a.a. 2024-25

COMPUTER VISION (2024-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, II semester, Subjects , ,
COMPUTER VISION (2024-25)
2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2023-24 (ex MEASUREMENT SYSTEMS FOR MECHATRONICS)
A.Y. 24-25
Code: 8039787
SSD: ING/INF/07

LEARNING OUTCOMES:
Learning of 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 mechatronics fields.

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

MAKING JUDGEMENTS:
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 of basic metrological definitions will be provided in support of background knwoledge.

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

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

SYLLABUS

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.

TEXTS

Digital image processing, Gonzalez and Woods, Prentice Hall, New York, 2002.

BiPM, I. E. C., IFCC, I., IUPAC, I., & ISO, O. (2012). The international vocabulary of metrology— basic and general concepts and associated terms (VIM). JCGM, 200, 2012.

ATTENDANCE

Although attendance is optional, it is strongly recommended to follow the lessons. The professor recommends the students to subscribe the course on the Delphi website. The teams platform will be used as a consequence to communicate with the Professor, ask for doubts, and download the materials used for the lessons.

On Board Energy Generation and Storage (C2 opt)

Simona Ranieri 1st year, 6 CFU, a.a. 2024-25, a.a. 2025-26, Block C2, c2-choice3, I sem, Subjects , , ,
On Board Energy Generation and Storage (C2 opt)
1 YEAR (Block C2)
 1 semester 6 CFU
Prof. Fabio Matteocci
 A.Y. 2024-25 (new)
Code: 80300150
SSD: ING-INF/01

 

The course requires a basic knowledge of nanotechnologies applied to the generation and storage of electric power, as well as a basic understanding of the functioning of solar cells and batteries.

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:

The main objectives of the course are the study of electric power generation and storage systems that can be implemented on vehicles. The lessons therefore focus on next-generation photovoltaics, thin-film deposition techniques, storage systems, supercapacitors, and thermoelectricity. The generation and storage technologies will then be studied from an application perspective through case studies.

KNOWLEDGE AND UNDERSTANDING:

Students will be able to:

a) To learn the working principles for energy generation and storage (EGS);
b) To understand and explain the solutions for EGS when applied in vehicles;
c) To solve simple problems concerning the use of design of integrated EGS systems;
d) To know how to design, develop and release a simple EGS system for vehicle integration.

APPLYING KNOWLEDGE AND UNDERSTANDING:

The student will be able to recognize the applicability areas for the various EGS systems. She/He will also be able to apply the knowledge and understanding developed during the course to study and understand recent literature.

MAKING JUDGEMENTS:

Students should be capable of identifying specific design scenarios and applying the most appropriate techniques for EGS. Additionally, they should be able to compare the effectiveness of various EGS system while evaluating their advantages and disadvantages.

COMMUNICATION SKILLS:

The student will be able to clearly and unequivocally communicate the course content to specialized interlocutors. He will also be able to communicate the main approches to the development of EGS systems. The student will also have a sufficient background to undertake a thesis/research work in EGS applications.

LEARNING SKILLS:

Being sufficiently skilled in the specific field to undertake subsequent studies characterized by a high degree of autonomy.

SYLLABUS

1. Introduction on Nanotechnology: Top Down and Bottom Up Approaches2. Physical, Chemical Deposition, Solution Processing (Working Principle and Applications) 3. Energy Generation: Conventional and Emergent Photovoltaics (Working Principle and Applications).
4. Case of Study: Perovskite solar Cells (Working Principle, Deposition Techniques and applications)
5. Storage: Conventional and Emergent technologies for Batteries
6. Electrical and Chemical Properties of Batteries (Working Principle)
7. System Integration of Energy Generation and Storage solutions
8. Oppurtinities and Limitations of vehicle-integrated solutions for Generation and Storage 9. Beyond Batteries: Supercapacitors and thermoelectricity

The lecture will be held in the classroom with the projection of slides that will be released to the students at the end of the lecture.

The student will only be admitted to the final exam if they have attended 80% of the course hours.

 

 

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

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, Block C, 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 (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.

ELECTRONICS OF IOT AND EMBEDDED SYSTEMS

Simona Ranieri 12 CFU, 2nd year, a.a. 2023-2024, a.a. 2024-25, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, Block D, Block E, I sem, Subjects ,
ELECTRONICS OF IOT AND EMBEDDED SYSTEMS

 

2 YEAR 1 semester 12 CFU
Patrick LONGHI (3cfu)
Giancarlo ORENGO (3cfu)
Gian Carlo CARDARILLI (4cfu)
Luca DI NUNZIO (2cfu)		 since A.Y. 2019-20
M-5519 – ELECTRONICS OF IOT (6cfu)
M-5520 – DESIGN OF EMBEDDED SYSTEMS FOR MECHATRONICS (6cfu)
Code: 8039795
SSD: ING-INF/01

EDUCATIONAL OBJECTIVES:
The objectives of the course are:
1) to provide the tools to carry out a radio link assessment in a real application context.
2) learn the fundamental parameters of the antennas used in IoT applications
3) provide the tools to interpret the electrical diagram of the RF front end of a typical trans receiver.

KNOWLEDGE AND UNDERSTANDING:
Provide the fundamental tools to understand the most advanced and updated content from publications, magazines, forums, blogs, etc., to always be updated on the state of the art.

ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
Practical radio link budget, electronic noise evaluation on receiver behavior, installation effects of the antennas, understanding of key parameters of commonly used antennas in the targeted scenario, analysis of an RF transceiver block diagram

AUTONOMY OF JUDGMENT:
With the enormous amount of information that is available today to developers of IoT applications, the course seeks to develop the ability of the student to select the highest quality and most validated content.

COMMUNICATION SKILLS:
The final test is based on an oral exam in which the student illustrates a part of the module

LEARNING ABILITY:
The course aims to develop in the student the ability to independently learn new and constantly updated content because the knowledge acquired today soon becomes obsolete.

SYLLABUS:

(Longhi):

Introduction to radiating elements and their key parameters.
Ideal and practical link budget.
The effect of noise in electronic receivers, figures of merit and mathematical modelling. Receiver G/T.
Practical aspects of IoT RF systems
RFID
Radiating elements key parameters, gain, directivity, HPBW, nulls, radiation pattern, polarization, and input impedance. Some practical cases: the mono/di-pole family, microstrip antennas, parabolic reflector, wearables
Introduction to RF transceiver systems and key-components (switches, HPA, LNA, mixers, frequency generators).

(G.Orengo):

Summary of Digital Electronics: digital encoding of information, binary (fixed and floating point), hexadecimal and ASCII; operators and main logic circuits, registers and memories, programmable devices. Prototyping boards for IoT (Arduino, Rasberry), Systems on Chip (SoC), architecture of a microcontroller, description of the Arduino Uno board. Programming languages ​​(assembly, compiled, interpreted), structure of an Arduino sketch (libraries, setups, loops, functions, interrupts), programming elements in C (variables, math and logical operations, cycles, conditional statements). Use of digital and analog I/O ports (A/D conversion, PWM output). Synchronous and asynchronous serial communication modes, wired (USB) and wireless with Bluetooth, RF and WiFi modules. Remote control of electronic modules (sensors, dc stepper and servo motors, LED/LCD displays etc.) from portable devices (Windows, IoS), through applications developed in Processing and Python, and mobile (Android), through Apps developed with the MIT App Inventor platform. Internet protocols for device local/remote control through WiFi modules connected as access points/clients to web platforms or public/private cloud servers controlled by laptops and/or mobile devices.

(G.Cardarilli):

– Introduction to the Internet of Things (IoT) and embedded systems
– Wireless and mobile communications
– The Sensors
– Low power processing
– IoT and machine learning applications
– Future developments in the field of IoT and embedded systems

 

CONTROL OF MECHANICAL SYSTEMS

Simona Ranieri 2nd year, 9 CFU, a.a. 2023-2024, a.a. 2024-25, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, Block D, Block E, I sem, Subjects ,
CONTROL OF MECHANICAL SYSTEMS
2 YEAR
 1 semester 9 CFU
Riccardo MARINO Since 2019-20
Code: 8039823
SSD: ING-INF/04

LEARNING OUTCOMES:

Ability to understand scientific papers on the control of mechanical systems

KNOWLEDGE AND UNDERSTANDING:

Knowledge of dynamic modeling of mechanical systems. Knowledge of basic feedback control techniques for single input single output systems and of decoupling techniques for multi input multi output nonlinear systems

APPLYING KNOWLEDGE AND UNDERSTANDING:

Ability to simulate using Matlab Simulink complex controlled mechanical systems

MAKING JUDGEMENTS:

Ability to evaluate stability, robustness, and performance of a control system

COMMUNICATION SKILLS: Ability to present and discuss an autonomous design project

LEARNING SKILLS: Ability to fully understand a scientific paper on the control of mechanical systems

SYLLABUS:

BASIC CONTROL TOOLS
Bounded- input bounded- output linear systems. Pole placement theorem for controllable and observable linear systems. Luenberger observers for observable systems. Design of dynamic compensators for linear systems. Integral feedback control to reject constant disturbances. PID control. System inverses for minimum phase linear systems. The combination of feedback and feedforward control actions.
ADVANCED CONTROL TOOLS
Linear approximations of nonlinear control systems about operating conditions. The definition of region of attraction for an operating condition. Output feedback compensators with integral actions to control nonlinear systems about a given operating condition. Liapunov matrix equations to determine quadratic Liapunov functions and assess the region of attraction. The definition of the sensitivity transfer function and its properties. The gang of four: sensitivity, complementary sensitivity, load sensitivity and noise sensitivity functions. How to determine the robustness of a control loop using the gang of four functions. Bode’s integral formula and the limitations imposed by unstable open loop poles. Youla parametrization to design stable compensation. Kalman filters, Riccati equations and robust control design.

CONTROL DESIGN FOR MULTIVARIABLE NONLINEAR SYSTEMS
Relative degree for a single input single output nonlinear system. State feedback control design for input-output linearization. State feedback linearization when the relative degree is equal to the state space dimension. The definition of nonlinear inverse systems. Relative degrees or decoupling indices for multivariable (multi-input, multi-output) nonlinear systems. The definition of the decoupling matrix. State feedback control design for input-output linearization when the decoupling matrix is full rank using the Penrose pseudoinverse. State feedback linearization when the sum of relative degrees is equal to the state space dimension and the decoupling matrix is full rank.

CASE STUDIES OF NONLINEAR MECHANICAL CONTROL SYSTEMS
Control of bycicles, robots, vehicles and aircrafts