Computer Engineering or Computer Science
Recent technological advances have made it possible to collect volumes of data on the evolution of spatio-temporal phenomena that are much greater than the existing capacity to analyze them and extract relevant information in various scientific areas. Therefore, tools capable of automating processes of quantitative analysis of spatio-temporal data are increasingly needed, guaranteeing levels of objectivity, precision and reproducibility compatible with the performance of scientific work. The activities presented below are part of the Automatic detection and representation of change task of the EES Data Lab project and continue the work carried out so far in the detection of spatio-temporal events. To evolve the results obtained in a master's thesis in the representation and automatic detection of events related to a space-time entity, already carried out in this context and scope of the project. It is intended: 1) complement the results achieved in the thesis and broaden the knowledge of the state of the art in the area in algorithms for detecting change of the point set registration class using machine learning, as a central part in the calculation of differences between pairs of geometric shapes that represent two states of a spatio-temporal entity; 2) select the most promising algorithm based on the choice; 3) experiment, compare and draw conclusions from the results obtained. Expected results: a) state of the art report carried out in 1) b) report of the experience carried out in 2) and 3) c) complete and publish a scientific article exposing the work performed and the results obtained; d) exchange activity report
Master's student or integrated master's student in the areas of computer science or computer engineering.
Minimum profile required
- current course average equal to or greater than 14 values;- not having overdue course units.
Since 28 Sep 2022 to 12 Oct 2022
Cluster / Centre
Computer Science / Human-Centered Computing and Information Science