-24 (ex MEASUREMENT SYSTEMS FOR MECHATRONICS)
A.Y. 2024-25: Computer Vision – program
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.

INTEGRATED SOLUTIONS FOR SUSTAINABLE MOBILITY AND ENERGY PRODUCTION – 6 CFU (C2)

Simona Ranieri 1st year, 6 CFU, a.a. 2024-25, a.a. 2025-26, Block C2, II semester, Subjects , ,
CEM
1 YEAR II semester 6 CFU
(from Mechanical)
Lorenzo BARTOLUCCI (3cfu)
Matteo BALDELLI (3cfu)
 A.Y. 2024-25
Code: 80300136
SSD: ING-IND/08
  • Prerequisites: No prior kknowledge is required, although notions about energy systems and an understanding of error and data analysis can facilitate the student. All the knowledge necessary to pass he exam will be provided during the course.
  • OBJECTIVES: The goal of the course is to provide students with both a detailed and holistic view of the energy landscape for sustainable mobility and its impact on the overall energy system. The course aims to bridge the production of key energy carriers (electricity, hydrogen, biofuels, etc.) with their use in mobility, addressing issues of integration and optimization. To this end, students will expand their understanding of the fundamental physics behind these technologies, combining theoretical/modeling aspects with experimental approaches through laboratory activities. Lastly, particular attention will be given to the presentation and critical analysis of data obtained both experimentally and through numerical modeling.

 

Electric Propulsion – 6 CFU (optC2.b)

Simona Ranieri 1st year, 6 CFU, a.a. 2024-25, a.a. 2025-26, Block C2, II semester, optC2.b, Subjects , , ,
Electric Propulsion – 6 CFU (optC2.b)
1 YEAR (Block C2)
 II semester 6 CFU
(from Mechanics – Energetics)
Prof. Marcello PUCCI
 A.Y. 2024-25 – program
Code: 80300151
SSD: ING-IND/32

LEARNING OUTCOMES:
The course aims to provide the students some theoretical instruments necessary for the comprehension and related application of the fundamentals of electric and hybrid electric propulsion systems, with particular emphasis to the on-wheel and ship propulsion.
The course will permit the students to acquire and apply the fundamentals of modelling and control of electric drives for the electric and hybrid electric on-wheel and ship propulsion, beside the supply and storage systems. The issues of the impact of electric vehicles on the power grid will also be discussed, with reference to modern vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies.

KNOWLEDGE AND UNDERSTANDING:
In order to improve understanding of the topics, the implementation of drive trains simulation models will be addressed by using Simscape Electrical libraries in the Matlab-Simulink environment. The students will acquire the capability of comprehend and demonstrate the aware knowledge of the behavior of electric and hybrid electric vehicles, with particular reference to their electric propulsion, to the electric motors, power converters and related control systems- to the supply and storage systems. The understanding will be enhanced by the comparison between different types of electric drives, power electronic converters and
related control systems, as well as different types of storage systems. Several kinds of supplies and storage systems will be analyzed as well, with particular emphasis to the fuel
cells supplied vehicles.

APPLYING KNOWLEDGE AND UNDERSTANDING:
At the end of the course students will have to show the ability to independently apply the concepts learned with particular reference to the sizing of the drive train for electric and hybrid electric vehicles, power sources as well as the issues related to the interaction of energy storage on board of vehicles with the distribution network in terms of vehicle-to-grid (V2G) and grid-to-vehicle (G2V).

MAKING JUDGEMENTS:
Students will be able to collect and process independently specialized technical information on the design and control of electric drives as well as on energy storage systems used in electric and hybrid electric propulsion by road and sea and finally verify their validity.

COMMUNICATION SKILLS:
Students will be able to interact with specialists in power electronics and electric drives in order to elaborate the technical information necessary for the development of a design activity to be carried out individually or in groups.

LEARNING SKILLS:

The expertises acquired during the course will allow students to undertake higher-level training courses or apply for specialist technical roles in companies in the sector with a good degree of autonomy.

Prerequisities

It is suggested to have the basic knowledge of Electrical Network Analysis and Power Electronics

 

SYLLABUS

The course will be articulated in the following way:
– Electric Vehicles
– Hybrid Electric Vehicles
– Electric Propulsion Systems for vehicles
– Series Hybrid Electric Drive Train Design
– Parallel Hybrid Electric Drive Train Design
– Energy Storages (Batteries, Supercapacitors, – Ultrahigh-Speed Flywheels, Hybrid)
– Fuel Cell Vehicles
– Ship propulsion systems
– Vehicle to Grid (V2G) and Grid to Vehicle (G2V)

TEXTS

Educational material provided by the teacher

– John M. Miller, Propulsion Systems for Hybrid Vehicles, IET, 2008
– Iqbal Husain, Electric and Hybrid Vehicles: Design Fundamentals, 2010, CRC Press
– Mehrdad Ehsani, Yimin Gao, Ali Emadi, Modern Electric, Hybrid Electric, and Fuel Cell
Vehicles: Fundamentals, Theory, and Design, 2017, CRC Press