The Resource Self-similar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Self-similar processes in telecommunications, Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
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The item Self-similar 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 Self-similar 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
- "Self-Similar Processes in Telecommunications considers the self-similar (fractal and multifractal) models of telecommunication traffic and efficiency based on the assumption that its traffic has fractal or multifractal properties (is self-similar). The theoretical aspects of the most well-known traffic models demonstrating self-similar properties are discussed in detail and the comparative analysis of the different models' efficiency for self-similar traffic is presented." "This book demonstrates how to use self-similar 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 self-similar 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 real-world 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 post-graduate 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
- Self-similar processes in telecommunications
- Title
- Self-similar processes in telecommunications
- Statement of responsibility
- Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
- Language
- eng
- Summary
- "Self-Similar Processes in Telecommunications considers the self-similar (fractal and multifractal) models of telecommunication traffic and efficiency based on the assumption that its traffic has fractal or multifractal properties (is self-similar). The theoretical aspects of the most well-known traffic models demonstrating self-similar properties are discussed in detail and the comparative analysis of the different models' efficiency for self-similar traffic is presented." "This book demonstrates how to use self-similar 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 self-similar 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 real-world 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 post-graduate 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)
- Self-similar processes
- Telecommunication systems
- Internetworking (Telecommunication)
- Self-similar processes
- Label
- Self-similar 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
- Self-similar 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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