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Applications of Geometric Algebra in Computer Science and Engineering
Geometric algebra has established itself as a powerful and valuable mathematical tool for solving problems in computer science, engineering, physics, and mathematics. The articles in this volume, written by experts in various fields, reflect an interdisciplinary approach to the subject, and highlight a range of techniques and applications. Relevant ideas are introduced in a self-contained manner and only a knowledge of linear algebra and calculus is assumed. Features and Topics: * The mathematical foundations of geometric algebra are explored * Applications in computational geometry include models of reflection and ray-tracing and a new and concise characterization of the crystallographic gr...
Topological Methods in Data Analysis and Visualization
Topology-based methods are of increasing importance in the analysis and visualization of datasets from a wide variety of scientific domains such as biology, physics, engineering, and medicine. Current challenges of topology-based techniques include the management of time-dependent data, the representation of large and complex datasets, the characterization of noise and uncertainty, the effective integration of numerical methods with robust combinatorial algorithms, etc. . The editors have brought together the most prominent and best recognized researchers in the field of topology-based data analysis and visualization for a joint discussion and scientific exchange of the latest results in the field. This book contains the best 20 peer-reviewed papers resulting from the discussions and presentations at the third workshop on "Topological Methods in Data Analysis and Visualization", held 2009 in Snowbird, Utah, US. The 2009 "TopoInVis" workshop follows the two successful workshops in 2005 (Slovakia) and 2007 (Germany).
Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration
The goal of visualization is the accurate, interactive, and intuitive presentation of data. Complex numerical simulations, high-resolution imaging devices and incre- ingly common environment-embedded sensors are the primary generators of m- sive data sets. Being able to derive scienti?c insight from data increasingly depends on having mathematical and perceptual models to provide the necessary foundation for effective data analysis and comprehension. The peer-reviewed state-of-the-art research papers included in this book focus on continuous data models, such as is common in medical imaging or computational modeling. From the viewpoint of a visualization scientist, we typically collaborate w...
See through
The problem of visualising multivariate data and tensor fields inherits its complexity from the data it targets. By definition, complex data is "hard to separate, analyse, or solve"1. This becomes evident through the fact that methods for "simple" data such as scalars and vectors do not trivially extend to multivariate data and tensors. In the light of increasing number of output variables from simulation models and measurements, this lack of methods leads to a limited choice in the analysis and to a lower fidelity of the analysis. In addition, split application of established methods to a subset of the data, for example the separate rendering of isosurfaces for the different scalar fields c...
Recent Advances in the Theory and Application of Fitness Landscapes
This book is concerned with recent advances in fitness landscapes. The concept of fitness landscapes originates from theoretical biology and refers to a framework for analysing and visualizing the relationships between genotypes, phenotypes and fitness. These relationships lay at the centre of attempts to mathematically describe evolutionary processes and evolutionary dynamics. The book addresses recent advances in the understanding of fitness landscapes in evolutionary biology and evolutionary computation. In the volume, experts in the field of fitness landscapes present these findings in an integrated way to make it accessible to a number of audiences: senior undergraduate and graduate students in computer science, theoretical biology, physics, applied mathematics and engineering, but also researcher looking for a reference or/and entry point into using fitness landscapes for analysing algorithms. Also practitioners wanting to employ fitness landscape techniques for evaluating bio- and nature-inspired computing algorithms can find valuable material in the book. For teaching proposes, the book could also be used as a reference handbook.
Data Visualization 2001
This book contains 33 papers presented at the Third Joint Visualization Symposium of the Eurographics Association and the Technical Committee on Visualization and Graphics of the IEEE Computer Society. The main topics treated are: visualization of geoscience data; multi-resolution and adaptive techniques; unstructured data, multi-scale and visibility; flow visualization; biomedical applications; information visualization; object representation; volume rendering; information visualization applications; and automotive applications.
Informatics
Informatics - 10 Years Back, 10 Years Ahead presents a unique collection of expository papers on major current issues in the field of computer science and information technology. The 26 contributions written by leading researchers on personal invitation assess the state of the art of the field by looking back over the past decade, presenting important results, identifying relevant open problems, and developing visions for the decade to come. This book marks two remarkable and festive moments: the 10th anniversary of the International Research and Conference Center for Computer Science in Dagstuhl, Germany and the 2000th volume published in the Lecture Notes in Computer Science series.
Visualization of Large Unstructured Grids. Efficient Point and Cell Location
Seminar paper from the year 2013 in the subject Engineering - Computer Engineering, grade: 1.0, University of Heidelberg (Computer Graphics and Visualization), course: Seminar Computer Vision, language: English, abstract: Visualization of large data sets, especially the visualization of unstructured grids, is a challenge due to the unstructured nature of the data which oftentimes causes large overheads in memory as well as performance problems on large grids. Problems emerge because existing solutions generally presuppose properties like uniform point distributions for datasets which are usually not existent in unstructured grids. These issues become particularly problematic on large grids s...
