Subjects

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.

 

Digital Modeling of Energy Conversion – 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 , ,
Digital Modeling of Energy Conversion – 6 CFU (block A-B) (since 2025-26)
1 YEAR
 1 semester 6 CFU
Vincenzo MULONE (3cfu)

Pietro MELE (3cfu)

 A.Y. 2025-26
didatticaweb
✅ Syllabus📑

Code:
SSD: ING-IND-08
(by Mechatronics Engineering)

Digital Modeling of Energy Conversion 9 – Block A-B

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 (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.