Huang / Ao Trends in Communication Technologies and Engineering Science

1. Auflage 2009
ISBN: 978-1-4020-9532-0
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 33, 500 Seiten

Reihe: Lecture Notes in Electrical Engineering

Comprised of research articles written for a major international conference, this book covers the state-of-the-art in communication systems and engineering science. Topics covered include network management, wireless networks, electronics, and many others.

Autoren/Hrsg.

Huang, Xu

Ao, Sio-Iong

Huang, He

Inhaltsverzeichnis

1;Preface;6
2;Contents;7
3;Contributors;10
4;Survivable Architecture with Dynamic Wavelength and Bandwidth Allocation Scheme in WDM-EPON;15
4.1;1.1 Introduction;15
4.2;1.2 Related Work;17
4.3;1.3 The Proposed CFT-WDM-EPON Architecture;19
4.4;1.4 The Proposed Dynamic Wavelength and Bandwidth Allocation;20
4.4.1;1.4.1 PFEBR Scheme with Early DBA Mechanism;21
4.4.1.1;1.4.1.1 The Operation of Early DBA Mechanism;21
4.4.1.2;1.4.1.2 PFEBR Scheme;22
4.4.2;1.4.2 Dynamic Wavelength Allocation;23
4.5;1.5 Performance Analysis;24
4.5.1;1.5.1 End-to-End Delay;25
4.5.2;1.5.2 EF Jitter Performance;26
4.6;1.6 Conclusion;27
5;Evaluation on Data Modeling Languages for Standardization of NETCONF-Based Network Management: Application of an Evaluation Framework for Data Modeling Languages in Network Management Domain;29
5.1;2.1 Introduction;29
5.2;2.2 Proposed Evaluation Framework;30
5.2.1;2.2.1 Modeling Approaches;30
5.2.2;2.2.2 Interoperability;31
5.2.2.1;2.2.2.1 Protocol Independence;31
5.2.2.2;2.2.2.2 Naming Independence;31
5.2.3;2.2.3 Readability;31
5.2.3.1;2.2.3.1 Human Readability;32
5.2.3.2;2.2.3.2 Machine Readability;32
5.2.4;2.2.4 Data Representation;32
5.2.4.1;2.2.4.1 Diversity of Data Types;32
5.2.4.2;2.2.4.2 Specification of Configuration Data, State Data and Statistics Data;33
5.2.5;2.2.5 Conformance;33
5.2.5.1;2.2.5.1 Backward Compatibility;33
5.2.5.2;2.2.5.2 Versioning;33
5.2.5.3;2.2.5.3 Definition of Event Notification Messages;33
5.2.5.4;2.2.5.4 Definition of Error Messages;34
5.2.6;2.2.6 Extensibility;34
5.2.6.1;2.2.6.1 Extensibility of Data Structures;34
5.2.6.2;2.2.6.2 Extensibility of Data Types;34
5.2.6.3;2.2.6.3 Extensibility of Elements and Attributes;34
5.2.7;2.2.7 Security Considerations;34
5.2.7.1;2.2.7.1 Granularity of Access Control;35
5.2.7.2;2.2.7.2 Lock Mechanism;35
5.3;2.3 Language Presentation;35
5.3.1;2.3.1 Structure of Management Information;35
5.3.2;2.3.2 Managed Object Format/Common Information Model;36
5.3.3;2.3.3 Structure of Management Information, Next Generation;36
5.4;2.4 Validation;36
5.4.1;2.4.1 Comparison;37
5.4.2;2.4.2 Summary;38
5.5;2.5 NETCONF-Based Data Modeling;38
5.5.1;2.5.1 XML Schema;38
5.5.2;2.5.2 Yang;39
5.6;2.6 Application of Proposed Framework to NETCONF-Based Data Modeling;39
5.7;2.7 Conclusions and Future Work;41
6;Ad Hoc Multiple Source Routing;43
6.1;3.1 Introduction;43
6.2;3.2 Related Works;44
6.3;3.3 The Protocol;45
6.4;3.4 Performance Evaluation;48
6.5;3.5 Conclusion;52
7;Efficient BER Improvement Mechanism for Wireless E1/E1 ATM Links;54
7.1;4.1 Introduction;54
7.2;4.2 Theoretical Background on RS Coding;55
7.3;4.3 Description of the Architecture and Design;56
7.3.1;4.3.1 Block Synchronization Design Issues;57
7.3.2;4.3.2 Calculation of Probability of Occurrence of False Alarm;58
7.3.3;4.3.3 Idle Cell Removal;59
7.3.4;4.3.4 Implementation;59
7.4;4.4 Test Results;60
7.4.1;4.4.1 Theoretical Calculations;60
7.4.2;4.4.2 Functional Testing;61
7.4.3;4.4.3 Performance Tests;62
7.5;4.5 Conclusions;64
8;Performance of Ad Hoc Routing Protocols in Mobile WiMAX Environment;65
8.1;5.1 Introduction;65
8.2;5.2 Ad Hoc Routing Protocols;66
8.2.1;5.2.1 Ad Hoc On-Demand Distance Vector (AODV);66
8.2.1.1;5.2.1.1 Route Discovery;67
8.2.1.2;5.2.1.2 Route Maintenance;68
8.2.2;5.2.2 Dynamic Source Routing (DSR);68
8.2.2.1;5.2.2.1 Route Discovery;68
8.2.2.2;5.2.2.2 Route Maintenance;69
8.2.3;5.2.3 Optimized Link State Routing (OLSR);70
8.2.3.1;5.2.3.1 Neighbor Sensing;70
8.2.3.2;5.2.3.2 Multipoint Relay Station;70
8.2.3.3;5.2.3.3 MPR Information Declaration;71
8.2.4;5.2.4 Zone Routing Protocol (ZRP);71
8.2.4.1;5.2.4.1 Intrazone Routing Protocol (IARP);72
8.2.4.2;5.2.4.2 Interzone Routing Protocol (IERP);72
8.3;5.3 Simulation Environment;73
8.4;5.4 Simulation Results;74
8.5;5.5 Conclusion;76
9;Predictive Mobility Management with Delay Optimizations in 802.11 Infrastructure Networks;78
9.1;6.1 Introduction;78
9.2;6.2 Related Works and Technologies;79
9.3;6.3 Mobility Management Architecture;79
9.4;6.4 Analysis of Mobility Prediction;81
9.5;6.5 Analysis of Delay Management and Resource Management;82
9.5.1;6.5.1 Delay Management;83
9.5.2;6.5.2 Resource Reservation Management;84
9.6;6.6 Performance Simulation Results;84
9.7;6.7 Conclusion;90
10;Decomposition of SQuaRE - Based Requirements for the Needs of SOA Applications;92
10.1;7.1 Introduction;92
10.2;7.2 Motivating Scenario;93
10.3;7.3 Related Work;94
10.4;7.4 Decomposition;96
10.4.1;7.4.1 SQuaRE -- Based SOA Quality Ontology;96
10.4.2;7.4.2 SWS QoS Ontology;98
10.4.3;7.4.3 Decomposition Model/Mappings;100
10.4.4;7.4.4 Example;101
10.5;7.5 Summary;104
11;Utilizing Web Directories for Translation Disambiguation inCross-Language Information Retrieval;106
11.1;8.1 Introduction;106
11.2;8.2 Related Work;107
11.3;8.3 Cross-Language Information Retrieval Using Web Directories;108
11.3.1;8.3.1 Method of Category Merging;109
11.3.2;8.3.2 Preprocessing Phase;109
11.3.3;8.3.3 Retrieval Phase;110
11.3.3.1;8.3.3.1 Relevant Category Selection;111
11.3.3.2;8.3.3.2 Query Translation;111
11.3.3.3;8.3.3.3 Retrieval of Documents;112
11.4;8.4 Experiments;112
11.4.1;8.4.1 Method of Experiments;112
11.4.2;8.4.2 Lower Bound of Feature Term in the Category;114
11.4.3;8.4.3 Result of Experiments;114
11.4.4;8.4.4 Discussion;116
11.4.4.1;8.4.4.1 Effectiveness of Proposed Method;116
11.4.4.2;8.4.4.2 Translation for Using Category Level;116
11.4.4.3;8.4.4.3 Appropriate Level of Using Category;116
11.5;8.5 Conclusion;117
12;Ontology Based Improvement of Response Time and Reliability of Web Service Repositories;119
12.1;9.1 Introduction;119
12.1.1;9.1.1 Why a Semantic Search Engine?;120
12.1.2;9.1.2 Traditional System;120
12.1.3;9.1.3 Semantic Web;120
12.1.4;9.1.4 Ontology;122
12.2;9.2 Previous Works in This Direction;122
12.3;9.3 The New Enhanced Search Engine;123
12.4;9.4 Structure of Major Subsystems;124
12.4.1;9.4.1 Service Retrieval Subsystem;125
12.4.2;9.4.2 Periodic Availability Checking;125
12.5;9.5 Experimental Evaluation;125
12.5.1;9.5.1 Improvement in Response Time;126
12.5.2;9.5.2 Automatic Availability Checking;127
12.6;9.6 Conclusion;129
12.7;9.7 Appendix: Ontology Building Tools;129
12.7.1;Protégé;130
12.7.2;OntoBuilder;130
13;Quad-Tree Based Adaptive Wavelet Packet Image Coding;132
13.1;10.1 Introduction;132
13.2;10.2 Background: Wavelet Transform and Wavelet Packet Transform;134
13.3;10.3 Wavelet Image Coding;135
13.3.1;10.3.1 The SPIHT Algorithm;136
13.4;10.4 Adaptive Wavelet Packet Image Coding;137
13.4.1;10.4.1 Rearrangement of Wavelet Packet Coefficients;137
13.4.2;10.4.2 Quad-Tree Based Adaptive Wavelet Packet Tree;138
13.4.3;10.4.3 The SPIAWPT Algorithm;140
13.5;10.5 Experiments of the Coding Algorithms;141
13.6;10.6 Conclusion;144
14;Plaque Boundary Extraction in Intravascular Ultrasound Image in Conjunction with Image Separability and Fuzzy Inference;148
14.1;11.1 Introduction;148
14.2;11.2 Intravascular Ultrasound (IVUS) Image and Conventional Boundary Extraction Method;149
14.2.1;11.2.1 IVUS Image;149
14.2.2;11.2.2 Conventional Boundary Extraction Method by Using Spline Function;150
14.3;11.3 Proposed Boundary Extraction Method Based on Fuzzy Inference;150
14.3.1;11.3.1 B-Mode Image;151
14.3.2;11.3.2 Separability for Boundary Extraction;151
14.3.3;11.3.3 Proposed Boundary Extraction by Applying Fuzzy Inference;152
14.4;11.4 Experimental Results;155
14.5;11.5 Conclusion;158
15;Dedicated Hardware for Hybrid Evolutionary Computation;160
15.1;12.1 Introduction;160
15.2;12.2 Preliminaries;161
15.2.1;12.2.1 Genetic Algorithm and Simulated Annealing;161
15.2.2;12.2.2 Previous Work;162
15.3;12.3 Architecture Methodology;162
15.4;12.4 Circuits;163
15.4.1;12.4.1 Hardware of Genetic Algorithm;164
15.4.1.1;12.4.1.1 Selection Operator;164
15.4.1.2;12.4.1.2 Crossover Operator;165
15.4.1.3;12.4.1.3 Mutation Operator;166
15.4.2;12.4.2 Hardware of Simulated Annealing;167
15.4.3;12.4.3 GA and SA Architecture;169
15.5;12.5 Experiments;169
15.6;12.6 Conclusion;169
16;Efficient Design of Arbitrary Complex Response Continuous-Time IIR Filter;171
16.1;13.1 Introduction;171
16.2;13.2 Vector Fitting;172
16.2.1;13.2.1 Problem Formulation;173
16.2.2;13.2.2 Pole Calculation;174
16.2.3;13.2.3 Building the Rational Function;175
16.3;13.3 Remarks;175
16.3.1;13.3.1 Numerical Consideration;176
16.3.2;13.3.2 Frequency Band Weighting;177
16.3.3;13.3.3 Stability Margin;177
16.4;13.4 Numerical Examples;177
16.4.1;13.4.1 Example 1;177
16.4.2;13.4.2 Example 2;179
16.5;13.5 Conclusions;183
17;Fixed Structure Robust Loop Shaping Controller for a Buck-Boost Converter Using Evolutionary Algorithm;185
17.1;14.1 Introduction;185
17.2;14.2 Converter Modeling;187
17.3;14.3 H8 Loop Shaping Control and Proposed Technique;188
17.4;14.4 Simulation and Experimental Results;191
17.5;14.5 Conclusion;195
18;Ant Colony Optimization (ACO) Technique in Economic Power Dispatch Problems;198
18.1;15.1 Introduction;198
18.2;15.2 Economic Dispatch Problem Formulation;199
18.2.1;15.2.1 Objective Function;199
18.2.2;15.2.2 Constraint Equations;200
18.2.3;15.2.3 Line Flow Constraints;201
18.2.4;15.2.4 Objective Function and Feasible Region;201
18.2.5;15.2.5 Parameter Setting;201
18.3;15.3 Ant Colony Optimization Technique;201
18.4;15.4 Ant Colony Optimization (ACO) Algorithm;202
18.5;15.5 Results And Discussion;207
18.6;15.6 Conclusion;209
19;Direction of Arrival Estimation Using a Phase Array Antenna;211
19.1;16.1 Introduction;211
19.2;16.2 Sensor Array System;212
19.3;16.3 Root-Music Algorithm;214
19.4;16.4 Computer Simulation Results;216
19.5;16.5 Sensor Spacing And Phase Sensitivity;220
19.6;16.6 Conclusion;223
20;Grid Computing for Ubiquitous Computing Environment (GCUCE) Mobility Model;226
20.1;17.1 Introduction;226
20.2;17.2 Architecture;227
20.2.1;17.2.1 Grid Layer;227
20.2.2;17.2.2 Context-Aware Layer;228
20.2.3;17.2.3 Ubiquitous Main Layer;229
20.3;17.3 Enterprise Model in GCUCE;229
20.3.1;17.3.1 Enterprise Model;230
20.3.2;17.3.2 Demand Model;231
20.3.3;17.3.3 Gain Maximization;233
20.4;17.4 Automata Model for Mobility in GCUCE;234
20.5;17.5 Experiment;234
20.6;17.6 Conclusion;237
21;A Novel Fault-Tolerant Multi-EPON System with Sharing Protection Scheme;240
21.1;18.1 Introduction;240
21.2;18.2 Literature Review;241
21.3;18.3 Proposed Novel Fault-Tolerant Multi-EPON System;243
21.3.1;18.3.1 Fault-Tolerant Architecture;243
21.3.2;18.3.2 Minimum Hop Count Relay Path Main Algorithm;244
21.3.2.1;18.3.2.1 Information Sending Phase;245
21.3.2.2;18.3.2.2 Waiting Information Phase;245
21.3.2.3;18.3.2.3 Deciding Relay Path Phase;246
21.3.2.4;18.3.2.4 Calculating Available Bandwidth Phase;246
21.3.3;18.3.3 Relay Mechanism and Local DBA;246
21.3.3.1;18.3.3.1 Relay Window Mechanism;246
21.3.3.2;18.3.3.2 Two DBA Schemes for Local ONUs;247
21.4;18.4 Performance Evaluation;250
21.4.1;18.4.1 Average End-to-End Delay;251
21.4.2;18.4.2 MAX End-to-End Delay;251
21.4.3;18.4.3 EF Jitter Performance;252
21.5;18.5 Conclusion;253
22;Design and Performance Evaluation of Knuth-BendixMulti-Completion System Using Boolean Constrained Reduction Orders;255
22.1;19.1 Introduction;255
22.2;19.2 Multi-Completion;257
22.3;19.3 Multi-Completion for Boolean Constrained Reduction Orders;259
22.3.1;19.3.1 Abstract Boolean Encoding;259
22.3.2;19.3.2 MKBOOL;260
22.4;19.4 Encoding for Recursive Path Orders;262
22.4.1;19.4.1 Basic Definitions;262
22.4.2;19.4.2 Encoding for Precendence and Status;263
22.4.3;19.4.3 Encoding for RPO;265
22.5;19.5 Implementation and Experiments;266
22.6;19.6 Conclusion;266
23;Generating Distributed Code from Cola Models;268
23.1;20.1 Introduction;268
23.2;20.2 Automated Code Generation;270
23.3;20.3 Running the Generated Code;277
23.3.1;20.3.1 Inter-Cluster Communication;277
23.3.2;20.3.2 Interfacing Sensors and Actuators;278
23.4;20.4 Case Study;279
23.4.1;20.4.1 Functionality of the Demonstrator;280
23.5;20.5 Conclusions;280
24;Outlining a Risk-Driven Development Model (RDD);283
24.1;21.1 Introduction;283
24.2;21.2 Method;284
24.3;21.3 Commonalities in Agile and Risk Management Process Models;285
24.3.1;21.3.1 Risk Definition;285
24.3.2;21.3.2 Organizational Levels and Stakeholders Involved;286
24.3.2.1;21.3.2.1 Pre-implementation Stage;286
24.3.2.2;21.3.2.2 Implementation Stage;286
24.3.2.3;21.3.2.3 Organizational Levels in the Process Models Studied;287
24.3.3;21.3.3 Stakeholders Involved;287
24.4;21.4 Process Integration Practice;288
24.4.1;21.4.1 Risk Definition and Identification;288
24.4.2;21.4.2 Organizational Levels and Stakeholders Involved;288
24.4.3;21.4.3 Integration Problems;290
24.5;21.5 RDD;290
24.5.1;21.5.1 Risk Definition and Identification;291
24.5.2;21.5.2 Organizational Levels and Stakeholders Involved;291
24.5.3;21.5.3 Stakeholders Involved;292
24.5.4;21.5.4 Communication Channels;293
24.5.5;21.5.5 Process Aspects;293
24.6;21.6 Epilogue;294
25;Mining Frequent Itemsets in Distributed Environment;297
25.1;22.1 Introduction;297
25.1.1;22.1.1 Notation and Problem Definition;299
25.2;22.2 Previous Work;299
25.2.1;22.2.1 The Apriori Algorithm;299
25.2.2;22.2.2 The Trie-Based Apriori;300
25.2.3;22.2.3 The FDM Algorithm;301
25.3;22.3 Our Implementation;301
25.3.1;22.3.1 The DTFIM Algorithm;301
25.3.2;22.3.2 The Revised DTFIM;302
25.4;22.4 Experimental Results;303
25.5;22.5 Conclusion;305
26;A Study on the Inequalities for Fast Similarity Search in Metric Spaces;308
26.1;23.1 Previous Works on Metric Search;310
26.1.1;23.1.1 Similarity Search in Metric Spaces;310
26.1.2;23.1.2 AESA;310
26.1.3;23.1.3 LAESA;311
26.1.4;23.1.4 Other Metric Search Algorithms;311
26.2;23.2 New Lower Bounds;312
26.2.1;23.2.1 Embedding of the Metric Space;312
26.2.2;23.2.2 New Bounds on the Unknown Distances;314
26.2.3;23.2.3 Extending to Generic Metric Spaces;315
26.3;23.3 Search Algorithms;315
26.3.1;23.3.1 PAESA;315
26.3.2;23.3.2 LPAESA;316
26.3.3;23.3.3 Pivot Selection;317
26.4;23.4 Experiments;317
26.5;23.5 Concluding Remarks;321
27;Discovering Knowledge of Association Using Coherent Rules;323
27.1;24.1 Introduction;323
27.2;24.2 Previous Work;324
27.3;24.3 Coherent Rules Framework;325
27.4;24.4 Coherent Rules Measure of Interest;327
27.4.1;24.4.1 Property of Coherent Rules Measure of Interest H ;329
27.5;24.5 Mining Coherent Rules;329
27.6;24.6 Experiments and Discussions;331
27.7;24.7 Conclusion;333
28;Particle Swarm Optimization with Diversive Curiosity and Its Identification;335
28.1;25.1 Introduction;335
28.2;25.2 Overview of EPSO;337
28.2.1;25.2.1 Basic EPSO;337
28.2.2;25.2.2 Fitness Functions;338
28.2.3;25.2.3 Convergence Speed;339
28.3;25.3 PSO/DC;339
28.3.1;25.3.1 Curiosity;339
28.3.2;25.3.2 Internal Indicator;340
28.3.3;25.3.3 Procedure of PSO/DC;341
28.4;25.4 Computer Experiments;342
28.4.1;25.4.1 Experimental Conditions;342
28.4.2;25.4.2 Results of EPSO;342
28.4.3;25.4.3 Results of PSO/DC;344
28.4.4;25.4.4 Feature of EPSO;345
28.5;25.5 Conclusions;347