The Resource Selfsimilar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Selfsimilar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
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The item Selfsimilar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Selfsimilar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
 Summary
 "SelfSimilar Processes in Telecommunications considers the selfsimilar (fractal and multifractal) models of telecommunication traffic and efficiency based on the assumption that its traffic has fractal or multifractal properties (is selfsimilar). The theoretical aspects of the most wellknown traffic models demonstrating selfsimilar properties are discussed in detail and the comparative analysis of the different models' efficiency for selfsimilar traffic is presented." "This book demonstrates how to use selfsimilar processes for designing new telecommunications systems and optimizing existing networks so as to achieve maximum efficiency and serviceability. The approach is rooted in theory, describing the algorithms (the logical arithmetical or computational procedures that define how a task is performed) for modeling these selfsimilar processes. However, the language and ideas are essentially accessible for those who have a general knowledge of the subject area and the advice is highly practical: all models, problems and solutions are illustrated throughout using numerous realworld examples." "The book will appeal to the wide range of specialists dealing with the design and exploitation of telecommunication systems. It will be useful for the postgraduate students, lecturers and researchers connected with communication networks disciplines."BOOK JACKET
 Language
 eng
 Extent
 xvii, 314 pages
 Contents

 Foreword.
 About the authors.
 Acknowledgements.
 1 Principal Concepts of Fractal Theory and Self Similar Processes.
 1.1 Fractals and Multifractals.
 1.1.1 Fractal Dimension of a Set.
 1.1.2 Multifractals.
 1.1.3 Fractal Dimension D0 and Informational Dimension D1.
 1.1.4 Legendre Transform.
 1.2 Self Similar Processes.
 1.2.1 Definitions and Properties of Self Similar Processes.
 1.2.2 Multifractal Processes.
 1.2.3 Long Range and Short Range Dependence.
 1.2.4 Slowly Decaying Variance.
 1.3 'Heavy Tails'.
 1.3.1 Distribution with 'Heavy Tails' (DHT).
 1.3.2 'Heavy Tails' Estimation.
 1.4 Hurst Exponent Estimation.
 1.4.1 Time Domain Methods of Hurst Exponent Estimation.
 1.4.2 Frequency Domain Methods of Hurst Exponent.
 Estimation.
 1.5 Hurst Exponent Estimation Problems.
 1.5.1 Estimation Problems.
 1.5.2 Nonstationarity Problems.
 1.5.3 Computational Problems.
 1.6 Self Similarity Origins in Telecommunication Traffic.
 1.6.1 User's Behaviour.
 1.6.2 Data Generation Data Structure and Its Search.
 1.6.3 Traffic Aggregation.
 1.6.4 Means of Network Control.
 1.6.5 Control Mechanisms based on Feedback.
 1.6.6 Network Development.
 References.
 2 Simulation Methods for Fractal Processes.
 2.1 Fractional Brownian Motion.
 2.1.1 RMD Algorithm for FBM Generation.
 2.1.2 SRA Algorithm for FBM Generation.
 2.2 Fractional Gaussian Noise.
 2.2.1 FFT Algorithm for FGN Synthesis.
 2.2.2 Advantages and Shortcomings of FBM/FGN Models.
 in Network Applications.
 2.3 Regression Models of Traffic.
 2.3.1 Linear Autoregressive (AR) Processes.
 2.3.2 Processes of Moving Average (MA).
 2.3.3 Autoregressive Models of Moving Average, ARMAethp; qT.
 2.3.4 Fractional Autoregressive Integrated Moving Average.
 (FARIMA) Process.
 2.3.5 Parametric Estimation Methods.
 2.3.6 FARIMAethp, d, qT Process Synthesis.
 2.4 Fractal Point Process.
 2.4.1 Statistical Characteristics of the Point Process.
 2.4.2 Fractal Structure of FPP.
 2.4.3 Methods of FPP Formation.
 2.5 Fractional Levy Motion and its Application to Network.
 Traffic Modelling.
 2.5.1 Fractional Levy Motion and Its Properties.
 2.5.2 Algorithm of Fractional Levy Motion Modelling.
 2.5.3 Fractal Traffic Formation Based on FLM.
 2.6 Models of Multifractal Network Traffic.
 2.6.1 Multiplicative Cascades.
 2.6.2 Modified Estimation Method of Multifractal Functions.
 2.6.3 Generation of Traffic the Multifractal Model.
 2.7 LRD Traffic Modelling with the Help of Wavelets.
 2.8 M/G/1Model.
 2.8.1 M/G/1Model and Pareto Distribution.
 2.8.2 M/G/1Model and Log Normal Distribution.
 References.
 3 Self Similarity of Real Time Traffic.
 3.1 Self Similarity of Real Time Traffic Preliminaries.
 3.2 Statistical Characteristics of Telecommunication Real Time Traffic.
 3.2.1 Measurement Organization.
 3.2.2 Pattern of TN Traffic.
 3.3 Voice Traffic Characteristics.
 3.3.1 Voice Traffi
 Isbn
 9780470014868
 Label
 Selfsimilar processes in telecommunications
 Title
 Selfsimilar processes in telecommunications
 Statement of responsibility
 Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
 Language
 eng
 Summary
 "SelfSimilar Processes in Telecommunications considers the selfsimilar (fractal and multifractal) models of telecommunication traffic and efficiency based on the assumption that its traffic has fractal or multifractal properties (is selfsimilar). The theoretical aspects of the most wellknown traffic models demonstrating selfsimilar properties are discussed in detail and the comparative analysis of the different models' efficiency for selfsimilar traffic is presented." "This book demonstrates how to use selfsimilar processes for designing new telecommunications systems and optimizing existing networks so as to achieve maximum efficiency and serviceability. The approach is rooted in theory, describing the algorithms (the logical arithmetical or computational procedures that define how a task is performed) for modeling these selfsimilar processes. However, the language and ideas are essentially accessible for those who have a general knowledge of the subject area and the advice is highly practical: all models, problems and solutions are illustrated throughout using numerous realworld examples." "The book will appeal to the wide range of specialists dealing with the design and exploitation of telecommunication systems. It will be useful for the postgraduate students, lecturers and researchers connected with communication networks disciplines."BOOK JACKET
 Cataloging source
 UKM
 http://library.link/vocab/creatorName
 Shelukhin, O. I.
 Dewey number
 621.382150151922
 Illustrations
 illustrations
 Index
 index present
 LC call number
 TK5102.5
 LC item number
 .S465 2007
 Literary form
 non fiction
 Nature of contents
 bibliography
 http://library.link/vocab/relatedWorkOrContributorName

 Smolskiy, Sergey M
 Osin, Andrey V
 http://library.link/vocab/subjectName

 Telecommunication systems
 Internetworking (Telecommunication)
 Selfsimilar processes
 Telecommunication systems
 Internetworking (Telecommunication)
 Selfsimilar processes
 Label
 Selfsimilar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Foreword.  About the authors.  Acknowledgements.  1 Principal Concepts of Fractal Theory and Self Similar Processes.  1.1 Fractals and Multifractals.  1.1.1 Fractal Dimension of a Set.  1.1.2 Multifractals.  1.1.3 Fractal Dimension D0 and Informational Dimension D1.  1.1.4 Legendre Transform.  1.2 Self Similar Processes.  1.2.1 Definitions and Properties of Self Similar Processes.  1.2.2 Multifractal Processes.  1.2.3 Long Range and Short Range Dependence.  1.2.4 Slowly Decaying Variance.  1.3 'Heavy Tails'.  1.3.1 Distribution with 'Heavy Tails' (DHT).  1.3.2 'Heavy Tails' Estimation.  1.4 Hurst Exponent Estimation.  1.4.1 Time Domain Methods of Hurst Exponent Estimation.  1.4.2 Frequency Domain Methods of Hurst Exponent.  Estimation.  1.5 Hurst Exponent Estimation Problems.  1.5.1 Estimation Problems.  1.5.2 Nonstationarity Problems.  1.5.3 Computational Problems.  1.6 Self Similarity Origins in Telecommunication Traffic.  1.6.1 User's Behaviour.  1.6.2 Data Generation Data Structure and Its Search.  1.6.3 Traffic Aggregation.  1.6.4 Means of Network Control.  1.6.5 Control Mechanisms based on Feedback.  1.6.6 Network Development.  References.  2 Simulation Methods for Fractal Processes.  2.1 Fractional Brownian Motion.  2.1.1 RMD Algorithm for FBM Generation.  2.1.2 SRA Algorithm for FBM Generation.  2.2 Fractional Gaussian Noise.  2.2.1 FFT Algorithm for FGN Synthesis.  2.2.2 Advantages and Shortcomings of FBM/FGN Models.  in Network Applications.  2.3 Regression Models of Traffic.  2.3.1 Linear Autoregressive (AR) Processes.  2.3.2 Processes of Moving Average (MA).  2.3.3 Autoregressive Models of Moving Average, ARMAethp; qT.  2.3.4 Fractional Autoregressive Integrated Moving Average.  (FARIMA) Process.  2.3.5 Parametric Estimation Methods.  2.3.6 FARIMAethp, d, qT Process Synthesis.  2.4 Fractal Point Process.  2.4.1 Statistical Characteristics of the Point Process.  2.4.2 Fractal Structure of FPP.  2.4.3 Methods of FPP Formation.  2.5 Fractional Levy Motion and its Application to Network.  Traffic Modelling.  2.5.1 Fractional Levy Motion and Its Properties.  2.5.2 Algorithm of Fractional Levy Motion Modelling.  2.5.3 Fractal Traffic Formation Based on FLM.  2.6 Models of Multifractal Network Traffic.  2.6.1 Multiplicative Cascades.  2.6.2 Modified Estimation Method of Multifractal Functions.  2.6.3 Generation of Traffic the Multifractal Model.  2.7 LRD Traffic Modelling with the Help of Wavelets.  2.8 M/G/1Model.  2.8.1 M/G/1Model and Pareto Distribution.  2.8.2 M/G/1Model and Log Normal Distribution.  References.  3 Self Similarity of Real Time Traffic.  3.1 Self Similarity of Real Time Traffic Preliminaries.  3.2 Statistical Characteristics of Telecommunication Real Time Traffic.  3.2.1 Measurement Organization.  3.2.2 Pattern of TN Traffic.  3.3 Voice Traffic Characteristics.  3.3.1 Voice Traffi
 Control code
 74967040
 Dimensions
 25 cm
 Extent
 xvii, 314 pages
 Isbn
 9780470014868
 Isbn Type
 (hbk.)
 Media category
 unmediated
 Media MARC source
 rdamedia
 Media type code

 n
 Other physical details
 illustrations
 System control number
 (OCoLC)74967040
 Label
 Selfsimilar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Foreword.  About the authors.  Acknowledgements.  1 Principal Concepts of Fractal Theory and Self Similar Processes.  1.1 Fractals and Multifractals.  1.1.1 Fractal Dimension of a Set.  1.1.2 Multifractals.  1.1.3 Fractal Dimension D0 and Informational Dimension D1.  1.1.4 Legendre Transform.  1.2 Self Similar Processes.  1.2.1 Definitions and Properties of Self Similar Processes.  1.2.2 Multifractal Processes.  1.2.3 Long Range and Short Range Dependence.  1.2.4 Slowly Decaying Variance.  1.3 'Heavy Tails'.  1.3.1 Distribution with 'Heavy Tails' (DHT).  1.3.2 'Heavy Tails' Estimation.  1.4 Hurst Exponent Estimation.  1.4.1 Time Domain Methods of Hurst Exponent Estimation.  1.4.2 Frequency Domain Methods of Hurst Exponent.  Estimation.  1.5 Hurst Exponent Estimation Problems.  1.5.1 Estimation Problems.  1.5.2 Nonstationarity Problems.  1.5.3 Computational Problems.  1.6 Self Similarity Origins in Telecommunication Traffic.  1.6.1 User's Behaviour.  1.6.2 Data Generation Data Structure and Its Search.  1.6.3 Traffic Aggregation.  1.6.4 Means of Network Control.  1.6.5 Control Mechanisms based on Feedback.  1.6.6 Network Development.  References.  2 Simulation Methods for Fractal Processes.  2.1 Fractional Brownian Motion.  2.1.1 RMD Algorithm for FBM Generation.  2.1.2 SRA Algorithm for FBM Generation.  2.2 Fractional Gaussian Noise.  2.2.1 FFT Algorithm for FGN Synthesis.  2.2.2 Advantages and Shortcomings of FBM/FGN Models.  in Network Applications.  2.3 Regression Models of Traffic.  2.3.1 Linear Autoregressive (AR) Processes.  2.3.2 Processes of Moving Average (MA).  2.3.3 Autoregressive Models of Moving Average, ARMAethp; qT.  2.3.4 Fractional Autoregressive Integrated Moving Average.  (FARIMA) Process.  2.3.5 Parametric Estimation Methods.  2.3.6 FARIMAethp, d, qT Process Synthesis.  2.4 Fractal Point Process.  2.4.1 Statistical Characteristics of the Point Process.  2.4.2 Fractal Structure of FPP.  2.4.3 Methods of FPP Formation.  2.5 Fractional Levy Motion and its Application to Network.  Traffic Modelling.  2.5.1 Fractional Levy Motion and Its Properties.  2.5.2 Algorithm of Fractional Levy Motion Modelling.  2.5.3 Fractal Traffic Formation Based on FLM.  2.6 Models of Multifractal Network Traffic.  2.6.1 Multiplicative Cascades.  2.6.2 Modified Estimation Method of Multifractal Functions.  2.6.3 Generation of Traffic the Multifractal Model.  2.7 LRD Traffic Modelling with the Help of Wavelets.  2.8 M/G/1Model.  2.8.1 M/G/1Model and Pareto Distribution.  2.8.2 M/G/1Model and Log Normal Distribution.  References.  3 Self Similarity of Real Time Traffic.  3.1 Self Similarity of Real Time Traffic Preliminaries.  3.2 Statistical Characteristics of Telecommunication Real Time Traffic.  3.2.1 Measurement Organization.  3.2.2 Pattern of TN Traffic.  3.3 Voice Traffic Characteristics.  3.3.1 Voice Traffi
 Control code
 74967040
 Dimensions
 25 cm
 Extent
 xvii, 314 pages
 Isbn
 9780470014868
 Isbn Type
 (hbk.)
 Media category
 unmediated
 Media MARC source
 rdamedia
 Media type code

 n
 Other physical details
 illustrations
 System control number
 (OCoLC)74967040
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