RAWFIE (Road-, Air-, and Water- based Future Internet Experimentation) is a project funded by the European Commission (Horizon H2020 program) under the Future Internet Research Experimentation (FIRE+) initiative that aims at providing research facilities for Internet of Things (IoT) devices. The DMML team has been involved in the RAWFIE project since early 2015 and delivered their final contributions in June 2019 as the the project ended. The members of the team invested in the project, Jason and Lionel, were responsible for the design and development of the platform's data analysis tools, enabling experimenters to apply machine learning algorithms on data collected via the platform's unmanned devices.
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MEDInA is an Innosuisse project, enabling the creation of low cost IoT self-adaptive Machine Learning based applications by developing an Artificial-Intelligence-as-a-Service (AIaaS) framework. Current work by the DMML team includes partnering with SixSq in order to develop an AI solution that runs on edge devices such as a Raspberry Pi 4, providing traffic volume information monitoring, to be used for adaptive smart lighting in Smart Cities.
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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 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. The percentage of improvement of the localization accuracy achieved so far lays in the range of 30%-55%, depending on the environment of the deployment.
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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.
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