6 CFU

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

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, a.a. 2025-26, Block C, c-choice2, II semester, 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
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.

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
Teaching programs (Schede d’Insegnamento-GOMP)📑

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.

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
All programs
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
 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