Author: Simona Ranieri

Call for Master and specialization courses A.Y. 2022-23

Simona Ranieri 2022, call, job, old news

VI Ediz- Master in Managment e Diritto della Facoltà di Economia (60 credits)

www.modsc.uniroma2.it

deadline 15/03/2023

Ref. dottrinasociale@uniroma2.it 3398747838

Awarding of n. 2 grants for tutoring and teaching-integrative activities

Simona Ranieri call, job, NEWS , , , ,

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

Information Theory and Data Science (opt C1.a)

Simona Ranieri 1st year, 6 CFU, a.a. 2022-23, Block C-Mechatronic Systems and ICT, c-choice3, I sem, optC1.a, optC1.b, Subjects , , ,
Information Theory and Data Science (opt C1.a)
1 YEAR I semester  6 CFU
Mauro De Sanctis ICT and Internet Engineering (6 of 9)
A.Y. 2023-24

 
Didatticaweb

✅ Syllabus📑 ICT – Information Theory and Data Science (ITDS) – M. De Sanctis

Code: 80300052
SSD: ING-INF/03

 

Multimedia Processing and Communication – 6 CFU (opt C1.a)

Simona Ranieri 1st year, 6 CFU, a.a. 2022-23, a.a. 2024-25, a.a. 2025-26, I sem, optC1.a, Subjects , , ,
Multimedia Processing and Communication – 6 CFU (opt C1.a)
1 YEAR I semester  6 CFU
Tommaso Rossi (3cfu)

Cesare Roseti (3cfu)

 ICT and Internet Engineering
didatticaweb
since A.Y. 2023-24

Code: 8039553 
SSD: ING-INF/03

 

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

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, Block C-Mechatronic Systems and ICT, 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 not be 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.