Cellular automata / Thomas M. Li, editor.

Format:

Book

Contributor:

Li, Thomas M.

Series:

Mathematics research developments series.

Computer science, technology and applications.

Mathematics research developments

Computer science, technology and applications

Language:

English

Subjects (All):

Cellular automata.

Physical Description:

1 online resource (309 p.)

Edition:

1st ed.

Place of Publication:

New York : Nova Science Publishers, Inc., c2011.

Language Note:

English

Summary:

A cellular automaton is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. It consists of a regular grid of cells, each in one of a finite number of states, such as "On" or "Off". The grid can be in any finite number of dimensions. For each cell, a set of cells called its neighborhood (usually including the cell itself) is defined relative to the specified cell. This book presents current research from across the globe in the study of cellular automata, including using cellular automata to solve optimization problems; modeling drug release science using cellular automata; using the cellular automata model to study the dispersion of aphids and ladybugs in a block of citric trees; and the reversibility of cellular automata.

Contents:

Intro

CELLULAR AUTOMATA

CONTENTS

PREFACE

Chapter 1 CA UPGRADING FOR EXTENDING THE OPTIMIZATION PROBLEM SOLVING ABILITY

ABSTRACT

1. INTRODUCTION

How Can We Guide the System by CA?

2. COMPLEX SYSTEMS

3. OPTIMIZATION

3.1. History

3.2. Objective Function

3.3. System Optimization

4. OPTIMIZATION BY CA

4.1. Optimization by CA+SA

4.1.1. Simulated annealing

4.1.2. Procedure

4.1.3. A Sample problem solving

4.2. Optimization by CA

4.2.1. Procedure

4.2.2. A Sample problem solving

5. CONCLUSION

REFERENCES

Chapter 2 MODELING DRUG RELEASE USING CELLULAR AUTOMATA: EVOLUTION AND TRENDS IN PHARMACEUTICAL SCIENCES

2. HISTORICAL REVIEW

3. MODELING MATRIX EROSION

3.1. Describing the Primary State of the Matrix

3.2. Step 1 of Polymer Erosion: Water Penetration in the Matrix

3.3. Step 2 of Polymer Erosion: Polymer Degradation

3.4. Step 3 of Polymer Erosion: Loss of Polymer Bulk

4. MODELING DRUG DIFFUSION

5. EVALUATING THE PREDICTIVE VALUE OF MODELS

6. CONCLUSION

Chapter 3 A MODEL OF CELLULAR AUTOMATA FOR THE SPATIAL ANALYSIS OF APHIDS AND LADYBUGS

1. PRELIMINARIES

1.1. Citrus Sudden Death

1.2. Cellular Automata

1.3. Fuzzy Rule-Based System

2. CELLULAR AUTOMATA MODEL

3. SIMULATIONS WITH CELLULAR AUTOMATA MODEL

CONCLUSIONS

ACKNOWLEDGMENTS

Chapter 4 CELLULAR AUTOMATA OPTIMIZATION VIA EVOLUTIONARY METHODS

INTRODUCTION

CELLULAR FORMULATION

COMBINED CELLULAR - GENETIC FORMULATION

LOCAL SEARCH ALGORITHM

RESULTS AND DISCUSSION

Chapter 5 PARALLEL CELLULAR AUTOMATA ON CHIP

2. A SIMPLE CELLULAR AUTOMATON

3. RECONFIGURABLE COMPUTING

4. CELLULAR AUTOMATA RECONFIGURABLE PROCESSOR.

4.1. Modeling of the Algorithm

4.2. Processor Design

4.3. Hardware Implementation

5. EXPERIMENTAL RESULTS

6. CONCLUSIONS AND FUTURE WORK

Chapter6 EVOLVINGCELLULARAUTOMATAFORFORMGENERATIONINARTIFICIALDEVELOPMENT

Abstract

1.Introduction

2.CellularGrowthTestbed

2.1.2DNeighborhoods

2.1.1.VonNeumannNeighborhood

2.1.2.MooreNeighborhood

2.1.3.2-RadialNeighborhood

2.1.4.MargolusNeighborhood

2.2.3DNeighborhood

2.3.NetLogoModels

3.MorphogeneticGradients

4.Genomes

5.GeneticAlgorithm

5.1.Chromosomestructure

5.1.1.Chromosomestructureforformgeneration

5.1.2.Chromosomestructureforpatterngeneration

5.2.Fitnessfunction

5.2.1.Onestructuralgene

5.2.2.Multiplestructuralgenes

6.FormGeneration

6.1.2Dshapes

6.2.3Dshapes

6.3.Chosenneighborhoodsforpatterngeneration

7.PatternGeneration

8.Discussion

9.Conclusion

References

Chapter7 STRUCTURALANDSYMMETRYANALYSISOFDISCRETEDYNAMICALSYSTEMS

2.DiscreteDynamics

2.1.DiscreteDynamicalModelswithSpace

2.1.1.ExampleofDiscreteModelwithEmergentSpace-time.

2.1.2.SpaceSymmetriesinMoreDetail.

2.1.3.UnificationofSpaceandInternalSymmetries.

3.StructuralAnalysisofDiscreteRelations

3.1.BasicDefinitionsandConstructions

3.1.1.Relations

3.1.2.CompatibilityofSystemsofRelations

3.1.3.DecompositionofRelations

3.1.4.OnRepresentationofRelationsinComputer

3.2.Illustration:ApplicationtoSomeCellularAutomata

3.2.1.J.Conway'sGameofLife

3.2.2.ElementaryCellularAutomata

4.Soliton-likeStructuresinDeterministicDynamics

CommentsonReversibilityinDiscreteSystems.

5.MesoscopicLatticeModels

5.1.StatisticalMechanics

5.2.Mesoscopy

5.2.1.LatticeModels.

5.3.PhaseTransitions

6.GaugeConnectionandQuantization

6.1.DiscreteGaugePrinciple.

6.2.QuantumBehaviorandGaugeConnection

6.2.1.IllustrativeExampleInspiredbyFreeParticle.

6.2.2.LocalQuantumModelsonRegularGraphs

6.3.GeneralDiscussionofQuantizationinFiniteSystems

6.3.1.PermutationsandLinearRepresentations

6.3.2.InterpretationofQuantumDescriptioninFiniteBackground

7.Conclusion

Acknowledgments

Chapter8 REVERSIBILITYOFCELLULARAUTOMATA

2.Quivers

2.1.DeBruijnQuiver

2.2.AdjacencyMatrices

3.CellularAutomata

3.1.WolframCellularAutomaton

3.2.CorrespondencetodeBruijnQuiver

3.3.GlobalTransitionofConfigurationAlgebra

3.4.TransitionMatrices

4.ReversibilityofCellularAutomata

4.1.PeriodicReductionsofWCA

4.2.Reversibilityofn-WCA

4.3.NecessaryConditionsforReversibilityofn-WCA

5.ReversibleRulesinECA

5.1.EquivalenceClassesofRules

5.2.ReversibilityofRule154

5.3.CompleteListofReversibleRules

6.Conclusion

Chapter9 FROMGLIDERSTOUNIVERSALITYOFCELLULARAUTOMATA:ANOTHER2D2-STATEUNIVERSALAUTOMATON

2.FormalisationsandNotations

2.1.SetofCellularAutomata

2.2.EvolutionofCellularAutomata

2.3.Isotropy

2.4.NumberofAutomata

2.5.QuiescentState

2.6.Patterns

2.6.1.Definition

2.6.2.Glider

2.7.GliderGun

3.GameofLife

3.1.TransitionRule

3.2.ANDGate

3.3.NOTGate

4.Gliders

4.1.EvolutionaryAlgorithm

4.2.Result

4.2.1.OrthogonalGliders

4.2.2.DiagonalGliders

5.Universality

5.1.TheR0Automaton:anExperimentalResult

5.2.Lookingforan"Eater"

5.2.1.EvolutionaryAlgorithm

5.2.2.TheEateroftheRAutomaton:anExperimentalResult

5.3.NANDGate

5.3.1.Collisions

5.3.2.NewPattern

5.3.3.AssemblingPatternsintoaNOTGate

5.4.SimulationofOneCelloftheGameofLife

5.5.SimulationoftheGameofLife

5.5.1.IntersectionofStreams

5.5.2.Synchronisation.

5.5.3.SimulationoftheGameofLifeinR

Chapter10 ANUMERICALIMPLEMENTATIONOFANENCRYPTIONSYSTEMOFCOMPRESSEDSIGNALSWITHACELLULARAUTOMATAAPPROACH

2.ElementaryCellularAutomata

3.EncryptionSystem

3.1.SynchronizationinCellularAutomata

3.1.1.Unidirectionalcoupling

3.1.2.Synchronization

3.2.TheBasicUnitCipher

4.PseudoRandomSequencesGenerator

4.1.ModifiedGenerator

4.2.PerformanceAnalysis

4.3.MultifractalPropertiesoftheMatrixHN

5.WaveletAnalysis

5.1.Introduction

5.2.WaveletTransform

5.3.CompressionScheme

6.NumericalImplementation

Chapter11 CANONICALFACTOROFCELLULARAUTOMATA

Introduction

1.Definitions

2.Traces

2.1.FactorSubshifts

2.2.Generators

2.3.ColumnFactors

3.TracesofCellularAutomata

4.Equicontinuity

5.Expansivity

6.Entropy

Conclusion

INDEX

Blank Page.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

Description based on print version record and CIP data provided by publisher.

ISBN:

1-62100-148-2

OCLC:

750173571