170,550 research outputs found

    The SSDC contribution to the improvement of knowledge by means of 3D data projections of minor bodies

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    The latest developments of planetary exploration missions devoted to minor bodies required new solutions to correctly visualize and analyse data acquired over irregularly shaped bodies. ASI Space Science Data Center (SSDC-ASI, formerly ASDC-ASI Science Data Center) worked on this task since early 2013, when started developing the web tool MATISSE (Multi-purpose Advanced Tool for the Instruments of the Solar System Exploration) mainly focused on the Rosetta/ESA space mission data. In order to visualize very high-resolution shape models, MATISSE uses a Python module (vtpMaker), which can also be launched as a stand-alone command-line software. MATISSE and vtpMaker are part of the SSDC contribution to the new challenges imposed by the "orbital exploration" of minor bodies: 1) MATISSE allows to search for specific observations inside datasets and then analyse them in parallel, providing high-level outputs; 2) the 3D capabilities of both tools are critical in inferring information otherwise difficult to retrieve for non-spherical targets and, as in the case for the GIADA instrument onboard Rosetta, to visualize data related to the coma. New tasks and features adding valuable capabilities to the minor bodies SSDC tools are planned for the near future thanks to new collaborations

    Spike sorting for large, dense electrode arrays

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    Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

    Using data visualization to deduce faces expressions

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    Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio
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