Parallel Computer Vision
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Deep Learning and Parallel Computing Environment for Bioengineering Systems
Author: Arun Kumar Sangaiah
language: en
Publisher: Academic Press
Release Date: 2019-07-26
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Parallel Computer Architectures
Parallel computer architectures are now going to real applications! This fact is demonstrated by the large number of application areas covered in this book (see section on applications of parallel computer architectures). The applications range from image analysis to quantum mechanics and data bases. Still, the use of parallel architectures poses serious problems and requires the development of new techniques and tools. This book is a collection of best papers presented at the first workshop on two major research activities at the Universitiit Erlangen-Niirnberg and Technis che Universitiit Miinchen. At both universities, more than 100 researchers are working in the field of multiprocessor systems and network configurations and methods and tools for parallel systems. Indeed, the German Science Founda tion (Deutsche Forschungsgemeinschaft) has been sponsoring the projects under grant numbers SFB 182 and SFB 342. Research grants in the form of a Sonder forschungsbereich are given to selected German Universities in portions of three years following a thoroughful reviewing process. The overall duration of such a research grant is restricted to 12 years. The initiative at Erlangen-Niirnberg was started in 1987 and has been headed since this time by Prof. Dr. H. Wedekind. Work at TU-Miinchen began in 1990, head of this initiative is Prof. Dr. A. Bode. The authors of this book are grateful to the Deutsche Forschungsgemeinschaft for its continuing support in the field of research on parallel processing. The first section of the book is devoted to hardware aspects of parallel systems.