Border authorities and Law Enforcement Agencies (LEAs) across Europe face important challenges in how they patrol and protect the borders. Their work becomes more problematic considering the heterogeneity of threats, the wideness of the surveyed area, the adverse weather conditions and the wide range of terrains. Although there are several research tools and works targeting these areas independently for border surveillance, nowadays border authorities do not have access to an intelligent holistic solution providing all aforementioned functionalities. Towards delivering such a solution, ROBORDER aims at developing and demonstrating a fully-functional autonomous border surveillance system with unmanned mobile robots including aerial, water surface, underwater and ground vehicles, capable of functioning both as standalone and in swarms, which will incorporate multimodal sensors as part of an interoperable network. The system will be equipped with adaptable sensing and robotic technologies that can operate in a wide range of operational and environmental settings. To provide a complete and detailed situational awareness picture that supports highly efficient operations, the network of sensors will include static networked sensors such as border surveillance radars, as well as mobile sensors customised and installed on board unmanned vehicles. To succeed implementing an operational solution, a number of supplementary technologies will also be applied that will enable the establishment of robust communication links between the command and control unit and the heterogeneous robots. On top of this, detection capabilities for early identification of criminal activities and hazardous incidents will be developed. This information will be forwarded to the command and control unit that will enable the integration of large volumes of heterogeneous sensor data and the provision of a quick overview of the situation at a glance to the operators, supporting them in their decisions.

The ehcoBUTLER Idea: Nowadays, it is a fact that Europe is ageing. A common characteristic of elders is the frequent occurrence of either physical or mild cognitive impairments. This situation brings new challenges in how to improve the independence and quality of life of elderly people and promote their good health in different ways. The ehcoBUTLER project addresses this challenge by developing an ICT technological platform with both leisure and care apps. The main objective of ehcoBUTLER is to demonstrate the socio-economic benefits from the deployment of several innovative and user led ICT pilot projects based on different business models in order to be able to translate promising results into scalable practice across Europe. How the objectives will be achieved: The ehcoBUTLER Consortium is composed by a multidisciplinary combination of specialist partners on their areas and responsibilities, in order to satisfy the requirements emerging from the EU Call and the particular PHC-20 topic. With this consortium we expect to contribute to break the technological barrier that exists nowadays between the elderly and the ICTs, encouraging the e-Inclusion, to facilitate psychological and cognitive techniques and support procedures, both for the elderly people and for the informal and formal caregivers, to develop an interoperable and open ICT platform particularly designed and adapted to elderly people, to demonstrate the ROI from several four business models based on the deployment of this ICT platform and to generate an ecosystem for apps provider that will allow end users to integrate all the leisure and care related activities in just one platform. To ensure that the platform can be scaled to an operational deployment in the European Market we will deploy ehcoBUTLER in 5 countries and 7 pilot sites to reach the higher number of users and to test the suitability of ehcoBUTLER in different but related business cases.

Health scientific discovery and innovation are expected to quickly move forward under the so called “fourth paradigm of science”, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments. Under this paradigm, the DeepHealth project will provide HPC computing power at the service of biomedical applications; and apply Deep Learning (DL) techniques on large and complex biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases. DeepHealth will develop a flexible and scalable framework for the HPC + Big Data environment, based on two new libraries: the European Distributed Deep Learning Library (EDDLL) and the European Computer Vision Library (ECVL). The framework will be validated in 14 use cases which will allow to train models and provide training data from different medical areas (migraine, dementia, depression, etc.). The resulting trained models, and the libraries, will be integrated and validated in 7 existing biomedical software platforms, which include: a) commercial platforms (e.g. PHILIPS Clinical Decision Support System from or THALES SIX PIAF; and b) research oriented platforms (e.g. CEA`s ExpressIF™ or CRS4`s Digital Pathology). Impact is measured by tracking the time-to-model-in-production (ttmip). Through this approach, DeepHealth will also standardise HPC resources to the needs of DL applications, and underpin the compatibility and uniformity on the set of tools used by medical staff and expert users. The final DeepHealth solution will be compatible with HPC infrastructures ranging from the ones in supercomputing centers to the ones in hospitals. DeepHealth involves 21 partners from 9 European Countries, gathering a multidisciplinary group from research organisations (9), health organisations (4) as well as (4) large and (4) SME industrial partners, with strong commitment towards innovation, exploitation and sustainability.