II semester

Mandatory Attendance – Second Semester Onwards – meeting 12th of March 2026

Simona Ranieri a.a. 2025-26, II semester, NEWS
Mandatory Attendance – Second Semester Onwards – meeting 12th of March 2026

⚠️ IMPORTANT NOTICE

Please note that, starting from the second semester, attendance is mandatory for all courses of the Master’s Degree in Mechatronics Engineering.

  • Minimum attendance required: 70% of lectures for each course

  • Attendance is monitored through a register

  • The requirement is mandatory to be admitted to the exam

  • For part-time students, the minimum percentage is proportionally reduced based on the agreed study duration

Students are invited to carefully review the course prerequisites in the course description section of the programme website.

More info:

Attendance is monitored through a register, and students must attend at least 70% of the lectures for each course to be admitted to the corresponding exam.

For part-time students (as regulated by Art. 13 of the Study Programme Regulations), the minimum attendance percentage is reduced proportionally. The required percentage is calculated by multiplying 70% by the ratio of the programme’s standard duration (2 years) to the agreed duration of the individual part-time study plan.
For example, if the agreed duration is 4 years, the coefficient is 2/4 = 0.5, and the minimum attendance requirement becomes 35%.

Dear First-Year Mechatronics Engineering Students,

you are strongly invited to participate in the meeting that will take place on the 12th of March 2026 at 1 p.m. (Aula Pitagora) and that will allow you to receive your presence-recording cards.

Students who are unable to attend must inform the Didactic Secretariat in due time.

Best regards,

Identification and Neural Networks – 6 CFU (since 24-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, a.a. 2025-26, II semester, optC1.b, Subjects , , , ,
Identification and Neural Networks – 6 CFU (since 24-25)
2 YEAR II semester  6 CFU
Patrizio Tomei (4cfu)
Eugenio Martinelli (2cfu)		 A.Y. 2023-24 ex Adaptive Systems (block C-opt) 
Giovanni Luca SANTOSUOSSO A.Y. 2024-25 not been activated
A.Y. 2025-26
(new name “Identification and Neural Networks”
Didatticaweb
✅ Syllabus📑

Code: 80300088
SSD: ING-INF/04

 

Quantum Computing (D-opt)

Simona Ranieri 2nd year, 8 CFU, a.a. 2025-26, Block D, II semester, optD, Subjects , , ,
Mathematical-Methods
2 YEAR (Block D)
 2 semester 8 CFU
(from Physics LM-17 )
Prof.  A.Y. 2025-26 activated
start in the a.y. 2026-27
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.

 

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

Simona Ranieri 1st year, 9 CFU, a.a. 2025-26, Block C, Block C2, 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 

✅ Syllabus📑
Code: 80300072
SSD: ING-INF/03

The Digital Signal Processing teaching modules offer students the opportunity to become designers, 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 3 credits of formative activities, with a focus on pre-assigned additional topics in the DSP realm.

 

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 (D-opt)

Simona Ranieri 2nd year, 8 CFU, a.a. 2025-26, Block D, II semester, optD, 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
didatticaweb
✅ Syllabus📑

Code: 8039787
SSD: ING/INF/07

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
 since A.Y. 2024-25

✅ Syllabus📑
didatticaweb

Code: 80300151
SSD: ING-IND/32

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 Storage (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