Projects

EDGI will develop middleware that consolidates the results achieved in the EDGeS project concerning the extension of Service Grids with Desktop Grids (DGs) in order to support European Grid Initiative (EGI) and National Grid Initiative user communities that are heavy users of Distributed Computing Infrastructures (DCIs) and require an extremely large number of CPUs and cores. EDGI will go beyond existing DCIs that are typically cluster Grids and supercomputer Grids, and will extend them with public and institutional Desktop Grids and Clouds.

The FP7 EDGeS project has successfully set up a production-level distributed computing infrastructure (DCI) consisting of more than 100.000 PCs from several volunteer and low-cost Desktop Grids, which have been connected to existing Service Grids (including EGEE, SEE-GRID, etc. with about 150.000 processors) based on the new 3G Bridge technology and application development methodology.

The CrossMedia e-science platform supports collaborative research communities by providing a comfortable solution to jointly develop semantic- and media search algorithms on common and challenging datasets processed by novel feature extractors. Users form communities on the CrossMedia portal where they can jointly create, build and share search algorithms and media datasets in an iterative way.

As a result of the CrossMedia project, researchers of MTA SZTAKI created a new testbed to support media retrieval research activities. The platform consists of a portal providing domain-specific functionality, collaborative features and a multimodal user interface; and is supported by a robust and scalable backend system. 

ManuCyte focuses on the development of an intelligent, modular manufacturing platform for flexible, patient-specific cell production which will increase efficiency of personalised cell production in terms of quality, throughput, and costs. This objective will be reached by combining several high-sophisticated technologies such as
- a modular and scalable concept for the setup of cell production platforms, which is based on plug-ins and allows a flexible composition of functionalities, extension of capacities, as well as fast ramp up.

The aim of the KOPI-Trans project is to create algorithms that enable locating and finding English translations of a Hungarian text, either on the internet, in digital libraries or collections. Finding translations is a yet to-be-solved problem on international level as well, even between English and German which are languages spoken by many, however finding a solution to it would mean a great advance in several fields.

SZTAKI Dictionary (SZTAKI Szótár) is the most popular Hungarian dictionary service with nearly 100.000 visitors a day, providing dictionaries in 7 languages (English, German, French, Italian, Dutch, Polish, Bulgarian). SZTAKI Dictionary started tp operate in 1995 and today is an infrastructural servcie for all foreign language learners and those in need of translating texts from or to Hungarian.

The EGI-InSPIRE project (Integrated Sustainable Pan-European Infrastructure for Researchers in Europe) started on 1 May 2010, co-funded by the European Commission (contract number: RI-261323) for four years, as a collaborative effort involving more than 50 institutions in over 40 countries. Its mission is to establish a sustainable European Grid Infrastructure (EGI).

THIS (Transport Hub Intelligent video System) is a research project funded by the European Union aimed at developing new and concrete solutions for handling the human control and identification of

people which could create risk situation in crowded scenario, by means of video analysis. THIS will propose software tools for people activities and behavior

recognition and surveillance modules, which can be integrated in existing platforms, and managed by inspector employers by means of Web interfaces.

In producing image analysis, video surveillance, remote sensing, medical diagnosis software tools, the usual problem is the actual parameterizing of the processing algorithms. In most cases the setting is done by applying the experience of retrieved training data from previous experiments.