ࡱ> QSP R1bjbjWW2H55( CCCCCWWW8W GGGGG{ $r"%pEC{{CCGG4@ CGCG:,GcGWo V 0 .%ov%%C % : wmYYe^;N[vxvzuhQe zW,gOo`h zS2109602 z Tyyf[{SƉS(Visualization in Scientific Computing)_f[b{:gf[b z'`(lQqQ Of[R2f[Rf[e32f[e z #N^gwm zRt^v\ql Ne4.21-25 NHS7-9TZf N11-13 N0Wpsl!h:SfIQj_;N|i413 z{NVisualization Module introduction: The module is expected to include both scientific and information visualization. Scientific visualization is a research area that focuses on the use of visualization techniques to help people understand and analyze data. While fields such as scientific visualization involve the presentation of data in their geometric correspondence. Information visualization focuses on abstract data using symbolic, tabular, networked, hierarchical, or textual information sources. The course also includes tailored design of visualization techniques to handle Big Data in nowadays application, and to integrate with the latest computing architecture such as Cloud. The project of the module will involve the solution of scientific problems using computer graphics, modeling, and visualization. Working in small groups, students will identify scientific problems, propose solutions involving computational modeling and visualization, evaluate the proposals, design and implement the solutions, apply them to the problems, evaluate their success, and report on results. The objectives of the course are Learn the principals involved in scientific and information visualization Learn about the variety of existing techniques and systems in scientific information visualization Develop skills in critiquing different visualization techniques as applied to particular tasks Learn how to evaluate visualization systems Gain a background that will aid the design of new, innovative visualizations The course will follow a lecture/seminar style with much discussion of assigned readings, as well as viewing of videos and hands-on experience with research and commercial visualization tools. The content include: 1) scalar visualization 2) volume visualization 3) vector and tensor visualization 4) basic of info vis 5) scalable visualization techniques 6) high dimensional visualization: sub-space clustering & dimension reduction 7) uncertainty visualization 8) visualization design 9) visualization in a cloud based environmentwmY;N^{NFeng Dong is Professor of Visual Computing at University of Bedfordshire. He joined the university in September 2007 from Brunel University. Prof Dong was awarded a BSc, MSc and PhD from Zhejiang University, where he became a member of academic staff at the State Key Lab of CAD and Computer Graphics, the leading computer graphics lab in China. He also worked as a lecturer at Lancaster University in the UK. Prof Dong has many research interests in computer graphics, medical visualization and image processing. His recent work has also developed new areas in visual analytics, pattern recognition, image-based rendering and figure animation. He is leading a research team which has been involved in large scale research grants in healthcare and creative learning from national (UK) and internal (Europe) funding bodies. His team is working on big data management and storage on a cloud environment, mining and visualization of massive data, text mining and semantics of web information. He is currently the coordinator of two active European projects. 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