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S. Venugopal

Publications and source records attributed to S. Venugopal.

2 recordsLinked to original sources

Using Modern Technologies to Capture and Share Indigenous Astronomical Knowledge

Indigenous Knowledge is important for Indigenous communities across the globe and for the advancement of our general scientific knowledge. In particular, Indigenous astronomical knowledge integrates many aspects of Indigenous Knowledge, including seasonal calendars, navigation, food economics, law, ceremony, and social structure. We aim to develop innovative ways of capturing, managing, and disseminating Indigenous astronomical knowledge for Indigenous communities and the general public for the future. Capturing, managing, and disseminating this knowledge in the digital environment poses a number of challenges, which we aim to address using a collaborative project involving experts in the higher education, library, and industry sectors. Using Microsoft's WorldWide Telescope and Rich Interactive Narratives technologies, we propose to develop software, media design, and archival management solutions to allow Indigenous communities to share their astronomical knowledge with the world on their terms and in a culturally sensitive manner.

physics.pop-ph

Economic and On Demand Brain Activity Analysis on Global Grids

The lack of computational power within an organization for analyzing scientific data, and the distribution of knowledge (by scientists) and technologies (advanced scientific devices) are two major problems commonly observed in scientific disciplines. One such scientific discipline is brain science. The analysis of brain activity data gathered from the MEG (Magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper describes a MEG data analysis system developed by us, leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. This paper explains the application of economy-based grid scheduling algorithms to the problem domain for on-demand processing of analysis jobs.

cs.DC