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.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
---|---|---|
I semestre | Oct 1, 2019 | Jan 31, 2020 |
II semestre | Mar 2, 2020 | Jun 12, 2020 |
Session | From | To |
---|---|---|
Sessione invernale d'esame | Feb 3, 2020 | Feb 28, 2020 |
Sessione estiva d'esame | Jun 15, 2020 | Jul 31, 2020 |
Sessione autunnale d'esame | Sep 1, 2020 | Sep 30, 2020 |
Session | From | To |
---|---|---|
Sessione Estiva | Jul 15, 2020 | Jul 15, 2020 |
Sessione Autunnale | Oct 16, 2020 | Oct 16, 2020 |
Sessione Autunnale Dicembre | Dec 11, 2020 | Dec 11, 2020 |
Sessione Invernale | Mar 17, 2021 | Mar 17, 2021 |
Period | From | To |
---|---|---|
Festa di Ognissanti | Nov 1, 2019 | Nov 1, 2019 |
Festa dell'Immacolata | Dec 8, 2019 | Dec 8, 2019 |
Vacanze di Natale | Dec 23, 2019 | Jan 6, 2020 |
Vacanze di Pasqua | Apr 10, 2020 | Apr 14, 2020 |
Festa della Liberazione | Apr 25, 2020 | Apr 25, 2020 |
Festa del lavoro | May 1, 2020 | May 1, 2020 |
Festa del Santo Patrono | May 21, 2020 | May 21, 2020 |
Festa della Repubblica | Jun 2, 2020 | Jun 2, 2020 |
Vacanze estive | Aug 10, 2020 | Aug 23, 2020 |
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Academic staff
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
Modules | Credits | TAF | SSD |
---|
Mathematical analysis 1
Computer Architecture
2° Year activated in the A.Y. 2020/2021
Modules | Credits | TAF | SSD |
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3° Year activated in the A.Y. 2021/2022
Modules | Credits | TAF | SSD |
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1 module to be chosen among the following
Modules | Credits | TAF | SSD |
---|
Mathematical analysis 1
Computer Architecture
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
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1 module to be chosen among the following
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.
Biomedical Data and Signal Processing (2021/2022)
Teaching code
4S008201
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
The teaching is organized as follows:
Teoria
Laboratorio
Learning outcomes
The aim of this course is to provide the basic knowledge of methods and models for biomedical sig- nal and image processing, developing the ability to analyze and solve problems of interest, mainly in the biomedical field. Each technique presented will be accompanied by applications on simulated and real signals. At the end of the course, the student will be able to show knowledge of the main methods of biomedical signals and image processing. They will possess the ability to formulate, ana- lyze and solve problems of interest in bioengineering, through the acquired theoretical and practical basic knowledge. Finally, the student will be able to evaluate the traditional methods in the biomedical sciences in order to devise new approaches to methodological problems with clinical reversibility.
Program
------------------------
MM: Teoria
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(1) Main biomedical signals and images. Origin, characteristics and acquisition of the main bioelectric signals (electroencephalographic signal - EEG, magnetoencephalographic – MEG, electrocardiographic - ECG, electromyographic - EMG, spontaneous and induced signals, evoked potentials - EP, event-related potentials - ERP); introduction to bioimaging. (2) Analysis techniques in the time and frequency domains. Fundamentals of digital signal processing and characterization in the time domain. Digital filtering methods, sampling, A/D conversion. Classic methods for frequency analysis; frequency bands and power spectrum, periodogram; time/frequency resolution; bispectra and coherence; feature extraction methods. Brain source imaging (direct and inverse problems for EEG and MEG signals) and functional and effective connectivity analysis methods. Applications on in-silico and real signals. (3) Statistical analysis of biomedical data. Review of basic concepts of descriptive and inferential statistics. Description of the measurement error, statistical description of the experimental data: statistical indices, confidence intervals, hypothesis test and significance level, simple and multivariate linear regression for biomedical signals and images. (4) Brain-computer interfaces. Introduction to the main data processing methods that allow to decode brain activity in real time and convert it into a control signal for a brain-computer interface. We will discuss the BCI model and its historical context, the invasive and non-invasive techniques allowing to measure in real time the responses of an individual to particular stimuli, the data interpretation (filtering, future extraction, classification) and the BCI technology.
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MM: Laboratorio
------------------------
The course includes a series of laboratories in the computer lab with hands-on activities mainly in MATLAB environment aimed at familiarizing students with the main analysis methods of biomedical signals and images (e.g. ECG, EMG, EEG, evoked potentials, functional magnetic resonance imaging - fMRI). The laboratories also foresee a project activity in small groups for the solution of problems related to the analysis of biomedical data. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills in the context of bioengineering. Teaching methods. Regular lectures with power point presentation and blackboard, laboratory exercises and projects. The course approach is "hands on" where students will experiment the design and data analysis with the most suitable methodologies to solve real-life clinical-medical problems. Educational material will be available to students enrolled in the course on the Moodle platform. This material includes lecture presentations in PDF format and material related to laboratory activities. For further details and supplementary materials, please refer to the reference books.
Bibliography
Examination Methods
Assessment is conducted via oral examination preceded by a discussion on the group project assigned during the lab. The final grade will be the average of the two grades (2/3 theory, 1/3 lab). To pass the exam, the students must show that: - they have understood the theoretical and practical concepts of the course; - they are able to use the knowledge acquired during the course to solve the assigned problems related to the processing of biomedical signals and data; - they are able to program in MATLAB environment in the context of signal and biomedical data processing.
Type D and Type F activities
years | Modules | TAF | Teacher |
---|---|---|---|
3° | The fashion lab (1 ECTS) | D |
Maria Caterina Baruffi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
3° | Control theory | D |
Riccardo Muradore
(Coordinator)
|
3° | Biomedical Data and Signal Processing | D |
Silvia Francesca Storti
(Coordinator)
|
3° | Python programming language | D |
Maurizio Boscaini
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
3° | CyberPhysical Laboratory | D |
Andrea Calanca
(Coordinator)
|
3° | C++ Programming Language | D |
Federico Busato
(Coordinator)
|
3° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
3° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
3° | Corso Europrogettazione | D | Not yet assigned |
3° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Matteo Cristani
|
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.
Graduation
List of thesis proposals
theses proposals | Research area |
---|---|
Analisi e percezione dei segnali biometrici per l'interazione con robot | AI, Robotics & Automatic Control - AI, Robotics & Automatic Control |
Integrazione del simulatore del robot Nao con Oculus Rift | AI, Robotics & Automatic Control - AI, Robotics & Automatic Control |
Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games |
Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) |
BS or MS theses in automated reasoning | Computing Methodologies - ARTIFICIAL INTELLIGENCE |
Domain Adaptation | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |
Domain Adaptation | Computing methodologies - Machine learning |
Dati geografici | Information Systems - INFORMATION SYSTEMS APPLICATIONS |
Analisi e percezione dei segnali biometrici per l'interazione con robot | Robotics - Robotics |
Integrazione del simulatore del robot Nao con Oculus Rift | Robotics - Robotics |
BS or MS theses in automated reasoning | Theory of computation - Logic |
BS or MS theses in automated reasoning | Theory of computation - Semantics and reasoning |
Proposte di tesi/collaborazione/stage in Intelligenza Artificiale Applicata | Various topics |
Proposte di Tesi/Stage/Progetto nell'ambito dell'analisi dei dati | Various topics |
Attendance modes and venues
As stated in the Teaching Regulations, attendance at the course of study is not mandatory.
Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.
The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus.
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.