Distributed Visualization Systems in Remote Sensing Data Processing Grid

Implementation of GEOSS/GMES initiative requires creation and integration of service providers, most of which provide geospatial data output from Grid system to interactive user. In this paper approaches of DOS- centers (service providers) integration used in Ukrainian segment of GEOSS/GMES will be considered and template solutions for geospatial data visualization subsystems will be suggested. Developed patterns are implemented in DOS center of Space Research Institute of National Academy of Science of Ukraine and National Space Agency of Ukraine (NASU-NSAU).

Open Access to Publications and Research Data in Horizon 2020: What Are the Requirements and How Can Institutional Repositories and OpenAIRE Help to Meet Them?

In the last decade the principle of Open Access to publicly funded research has been getting a growing support from policy makers and funders across Europe, both at national level and within the European Union context. At European level some of the first relevant steps taken by the European Research Council (ERC) with a statement supporting Open Access (2006), shortly followed by guidelines for researchers funded by the ERC (2007) stating that all peer-reviewed publications from ERC funded projects should be made openly accessible shortly after their publication. Those guidelines were revised in October 2013, reinforcing the mandatory character of the requirements and expanding them to monographs.

Intellect Sensing of Neural Network that Trained to Classify Complex Signals

An experimental comparison of information features used by neural network is performed. The sensing method was used. Suboptimal classifier agreeable to the gaussian model of the training data was used as a probe. Neural nets with architectures of perceptron and feedforward net with one hidden layer were used. The experiments were carried out with spatial ultrasonic data, which are used for car’s passenger safety system neural controller learning. In this paper we show that a neural network doesn’t fully make use of gaussian components, which are first two moment coefficients of probability distribution. On the contrary, the network can find more complicated regularities inside data vectors and thus shows better results than suboptimal classifier. The parallel connection of suboptimal classifier improves work of modular neural network whereas its connection to the network input improves the specialization effect during training.