Master of Science – Two-year program
SELEZIONE PER il conferimento di n. 2 assegni per attività di tutorato e didattico-integrative propedeutiche e di recupero DA DESTINARE A studenti del Corso di Laurea Magistrale in Mechatronics Engineering e del Corso di Dottorato in Ingegneria Elettronica dell’Università degli Studi Roma “Tor Vergata”
Bando_tutorato_Mechatronics 2022
DEADLINE: 03/10/2022
| 2 YEAR | I semester | 6 CFU |
| Mauro De Sanctis | ICT and Internet Engineering (6 of 9) |
| A.Y. 2023-24 | |
| Didatticaweb
Code: 80300052 |
| 2 YEAR | I semester | 6 CFU |
| Tommaso Rossi
Cesare Roseti |
ICT and Internet Engineering |
| A.Y. 2023-24 | |
| Code: SSD: ING-INF/03 |
FORMATIVE OBJECTIVES
The course module provides an overview of the technologies involved in the multimedia application evolution from analogue to digital, from linear television to video on demand. To this aim, the module addresses the main TV standards, the TCP/IP protocols involved in modern streaming services, the network architectures and the different service modes.
PREREQUISITES: A good background in TCP/IP protocols.
SYLLABUS:
PARTE I – Digital TV standards, MPEG-2 and Transport Stream, IP encapsulation over DVB.
PARTE II – IP multicast, IGMP, IP multicast routing
PARTE III – Transport protocols for IP multimedia applications; Video streaming applications and CDN, the multimedia protocol stack, RTP and RTCP, multimedia signalling protocols: RTSP, SDP and SIP, Key Performance Indicators.
PARTE IV -Adaptive Streaming over HTTP, MPEG-DASH, Support to multimedia applications over 5G.
| 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.
| 2 YEAR | II semester | 6 CFU |
| Cristiano M. Verrelli | A.Y. 2021-22 to A.Y. 2024-25 |
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A.Y. 2025-26 (new name CONTROL OF ELECTRICAL MOTORS AND VEHICLES ) |
| Code:8039782 SSD: ING-INF/04 |
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.
TEXTS
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 |
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| Code: 8039787 SSD: ING/INF/07 |
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.
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 basic metrological definitions will be provided in support of background knowledge.
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 a group to demonstrate working group capabilities.
LEARNING SKILLS:
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.
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.
| 2 YEAR | 1 semester | 12 CFU |
| Patrick LONGHI (3cfu)
Giancarlo ORENGO (3cfu) Gian Carlo CARDARILLI (4cfu) Luca DI NUNZIO (2cfu) |
A.Y. 2021-22
A.Y. 2022-23 A.Y. 2023-24 |
| MODULI:
M-5519 – ELECTRONICS OF IOT (6cfu) M-5520 – DESIGN OF EMBEDDED SYSTEMS FOR MECHATRONICS (6cfu) |
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| 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 behaviour, 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:
In the enormous amount of information that is available today to developers of IoT applications, the course seeks to develop in the student the ability 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
| 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
| 1 YEAR | I semester | 6 CFU |
| Marco Re |
A.Y. 2021-22
A.Y. 2022-23 |
| A.Y. 2023-24 | |
| Didatticaweb
Code: 80300061 |