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Positioning Patterns from Multidimensional Data and Its Applications in Meteorology: (English)

Positioning Patterns from Multidimensional Data and Its Applications in Meteorology: (English)

          
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About the Book

This dissertation, "Positioning Patterns From Multidimensional Data and Its Applications in Meteorology" by Ka-yan, Wong, 王嘉欣, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "Positioning patterns from multidimensional data and its applications in meteorology" Submitted by WONG Ka Yan for the degree of Doctor of Philosophy at The University of Hong Kong in February 2008 Large, complex, multidimensional data are encountered in many ap- plication domains such as meteorology, surveillance, military, medicine and industry. Researchers in different disciplines work to identify and position patterns of interest from their respective data sources. Unfortu- nately, the volume and complexity of data in many application domains are too high for manual analysis. With the continuous growth of data rate, volumeanddimension, efficientandeffectivemethodsforidentifica- tion and positioning patterns of interest from multidimensional data are needed. In this dissertation, original algorithms for positioning patterns from multidimensional data are presented. As an application example, the developed techniques are applied on meteorological data for weather system positioning, with emphasis on efficiency, effectiveness, noise toler- ance, adaptiveness, objectiveness and temporal continuity of the results. In particular, the focus is not only limited to positioning static patterns, but also continuously changing patterns including vector fields. The contributions of this dissertation are manifold. They include the research and development of a genetic algorithm-based framework for ef- ficient and effective matching of patterns on multidimensional data; an efficient and noise tolerant method for positioning centers of a family of flow patterns; a method for automatic and adaptive template generation; and a set of three-dimensional block-based motion estimation techniques using Platonic solids. These methods are applied in an adaptive, effi- cient and effective weather system positioning framework that could give temporally continuous positioning results. This dissertation also con-tributes to meteorological computing by the design and development of two models of tropical cyclones, a generic model of weather systems us- ing adjoining segments of arcs, and a genetic algorithm-based framework for line-shaped weather system positioning. These led to the design of one of the first radar-based automatic tropical cyclone center positioning systems. The algorithms and methods developed are generic and can also be usedinotherdomains. Oneofthepotentialapplicationsistheanalysisof video sequences for detecting the centers of rotating or spiraling objects. Through the study, contributions have been made to both computer sci- ence and meteorology, giving a starting point for future research in both fields. (Word counts: 340) DOI: 10.5353/th_b3955863 Subjects: Meteorology - Data processing Genetic algorithms Mathematical statistics - Data processing


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Product Details
  • ISBN-13: 9781361433683
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 260
  • Spine Width: 14 mm
  • Width: 216 mm
  • ISBN-10: 136143368X
  • Publisher Date: 27 Jan 2017
  • Binding: Paperback
  • Language: English
  • Series Title: English
  • Weight: 608 gr


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