Deep Learnig and applications (block C-opt)

1 YEAR II semester  6 CFU

Eugenio Martinelli
A.Y. 2024-25

Description: The course, starting from the principles of deep learning, will bring the students to study, analyze, and use all the main DL algorithms in different application scenarios. During the course, theory lessons will also be coupled with practical sessions where the algorithm will be applied to real data.

Electronic Interfaces (block B-opt) (since 2022-23)

Electronic Interfaces (block B-opt) (since 2022-23)
1 YEAR II semester  6 CFU
Christian Falconi A.Y. 2022-23 (new)
Code: 80300103


The goal is to teach the fundamental principles and tools for designing electronic interfaces.
The contents of the course have general validity, but the focus will be on electronic interfaces for mechatronics.
The course is oriented toward design.

Students will need to know and understand the fundamental principles and tools for the analysis and design of electronic interfaces.

Students will have to demonstrate that they are able to design electronic interfaces.

Students will be able to evaluate the design of electronic interfaces.

The students, in addition to illustrating the fundamental principles and tools for the design of electronic interfaces, must be able to explain each design choice.

Students must be able to read and understand scientific texts and articles (also in English) concerning electronic interfaces.


Thévenin equivalent circuit.
Norton equivalent circuit.
Laplace transform
Fourier transform


Fundamentals on electronic devices.
Equivalent circuits (mechanic systems, thermal systems,…).
Diode circuits.
Transistor circuits.
Operational amplifiers (op amps).
Universal active devices.
Non-idealities of op-amps and other universal active devices.
Op-amp circuits.
Simulations of electronic circuits (SPICE).
Electronic interfaces.
Circuits for mechatronics (design examples).

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

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 (new name “Identification and Neural Networks”
Code: 80300088

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.


Adaptive Filtering Prediction and Control, Graham C. Goodwin, Kwai Sang Sin, Dover Publications, 2009.

Control of Electrical Machines (B-C-E) –> CONTROL OF ELECTRICAL MOTORS AND VEHICLES (B-C1-C2-E) (25-26)

2 YEAR II semester 6 CFU
Cristiano M. Verrelli A.Y. 2021-22 to A.Y. 2024-25




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.


R. Marino, P. Tomei, C.M. Verrelli, Induction Motor Control Design, Springer, 2010.
Latest journal papers.




2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2021-22

A.Y. 2022-23

A.Y. 2023-24

Computer Vision A.Y. 24-25

Code: 8039787

LEARNING OUTCOMES: Learning 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 image medical fields.

KNOWLEDGE AND UNDERSTANDING: The student acquires 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 with the aim of being able to autonomously develop new codes for the solution of specific problems in different application fields.

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 basic metrological definitions will be provided in support of background knowledge.

The student solves a written test and develops a project in Matlab that illustrates during the oral exam. The project can be done in a group to demonstrate working group capabilities.

Students will be able to read and understand scientific papers and books in English and also to deepen some topics. In some cases, students will develop also experimental tests with time-lapse microscopy acquisition in the department laboratory.



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.


1 YEAR (Block C)

2 YEAR (Blocks A|B|D|E)

II semester  9 CFU
Stefano CORDINER (6/9 cfu)
Lorenzo BARTOLUCCI (3/9 cfu)
A.Y. 2021-22

Internal Combustion Engines

Since A.Y. 2022-23


Code: 80300079
(by Mechanical Engineering)


The aim of the course is to provide students with in-depth scientific training to properly address the design, selection and management of internal combustion engines and their interaction with the environment, as well as to create the conditions for the development of innovative solutions. To this aim, students will develop in-depth knowledge of the principles of engine operation and learn simulation procedures for testing and sizing an alternative internal combustion engine and its main components. Special attention is also given to the latest technological development of internal combustion engine technology aimed at exceeding current limits in terms of emissions and efficiency and defining innovative scenarios of sustainable mobility.

The course aim is to provide the students with tools for the analysis of the performances and the evaluation of proper design solutions for internal combustion engines and their core components. At the end of the course, the student will be able to independently understand the functional link between design variables and the performance of internal combustion engines also in case of innovative design,

The course, through the analysis of specific problems and quantitative data, is aimed at providing the tools for analysis and evaluation of the effects of different design choices. The theme of energy efficiency and pollution reduction are at the heart of the teaching organization. The student will be able to interpret and propose design solutions, even innovative ones, adapted to the specificity of the problems that are presented to him.

By studying theoretical and practical aspects of engine design and critically assessing the influence of different design variables, the student will be able to improve his judgment and proposal in relation to design. and the management of internal combustion engines.

The presentation of the theoretical and application profiles underlying the operation of internal combustion engines will be carried out to allow the knowledge of the technical language of the appropriate specialist terminology; The development of communication skills, both oral and written, will also be stimulated through classroom discussion, participation in seminary activities and through final tests.

The learning capacity, even individual, will be stimulated through numerical exercises, the drafting of papers on specialized topics, the discussion in the classroom, also aimed at verifying the actual understanding of the topics treated. The learning capacity will also be stimulated by integrative educational aids (journal articles and economic newspapers) in order to develop autonomous application capabilities.


General information on internal combustion engines: Characteristics and Classification, thermodynamic and performance analysis. Experimental analysis of the performance of an internal combustion engine Air Supply for 4-stroke engines: volumetric efficiency and its evaluation, quasi-stationary effects; valve sizing; the influence of other engine parameters; Variable Valve Actuation systems; non-stationary phenomena in the intake and exhaust: inertia and wave propagation; variable valve geometry systems, computational models; 2-stroke engines: construction schemes; Supercharging; In cylinder charge motion: Turbulence; swirl, squish, tumble, stratified charge engines Traditional and alternative fuels; Fuels general properties: fuel, air stoichiometric; calorific value gaseous fuels: natural gas, hydrogen and mixtures thereof. bioethanol , bio-diesel and DME. Features and their use in engines: technical solutions, performance and emissions Fuel metering. Otto engines: carburetor; injection systems; lambda probe. Diesel engines: fuel injectors and injection systems, dimensioning. Experimental tests on a diesel injection system Common Rail Combustion: Fundamentals of analytical study of combustion, thermodynamics of combustion processes, calculation of the chemical composition and temperature in adiabatic equilibrium transport phenomena (notes), chemical kinetics (notes). Combustion in Otto and Diesel engines. Emissions and their control systems: emissions formation mechanisms, effects on health and environment, measurement of emissions; influence of engine parameters, test cycles and legislation; procedures and systems for the reduction of emissions in engines. Experimental tests. Cooling system: Heat flows, heat transfer in the engine cooling systems, liquid and air: structural layouts and sizing; thermal stress of the mechanical parts. Sustainable mobility. Principles of operation of hybrid vehicles: series and parallel solution; engines there and electrical workers, regenerative braking, lithium batteries, performance and prospects. Plug-in hybrid vehicles, engines c.i. ” Range extender “. Electric vehicles, characteristics and perspectives For all the topics of the course the numerical simulation tools will be presented


Digital Signal Processing (block C-opt)

Digital Signal Processing (block C-opt)
1 YEAR II semester  6 CFU
ICT and Internet Engineering


A.Y. 2023-24
Code: 8039514


LEARNING OUTCOMES: The course aims at providing to the students the theoretical and practical tools for the development of design capabilities and implementation awareness of Digital Signal Processing (DSP) systems and applications.

KNOWLEDGE AND UNDERSTANDING: Students are envisaged to understand the DSP theoretical, design and algorithm elements and to be able to apply them in design exercises.

APPLYING KNOWLEDGE AND UNDERSTANDING: Students are envisaged to apply broadly and to personalize the design techniques and algorithm approaches taught during the lessons.

MAKING JUDGEMENTS: Students are envisaged to provide a reasoned description of the design and algorithm techniques and tools, with proper integrations and links.

COMMUNICATION SKILLS: Students are envisaged to describe analytically the theoretical elements and to provide a description of the design techniques and the algorithm steps, also providing eventual examples.

LEARNING SKILLS: Students are envisaged to deal with design tools and manuals. The correlation of topics is important, particularly when design trade-offs are concerned.


A good mathematical background (in particular on complex numbers, series, functions of complex variable) is strongly recommended.


PART I – Discrete-time signals and systems; sampling process; Discrete-time Fourier transform (DTFT); Z-transform; Discrete Fourier Series (DFS).
PART II – Processing algorithms: introduction to processing; Discrete Fourier Transform (DFT); finite and long processing; DFT-based Processing; Fast Fourier Transform (FFT); processing with FFT.
PART III – Filter Design: introduction to digital filters: FIR and IIR classification; structures, design and implementation of IIR and FIR filters; analysis of finite word length effects; DSP system design and applications; VLAB and applications (Dr. Tommaso Rossi) with design examples and applications of IIR and FIR filters, Matlab-based lab and exercises (optional).


[1] “Digital Signal Processing Exercises and Applications”, Marina Ruggieri, Michele Luglio, Marco Pratesi. Aracne Editrice, ISBN: 88-7999-907-9.
[2] The River Publishers’ Series in Signal, Image & Speech Processing, “An Introduction to Digital Signal Processing: A Focus on Implementation”, Stanley Henry Mneney. River Publishers, ISBN: 978-87-92329-12-7.
[3] Slides (exercises are also included therein), published on the teaching website.