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Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design
Describing state-of-the-art solutions in distributed system architectures, Integration of Services into Workflow Applications presents a concise approach to the integration of loosely coupled services into workflow applications. It discusses key challenges related to the integration of distributed systems and proposes solutions, both in terms of theoretical aspects such as models and workflow scheduling algorithms, and technical solutions such as software tools and APIs. The book provides an in-depth look at workflow scheduling and proposes a way to integrate several different types of services into one single workflow application. It shows how these components can be expressed as services that can subsequently be integrated into workflow applications. The workflow applications are often described as acyclic graphs with dependencies which allow readers to define complex scenarios in terms of basic tasks. Presents state-of-the-art solutions to challenges in multi-domain workflow application definition, optimization, and execution Proposes a uniform concept of a service that can represent executable components in all major distributed software architectures used today Discusses an extended model with determination of data flows among parallel paths of a workflow application Since workflow applications often process big data, the book explores the dynamic management of data with various storage constraints during workflow execution. It addresses several practical problems related to data handling, including data partitioning for parallel processing next to service selection and scheduling, processing data in batches or streams, and constraints on data sizes that can be processed at the same time by service instances. Illustrating several workflow applications that were proposed, implemented, and benchmarked in a real BeesyCluster environment, the book includes templates for
Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.
Today's enterprise cannot effectively function without a network, and today's enterprise network is almost always based on LAN technology. In a few short years, LANs have become an essential element of today's business environment. This time in the spotlight, while well deserved, has not come without a price. Businesses now insist that LANs deliver vast and ever-increasing quantities of business-critical information and that they do it efficiently, flawlessly, without fail, and most of all, securely. Today's network managers must consistently deliver this level of performance, and must do so while keeping up with ever changing, ever increasing demands without missing a beat. At the same time, today's IT managers must deliver business-critical information systems in an environment that has undergone radical paradigm shifts in such widely varied fields as computer architecture, operating systems, application development, and security.
Experts from Andersen Consulting show you how to combine computing, communications, and knowledge to deliver a uniquely new-and entirely indispensable-competitive advantage.
One critical barrier leading to successful implementation of flexible manufacturing and related automated systems is the ever-increasing complexity of their modeling, analysis, simulation, and control. Research and development over the last three decades has provided new theory and graphical tools based on Petri nets and related concepts for the design of such systems. The purpose of this book is to introduce a set of Petri-net-based tools and methods to address a variety of problems associated with the design and implementation of flexible manufacturing systems (FMSs), with several implementation examples.There are three ways this book will directly benefit readers. First, the book will allow engineers and managers who are responsible for the design and implementation of modern manufacturing systems to evaluate Petri nets for applications in their work. Second, it will provide sufficient breadth and depth to allow development of Petri-net-based industrial applications. Third, it will allow the basic Petri net material to be taught to industrial practitioners, students, and academic researchers much more efficiently. This will foster further research and applications of Petri nets in aiding the successful implementation of advanced manufacturing systems.
This book contains the papers presented at the Parallel
Computational Fluid Dynamics 1998 Conference.
This volume contains information about the automatic acquisition of biographic knowledge from encyclopedic texts, Web interaction and the navigation problem in hypertext.
The fourth in the "Inside" series, this volume includes four theses
completed under the editor's direction at the Institute for the
Learning Sciences at Northwestern University. This series bridges
the gap between Schank's books introducing (for a popular audience)
the theories behind his work in artificial intelligence (AI) and
the many articles and books written by Schank and other AI
researchers for their colleagues and students. The series will be
of interest to graduate students in AI and professionals in other
academic fields who seek the retraining necessary to join the AI
effort or to understand it at the professional level.
Classical and Fuzzy Concepts in Mathematical Logic and Applications provides a broad, thorough coverage of the fundamentals of two-valued logic, multivalued logic, and fuzzy logic. Exploring the parallels between classical and fuzzy mathematical logic, the book examines the use of logic in computer science, addresses questions in automatic deduction, and describes efficient computer implementation of proof techniques. Specific issues discussed include: oPropositional and predicate logic oLogic networks oLogic programming oProof of correctness oSemantics oSyntax oCompletenesss oNon-contradiction oTheorems of Herbrand and Kalman The authors consider that the teaching of logic for computer science is biased by the absence of motivations, comments, relevant and convincing examples, graphic aids, and the use of color to distinguish language and metalanguage. Classical and Fuzzy Concepts in Mathematical Logic and Applications discusses how the presence of these facts trigger a stirring, decisive insight into the understanding process. This view shapes this work, reflecting the authors' subjective balance between the scientific and pedagogic components of the textbook. Usually, problems in logic lack relevance, creating a gap between classroom learning and applications to real-life problems. The book includes a variety of application-oriented problems at the end of almost every section, including programming problems in PROLOG III. With the possibility of carrying out proofs with PROLOG III and other software packages, readers will gain a first-hand experience and thus a deeper understanding of the idea of formal proof.
The fourth in the "Inside" series, this volume includes four theses
completed under the editor's direction at the Institute for the
Learning Sciences at Northwestern University. This series bridges
the gap between Schank's books introducing (for a popular audience)
the theories behind his work in artificial intelligence (AI) and
the many articles and books written by Schank and other AI
researchers for their colleagues and students. The series will be
of interest to graduate students in AI and professionals in other
academic fields who seek the retraining necessary to join the AI
effort or to understand it at the professional level.
Designed for introductory parallel computing courses at the advanced undergraduate or beginning graduate level, Elements of Parallel Computing presents the fundamental concepts of parallel computing not from the point of view of hardware, but from a more abstract view of algorithmic and implementation patterns. The aim is to facilitate the teaching of parallel programming by surveying some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. The presentation is friendly and informal. The content of the book is language neutral, using pseudocode that represents common programming language models. The first five chapters' present core concepts in parallel computing. SIMD, shared memory, and distributed memory machine models are covered, along with a brief discussion of what their execution models look like. The book also discusses decomposition as a fundamental activity in parallel algorithmic design, starting with a naive example, and continuing with a discussion of some key algorithmic structures. Important programming models are presented in depth, as well as important concepts of performance analysis, including work-depth analysis of task graphs, communication analysis of distributed memory algorithms, key performance metrics, and a discussion of barriers to obtaining good performance. The second part of the book presents three case studies that reinforce the concepts of the earlier chapters. One feature of these chapters is to contrast different solutions to the same problem, using select problems that aren't discussed frequently in parallel computing textbooks. They include the Single Source Shortest Path Problem, the Eikonal equation, and a classical computational geometry problem: computation of the two-dimensional convex hull. After presenting the problem and sequential algorithms, each chapter first discusses the sources of parallelism then
Addresses the major issues involved in computer design and architectures. Dealing primarily with theory, tools, and techniques as related to advanced computer systems, it provides tutorials and surveys and relates new important research results. Each chapter provides background information, describes and analyzes important work done in the field, and provides important direction to the reader on future work and further readings. The topics covered include hierarchical design schemes, parallel and distributed modeling and simulation, parallel simulation tools and techniques, theoretical models for formal and performance modeling, and performance evaluation techniques.
Digital Signal Processing Algorithms describes computational number theory and its applications to deriving fast algorithms for digital signal processing. It demonstrates the importance of computational number theory in the design of digital signal processing algorithms and clearly describes the nature and structure of the algorithms themselves. The book has two primary focuses: first, it establishes the properties of discrete-time sequence indices and their corresponding fast algorithms; and second, it investigates the properties of the discrete-time sequences and the corresponding fast algorithms for processing these sequences.
Provides a readily accessible introduction to the analysis and design of digital circuits at a logic instead of electronics level. Second Edition features a new and improved arrangement of chapters, a balance of theoretical and practical implementation aspects and in-text examples in each chapter, 21 experiments using standard TTL type of ICs, updated end-of-chapter problems with answers to selected problems (answers provided in a Solutions Manual for Instructors only), and more.
This textbook serves as an introduction to fault-tolerance, intended for upper-division undergraduate students, graduate-level students and practicing engineers in need of an overview of the field. Readers will develop skills in modeling and evaluating fault-tolerant architectures in terms of reliability, availability and safety. They will gain a thorough understanding of fault tolerant computers, including both the theory of how to design and evaluate them and the practical knowledge of achieving fault-tolerance in electronic, communication and software systems. Coverage includes fault-tolerance techniques through hardware, software, information and time redundancy. The content is designed to be highly accessible, including numerous examples and exercises. Solutions and powerpoint slides are available for instructors.
Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
Based on a symposium honoring the extensive work of Allen Newell --
one of the founders of artificial intelligence, cognitive science,
human-computer interaction, and the systematic study of
computational architectures -- this volume demonstrates how
unifying themes may be found in the diversity that characterizes
current research on computers and cognition. The subject matter
includes:
Microprocessors and Microcomputer-Based System Design, Second Edition, builds on the concepts of the first edition. It discusses the basics of microprocessors, various 32-bit microprocessors, the 8085 microprocessor, the fundamentals of peripheral interfacing, and Intel and Motorola microprocessors. This edition includes new topics such as floating-point arithmetic, Program Array Logic, and flash memories. It covers the popular Intel 80486/80960 and Motorola 68040 as well as the Pentium and PowerPC microprocessors. The final chapter presents system design concepts, applying the design principles covered in previous chapters to sample problems.
Composed of three sections, this book presents the most popular
training algorithm for neural networks: backpropagation. The first
section presents the theory and principles behind backpropagation
as seen from different perspectives such as statistics, machine
learning, and dynamical systems. The second presents a number of
network architectures that may be designed to match the general
concepts of Parallel Distributed Processing with backpropagation
learning. Finally, the third section shows how these principles can
be applied to a number of different fields related to the cognitive
sciences, including control, speech recognition, robotics, image
processing, and cognitive psychology. The volume is designed to
provide both a solid theoretical foundation and a set of examples
that show the versatility of the concepts. Useful to experts in the
field, it should also be most helpful to students seeking to
understand the basic principles of connectionist learning and to
engineers wanting to add neural networks in general -- and
backpropagation in particular -- to their set of problem-solving
methods.
Composed of three sections, this book presents the most popular
training algorithm for neural networks: backpropagation. The first
section presents the theory and principles behind backpropagation
as seen from different perspectives such as statistics, machine
learning, and dynamical systems. The second presents a number of
network architectures that may be designed to match the general
concepts of Parallel Distributed Processing with backpropagation
learning. Finally, the third section shows how these principles can
be applied to a number of different fields related to the cognitive
sciences, including control, speech recognition, robotics, image
processing, and cognitive psychology. The volume is designed to
provide both a solid theoretical foundation and a set of examples
that show the versatility of the concepts. Useful to experts in the
field, it should also be most helpful to students seeking to
understand the basic principles of connectionist learning and to
engineers wanting to add neural networks in general -- and
backpropagation in particular -- to their set of problem-solving
methods.
"The Encyclopedia of Microcomputers serves as the ideal companion reference to the popular Encyclopedia of Computer Science and Technology. Now in its 10th year of publication, this timely reference work details the broad spectrum of microcomputer technology, including microcomputer history; explains and illustrates the use of microcomputers throughout academe, business, government, and society in general; and assesses the future impact of this rapidly changing technology."
This unique reference presents in-depth coverage of the latest methods and applications of digital image processing describing various computer architectures ideal for satisfying specific image processing demands.
This book gives a solution to the problem of constructing lightwave paths in free spaces by proposing the concept of a Self-Organized Lightwave Network (SOLNET). This concept enables us to form self-aligned coupling optical waveguides automatically. SOLNETs are fabricated by self-focusing of lightwaves in photosensitive media, in which the refractive index increases upon light beam exposure, to realize the following functions: 1) Optical solder: Self-aligned optical couplings between misaligned devices with different core sizes 2) Three-dimensional optical wiring 3) Targeting lightwaves onto specific objects SOLNETs are expected to reduce the efforts to implement lightwaves into electronic systems and allow us to create new architectures, thus reducing costs and energy dissipation and improving overall system performance. SOLNETs are also expected to be applied to a wide range of fields where lightwaves are utilized, for example, solar energy conversion systems and biomedical technologies, especially photo-assisted cancer therapies. Readers will systematically learn concepts and features of SOLNETs, SOLNET performance predicted by computer simulations, experimental demonstrations for the proof of concepts, and expected applications. They will also be prepared for future challenges of the applications. This book is intended to be read by scientists, engineers, and graduate students who study advanced optoelectronic systems such as optical interconnects within computers and optical networking systems, and those who produce new ideas or strategies on lightwave-related subjects.
"The Encyclopedia of Microcomputers serves as the ideal companion reference to the popular Encyclopedia of Computer Science and Technology. Now in its 10th year of publication, this timely reference work details the broad spectrum of microcomputer technology, including microcomputer history; explains and illustrates the use of microcomputers throughout academe, business, government, and society in general; and assesses the future impact of this rapidly changing technology." |
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