Build your very own 2D physics-based game engine simulation system for rigid body dynamics. Beginning from scratch, in this book you will cover the implementation technologies, HTML5 and JavaScript; assemble a simple and yet complete fundamental mathematics support library; define basic rigid body behaviors; detect and resolve rigid body collisions; and simulate collision responses after the collisions. In this way, by the end of Building a 2D Game Physics Engine, you will have an in‐depth understanding of the specific concepts and events, implementation details, and actual source code of a physics game engine that is suitable for building 2D games or templates for any 2D games you can create and can be played across the Internet via popular web‐browsers. What You'll Learn Gain an understanding of 2D game engine physics and how to utilize it in your own games Describe the basic behaviors of rigid bodies Detect collisions between rigid bodies Resolve interpretations after rigid body collisions Model and implement rigid body impulse responses Who This Book Is For Game enthusiasts, hobbyists, and anyone who is interested in building their own 2D physics game engines but is unsure of how to begin.
This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.
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