Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory activities for Smart Systems & Data Analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05

2° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
6
B/C
ING-INF/05
Final exam
24
E
-
ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory activities for Smart Systems & Data Analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
6
B/C
ING-INF/05
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Further activities
3
F
-
Between the years: 1°- 2°
Training
3
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009011

Credits

6

Coordinator

Francesco Setti

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

The teaching is organized as follows:

Teoria

Credits

5

Period

Primo semestre

Academic staff

Francesco Setti

Laboratorio

Credits

1

Period

Primo semestre

Academic staff

Francesco Setti

Learning outcomes

The course aims to provide students with skills in: i) analyzing data using univariate, multivariate and high-dimensional statistics methods; ii) identification of anomalous situations; iii) analysis of heterogeneous data; iv) analysis of dynamic and non-stationary processes; v) time series prediction. At the end of the course the student will have to demonstrate that he is able to manage the monitoring of an industrial process. In particular, he will have to demonstrate that he is able to: i) identify potential failure modes; ii) design a data acquisition system on the production line; iii) identify anomalies in the process; iv) optimize the process parameters according to predefined objectives (rejection rate, time reduction, etc.); v) analyze the causes of unexpected failures (root cause analysis); vi) manage the maintenance of the system with predictive techniques.

Program

Measurement and sensors:
- Foundamentals of industrial metrology: basic definitions, international system of units, measurement system model, errors, static and dynamic calibration
- Displacement measurement: resistive potentiometers, linear variable differential transformers, eddy current transducers, triangulation photodiodes, encoders, strain gauges
- Vibration measurement: vibrometers and accelerometers
- Flow measurement: pitot tube, hot-wire anemometer, pressure drop flowmeters, drag force flowmeter, ultrasonic flowmeter
- Thermal measurement: bimetallic thermometers, thermocouples, resistance temperature detectors, thermistors, bolometers and thermal imaging

Data analysis:
- Monitoring charts: Shewhart, cumulative sum, moving average, exponentially weighted moving average, Western Electric rules
- Univariate monitoring schemes: hypothesis testing, generalized likelihood ratio, Kullback-Leibler divergence, Hellinger distance, ordinary least square, ridge regression, principal component analysis and regression
- Multivariate monitoring schemes: multivariate monitoring charts, dynamic latent variable regression
- Unsupervised data analysis: hierarchical clustering, mean shift, k-Nearest neighbours, k-means, one-class SVM, support vector data description
- Fault isolation techniques

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Examination Methods

The exam involves the discussion of a project proposing a solution to an industrial problem.
The student will present his/her work in about 15 minutes (with or without the use of support material such as slides, written report, demo, etc.), followed by a Q&A session.
For the generation of the mark it will be taken into account:
- performance of the developed system (with different metrics depending on the problem);
- theoretical motivation behind the student's design choices;
- ability to clearly and concisely present the key points of the project;
- ability to support a discussion on possible alternative solutions and potential causes of failure of the solution developed.
The student must also demonstrate mastery of all the topics in the program (even those not addressed during the project).

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE