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st This volume contains the proceedings of two conferences held as part of the 21 IFIP World Computer Congress in Brisbane, Australia, 20–23 September 2010. th The first part of the book presents the proceedings of DIPES 2010, the 7 IFIP Conference on Distributed and Parallel Embedded Systems. The conference, int- duced in a separate preface by the Chairs, covers a range of topics from specification and design of embedded systems through to dependability and fault tolerance. rd The second part of the book contains the proceedings of BICC 2010, the 3 IFIP Conference on Biologically-Inspired Collaborative Computing. The conference is concerned with emerging techniques from research areas such as organic computing, autonomic computing and self-adaptive systems, where inspiraton for techniques - rives from exhibited behaviour in nature and biology. Such techniques require the use of research developed by the DIPES community in supporting collaboration over multiple systems. We hope that the combination of the two proceedings will add value for the reader and advance our related work.
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
The increasing presence of mobile robots in our everyday lives introduces the requirements for their intelligent and autonomous features. Therefore the next generation of mobile robots should be more self-capable, in respect to: increasing of their functionality in unforeseen situations, decreasing of the human involvement in their everyday operations and their maintenance; being robust; fault tolerant and reliable in their operation. Although mobile robotic systems have been a topic of research for decades and aside the technology improvements nowadays, the subject on how to program and making them more autonomous in their operations is still an open field for research. Applying bio-inspired, organic approaches in robotics domain is one of the methodologies that are considered that would help on making the robots more autonomous and self-capable, i.e. having properties such as: self-reconfiguration, self-adaptation, self-optimization, etc. In this book several novel biologically inspired approaches for walking robots (multi-legged and humanoid) domain are introduced and elaborated. They are related to self-organized and self-stabilized robot walking, anomaly detection within robot systems using self-adaptation, and mitigating the faulty robot conditions by self-reconfiguration of a multi-legged walking robot. The approaches presented have been practically evaluated in various test scenarios, the results from the experiments are discussed in details and their practical usefulness is validated.
The two-volume set, CCIS 681 and CCIS 682, constitutes the proceedings of the 11th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2016, held in Xi'an, China, in October 2016.The 115 revised full papers presented were carefully reviewed and selected from 343 submissions. The papers of Part I are organized in topical sections on DNA Computing; Membrane Computing; Neural Computing; Machine Learning. The papers of Part II are organized in topical sections on Evolutionary Computing; Multi-objective Optimization; Pattern Recognition; Others.
The evolution of the Internet has led us to the new era of the information infrastructure. As the information systems operating on the Internet are getting larger and more complicated, it is clear that the traditional approaches based on centralized mechanisms are no longer meaningful. One typical example can be found in the recent growing interest in a P2P (peer-to-peer) computing paradigm. It is quite different from the Web-based client-server systems, which adopt essentially centralized management mechanisms. The P2P computing environment has the potential to overcome bottlenecks in Web computing paradigm, but it introduces another difficulty, a scalability problem in terms of information found, if we use a brute-force flooding mechanism. As such, conventional information systems have been designed in a centralized fashion. As the Internet is deployed on a world scale, however, the information systems have been growing, and it becomes more and more difficult to ensure fau- free operation. This has long been a fundamental research topic in the field. A complex information system is becoming more than we can manage. For these reasons, there has recently been a significant increase in interest in biologically inspired approaches to designing future information systems that can be managed efficiently and correctly.
This book constitutes the refereed proceedings of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, held in Salamanca, Spain in June 2009. The 167 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from over 230 submissions. The papers are organized in thematic sections on theoretical foundations and models; learning and adaptation; self-organizing networks, methods and applications; fuzzy systems; evolutionary computation and genetic algoritms; pattern recognition; formal languages in linguistics; agents and multi-agent on intelligent systems; brain-computer interfaces (bci); multiobjetive optimization; robotics; bioinformatics; biomedical applications; ambient assisted living (aal) and ambient intelligence (ai); other applications.
This book constitutes the proceedings of the 9th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2014, held in Wuhan, China, in October 2014. The 109 revised full papers presented were carefully reviewed and selected from 204 submissions. The papers focus on four main topics, namely evolutionary computing, neural computing, DNA computing, and membrane computing.
This monograph presents examples of best practices when combining bioinspired algorithms with parallel architectures. The book includes recent work by leading researchers in the field and offers a map with the main paths already explored and new ways towards the future. Parallel Architectures and Bioinspired Algorithms will be of value to both specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as computer science students trying to understand the present and the future of Parallel Architectures and Bioinspired Algorithms.
This book constitutes the proceedings of the 12th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2017, held in Harbin, China, December 2017. The 50 full papers presented were selected from 143 submissions. The papers deal with studies abstracting computing ideas such as data structures, operations with data, ways to control operations, computing models from living phenomena or biological systems such as evolution, cells, tissues, neural networks, immune systems, and ant colonies.
Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication. The work is organized into five parts: * Algorithms and models * Sequence analysis and microarrays * Phylogenetics * Protein folding * Platforms and enabling technologies Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.