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 |
---|---|---|
Primo semestre | Oct 4, 2021 | Jan 28, 2022 |
Secondo semestre | Mar 7, 2022 | Jun 10, 2022 |
Session | From | To |
---|---|---|
Sessione invernale d'esame | Jan 31, 2022 | Mar 4, 2022 |
Sessione estiva d'esame | Jun 13, 2022 | Jul 29, 2022 |
Sessione autunnale d'esame | Sep 1, 2022 | Sep 30, 2022 |
Session | From | To |
---|---|---|
Sessione Estiva | Jul 12, 2022 | Jul 12, 2022 |
Sessione Autunnale | Oct 18, 2022 | Oct 18, 2022 |
Sessione Autunnale Dicembre | Dec 6, 2022 | Dec 6, 2022 |
Sessione Invernale | Mar 13, 2023 | Mar 13, 2023 |
Period | From | To |
---|---|---|
Festa di Tutti i Santi | Nov 1, 2021 | Nov 1, 2021 |
Festa dell'Immacolata Concezione | Dec 8, 2021 | Dec 8, 2021 |
Festività natalizie | Dec 24, 2021 | Jan 2, 2022 |
Festa dell'Epifania | Jan 6, 2022 | Jan 7, 2022 |
Festività pasquali | Apr 15, 2022 | Apr 19, 2022 |
Festa della Liberazione | Apr 25, 2022 | Apr 25, 2022 |
Festività Santo Patrono di Verona | May 21, 2022 | May 21, 2022 |
Festa della Repubblica | Jun 2, 2022 | Jun 2, 2022 |
Chiusura estiva | Aug 15, 2022 | Aug 20, 2022 |
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
Ugolini Stefania
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 |
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Mathematical analysis 1
Computer Architecture
2° Year activated in the A.Y. 2022/2023
Modules | Credits | TAF | SSD |
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3° Year activated in the A.Y. 2023/2024
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Mathematical analysis 1
Computer Architecture
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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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 (2023/2024)
Teaching code
4S008201
Credits
6
Language
Italian
Also offered in courses:
- Modeling and Analysis of Biological Systems of the course Bachelor's degree in Human Centered Medical System Engineering
- Modeling and Analysis of Biological Systems of the course Bachelor's degree in Human Centered Medical System Engineering
Scientific Disciplinary Sector (SSD)
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Courses Single
Authorized
The teaching is organized as follows:
Modulo B Teoria
Modulo A Teoria
Modulo B Laboratorio
Modulo A Laboratorio
Learning objectives
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.
Prerequisites and basic notions
-
Program
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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
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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.
Bibliography
Didactic methods
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.
Learning assessment procedures
Assessment is conducted via oral examination preceded by a discussion on the group project assigned during the lab.
Evaluation criteria
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.
Criteria for the composition of the final grade
The final grade will be the average of the two grades (2/3 theory, 1/3 lab).
Exam language
Italiano
Type D and Type F activities
Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al Regolamento Didattico del Corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. Le attività formative di tipologia D o F possono essere ricoperte dalle seguenti attività.
1. Insegnamenti impartiti presso l'Università di Verona
Comprendono gli insegnamenti sotto riportati e/o nel Catalogo degli insegnamenti (che può essere filtrato anche per lingua di erogazione tramite la Ricerca avanzata).
Modalità di inserimento a libretto: se l'insegnamento è compreso tra quelli sottoelencati, lo studente può inserirlo autonomamente durante il periodo in cui il piano di studi è aperto; in caso contrario, lo studente deve fare richiesta alla Segreteria, inviando a carriere.scienze@ateneo.univr.it il modulo nel periodo indicato.
2. Attestato o equipollenza linguistica CLA
Oltre a quelle richieste dal piano di studi, per gli immatricolati dall'A.A. 2021/2022 vengono riconosciute:
- Lingua inglese: vengono riconosciuti 3 CFU per ogni livello di competenza superiore a quello richiesto dal corso di studio (se non già riconosciuto nel ciclo di studi precedente).
- Altre lingue e italiano per stranieri: vengono riconosciuti 3 CFU per ogni livello di competenza a partire da A2 (se non già riconosciuto nel ciclo di studi precedente).
Tali cfu saranno riconosciuti, fino ad un massimo di 6 cfu complessivi, di tipologia F se il piano didattico lo consente, oppure di tipologia D. Ulteriori crediti a scelta per conoscenze linguistiche potranno essere riconosciuti solo se coerenti con il progetto formativo dello studente e se adeguatamente motivati.
Gli immatricolati fino all'A.A. 2020/2021 devono consultare le informazioni che si trovano qui.
Modalità di inserimento a libretto: richiedere l’attestato o l'equipollenza al CLA e inviarlo alla Segreteria Studenti - Carriere per l’inserimento dell’esame in carriera, tramite mail: carriere.scienze@ateneo.univr.it
3. Competenze trasversali
Scopri i percorsi formativi promossi dal TALC - Teaching and learning center dell'Ateneo, destinati agli studenti regolarmente iscritti all'anno accademico di erogazione del corso https://talc.univr.it/it/competenze-trasversali
Modalità di inserimento a libretto: non è previsto l'inserimento dell'insegnamento nel piano di studi. Solo in seguito all'ottenimento dell'Open Badge verranno automaticamente convalidati i CFU a libretto. La registrazione dei CFU in carriera non è istantanea, ma ci saranno da attendere dei tempi tecnici.
4. Periodo di stage/tirocinio
Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didattico): qui informazioni su come attivare lo stage.
Insegnamenti e altre attività che si possono inserire autonomamente a libretto
years | Modules | TAF | Teacher |
---|---|---|---|
2° 3° | The fashion lab (1 ECTS) | D |
Caterina Fratea
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
2° 3° | Introduction to Robotics for students of scientific courses. | D |
Paolo Fiorini
(Coordinator)
|
2° 3° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
2° 3° | The fashion lab (1 ECTS) | D |
Caterina Fratea
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
2° 3° | Introduction to Robotics for students of scientific courses. | D |
Paolo Fiorini
(Coordinator)
|
2° 3° | Introduction to 3D printing | D |
Franco Fummi
(Coordinator)
|
2° 3° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
2° 3° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
2° 3° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
2° 3° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Roberto Giacobazzi
(Coordinator)
|
years | Modules | TAF | Teacher | |
---|---|---|---|---|
1° | Subject requirements: mathematics | D |
Franco Zivcovich
|
|
2° 3° | Python programming language | D |
Giulio Mazzi
(Coordinator)
|
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.