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GPU Pro
This book covers essential tools and techniques for programming the graphics processing unit. Brought to you by Wolfgang Engel and the same team of editors who made the ShaderX series a success, this volume covers advanced rendering techniques, engine design, GPGPU techniques, related mathematical techniques, and game postmortems. A special emphasi
Graph Embedding for Pattern Analysis
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.
Rendering Techniques 2001
This book contains the proceedings of the lih Eurographics Workshop on Rendering, th which took place from the 25 to the 27th of June, 2001, in London, United Kingdom. Over the past 11 years, the workshop has become the premier forum dedicated to research in rendering. Much of the work in rendering now appearing in other conferences and journals builds on ideas originally presented at the workshop. This year we received a total of 74 submissions. Each paper was carefully reviewed by two of the 28 international programme committee members, as well as external reviewers, selected by the co-chairs from a pool of 125 individuals. In this review process, all submissions and reviews were handled e...
Generative Adversarial Networks and Deep Learning
This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various appl...
Computational Science -- ICCS 2005
The three-volume set LNCS 3514-3516 constitutes the refereed proceedings of the 5th International Conference on Computational Science, ICCS 2005, held in Atlanta, GA, USA in May 2005. The 464 papers presented were carefully reviewed and selected from a total of 834 submissions for the main conference and its 21 topical workshops. The papers span the whole range of computational science, ranging from numerical methods, algorithms, and computational kernels to programming environments, grids, networking, and tools. These fundamental contributions dealing with computer science methodologies and techniques are complemented by papers discussing computational applications and needs in virtually all scientific disciplines applying advanced computational methods and tools to achieve new discoveries with greater accuracy and speed.
DNA Computing
This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Meeting on DNA Computing, DNA 14, held in Prague, Czech Republic, in June 2008. The 15 revised full papers presented were carefully reviewed and selected from 59 submissions. Their topics include theoretical models of biomolecular computing, demonstrations of biomolecular computing processes, self-assembly systems, DNA nanostructures and nanomachines, biotechnological and other applications of DNA computing, and other related themes.
The Oxford Handbook of Interactive Audio
What does it mean to interact with sound? How does interactivity alter our experience as creators and listeners? What does the future hold for interactive musical and sonic experiences? This book answers these questions with newly-commissioned chapters that explore the full range of interactive audio in games, performance, design, and practice.
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Gas Dynamics with Applications in Industry and Life Sciences
This proceedings volume gathers selected contributions presented at two instances of the "JSPS/SAC Seminar: On Gas Kinetic/Dynamics and Life Science", held by the Chalmers University of Technology and University of Gothenburg, Sweden, on March 25-26, 2021 (virtual) and March 17-18, 2022 (virtual). Works in this book provide a concise approach to the theoretical and numerical analysis of kinetic type equations that arise, for example, in modeling industrial, medical, and environmental problems. Readers will find some of the most recent theoretical results, newly developed numerical methods in the field, and some open problems. Possible application areas encompass fission/fusion energy, electr...
Deep Learning
An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also...
