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Polynomial constraints and their applications to robust indoor localization

Martedì 21 maggio alle 16:00 in aula M1.9, edificio Matematica, Dipartimento FIM, Modena

Relatore: dott.ssa Stefania Monica

Abstract: Polynomial constraints over finite domains have been recently studied to support effective reasoning on complex constraint satisfaction problems. The literature documents various approaches that can be used to effectively reason on such constraints, and to support their applications to real-world problems. One of such problems is accurate and robust indoor localization. In the talk, first the three major approaches to treat polynomial constraints are briefly recalled. Then, the localization problem is presented in terms of the localization as optimization approach. Finally, recent experimental results on the use of polynomial constraints for indoor localization are presented.
Discussed results show that accurate and robust indoor localization is feasible in both high-profile industrial environments and ordinary scenarios.

Short Bio: Stefania Monica obtained a B.Sc. and a M.Sc. in Mathematics from the University of Parma. She also obtained a Ph.D. in Information Technology from the same University. Currently, she is a researcher (RTD-A) at the Department of Mathematics, Physics, and Computer Science of the University of Parma. Starting from her doctoral studies, her research interests include accurate and robust indoor localization in industrial environments using UWB signaling. Recently, her interest on indoor localization focused on the use of other signaling technologies, and on the proposal of the localization as optimization approach. Stefania is also interested in constraint satisfaction problems and their applications. In particular, her contribution on this subject concerns the study of effective algorithms to treat polynomial constraints over finite domains. Finally, she has recently started a research on the study of long-term asymptotic properties of large multi-agent systems using ideas and methods borrowed from mathematical kinetic theories. Artificial Intelligence Laboratory - Università degli Studi di Parma

Host: Giacomo Cabri (giacomo.cabri@unimore.it)

[Ultimo aggiornamento: 01/04/2021 12:23:27]