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8.6. E-INFRASTRUCTURES
The Internet of Living Things
University of Szeged
Consortium leader: Project description:
Project code: By establishing the IoLT research group our aim is to create a platform based on our
GINOP-2.2.1-15-2017-00073 former R&D results that will facilitate the development of IoLT applications through
the high-level programming of ultra-low-resource sensors for adaptive data collection
Project leader institution: and processing. The platform is open source, so it will potentially attract special at-
University of Szeged tention and have a major role in international projects.
Project leader: The resulting IoLT (Internet of Living Things) research group constitutes a novelty
Tibor Gyimóthy even at international level, as it integrates research in IoT basic technologies and areas
of biological and medical applications. The goal is to develop an integrated IoLT re-
Project leader’s contacts: search group, in which the participating researchers and international research teams
gyimi@inf.u-szeged.hu involved through their relationships build up a knowledge base of excellence. Based
on the results of previous international industrial cooperation, the project will estab-
Project partner: lish an open source IoLT platform that allows even non-IT researchers to develop
MTA Biological Research Centre biological, medical, and other IoT applications. The platform will work on ultra-low-
in Szeged resource, ultra-cheap IoT devices as well. To this end, we are engaging in R&D in in-
ternational cooperation in the following areas: JavaScript execution engine, IoT
Amount of funding: development environment, device drivers, communications, IoT cloud infrastructure,
HUF 810,986,542 algorithms for data safety and security.
Website: Within IoLT the focus is on biological and medical applications. The “Smart Pot”
https://www.u-szeged.hu/fejlesz- sub-project is dedicated to the examination of the impact of environmental factors
tesiprojektek/ginop-2-3-2-15- determining the growth and stress reactions of individual plants. Our actigraphic re-
2016-170525/ginop-2-3-2-15-20 search is aimed at identifying new ultradian and slower periodic and stochastic com-
16 ponents in human physical activity patterns and at establishing their correlation to
physical conditions, activities, and certain psychiatric disorders. The applicability of
the results will be tested in medical practice. The development of “Lab-on-a-chip”
systems greatly increases the effectiveness of work on cell cultures. This facilitates the
high throughput testing of potential active substances. To increase the efficiency of
personalised therapies we develop image processing and machine learning algorithms
for the analysis of microscopic images and the determination of automatic classifica-
tion planned to be implemented with feature extraction methods based on local and
cell neighbourhood.
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