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Yongjie Jessica Zhang

Geometric Modeling and Mesh Generation from Scanned Images

Ebook (PDF Format)

Cutting-Edge Techniques to Better Analyze and Predict Complex Physical PhenomenaGeometric Modeling and Mesh Generation from Scanned Images shows how to integrate image processing, geometric modeling, and mesh generation with the finite element method (FEM) to solve problems in computational biology, medicine, materials science, and engineering. Based on the author's recent research and course at Carnegie Mellon University, the text explains the fundamentals of medical imaging, image processing, computational geometry, mesh generation, visualization, and finite element analysis. It also explores novel and advanced applications in computational… Mehr

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Produktdetails


  • ISBN: 978-1-315-36256-4
  • EAN: 9781315362564
  • Produktnummer: 31314032
  • Verlag: Taylor & Francis Ltd.
  • Sprache: Englisch
  • Erscheinungsjahr: 2018
  • Seitenangabe: 364 S.
  • Plattform: PDF
  • Masse: 248'657 KB
  • Auflage: 1. Auflage
  • Abbildungen: 156 farbige Abbildungen, 4 farbige Tabellen

Über den Autor


Yongjie Jessica Zhang is an associate professor in the Department of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in the Department of Biomedical Engineering. She is the co-author of more than 130 publications in peer-reviewed international journals and conference proceedings. She has been a recipient of the Presidential Early Career Award for Scientists and Engineers, NSF CAREER Award, Office of Naval Research Young Investigator Award, USACM Gallagher Young Investigator Award, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering, George Tallman Ladd Research Award, and Donald L. and Rhonda Struminger Faculty Fellow. Her research interests include computational geometry, mesh generation, computer graphics, visualization, finite element method, isogeometric analysis, and their application in computational biomedicine and engineering. She earned a PhD in computational engineering and sciences from the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin.

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