| KEYWORD |
ECG signal denoising method based on deep convolutional network
Thesis in external company
keywords MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, OPTIMIZATION, INTELLIGENT TRANSPORTATION SYSTEMS
Reference persons ALESSANDRO ALIBERTI, EDOARDO PATTI
External reference persons Mattia Tarchini Bojczuk
Research Groups DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, EDA Group, ELECTRONIC DESIGN AUTOMATION - EDA, Energy Center Lab, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Thesis type APPLIED RESEARCH
Description In the context of a company project, the student will investigate the state of the art of bio-signal denoising techniques (i.e., ECG), with a focus on techniques based on deep convolutional networks, to develop innovative methodologies by leveraging the latest Machine Learning techniques
Required skills python
Deadline 17/01/2025
PROPONI LA TUA CANDIDATURA