Monthly Archives: February 2018

SimGait: Modeling pathological gait resulting from motor impairments

Within this project we seek to develop machine learning methods for the modelling of pathological human locomotion. This is a collaborative Sinergia project funded by the Swiss National Science Foundation.

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OrbiLoc: Learning to position IoT devices in outdoor environments

OrbiLoc is an Innosuisse project in which the Data Mining and Machine Learning (DMML) group collaborates with the company Orbiwise. The goal of this project is to improve the localization accuracy achieved by IoT devices utilizing the LoRa network of Orbiwise. In the context of this project, DMML introduces machine learning techniques for localization, achieving significant performance improvements, having as a baseline the trilateration method currently used by Orbiwise

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SMELL: Learning olfactory models to support the perfume creation process

SMELL  is an Innosuisse project undertaken with collabration with the company FirmenichThe goal of  the project is to develop a data-driven methodology which will allow us to uncover the olfactory perception mechanisms related to perfume creation and exploit them to build rational solutions that improve product performance and differentiation. We designed  and developed novel machine learning algorithms that exploit side information to reliably predic olfaction of the product  and exploit the similarities of ingredients with respect to olfaction.

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RAWFIE: Road-, Air-, and Water- based Future Internet Experimentation

RAWFIE (Road-, Air-, and Water- based Future Internet Experimentation) is a project funded by the European Commission (Horizon H2020 programme) under the Future Internet Research Experimentation (FIRE+) initiative that aims at providing research facilities for Internet of Things (IoT) devices.  

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