QS World University Rankings 2026: the unstoppable rise of the University of Rome Tor Vergata – second in Italy for international students, eighth in the QS rankings.
Link to access the QS WUR 2026 rankin: https://www.topuniversities.com/world-university-rankings
EVENT POSTPONED!!!!!
Unfortunately, due to the general public transport strike scheduled for Friday, 20 June and the limited guaranteed service hours (5:00 PM-8:00 PM), we regret to inform you that the event has to be postponed. This decision has been taken to ensure equal participation opportunities for everyone.
We will notify you of the new date as soon as possible.
However, since some of you are soon departing, we wish to ensure the timely distribution of certificates (for incoming students) and of kits (for outgoing students).
If your departure is imminent, you may collect these items starting Monday, 23 June at the Central Erasmus Office during opening hours: Monday 9:00 AM – 12:00 PM; Wednesday 9:00 AM – 12:00 PM and 2:00 PM – 4:00 PM; Friday 9:00 AM – 12:00 PM.
For those unable to collect certificates in person, we will email them to you.
We look forward to seeing you soon!
____________________________________
Come and discover the Erasmus+ World at Tor Vergata University of Rome:
Friday, 20 June 2025 5:00 PM School of Economics – Tor Vergata University of Rome
A special event designed for you, full of stories, dreams, and emotions
The full programme will be available soon!
—>>>> Register now: https://forms.office.com/e/dwM3rZYsda
Don’t miss out: it’s going to be an unforgettable journey!
| 2 YEAR (Block D) |
2 semester | 8 CFU |
| (from Physics LM-17 ) | |
| Prof. Michele BUZZICOTTI | A.Y. 2025-26 |
| Code: 80300140 SSD: FIS/01 https://www.master-mass.eu/ |
8th & 14th of May 2025
EN: https://web.uniroma2.it/en/contenuto/webinar-for-bachelors-degree-programs
Available sessions (CEST time):
| 1 YEAR | II semester | 9 CFU |
| ICT and Internet Engineering | |
| Marina RUGGIERI (cfu)
Tommaso ROSSI (cfu) |
A.Y. 2025-26 |
| Code: 80300072 SSD: ING-INF/03 |
OBJECTIVES
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.
BACKGROUND
A good mathematical background (in particular on complex numbers, series, functions of complex variable) is strongly recommended.
PROGRAMME
(Prof. M.RUGUERI)
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;
PART IV – Random sequences; processing of random sequences with digital filters; introduction to random sequence estimation; estimators of mean, variance and auto-covariance of random sequences with performance analysis; power spectrum estimation; periodogram and performance analysis; smoothed estimators of the power spectrum and performance analysis; use of FFT in power spectrum estimation.
(Dott. Tommaso ROSSI)
PART V – VLAB: applications with design examples and applications of IIR and FIR filters, Matlab-based lab and exercises; use of Matlab in the power spectrum estimation.
VERIFICATION CRITERIA
a) Combination of: design test (written); deepening on DSP System development (written); oral.
The design test is propedeutic to the oral one.
The course offers a verify in progress (with a related recovery date) that if passed exempts from the design test of the exam session.
b) The written exam includes design exercises.
The oral part envisages questions on the whole program and a discussion on the design test.
c) The written exam is scored from FAIL to EXCELLENT. The design test and the oral concur almost evenly to the final score (x/30).
d) The final score is based on the level of knowledge of the theoretical, design and algorithm elements and tools as well as on their effective use in design exercises; in particular, the final evaluation refers to 70% of the student’s knowledge level and for 30% to her/his capability of expressing the knowledge and providing an autonomous judgment in the design and oral exam phases.
The detailed final evaluation criteria are as follows:
Failed exam: deep lack and/or inaccuracy of knowledge and comprehension of topics and design techniques; limited capabilities in analysis and synthesis, critical ability and judgment; designs and topics are presented with a non-coherent and technically inadequate approach.
18-20: sufficient knowledge and comprehension of topics and design techniques with possible imperfections; sufficient capabilities in analysis, synthesis and autonomous judgment; designs and topics are presented with a not too much coherent and technically appropriate approach.
21-23: flat knowledge and comprehension of topics and design techniques; appropriate capabilities in analysis and synthesis with fair autonomous judgment; designs and topics are presented with sufficient coherency and technically appropriate approach.
24-26: more than fair knowledge and comprehension of topics and design techniques; good capabilities in analysis and synthesis with good autonomous judgment; designs and topics are presented with coherency and technically appropriate approach.
27-29: complete knowledge and very good comprehension of topics and design techniques; remarkable capabilities in analysis and synthesis with very good autonomous judgment; designs and topics are presented with a rigorous and technically very appropriate approach.
30-30L: excellent knowledge and complete comprehension of topics and design techniques; excellent capabilities in analysis and synthesis with excellent autonomous judgment and originality; designs and topics are presented with a rigorous and technically excellent approach.
TEXTBOOKS
[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.
| 2 YEAR | II semester | 6 CFU |
| Cristiano M. Verrelli | |
|
|
A.Y. 2025-26 (ex Control of Electrical Machines (B-C-E) |
| 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.
VERIFICATION OF THE KNOWLEDGE
Verify the knowledge and skills acquired by the student on the topics covered by the program. The intermediate exams, the final written tests, and the oral exam will consist of questions related to the topics covered by the program of the course. The questions are aimed at ascertaining the student’s knowledge and his/her reasoning skills in making logical connections between the different topics.
The final vote of the exam is expressed out of thirty and will be obtained through the following graduation system:
Not pass: important deficiencies in the knowledge and in the understanding of the topics; limited capacity for analysis and synthesis, frequent mistakes and limited critical and judgmental capacity, inconsistent reasoning, inappropriate language.
18-21: the student has acquired the basic concepts of the discipline and has an analytical capacity that comes out only with the help of the teacher. The way of speaking and the language used are almost correct, though not precise.
22-25: the student has acquired the basic concepts of the discipline in a discrete way; he/she knows how to discuss the various topics; he/she has an autonomous analysis capacity while adopting a correct language.
26-29: the student has a well-structured knowledge base. He/She is able to independently adopt a correct logical reasoning; notations and technical language are correct.
30 and 30 cum laude: the student has a complete and in-depth knowledge base. The cultural references are rich and up-to-date while being expressed by means of brilliant technical language.
| 1 YEAR |
1 semester | 6 CFU |
| Vincenzo MULONE (3cfu)
TBD (3cfu) |
A.Y. 2025-26 |
| Code: SSD: ING-IND-08 (by Mechatronics Engineering) |
| 1 YEAR |
1 semester | 6 CFU |
| Marco Ceccarelli | A.Y. 2025-26 |
| 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
UNIVERSITA' DEGLI STUDI ROMA "TOR VERGATA"