Monthly Archives: February 2018
Modeling pathological gait resulting from motor impairments: compare and combine neuromechanical simulation and machine learning approaches
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. Continue reading
Location-based services are expanding at an ever increasing speed. They address individual consumer demands as well as the needs of industrial and service organizations active in the private or the public sector. To deliver their expected value location-based services require adapted sensing and network technologies, in particular for large areas. Within this project we will develop machine learning and neural networks to ascertain sensor location.
SMELL is an Innosuisse project undertaken with collabration with the company Firmenich. The 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.
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.