Frank Klawonn – författare

Computer graphics comprises the creation and representation of simple graphical elements and images as well as modern techniques for virtual reality. In order to apply computer graphics techniques correctly, a basic understanding of the fundamental concepts is required.

This book provides an introduction to the most important basic concepts coupling the technical background and theory immediately with practical examples and applications. The reader can follow up the theory and then literally see the theory at work in numerous example programs. With only elementary knowledge of the programming language Java, the reader will be able to create his or her own images and animations immediately using Java 2D and Java 3D.

Written for second year undergraduate and above students, this book will be an essential tool for those studying computer science and computer engineering. A website for this book includes programs with source code, exercises with solutions and slides as teaching material.

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.

Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.

Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.

A basic understanding of the key techniques in computer graphics can open the door to this exciting field and its many applications, including for video games and for augmented and virtual reality.

This easy-to-follow textbook and reference introduces the fundamental concepts of computer graphics, integrating both technical background and theory with practical examples and applications throughout. Thoroughly revised and updated, this new edition continues to present a user-friendly approach to creating images and animations, complementing the expanded coverage of topics with usage of example programs and exercises.

Topics and features:

This reader-friendly textbook is an essential tool for second-year undergraduate students and above, providing clear and concise explanations of the basic concepts of computer graphics. It will enable readers to immediately implement these concepts using the OpenGL and Java (with only elementary knowledge of the programming language).

This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, held in Yangzhou, China, in October 2016.

The 68 full papers presented were carefully reviewed and selected from 115 submissions. They provide a valuable and timely sample of latest research outcomes in data engineering and automated learning ranging from methodologies, frameworks, and techniques to applications including various topics such as evolutionary algorithms; deep learning; neural networks; probabilistic modeling; particle swarm intelligence; big data analysis; applications in regression, classification, clustering, medical and biological modeling and predication; text processing and image analysis.