Error-tolerant Products
Error
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Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: False Friend, Spoonerism, Mondegreen, List of Misquotations, Observational Error, Faux Pas, Error, List of Commonly Misused English Words, Biblical Inerrancy, Type I and Type Ii Errors, Source Criticism, Ringing Artifacts, Commonly Misspelled Words, Margin of Error, False Etymology, Controversies About the Word "Niggardly", Miscarriage of Justice, Trial and Error, Healthcare Error Proliferation Model, Near Miss, Errors and Residuals in Statistics, Studentized Residual, Typographical Error, Ras Syndrome, User Error, Literal Translation, Percussive Maintenance, List of Misconceptions About the Brain, List of Erroneous Newspaper Headlines, Pocket Dialing, Mistaken Identity, Error Analysis, Mistake of Law, Drunk Dialing, Human Error Model, Postage Stamp Design Error, Mistake, Transcription Error, Errors in the United States Constitution, Stein's Unbiased Risk Estimate, Artifact, Misdialed Call, Error-Tolerant Design, Correction, Sampling Error, Probability of Error, Forecast Error, Digital Artifact, Personal Equation, Clerical Error, Popular Errors, Latent Human Error, Paper Street, Berkson Error Model, Non-Sampling Error, Abbe Error, Innovations Vector, Talko, Developmental Abnormality. Excerpt: Abbe error , named after Ernst Abbe , also called sine error , describes the magnification of angular error over distance. For example, when one measures a point that is 1 meter away at 45 degrees, an angular error of 1 degree corresponds to a positional error of over 1.745 cm, equivalent to a distance-measur ement error of 1.745 %.In machine design, some components are particularly sensitive to angular errors. For example, if the shaft of a lathe is angled slightly, the part will have errors in diameter far down the part.Abbe error can be detrimental to dead reckoning .Websites (URLs onlin...
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Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: False Friend, Spoonerism, Mondegreen, List of Misquotations, Observational Error, Faux Pas, Error, List of Commonly Misused English Words, Biblical Inerrancy, Type I and Type Ii Errors, Source Criticism, Ringing Artifacts, Commonly Misspelled Words, Margin of Error, False Etymology, Controversies About the Word "Niggardly", Miscarriage of Justice, Trial and Error, Healthcare Error Proliferation Model, Near Miss, Errors and Residuals in Statistics, Studentized Residual, Typographical Error, Ras Syndrome, User Error, Literal Translation, Percussive Maintenance, List of Misconceptions About the Brain, List of Erroneous Newspaper Headlines, Pocket Dialing, Mistaken Identity, Error Analysis, Mistake of Law, Drunk Dialing, Human Error Model, Postage Stamp Design Error, Mistake, Transcription Error, Errors in the United States Constitution, Stein's Unbiased Risk Estimate, Artifact, Misdialed Call, Error-Tolerant Design, Correction, Sampling Error, Probability of Error, Forecast Error, Digital Artifact, Personal Equation, Clerical Error, Popular Errors, Latent Human Error, Paper Street, Berkson Error Model, Non-Sampling Error, Abbe Error, Innovations Vector, Talko, Developmental Abnormality. Excerpt: Abbe error , named after Ernst Abbe , also called sine error , describes the magnification of angular error over distance. For example, when one measures a point that is 1 meter away at 45 degrees, an angular error of 1 degree corresponds to a positional error of over 1.745 cm, equivalent to a distance-measur ement error of 1.745 %.In machine design, some components are particularly sensitive to angular errors. For example, if the shaft of a lathe is angled slightly, the part will have errors in diameter far down the part.Abbe error can be detrimental to dead reckoning .Websites (URLs onlin...
Ambient Intelligence
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Ambient intelligence is the vision of a technology that will become invisibly embedded in our natural surroundings, present whenever we need it, enabled by simple and effortless interactions, attuned to all our senses, adaptive to users and context-sensiti ve, and autonomous. High-quality information access and personalized content must be available to everybody, anywhere, and at any time. This book addresses ambient intelligence used to support human contacts and accompany an individual's path through the complicated modern world. From the technical standpoint, distributed electronic intelligence is addressed as hardware vanishing into the background. Devices used for ambient intelligence are small, low-power, low weight, and (very importantly) low-cost; they collaborate or interact with each other; and they are redundant and error-tolerant. This means that the failure of one device will not cause failure of the whole system. Since wired connections often do not exist, radio methods will play an important role for data transfer. This book addresses various aspects of ambient intelligence, from applications that are imminent since they use essentially existing technologies, to ambitious ideas whose realization is still far away, due to major unsolved technical challenges.
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Ambient intelligence is the vision of a technology that will become invisibly embedded in our natural surroundings, present whenever we need it, enabled by simple and effortless interactions, attuned to all our senses, adaptive to users and context-sensiti ve, and autonomous. High-quality information access and personalized content must be available to everybody, anywhere, and at any time. This book addresses ambient intelligence used to support human contacts and accompany an individual's path through the complicated modern world. From the technical standpoint, distributed electronic intelligence is addressed as hardware vanishing into the background. Devices used for ambient intelligence are small, low-power, low weight, and (very importantly) low-cost; they collaborate or interact with each other; and they are redundant and error-tolerant. This means that the failure of one device will not cause failure of the whole system. Since wired connections often do not exist, radio methods will play an important role for data transfer. This book addresses various aspects of ambient intelligence, from applications that are imminent since they use essentially existing technologies, to ambitious ideas whose realization is still far away, due to major unsolved technical challenges.
729 Focus I Pips In
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Speed: 10 Spin: 12 Control: 12 Forward: 9 729 Focus I rubber is a Chinese rubber with a new concept of pimple structure (with more soft smaller pimples). It increases the effective contact surface and dwell time so that it increased ball control, feel and spin. With elastic sponge, it is powerful too. As its name implies, Focus I has an excellent error tolerant feature and particularly great as a backhand rubber. Fitted Play: Stable and Great Control.
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Speed: 10 Spin: 12 Control: 12 Forward: 9 729 Focus I rubber is a Chinese rubber with a new concept of pimple structure (with more soft smaller pimples). It increases the effective contact surface and dwell time so that it increased ball control, feel and spin. With elastic sponge, it is powerful too. As its name implies, Focus I has an excellent error tolerant feature and particularly great as a backhand rubber. Fitted Play: Stable and Great Control.
Ambient Intelligence
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Ambient intelligence is the vision of a technology that will become invisibly embedded in our natural surroundings, present whenever we need it, enabled by simple and effortless interactions, attuned to all our senses, adaptive to users and context-sensiti ve, and autonomous. High-quality information access and personalized content must be available to everybody, anywhere, and at any time. This book addresses ambient intelligence used to support human contacts and accompany an individual's path through the complicated modern world. From the technical standpoint, distributed electronic intelligence is addressed as hardware vanishing into the background. Devices used for ambient intelligence are small, low-power, low weight and (very importantly) low-cost; they collaborate or interact with each other; and they are redundant and error-tolerant. This means that the failure of one device will not cause failure of the whole system. Sincewired connections often do not exist,radio methods will play an important role for data transfer. This bookaddresses various aspects of ambient intelligence, from applications that wait around the corner using essentially existing technologies to ambitious ideas whose realization is still far away due to major unsolved technical challenges. Promise Angels is dedicated to bringing you great books at great prices. Whether you read for entertainment, to learn, or for literacy - you will find what you want at promiseangels.c om! Author Information: W. Weber
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Ambient intelligence is the vision of a technology that will become invisibly embedded in our natural surroundings, present whenever we need it, enabled by simple and effortless interactions, attuned to all our senses, adaptive to users and context-sensiti ve, and autonomous. High-quality information access and personalized content must be available to everybody, anywhere, and at any time. This book addresses ambient intelligence used to support human contacts and accompany an individual's path through the complicated modern world. From the technical standpoint, distributed electronic intelligence is addressed as hardware vanishing into the background. Devices used for ambient intelligence are small, low-power, low weight and (very importantly) low-cost; they collaborate or interact with each other; and they are redundant and error-tolerant. This means that the failure of one device will not cause failure of the whole system. Sincewired connections often do not exist,radio methods will play an important role for data transfer. This bookaddresses various aspects of ambient intelligence, from applications that wait around the corner using essentially existing technologies to ambitious ideas whose realization is still far away due to major unsolved technical challenges. Promise Angels is dedicated to bringing you great books at great prices. Whether you read for entertainment, to learn, or for literacy - you will find what you want at promiseangels.c om! Author Information: W. Weber
iC-MA DFN10 8-Bit Angular Hall Sensor / E- By iC-Haus
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The CMOS device i C-MA consists of a quadruple hall sensor array which has been optimized for the magnetic measurement of angles of rotation. This array permits error-tolerant adjustment of the magnet, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output. The sensor generates one sine cycle per each full rotation of the magnet, enabling the angle to be clearly determined.
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The CMOS device i C-MA consists of a quadruple hall sensor array which has been optimized for the magnetic measurement of angles of rotation. This array permits error-tolerant adjustment of the magnet, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output. The sensor generates one sine cycle per each full rotation of the magnet, enabling the angle to be clearly determined.
Fault Tolerance: Fail-Safe, Uninterruptible Power Supply, Paxos Algori
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Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Fail-Safe, Uninterruptible Power Supply, Paxos Algorithm, State Machine Replication, Quantum Error Correction, Quantum Byzantine Agreement, Hot Swapping, Fault-Tolerant System, Self-Stabilizat ion, Control Reconfiguration , Byzantine Fault Tolerance, Application Checkpointing, Fault-Tolerant Computer Systems, Fault-Tolerant Design, Spacewire, Embryonics Project, Capability-Base d Addressing, Uptime, N-Version Programming, Double Switching, Chandra-Toueg Consensus Algorithm, Reliability, Availability and Serviceability, Superstabilizat ion, Error-Tolerant Design, Lockstep, Capillary Routing, Fault, Intrusion Tolerance, Systematic Fault. Excerpt: Checkpointing is a technique for inserting fault tolerance into computing systems. It basically consists of storing a snapshot of the current application state, and later on, use it for restarting the execution in case of failure . Checkpointing techniques properties There are many different points of view and techniques for achieving application checkpointing. Depending on the specific implementation, a tool can be classified as having several properties: Each design decision made affects the properties and efficiency of the final product. For instance, deciding to store the entire application state will allow for a more straightforward implementation , since no analysis of the application will be needed, but it will deny the portability of the generated state files, due to a number of non-portable structures (such as application stack or heap ) being stored along with application data. Checkpointing in distributed shared memory systems In Distributed shared memory , checkpointing is a technique that helps tolerate the errors leading to losing the effect of work of long-running applications. The main property which should be induced by chec...
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Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Fail-Safe, Uninterruptible Power Supply, Paxos Algorithm, State Machine Replication, Quantum Error Correction, Quantum Byzantine Agreement, Hot Swapping, Fault-Tolerant System, Self-Stabilizat ion, Control Reconfiguration , Byzantine Fault Tolerance, Application Checkpointing, Fault-Tolerant Computer Systems, Fault-Tolerant Design, Spacewire, Embryonics Project, Capability-Base d Addressing, Uptime, N-Version Programming, Double Switching, Chandra-Toueg Consensus Algorithm, Reliability, Availability and Serviceability, Superstabilizat ion, Error-Tolerant Design, Lockstep, Capillary Routing, Fault, Intrusion Tolerance, Systematic Fault. Excerpt: Checkpointing is a technique for inserting fault tolerance into computing systems. It basically consists of storing a snapshot of the current application state, and later on, use it for restarting the execution in case of failure . Checkpointing techniques properties There are many different points of view and techniques for achieving application checkpointing. Depending on the specific implementation, a tool can be classified as having several properties: Each design decision made affects the properties and efficiency of the final product. For instance, deciding to store the entire application state will allow for a more straightforward implementation , since no analysis of the application will be needed, but it will deny the portability of the generated state files, due to a number of non-portable structures (such as application stack or heap ) being stored along with application data. Checkpointing in distributed shared memory systems In Distributed shared memory , checkpointing is a technique that helps tolerate the errors leading to losing the effect of work of long-running applications. The main property which should be induced by chec...
Bridging the Gap Between Graph Edit Distance and Kernel Machines Series in Machine Perception and Artifical Intelligence
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In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbo r classifiers, both in terms of classification accuracy and running time.
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In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbo r classifiers, both in terms of classification accuracy and running time.
iC-ML TSSOP20 Hall Position Sensor / Enco- By iC-Haus
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The CMOS device i C-ML consists of four hall sensors arranged in a line and optimized to read out magnetic tapes with 2.56 mm pole spacing. This sensor array permits error-tolerant adjustment of the magnetic tape, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output.
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The CMOS device i C-ML consists of four hall sensors arranged in a line and optimized to read out magnetic tapes with 2.56 mm pole spacing. This sensor array permits error-tolerant adjustment of the magnetic tape, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output.
Pooling Designs And Nonadaptive Group Testing: Important Tools for DNA Sequencing
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Pooling designs have been widely used in various aspects of DNA sequencing. In biological applications, the well-studied mathematical problem called ?group testing? shifts its focus to nonadaptive algorithms while the focus of traditional group testing is on sequential algorithms. Biological applications also bring forth new models not previously considered, such as the error-tolerant model, the complex model, and the inhibitor model. This book is the first attempt to collect all the significant research on pooling designs in one convenient place. The coverage includes many real biological applications such as clone library screening, contig sequencing, exon boundary finding and protein?protein interaction detecting and introduces the mathematics behind it.
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Pooling designs have been widely used in various aspects of DNA sequencing. In biological applications, the well-studied mathematical problem called ?group testing? shifts its focus to nonadaptive algorithms while the focus of traditional group testing is on sequential algorithms. Biological applications also bring forth new models not previously considered, such as the error-tolerant model, the complex model, and the inhibitor model. This book is the first attempt to collect all the significant research on pooling designs in one convenient place. The coverage includes many real biological applications such as clone library screening, contig sequencing, exon boundary finding and protein?protein interaction detecting and introduces the mathematics behind it.
iC-MA EVAL MA1D Evaluation Board iC-MA (i- By iC-Haus
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The CMOS device i C-MA consists of a quadruple hall sensor array which has been optimized for the magnetic measurement of angles of rotation. This array permits error-tolerant adjustment of the magnet, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output. The sensor generates one sine cycle per each full rotation of the magnet, enabling the angle to be clearly determined.
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The CMOS device i C-MA consists of a quadruple hall sensor array which has been optimized for the magnetic measurement of angles of rotation. This array permits error-tolerant adjustment of the magnet, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output. The sensor generates one sine cycle per each full rotation of the magnet, enabling the angle to be clearly determined.
Perspectives on Multimedia: Communication, Media and Information Tech
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The uses of multimedia are rapidly increasing. Its power to present information in ways not previously possible and its Integration of resources, allow for the Creation of rich learning environments. Perspectives on Multimedia: Communication, Media and Information Technology provides a critical examination of the latest multimedia developments and approaches, such as Interface Technology and Qo S architectures. Topics covered include: *The key concepts intrinsic to digital forms of multimedia: integration, interactivity, hypermedia, immersion, narrativity and hybridity. *The development of Information Technology (IT) usage in Society and in the Business community. *How modern IT enables private Companies and public organisations to support business operations, create new business opportunities, and to promote more proactive service management. *Multimedia from a Computer Science perspective and how computer science provides the Technical foundation for the computer Software and Hardware that drives the information age. Gives a broad range of perspectives on key issues for interactive multimedia in organisations and industry today. This book will be of interest to practitioners involved in multimedia development in an organisation, management consultants giving Professional advice on digital Solutions and information technology matters to their Customer organisations and academics focusing on business and technical aspects of multimedia frameworks.Fore word. Preface. 1. Multimedia: Back To The Future! (by Robert Burnett). 1.1 Introduction. 1.2 Multimedia as Art and Science. 1.3 Secret History of Multimedia. 1.4 Multimedia as Art and Performance. 1.5 Summing Up. 1.6 References. 2. Alternative Approaches to Interface Technology (by Steve Gibson). 2.1 Introduction. 2.2 Cybernetic Systems. 2.3 'Hands-free' Tracking Systems. 2.4 MIDI Instruments as Visual Triggers. 2.5 Conclusions. 2.6 References. 3. Transparency, Standardization and Servitude: the Paradoxes of Friendly Software (by Andreas Kitzmann). 3.1 Introduction. 3.2 Digital Promises. 3.3 Find Where Everything Is. 3.4 The Question of Practice. 3.5 References. 4. Business Modelling as a Foundation for Multimedia Development-Con cerning Strategic, Process and Systems Levels in Organizations (by Anders G. Nilsson). 4.1 Business Modelling-Some Needs in Practice. 4.2 Business Modelling-Thre e Levels of Development Work. 4.3 Business Modelling and Multimedia. 4.4 Business Modelling-Syste matic Work in Phases. 4.5 Business Modelling of Values, Operations and Objects. 4.6 Business Modelling for Multimedia-Main Messages. 4.7 References. 5. How Should Interactive Media Be Discussed For Successful Requirements Engineering? (by Lennart Molin and John Soren Pettersson). 5.1 Specifying Requirements. 5.2 Requirements Work In Multimedia Systems Development. 5.3 Ozlab-A Simple Tool for Prototyping Interactivity. 5.4 Challenges for Multimedia Requirements Work. 5.5 Writing Requirements vs Visualizing Requirements. 5.6 Requirements Specification By Content Professionals. 5.7 Concluding Remarks. 5.8 References. 6. Evaluating Interactive Multimedia in an Inexpensive and Timesaving Way-Illustrated by Business Case Studies (by Louise Ulfhake). 6.1 Background. 6.2 The Multimedia Case Studies. 6.3 Evaluation Methods. 6.4 What to Evaluate. 6.5 Evaluation of the Structure. 6.6 Evaluation of the Interaction. 6.7 Evaluation of the Usability. 6.8 Evaluation of the Productivity. 6.9 Conclusion. 6.10 References. 7. Conceptual Modelling for Creating Multimedia Databases (by Lars Erik Axelsson). 7.1 Introduction. 7.2 The ISO Report. 7.3 Conceptual Modelling in a Multimedia Environment. 7.4 Conclusions. 7.5 References. 8. Adding Security to Qo S Architectures (by Stefan Lindskog and Erland Jonsson). 8.1 Introduction. 8.2 An Overview of Qo S. 8.3 Introducing Security in Qo S Architectures. 8.4 Further Readings. 8.5 Concluding Remarks. 8.6 Acknowledgments . 8.7 References. 9. Partially Reliable Multimedia Transport (by Katarina Asplund and Anna Brunstrom). 9.1 Introduction. 9.2 Transmission Control Protocol-TCP. 9.3 Design of PRTP. 9.4 Performance Evaluation. 9.5 Further Reading. 9.6 Concluding Remarks. 9.7 References. 10. Bit Error Tolerant Multimedia Transport (by Stefan Alfredsson and Anna Brunstrom). 10.1 Introduction. 10.3 Performance Evaluation. 10.4 Experiment Results. 10.5 Further Readings. 10.6 Concluding Remarks. 10.7 References. 11. Transcoding of Image Data (by Johan Garcia and Anna Brunstrom). 11.1 Introduction. 11.2 JPEG Coding. 11.3 JPEG Compression Transcoding. 11.4 Robust JPEG Transcoding. 11.5 Further Reading. 11.6 Concluding Remarks. 11.7 References. About the Authors. Index.
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The uses of multimedia are rapidly increasing. Its power to present information in ways not previously possible and its Integration of resources, allow for the Creation of rich learning environments. Perspectives on Multimedia: Communication, Media and Information Technology provides a critical examination of the latest multimedia developments and approaches, such as Interface Technology and Qo S architectures. Topics covered include: *The key concepts intrinsic to digital forms of multimedia: integration, interactivity, hypermedia, immersion, narrativity and hybridity. *The development of Information Technology (IT) usage in Society and in the Business community. *How modern IT enables private Companies and public organisations to support business operations, create new business opportunities, and to promote more proactive service management. *Multimedia from a Computer Science perspective and how computer science provides the Technical foundation for the computer Software and Hardware that drives the information age. Gives a broad range of perspectives on key issues for interactive multimedia in organisations and industry today. This book will be of interest to practitioners involved in multimedia development in an organisation, management consultants giving Professional advice on digital Solutions and information technology matters to their Customer organisations and academics focusing on business and technical aspects of multimedia frameworks.Fore word. Preface. 1. Multimedia: Back To The Future! (by Robert Burnett). 1.1 Introduction. 1.2 Multimedia as Art and Science. 1.3 Secret History of Multimedia. 1.4 Multimedia as Art and Performance. 1.5 Summing Up. 1.6 References. 2. Alternative Approaches to Interface Technology (by Steve Gibson). 2.1 Introduction. 2.2 Cybernetic Systems. 2.3 'Hands-free' Tracking Systems. 2.4 MIDI Instruments as Visual Triggers. 2.5 Conclusions. 2.6 References. 3. Transparency, Standardization and Servitude: the Paradoxes of Friendly Software (by Andreas Kitzmann). 3.1 Introduction. 3.2 Digital Promises. 3.3 Find Where Everything Is. 3.4 The Question of Practice. 3.5 References. 4. Business Modelling as a Foundation for Multimedia Development-Con cerning Strategic, Process and Systems Levels in Organizations (by Anders G. Nilsson). 4.1 Business Modelling-Some Needs in Practice. 4.2 Business Modelling-Thre e Levels of Development Work. 4.3 Business Modelling and Multimedia. 4.4 Business Modelling-Syste matic Work in Phases. 4.5 Business Modelling of Values, Operations and Objects. 4.6 Business Modelling for Multimedia-Main Messages. 4.7 References. 5. How Should Interactive Media Be Discussed For Successful Requirements Engineering? (by Lennart Molin and John Soren Pettersson). 5.1 Specifying Requirements. 5.2 Requirements Work In Multimedia Systems Development. 5.3 Ozlab-A Simple Tool for Prototyping Interactivity. 5.4 Challenges for Multimedia Requirements Work. 5.5 Writing Requirements vs Visualizing Requirements. 5.6 Requirements Specification By Content Professionals. 5.7 Concluding Remarks. 5.8 References. 6. Evaluating Interactive Multimedia in an Inexpensive and Timesaving Way-Illustrated by Business Case Studies (by Louise Ulfhake). 6.1 Background. 6.2 The Multimedia Case Studies. 6.3 Evaluation Methods. 6.4 What to Evaluate. 6.5 Evaluation of the Structure. 6.6 Evaluation of the Interaction. 6.7 Evaluation of the Usability. 6.8 Evaluation of the Productivity. 6.9 Conclusion. 6.10 References. 7. Conceptual Modelling for Creating Multimedia Databases (by Lars Erik Axelsson). 7.1 Introduction. 7.2 The ISO Report. 7.3 Conceptual Modelling in a Multimedia Environment. 7.4 Conclusions. 7.5 References. 8. Adding Security to Qo S Architectures (by Stefan Lindskog and Erland Jonsson). 8.1 Introduction. 8.2 An Overview of Qo S. 8.3 Introducing Security in Qo S Architectures. 8.4 Further Readings. 8.5 Concluding Remarks. 8.6 Acknowledgments . 8.7 References. 9. Partially Reliable Multimedia Transport (by Katarina Asplund and Anna Brunstrom). 9.1 Introduction. 9.2 Transmission Control Protocol-TCP. 9.3 Design of PRTP. 9.4 Performance Evaluation. 9.5 Further Reading. 9.6 Concluding Remarks. 9.7 References. 10. Bit Error Tolerant Multimedia Transport (by Stefan Alfredsson and Anna Brunstrom). 10.1 Introduction. 10.3 Performance Evaluation. 10.4 Experiment Results. 10.5 Further Readings. 10.6 Concluding Remarks. 10.7 References. 11. Transcoding of Image Data (by Johan Garcia and Anna Brunstrom). 11.1 Introduction. 11.2 JPEG Coding. 11.3 JPEG Compression Transcoding. 11.4 Robust JPEG Transcoding. 11.5 Further Reading. 11.6 Concluding Remarks. 11.7 References. About the Authors. Index.
Mining Graph Data
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This text takes a focused and comprehensive look at Mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be Acknowledgments . Contributors. 1 introduction (Lawrence B. Holder and Diane J. Cook). 1.1 Terminology. 1.2 Graph Databases. 1.3 Book Overview. References. Part I GRAPHS. 2 graph matching exact and error-Tolerant methods and the automatic learning of edit costs (Horst Bunke and Michel Neuhaus). 2.1 Introduction. 2.2 Definitions and Graph Matching Methods. 2.3 Learning Edit Costs. 2.4 Experimental Evaluation. 2.5 Discussion and Conclusions. References. 3 graph visualization and data mining (Walter Didimo and Giuseppe Liotta). 3.1 Introduction. 3.2 Graph Drawing Techniques. 3.3 Examples of Visualization Systems. 3.4 Conclusions. References. 4 graph patterns and the r-Mat generator (Deepayan Chakrabarti and Christos Faloutsos). 4.1 Introduction. 4.2 Background and Related Work. 4.3 Net Mine and R-MAT. 4.4 Experiments. 4.5 Conclusions. References. Part ii mining techniques. 5 Discovery of frequent substructures (Xifeng Yan and Jiawei Han). 5.1 Introduction. 5.2 Preliminary Concepts. 5.3 Apriori-based Approach. 5.4 Pattern Growth Approach. 5.5 Variant Substructure Patterns. 5.6 Experiments and Performance Study. 5.7 Conclusions. References. 6 finding topological frequent patterns from graph datasets (Michihiro Kuramochi and George Karypis). 6.1 Introduction. 6.2 Background Definitions and Notation. 6.3 Frequent Pattern Discovery from Graph Datasets Problem Definitions. 6.4 FSG for the Graph-Transacti on Setting. 6.5 SIGRAM for the Single-Graph Setting. 6.6 GREW Scalable Frequent Subgraph Discovery Algorithm. 6.7 Related Research. 6.8 Conclusions. References. 7 unsupervised and supervised pattern learning in graph data (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar). 7.1 Introduction. 7.2 Mining Graph Data Using Subdue. 7.3 Comparison to Other Graph-Based Mining Algorithms. 7.4 Comparison to Frequent Substructure Mining Approaches. 7.5 Comparison to ILP Approaches. 7.6 Conclusions. References. 8 graph grammar learning (Istvan Jonyer). 8.1 Introduction. 8.2 Related Work. 8.3 Graph Grammar Learning. 8.4 Empirical Evaluation. 8.5 Conclusion. References. 9 constructing decision tree based on chunkingless graph-Based induction (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio). 9.1 Introduction. 9.2 Graph-Based Induction Revisited. 9.3 Problem Caused by Chunking in B-GBI. 9.4 Chunkingless Graph-Based Induction (Cl-GBI). 9.5 Decision Tree Chunkingless Graph-Based Induction (DT-Cl GBI). 9.6 Conclusions. References. 10 some links between formal concept analysis and graph mining (Michel Liquire). 10.1 Presentation. 10.2 Basic Concepts and Notation. 10.3 Formal Concept Analysis. 10.4 Extension Lattice and Description Lattice Give Concept Lattice. 10.5 Graph Description and Galois Lattice. 10.6 Graph Mining and Formal Propositionaliz ation. 10.7 Conclusion. References. 11 kernel methods for graphs (Thomas Grtner, Tams Horvth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel). 11.1 Introduction. 11.2 Graph Classification. 11.3 Vertex Classification. 11.4 Conclusions and Future Work. References. 12 kernels as link analysis measures (Masashi Shimbo and Takahiko Ito). 12.1 Introduction. 12.2 Preliminaries. 12.3 Kernel-based Unified Framework for Importance and Relatedness. 12.4 Laplacian Kernels as a Relatedness Measure. 12.5 Practical Issues. 12.6 Related Work. 12.7 Evaluation with Bibliographic Citation Data. 12.8 Summary. References. 13 ENTITY Resolution in graphs (Indrajit Bhattacharya and Lise Getoor). 13.1 Introduction. 13.2 Related Work. 13.3 Motivating Example for Graph-Based Entity Resolution. 13.4 Graph-Based Entity Resolution: Problem Formulation. 13.5 Similarity Measures for Entity Resolution. 13.6 Graph-Based Clustering for Entity Resolution. 13.7 Experimental Evaluation. 13.8 Conclusion. References. Part iii applications. 14 mining from chemical graphs (Takashi Okada). 14.1 Introduction and Representation of Molecules. 14.2 Issues for Mining. 14.3 CASE: A Prototype Mining System in Chemistry. 14.4 Quantitative Estimation Using Graph Mining. 14.5 Extension of Linear Fragments to Graphs. 14.6 Combination of Conditions. 14.7 Concluding Remarks. References. 15 unified approach to rooted tree mining: Algorithms and applications (Mohammed Zaki). 15.1 Introduction. 15.2 Preliminaries. 15.3 Related Work. 15.4 Generating Candidate Subtrees. 15.5 Frequency Computation. 15.6 Counting Distinct Occurrences. 15.7 The SLEUTH Algorithm. 15.8 Experimental Results. 15.9 Tree Mining Applications in Bioinformatics. 15.10 Conclusions. References. 16 dense subgraph extraction (Andrew Tomkins and Ravi Kumar). 16.1 Introduction. 16.2 Related Work. 16.3 Finding the densest subgraph. 16.4 Trawling. 16.5 Graph Shingling. 16.6 Connection Subgraphs. 16.7 Conclusions. References. 17 social network analysis (Sherry E. Marcus,
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This text takes a focused and comprehensive look at Mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be Acknowledgments . Contributors. 1 introduction (Lawrence B. Holder and Diane J. Cook). 1.1 Terminology. 1.2 Graph Databases. 1.3 Book Overview. References. Part I GRAPHS. 2 graph matching exact and error-Tolerant methods and the automatic learning of edit costs (Horst Bunke and Michel Neuhaus). 2.1 Introduction. 2.2 Definitions and Graph Matching Methods. 2.3 Learning Edit Costs. 2.4 Experimental Evaluation. 2.5 Discussion and Conclusions. References. 3 graph visualization and data mining (Walter Didimo and Giuseppe Liotta). 3.1 Introduction. 3.2 Graph Drawing Techniques. 3.3 Examples of Visualization Systems. 3.4 Conclusions. References. 4 graph patterns and the r-Mat generator (Deepayan Chakrabarti and Christos Faloutsos). 4.1 Introduction. 4.2 Background and Related Work. 4.3 Net Mine and R-MAT. 4.4 Experiments. 4.5 Conclusions. References. Part ii mining techniques. 5 Discovery of frequent substructures (Xifeng Yan and Jiawei Han). 5.1 Introduction. 5.2 Preliminary Concepts. 5.3 Apriori-based Approach. 5.4 Pattern Growth Approach. 5.5 Variant Substructure Patterns. 5.6 Experiments and Performance Study. 5.7 Conclusions. References. 6 finding topological frequent patterns from graph datasets (Michihiro Kuramochi and George Karypis). 6.1 Introduction. 6.2 Background Definitions and Notation. 6.3 Frequent Pattern Discovery from Graph Datasets Problem Definitions. 6.4 FSG for the Graph-Transacti on Setting. 6.5 SIGRAM for the Single-Graph Setting. 6.6 GREW Scalable Frequent Subgraph Discovery Algorithm. 6.7 Related Research. 6.8 Conclusions. References. 7 unsupervised and supervised pattern learning in graph data (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar). 7.1 Introduction. 7.2 Mining Graph Data Using Subdue. 7.3 Comparison to Other Graph-Based Mining Algorithms. 7.4 Comparison to Frequent Substructure Mining Approaches. 7.5 Comparison to ILP Approaches. 7.6 Conclusions. References. 8 graph grammar learning (Istvan Jonyer). 8.1 Introduction. 8.2 Related Work. 8.3 Graph Grammar Learning. 8.4 Empirical Evaluation. 8.5 Conclusion. References. 9 constructing decision tree based on chunkingless graph-Based induction (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio). 9.1 Introduction. 9.2 Graph-Based Induction Revisited. 9.3 Problem Caused by Chunking in B-GBI. 9.4 Chunkingless Graph-Based Induction (Cl-GBI). 9.5 Decision Tree Chunkingless Graph-Based Induction (DT-Cl GBI). 9.6 Conclusions. References. 10 some links between formal concept analysis and graph mining (Michel Liquire). 10.1 Presentation. 10.2 Basic Concepts and Notation. 10.3 Formal Concept Analysis. 10.4 Extension Lattice and Description Lattice Give Concept Lattice. 10.5 Graph Description and Galois Lattice. 10.6 Graph Mining and Formal Propositionaliz ation. 10.7 Conclusion. References. 11 kernel methods for graphs (Thomas Grtner, Tams Horvth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel). 11.1 Introduction. 11.2 Graph Classification. 11.3 Vertex Classification. 11.4 Conclusions and Future Work. References. 12 kernels as link analysis measures (Masashi Shimbo and Takahiko Ito). 12.1 Introduction. 12.2 Preliminaries. 12.3 Kernel-based Unified Framework for Importance and Relatedness. 12.4 Laplacian Kernels as a Relatedness Measure. 12.5 Practical Issues. 12.6 Related Work. 12.7 Evaluation with Bibliographic Citation Data. 12.8 Summary. References. 13 ENTITY Resolution in graphs (Indrajit Bhattacharya and Lise Getoor). 13.1 Introduction. 13.2 Related Work. 13.3 Motivating Example for Graph-Based Entity Resolution. 13.4 Graph-Based Entity Resolution: Problem Formulation. 13.5 Similarity Measures for Entity Resolution. 13.6 Graph-Based Clustering for Entity Resolution. 13.7 Experimental Evaluation. 13.8 Conclusion. References. Part iii applications. 14 mining from chemical graphs (Takashi Okada). 14.1 Introduction and Representation of Molecules. 14.2 Issues for Mining. 14.3 CASE: A Prototype Mining System in Chemistry. 14.4 Quantitative Estimation Using Graph Mining. 14.5 Extension of Linear Fragments to Graphs. 14.6 Combination of Conditions. 14.7 Concluding Remarks. References. 15 unified approach to rooted tree mining: Algorithms and applications (Mohammed Zaki). 15.1 Introduction. 15.2 Preliminaries. 15.3 Related Work. 15.4 Generating Candidate Subtrees. 15.5 Frequency Computation. 15.6 Counting Distinct Occurrences. 15.7 The SLEUTH Algorithm. 15.8 Experimental Results. 15.9 Tree Mining Applications in Bioinformatics. 15.10 Conclusions. References. 16 dense subgraph extraction (Andrew Tomkins and Ravi Kumar). 16.1 Introduction. 16.2 Related Work. 16.3 Finding the densest subgraph. 16.4 Trawling. 16.5 Graph Shingling. 16.6 Connection Subgraphs. 16.7 Conclusions. References. 17 social network analysis (Sherry E. Marcus,
Human Reliability
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Human reliability is related to the field of human factors engineering, and refers to the reliability of humans in fields such as manufacturing, transportation, the military, or medicine. Human performance can be affected by many factors such as age, state of mind, physical health, attitude, emotions, propensity for certain common mistakes, errors and cognitive biases, etc. Human reliability is very important due to the contributions of humans to the resilience of systems and to possible adverse consequences of human errors or oversights, especially when the human is a crucial part of the large socio-technical systems as is common today. User-centered design and error-tolerant design are just two of many terms used to describe efforts to make technology better suited to operation by humans.
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Human reliability is related to the field of human factors engineering, and refers to the reliability of humans in fields such as manufacturing, transportation, the military, or medicine. Human performance can be affected by many factors such as age, state of mind, physical health, attitude, emotions, propensity for certain common mistakes, errors and cognitive biases, etc. Human reliability is very important due to the contributions of humans to the resilience of systems and to possible adverse consequences of human errors or oversights, especially when the human is a crucial part of the large socio-technical systems as is common today. User-centered design and error-tolerant design are just two of many terms used to describe efforts to make technology better suited to operation by humans.
iC-ML EVAL ML1D Evaluation Board iC-ML (i- By iC-Haus
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The CMOS device i C-ML consists of four hall sensors arranged in a line and optimized to read out magnetic tapes with 2.56 mm pole spacing. This sensor array permits error-tolerant adjustment of the magnetic tape, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output.
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The CMOS device i C-ML consists of four hall sensors arranged in a line and optimized to read out magnetic tapes with 2.56 mm pole spacing. This sensor array permits error-tolerant adjustment of the magnetic tape, reducing assembly efforts. The integrated signal conditioning unit provides a differential sine/cosine signal at the output.
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