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
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Computer Engineering for intelligent Systems - Enrollment from 2025/2026The 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
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2° Year It will be activated in the A.Y. 2025/2026
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3 modules among the following| Modules | Credits | TAF | SSD |
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3 modules among the following| Modules | Credits | TAF | SSD |
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4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics
- 2nd year: Advanced control systemsLegend | 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.
Sistemi intelligenti per la sanità (2024/2025)
Teaching code
4S012363
Credits
12
Coordinator
Language
English
Courses Single
Authorized
The teaching is organized as follows:
Learning objectives
The course consists of two modules: the first, Digital Health Systems, aims to provide skills on methods and procedures for the creation of applications in the healthcare sector, including the software and architectural components of a healthcare information system, the reference standards for the classification and exchange of healthcare data, the management of the flow of work, the management of data security and privacy, issues relating to network and system storage systems. The second, Advanced Medical Image Analysis, aims at providing competence about the representation and coding of biomedical signals, covering the whole path from modeling to standards. The student will become acquainted with the signal and image processing methods required for effectively representing multi-dimensional biomedical signals (from 1D to ND) in different domains including Fourier, time/space-frequency (multiscale) and latent-space representations. Emphasis will also be given to the analysis of the structure and properties of the representation space for a clear understanding of its suitability for compression and coding applications, as well as to the main performance and quality assessment methods. Different state-of-the-art coding standards will be considered, suitable for data of different dimensionality and nature. At the end of the course the students will be able to effectively manage biomedical signals and devise the most appropriate method for their processing and coding.
