II semester

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

Simona Ranieri 1st year, 9 CFU, a.a. 2025-26, Block C-Mechatronic Systems and ICT, Block C2-ICT IEVE, 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.

 

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

Simona Ranieri 2nd year, a.a. 2025-26, Block C1-ICT LC, Block C2-ICT IEVE, BLOCK E–Electromechanics, c1-no-opt, 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)

 
didatticaweb
 ✅ Syllabus📑

Code:8039782
SSD: ING-INF/04

 

Radar and Localization – 6 CFU (optC2.a)

Simona Ranieri 2nd year, 6 CFU, a.a. 2025-26, Block C2-ICT IEVE, 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-Computational Methods, 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/02

SSD2024: PHYS-02/A
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-Mechanics and Digital Transition, Block B-Electronics and Digital Transition, Block C-Mechatronic Systems and ICT, Block C1-ICT LC, Block C2-ICT IEVE, c1-no-opt, 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-ICT IEVE, c2-no-opt, II semester, Subjects , ,
CEM
PROFESSOR 1st Year – II semester 6 CFU
Lorenzo BARTOLUCCI (3 CFU)
Matteo BALDELLI (3 CFU)
 since A.Y. 2024-25
👉(provided by Mechanical Engineering) 

 🧩 Block: C2 – INTERCONNECTED ELECTRIC VEHICLE ENGINEERING

📌 Course Code: 80300136 (6CFU)

🏷 SSD2015: ING-IND/08
🏷 SSD2024:

📑 Grading criteria

https://t0.gstatic.com/faviconV2?client=SOCIAL&type=FAVICON&fallback_opts=TYPE,SIZE,URL&url=https://didatticaweb.uniroma2.it&size=32  DidatticaWeb

Syllabus

The course program includes the presentation and discussion of the following topics:

  • Introduction to key energy carriers and the concept of sustainability
  • Technologies for the production of energy carriers: electricity, hydrogen, biofuels
  • Technological challenges in integrating these energy carriers into sustainable mobility
  • The concept of digital twins and system optimization
  • Development of a numerical-experimental project in mobility using one or more energy carriers

 

Electric Propulsion – 6 CFU (optC2.b)

Simona Ranieri 1st year, 6 CFU, a.a. 2024-25, a.a. 2025-26, Block C2-ICT IEVE, 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

Electronic Interfaces – 6 CFU (block E, optB, optC1.b/optC2.b)

Simona Ranieri 1st year, 6 CFU, a.a. 2023-24, a.a. 2024-25, a.a. 2025-26, b-choice2A, Block B-Electronics and Digital Transition, Block C-Mechatronic Systems and ICT, Block C2-ICT IEVE, BLOCK E–Electromechanics, II semester, optB, optC1.b, optC2.b, Subjects , ,
Electronic Interfaces – 6 CFU (block E, optB, optC1.b/optC2.b)
1 YEAR II semester  6 CFU
Christian Falconi A.Y. 2022-23 (since)
A.Y. 2023-24 (new block E) 
Christian Falconi (4)

Usman Khan (2)

 A.Y. 2025-26
didatticaweb

✅ Syllabus📑

Code: 80300103
SSD: ING-INF/01

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

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, Block C-Mechatronic Systems and ICT, 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 not be activated
A.Y. 2025-26
(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.