Gelman Electronic Engineering and Computing Technology

1. Auflage 2010
ISBN: 978-90-481-8776-8
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 500 Seiten

Electronic Engineering and Computing Technology contains sixty-one revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Control Engineering, Network Management, Wireless Networks, Biotechnology, Signal Processing, Computational Intelligence, Computational Statistics, Internet Computing, High Performance Computing, and industrial applications. Electronic Engineering and Computing Technology will offer the state of art of tremendous advances in electronic engineering and computing technology and also serve as an excellent reference work for researchers and graduate students working with/on electronic engineering and computing technology.

Ao, Sio Iong, Ph.D. Education & Post-Doctoral Research Harvard University: Post-Doctoral Fellow in Computer Science (Harvard School of Engineering and Applied Sciences, and Time Series Center, Initiative in Innovative Computing) University of Oxford: Post-Doctoral Researcher (Oxford University Computing Laboratory) The University of Hong Kong: Doctor of Philosophy (Mathematics) The Chinese University of Hong Kong: Master of Philosophy (Systems Engineering & Engineering Management) Scholarships & Awards Dean List of Class (1994/95), Keio University Award (1996/97), CUHK Studentship Award (2001-2003), CUHK Postgraduate Student Grants Award for Overseas Academic Activities (2003), HKU Studentship Award (2004-2007), HKU Postgraduate Student Grants Award for Overseas Academic Activities (2005), Postgraduate Student Grant for MSRI Summer Graduate Workshop (MSRI, Berkeley, USA, 2006), International Association of Engineers Academics Fellowship Award (2007/09) Other Honors: Selected for the Who's Who in the World (Marquis Who's Who, 2007 & 2008), Selected for 2000 Outstanding Intellectuals of the 21st Century, (2008, International Biographical Centre, Cambridge, England); - Commended for the Thirty-Fourth Edition of the Dictionary of International Biography (2008, International Biographical Centre, Cambridge, England), Best Paper Award of The 2007 International Conference of Information Engineering, Certificate of Commendation (The University of Hong Kong, 2007), Selected for the First Edition of Who's Who in Asia (Marquis Who's Who, 2006), Best Student Paper Award of The International MultiConference of Engineers and Computer Scientists 2006 and The 2006 IAENG International Workshop on Financial Engineering   Professor Len Gelman: Chair in Vibro-Acoustic Monitoring; Chairman of Condition Monitoring Technical Committee, British Institute of NDT; Director, Centre of Vibro-Acoustics and Fatigue; Director, International Institute of Acoustics and Vibration School of Engineering, Cranfield University Brief Description Professor Len Gelman, PhD, Dr of Sciences (habilitation) is Director of Centre of Vibro-Acoustics and Fatigue, Cranfield University. He has more than 25 year's experience in signal processing and vibro-acoustic monitoring of complex mechanical systems (e.g. rotating, reciprocating machinery, etc.) both in industry and academia. He has been Principal Investigator on numerous contracts and grants, including grants from the USA National Academy of Sciences, USA National Research Council, USA International Science Foundation, USA Civilian Research and Development Foundation (twice), USA MacArthur Foundation, Lady Davis, Israel, Centro Volta, Italy. He is Principal Investigator on UK EPSRC, UK DTI (three times), UK Royal Society, Rolls Royce (two times) and Shell grants. He is a Fellow of the British Institute of Non-Destructive Testing (BINDT) and UK Institution of Diagnostic Engineers, Chairman of the Condition Monitoring and Diagnostic Technology Committee of the BINDT and Director of the International Institute of Acoustics and Vibration (USA). He is the author of over200 publications (including 17 patents) and 11 keynote papers. He is editor-in-chief of the book series 'Condition monitoring' (Coxmoor, UK), Chair of 2007 World Congress on Engineering Asset Management, Honorary Co-Chair of 2007 and 2008 World Congresses of Engineering and Chair of 2008 Condition Monitoring Conference. He has participated in the scientific boards of numerous international conferences, has spent a large amount of time lecturing and consulting to industry in all parts of the world and has held visiting professor positions at 5 overseas universities.

Autoren/Hrsg.

Gelman, Len

Inhaltsverzeichnis

1;Electronic Engineering
and Computing Technology
;2
1.1;Preface
;6
1.2;Contents
;8
1.3;1 On the Experimental Control of a Separately Excited DC Motor;14
1.3.1;1.1 Introduction;14
1.3.2;1.2 Modeling;15
1.3.2.1;1.2.1 Load Torque Model;16
1.3.2.2;1.2.2 State Space Representation;16
1.3.2.3;1.2.3 State Observer;17
1.3.2.4;1.2.4 Controller;18
1.3.3;1.3 Experimental Setup;19
1.3.3.1;1.3.1 Real-Time Platform;19
1.3.3.2;1.3.2 Rotor Position Measurement Design;20
1.3.3.3;1.3.3 Power Converter;22
1.3.4;1.4 Experimental Results;23
1.3.5;References;25
1.4;2 MASH Digital Delta--Sigma Modulator with Multi-Moduli;26
1.4.1;2.1 Introduction;26
1.4.2;2.2 Previous MASH Architectures;27
1.4.3;2.3 The Adequacy and Effect of the Multi-Modulus MASH-DDSM;28
1.4.3.1;2.3.1 The Suitability of the Multi-Modulus MASH-DDSM;29
1.4.3.2;2.3.2 The Effect of the Multi-Moduli on the Modulator Sequence Length;31
1.4.4;2.4 The Proposed Structure and Simulation Results;34
1.4.5;2.5 The Simulation Results;35
1.4.6;2.6 Conclusions;37
1.4.7;References;37
1.5;3 Sensorless PM-Drive Aspects;38
1.5.1;3.1 Introduction;38
1.5.2;3.2 Sensorless Control Mode;40
1.5.2.1;3.2.1 Nonlinear Motor Model;40
1.5.2.2;3.2.2 Simplified Block Diagram of Closed-Loop Control;42
1.5.2.3;3.2.3 Injected High Frequency Test Signals;42
1.5.3;3.3 Space Dependent Inductances;45
1.5.3.1;3.3.1 Set-Up for the Saturation Dependent Differential Inductances Evaluation;45
1.5.3.2;3.3.2 Saturation Dependent Differential and Main Synchronous Inductances;46
1.5.4;3.4 Conclusion;47
1.5.5;References;47
1.6;4 Design of Silicon Resonant Micro Accelerometer Based on Electrostatic Rigidity;49
1.6.1;4.1 Introduction;49
1.6.2;4.2 Operating Principle;50
1.6.3;4.3 Theory Analysis;51
1.6.4;4.4 Structure Design;54
1.6.5;4.5 Conclusion;56
1.6.6;References;56
1.7;5 Longer MEMS Switch Lifetime Using Novel Dual-Pulse Voltage Driver;58
1.7.1;5.1 Introduction;58
1.7.2;5.2 Dielectric Charging;59
1.7.3;5.3 Modeling of Dielectric Charging;60
1.7.3.1;5.3.1 Mathematical Model;60
1.7.3.2;5.3.2 Equivalent Circuit Model;62
1.7.4;5.4 Dual-Pulse Actuation Signal;63
1.7.5;5.5 Novel Dual-Pulse Actuation Signal;64
1.7.6;5.6 Experimental Setup;65
1.7.7;5.7 Conclusion;67
1.7.8;References;68
1.8;6 Optimal Control of Full Envelope Helicopter;69
1.8.1;6.1 Introduction;69
1.8.2;6.2 Manual Control of Helicopter;70
1.8.3;6.3 Mathematical Modelling of Helicopter;70
1.8.3.1;6.3.1 Coordinate Frames and Transformations;70
1.8.3.2;6.3.2 Dynamic Equations of Motion;71
1.8.3.3;6.3.3 Kinematic Equations;71
1.8.3.4;6.3.4 Force and Moments Acting on Helicopter;72
1.8.3.5;6.3.5 Trimming and Linearization;73
1.8.3.6;6.3.6 Obtaining the Stability Derivatives;73
1.8.4;6.4 Controller Design;74
1.8.4.1;6.4.1 State Feedback Controller;75
1.8.4.2;6.4.2 State Integrator;76
1.8.4.3;6.4.3 PI Controller;76
1.8.5;6.5 Simulations;76
1.8.5.1;6.5.1 Movement to Point;76
1.8.5.2;6.5.2 Movement Through Waypoints;76
1.8.6;6.6 Conclusion;78
1.8.7;References;79
1.9;7 Determination of Consistent Induction Motor Parameters;80
1.9.1;7.1 Introduction;80
1.9.2;7.2 Power Balance;81
1.9.3;7.3 Determination of Parameters;83
1.9.3.1;7.3.1 Measurement or Estimation of Core Losses;83
1.9.3.2;7.3.2 Measurement or Estimation of Friction Losses;84
1.9.3.3;7.3.3 Calculation of Stray-Load Losses;84
1.9.3.4;7.3.4 Estimation of Stator Resistance ;84
1.9.3.5;7.3.5 Measurement or Estimation of No Load Current;85
1.9.3.6;7.3.6 Determination of Stator Inductance;86
1.9.3.7;7.3.7 Determination of Stray Factor and Rotor Time Constant;86
1.9.3.8;7.3.8 Determining Magnetizing Inductance, Rotor Inductance and Rotor Resistance;87
1.9.4;7.4 Measurement and Calculation Results;88
1.9.5;7.5 Conclusions;89
1.9.6;References;89
1.10;8 Broken Rotor Bars in Squirrel Cage Induction Machines -- Modeling and Simulation;90
1.10.1;8.1 Introduction;90
1.10.2;8.2 Model of Stator Winding;91
1.10.3;8.3 Model of Rotor Winding;92
1.10.4;8.4 Torque Equation;94
1.10.5;8.5 Theoretical Background of Rotor Faults;94
1.10.6;8.6 Investigated Machine ;95
1.10.7;8.7 Simulation Results;96
1.10.8;8.8 Measurement Results;98
1.10.9;8.9 Rotor Fault Detection Methods;99
1.10.10;8.10 Conclusions;100
1.10.11;References;100
1.11;9 Different Designs of Large Chipper Drives;101
1.11.1;9.1 Introduction;101
1.11.1.1;9.1.1 Slip Ring Motor with Rotor Rheostat;102
1.11.1.2;9.1.2 Speed Controlled Squirrel Cage Motor with Inverter;102
1.11.1.3;9.1.3 Technical Data;103
1.11.2;9.2 Simulation Models;103
1.11.2.1;9.2.1 Slip Ring Motor with Additional Rotor Resistances;105
1.11.2.2;9.2.2 Speed Controller Squirrel Cage Motor with Inverter;107
1.11.3;9.3 Simulation Results;109
1.11.3.1;9.3.1 Slip Ring Motor;110
1.11.3.2;9.3.2 Squirrel Cage Motor;110
1.11.4;9.4 Conclusions;111
1.11.5;References;112
1.12;10 Macro Cell Placement: Based on a Force Directed Flow;113
1.12.1;10.1 Introduction;113
1.12.2;10.2 Placement Tools;114
1.12.3;10.3 Standard Cells Versus Macro-cells;115
1.12.4;10.4 Force Directed Graph Drawing Algorithms;116
1.12.5;10.5 Implementation Details;118
1.12.5.1;10.5.1 Non-zero Size Vertices Implementation;119
1.12.5.2;10.5.2 Fixed Node Support;120
1.12.5.3;10.5.3 Input/Output Format;120
1.12.6;10.6 Experimentation and Results;121
1.12.7;10.7 Future Work and Conclusion;122
1.12.8;References;123
1.13;11 Surface Roughness Scattering in MOS Structures;124
1.13.1;11.1 Introduction;124
1.13.2;11.2 Physics of the Problem;125
1.13.3;11.3 Associated Scattering Potentials;126
1.13.4;11.4 Relative Strength of the Scattering Potentials;129
1.13.5;11.5 Remote Surface Roughness Scattering;129
1.13.5.1;11.5.1 Observed Trend in RSR Mobility;133
1.13.6;References;134
1.14;12 A Novel Transform Domain Based Hybrid Recurrent Neural Equaliser for Digital Communication Channel;135
1.14.1;12.1 Introduction;135
1.14.2;12.2 Proposed Hybrid Recurrent Neural Equaliser;137
1.14.2.1;12.2.1 Training Algorithm of Hybrid Neural Structure;137
1.14.3;12.3 Simulation Study and Discussions;141
1.14.4;12.4 Conclusion;145
1.14.5;References;145
1.15;13 Embedding Interconnection Networks in Crossed Cubes;146
1.15.1;13.1 Introduction;146
1.15.2;13.2 Definitions and Notations;147
1.15.3;13.3 Embedding Complete Binary Trees;149
1.15.4;13.4 Embedding Complete Quad Trees;150
1.15.5;13.5 Embedding Cycles;151
1.15.6;13.6 Conclusions and Future Work;155
1.15.7;References;155
1.16;14 Software Fault Tolerance: An Aspect Oriented Approach;157
1.16.1;14.1 Introduction;157
1.16.2;14.2 ROC Plausibility Based Error Detection and Recovery;158
1.16.3;14.3 Aspect Oriented Exception Handling Patterns;159
1.16.3.1;14.3.1 Error Detection and Exception Throwing Aspect;161
1.16.3.2;14.3.2 ROC Plausibility Check Aspect;162
1.16.3.3;14.3.3 Catcher Handler Aspect;162
1.16.3.4;14.3.4 Dynamics of Cather Handler Aspect;162
1.16.4;14.4 Case Study;164
1.16.5;14.5 Result and Discussion;164
1.16.6;14.6 Conclusions and Future Work;167
1.16.7;References;168
1.17;15 An Adaptive Multibiometric System for Uncertain Audio Condition;169
1.17.1;15.1 Introduction;169
1.17.2;15.2 Support Vector Machine Classifier;172
1.17.3;15.3 Visual Front-End Subsystem;173
1.17.4;15.4 Audio Front-End Subsystem;174
1.17.5;15.5 Fusion System Implementation;175
1.17.6;15.6 Results and Discussions;175
1.17.7;15.7 Conclusions;179
1.17.8;References;180
1.18;16 Analysis of Performance Impact due to Hardware Virtualization Using a Purely Hardware-Assisted VMM;182
1.18.1;16.1 Introduction;182
1.18.2;16.2 Hardware Virtualization;183
1.18.2.1;16.2.1 86 Architectural Limitations;183
1.18.2.2;16.2.2 AMD SVM Architectural Extensions;184
1.18.3;16.3 The Hardware-Assisted Virtual Machine Monitor;185
1.18.3.1;16.3.1 HVMM Design;185
1.18.3.2;16.3.2 HVMM Implementation;188
1.18.4;16.4 Performance Analysis;189
1.18.4.1;16.4.1 Test Setup;189
1.18.4.2;16.4.2 Analysis;189
1.18.5;16.5 Conclusion;190
1.18.6;16.6 Future Works;191
1.18.7;References;191
1.19;17 Metrics-Driven Software Quality Prediction Without Prior Fault Data;192
1.19.1;17.1 Introduction;193
1.19.2;17.2 Related Work;195
1.19.3;17.3 Clustering;196
1.19.3.1;17.3.1 Clustering Basics;196
1.19.3.2;17.3.2 Clustering Algorithms;197
1.19.3.2.1;17.3.2.1 K-Means;197
1.19.3.2.2;17.3.2.2 X-Means;197
1.19.4;17.4 Empirical Case Studies;198
1.19.4.1;17.4.1 Performance Evaluation Parameters;198
1.19.4.2;17.4.2 Results and Analysis;199
1.19.5;17.5 Conclusion and Future Work;201
1.19.6;References;202
1.20;18 Models of Computation for Heterogeneous Embedded Systems;203
1.20.1;18.1 Introduction;204
1.20.1.1;18.1.1 Embedded Systems;204
1.20.2;18.2 System Level Design;204
1.20.2.1;18.2.1 System Level Design Approaches;205
1.20.3;18.3 Hardware/Software Co-design;205
1.20.3.1;18.3.1 Specification and Modeling;206
1.20.3.2;18.3.2 Design and Refinement;206
1.20.3.3;18.3.3 Validation;206
1.20.4;18.4 Specification and Modeling;207
1.20.4.1;18.4.1 Models of Computation;207
1.20.4.1.1;18.4.1.1 Finite State Machines (FSM);207
1.20.4.1.2;18.4.1.2 Discrete-Event Systems;208
1.20.4.1.3;18.4.1.3 Petri Nets;208
1.20.4.1.4;18.4.1.4 Data Flow Graphs;209
1.20.4.1.5;18.4.1.5 Synchronous/Reactive Models;209
1.20.4.1.6;18.4.1.6 Heterogeneous Models;209
1.20.4.2;18.4.2 Comparison of Models of Computation;210
1.20.4.3;18.4.3 Specification Languages;210
1.20.4.3.1;18.4.3.1 Formal Description Languages;210
1.20.4.3.2;18.4.3.2 Real Time Languages;210
1.20.4.3.3;18.4.3.3 Hardware Description Languages (HDL);212
1.20.4.3.4;18.4.3.4 System Level Design Languages (SLDL);212
1.20.4.4;18.4.4 Requirements for Specification Languages;214
1.20.5;References;214
1.21;19 A Quotient-Graph for the Analysis of Reflective Petri Nets;216
1.21.1;19.1 Introduction;216
1.21.2;19.2 WN's Basic Notions;217
1.21.2.1;19.2.1 The Symbolic Marking;218
1.21.3;19.3 Reflective Petri Nets Layout;218
1.21.4;19.4 State-Transition Semantics for Reflective Nets;220
1.21.4.1;19.4.1 Handling Equivalent Evolutions;221
1.21.5;19.5 The Dynamic Philosophers Example;222
1.21.6;19.6 Related Works;224
1.21.7;19.7 Conclusions and Future Work;225
1.21.8;References;226
1.22;20 Using Xilinx System Generator for Real Time Hardware Co-simulation of Video Processing System;227
1.22.1;20.1 Introduction;227
1.22.2;20.2 Design Methodology for Implementation on FPGA with Xilinx System Generator;228
1.22.3;20.3 Study Case: Color Space Conversion RGB to YCbCr;230
1.22.3.1;20.3.1 Overwiew;230
1.22.3.2;20.3.2 YCbCr Color Model;231
1.22.4;20.4 Implementation Results, Simulation and Comparisons;231
1.22.4.1;20.4.1 Hardware Co-simulation;231
1.22.4.2;20.4.2 Simulation;232
1.22.5;20.5 Discussion;234
1.22.6;References;235
1.23;21 Perception-Based Road Traffic Congestion Classification Using Neural Networks and Decision Tree;237
1.23.1;21.1 Introduction;238
1.23.2;21.2 Review Work of the Road Traffic Congestion Estimation;239
1.23.3;21.3 Methodology;239
1.23.3.1;21.3.1 Data Collection and Tools;239
1.23.3.2;21.3.2 Data Classification;241
1.23.4;21.4 Results and Evaluations;242
1.23.4.1;21.4.1 Performance Evaluations;242
1.23.4.2;21.4.2 Evaluations with the Existing System;244
1.23.5;21.5 Conclusion;247
1.23.6;References;247
1.24;22 RDFa Ontology-Based Architecture for String-Based Web Attacks: Testing and Evaluation;249
1.24.1;22.1 Introduction;249
1.24.1.1;22.1.1 Data Validation Process;250
1.24.2;22.2 Related Work;250
1.24.3;22.3 NDVS Design;251
1.24.3.1;22.3.1 Functional Overview;252
1.24.3.2;22.3.2 Overview of the Framework Architecture;253
1.24.3.3;22.3.3 Case Study;255
1.24.4;22.4 Implementation and Evaluation of NDVS;256
1.24.4.1;22.4.1 Case Study: Security Objective;256
1.24.4.2;22.4.2 End-to-End Performance Evaluation;257
1.24.5;22.5 Summary;258
1.24.6;References;258
1.25;23 Classification of Road Traffic Congestion Levels from Vehicle's Moving Patterns: A Comparison Between Artificial Neural Network and Decision Tree Algorithm;260
1.25.1;23.1 Introduction;260
1.25.2;23.2 Related Works;261
1.25.3;23.3 Methodology;262
1.25.3.1;23.3.1 Collection of Empirical Data;262
1.25.3.2;23.3.2 Data Preparation;263
1.25.3.2.1;23.3.2.1 Smoothening Out Instantaneous Velocity;263
1.25.3.2.2;23.3.2.2 Extracting Vehicle's Moving Patterns;264
1.25.3.2.3;23.3.2.3 Balancing Class Distributions;266
1.25.3.3;23.3.3 Data Classifications;266
1.25.4;23.4 Results and Evaluations;267
1.25.4.1;23.4.1 Classification Model;267
1.25.4.2;23.4.2 Performance Evaluations;267
1.25.5;23.5 Conclusion;269
1.25.6;References;270
1.26;24 Fuzzy Parameters and Cutting Forces Optimization via Genetic Algorithm Approach;271
1.26.1;24.1 Introduction;271
1.26.2;24.2 Genetic Algorithm Optimization Methodology Approach;272
1.26.3;24.3 Synthesizing Process of Analytic Fuzzy Logic Controller;275
1.26.4;24.4 Parameter Optimization Procedure;276
1.26.5;24.5 Experiments and Results;277
1.26.6;24.6 Cutting Forces Optimization via Genetic Algorithm;279
1.26.7;24.7 Conclusion;280
1.26.8;References;281
1.27;25 Encoding Data to Use with a Sparse Distributed Memory;283
1.27.1;25.1 Introduction;283
1.27.2;25.2 Sparse Distributed Memories ;284
1.27.3;25.3 Experimental Setup;285
1.27.4;25.4 Practical Problems;286
1.27.5;25.5 Binary Codes and Distances;287
1.27.5.1;25.5.1 Sorting the Bytes;288
1.27.5.2;25.5.2 Using a Sum-Code;289
1.27.6;25.6 Tests and Results;290
1.27.6.1;25.6.1 Results;290
1.27.6.2;25.6.2 Analysis of the Results;291
1.27.7;25.7 Conclusions and Future Work;292
1.27.8;References;293
1.28;26 A Proposal for Integrating Formal Logic and Artificial Neural Systems: A Practical Exploration;294
1.28.1;26.1 Introduction;294
1.28.2;26.2 Constructive Type Theory;295
1.28.3;26.3 The Theoretical Framework;295
1.28.4;26.4 The Prototype;297
1.28.5;26.5 A Feedforward Neural Network;299
1.28.6;26.6 Forecasting the Dover Tides;300
1.28.7;26.7 A Self Organising Map;302
1.28.8;26.8 Conclusion;304
1.28.9;References;304
1.29;27 A Clustering Application in Portfolio Management;306
1.29.1;27.1 Introduction;306
1.29.2;27.2 The Asset Allocation Model;307
1.29.2.1;27.2.1 The Optimization Problem;307
1.29.2.2;27.2.2 Asset Allocation Methods;309
1.29.2.2.1;27.2.2.1 The 1/ Allocation;309
1.29.2.2.2;27.2.2.2 The Markowitz MVP Allocation;310
1.29.2.2.3;27.2.2.3 The Modified Tobin Tangency Allocation;310
1.29.2.3;27.2.3 The Optimization Method;310
1.29.2.4;27.2.4 Data and Performance Indicators;312
1.29.3;27.3 Computational Results;313
1.29.3.1;27.3.1 Portfolio Instability;313
1.29.3.2;27.3.2 Sharpe Ratios and Return Distribution;314
1.29.3.3;27.3.3 Cluster Properties: the Sharpe Ratio and the Euclidean Distance;315
1.29.4;27.4 Conclusion;317
1.29.5;References;318
1.30;28 Building an Expert System for HIV and Aids Information;319
1.30.1;28.1 Introduction;319
1.30.2;28.2 Health Expert Systems;321
1.30.3;28.3 HIV and Aids Expert System;323
1.30.4;28.4 Development of the System;324
1.30.5;28.5 System Interactivity;327
1.30.6;28.6 Conclusion;328
1.30.7;References;329
1.31;29 A Multi-Objective Approach to Generate an Optimal Management Plan in an IMS-QSE;330
1.31.1;29.1 Introduction;331
1.31.2;29.2 A Brief Recall on the New Process-Based Approach;332
1.31.3;29.3 A Multi-Objective Approach for a QSE Management Plan ;333
1.31.3.1;29.3.1 Bow Tie Method;334
1.31.3.2;29.3.2 Multi-Objective Influence Diagrams;335
1.31.3.3;29.3.3 Transformation of Bow Ties into a MID;337
1.31.4;29.4 Case Study;339
1.31.5;29.5 Conclusion;340
1.31.6;References;342
1.32;30 Topological Spatial Relations for Circular Spatially Extended Points: An Overview;343
1.32.1;30.1 Introduction;343
1.32.2;30.2 Qualitative Spatial Reasoning;345
1.32.2.1;30.2.1 Direction Spatial Relations;345
1.32.2.2;30.2.2 Distance Spatial Relations;346
1.32.2.3;30.2.3 Topological Spatial Relations;346
1.32.3;30.3 Topological Spatial Relations Between a CSEP and a Region;346
1.32.4;30.4 Topological Spatial Relations Between a CSEP and a Line;348
1.32.5;30.5 Topological Spatial Relations Between Two CSEPS;349
1.32.6;30.6 Conclusion;351
1.32.7;References;353
1.33;31 Spatial Neighbors for Topological Spatial Relations: The Case of a Circular Spatially Extended Point;354
1.33.1;31.1 Introduction;354
1.33.2;31.2 Conceptual Neighborhood Graphs: Snapshot Model;355
1.33.3;31.3 Conceptual Neighborhood Graph: A CSEP and a Region;356
1.33.4;31.4 Conceptual Neighborhood Graph: A CSEP and a Line;359
1.33.5;31.5 Conceptual Neighborhood Graph: Two CSEPS;362
1.33.6;31.6 Conclusion;363
1.33.7;References;364
1.34;32 Multi-Agent Exploration Inside Structural Collapses;366
1.34.1;32.1 Introduction;366
1.34.2;32.2 Converting Real World to Simulated World;367
1.34.3;32.3 Simulated Agent Tasks;370
1.34.4;32.4 Simulated 2D Search Field ;371
1.34.5;32.5 Discriminative Index;372
1.34.6;32.6 Validation;374
1.34.7;32.7 Discussion;375
1.34.8;References;375
1.35;33 Diagnostic Problem Solving by Means of Neuro-Fuzzy Learning, Genetic Algorithm and Chaos Theory Principles Applying;377
1.35.1;33.1 Introduction;377
1.35.2;33.2 A Mamdani Neuro-Fuzzy Network Module;379
1.35.3;33.3 Problem Solving by Means of Chaos Theory Principles;381
1.35.4;33.4 Obtained Results;385
1.35.5;33.5 Conclusion;386
1.35.6;References;387
1.36;34 The New Measure of Robust Principal Component Analysis;388
1.36.1;34.1 Introduction;388
1.36.2;34.2 The Classical Principal Component Analysis (PCA);390
1.36.3;34.3 The Robust PCA Using Minimum Vector Variance (MVV);392
1.36.4;34.4 The Performance of MVV Robust PCA;396
1.36.4.1;34.4.1 The Clustering Flower Images;396
1.36.4.2;34.4.2 The Identification of Anomalous Data in High and Large Dimension;396
1.36.4.3;34.4.3 The Computation Time of MVV Robust PCA;397
1.36.5;34.5 Conclusion;398
1.36.6;References;398
1.37;35 The Affects of Demographics Differentiations on Authorship Identification;400
1.37.1;35.1 Introduction;400
1.37.2;35.2 Related Work;401
1.37.3;35.3 Feature Set;402
1.37.4;35.4 Framework;403
1.37.5;35.5 Results;404
1.37.5.1;35.5.1 Demographics Differentiations;405
1.37.5.1.1;35.5.1.1 Personality;405
1.37.5.1.2;35.5.1.2 Gender and Age;406
1.37.6;35.6 Conclusion;407
1.37.7;References;408
1.38;36 Anonymous ID Assignment and Opt-Out;409
1.38.1;36.1 Introduction;409
1.38.2;36.2 Anonymous ID Assignment Algorithms;411
1.38.2.1;36.2.1 AIDA-H -- Using Edge-Distjoint Hamiltonian Cycles;411
1.38.2.2;36.2.2 AIDA-D -- Decentralized Anonymous Algorithm;412
1.38.3;36.3 Performance Analysis;413
1.38.4;36.4 Anonymous ID Assignment and Opt-Out;415
1.38.5;36.5 Concluding Remarks;418
1.38.6;References;421
1.39;37 Clustering Biological Data Using Enhanced k-Means Algorithm;422
1.39.1;37.1 Introduction;422
1.39.2;37.2 k-Means Clustering Algorithm;423
1.39.3;37.3 Literature Survey;424
1.39.4;37.4 Proposed Method;425
1.39.5;37.5 Computational Complexity;427
1.39.6;37.6 Experimental Results;428
1.39.7;37.7 Conclusion;428
1.39.8;References;430
1.40;38 The Ornstein--Uhlenbeck Processes Driven by Lévy Process and Application to Finance;432
1.40.1;38.1 Introduction;432
1.40.2;38.2 Levy Processes;433
1.40.3;38.3 Ornstein-Uhlenbeck Processes;435
1.40.4;38.4 The Modelling of Data;439
1.40.5;38.5 Conclusion;441
1.40.6;References;441
1.41;39 Discrete Orthogonality of Zernike Functionsand Its Application to Corneal Measurements;443
1.41.1;39.1 Introduction;443
1.41.1.1;39.1.1 Zernike Functions;444
1.41.1.2;39.1.2 Zernike Functions in Ophthalmology;447
1.41.1.2.1;39.1.2.1 Corneal Measurements and Modelling Corneal Surface;447
1.41.1.2.2;39.1.2.2 Utilizing Discrete Orthogonality;448
1.41.2;39.2 Continuous Zernike Functions;449
1.41.3;39.3 Discretization of Zernike Functions;451
1.41.4;39.4 Computing the Discrete Zernike Coefficients;453
1.41.5;39.5 Conclusions and Future Work;456
1.41.6;References;457
1.42;40 A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation;458
1.42.1;40.1 Introduction;458
1.42.2;40.2 Extended Dependency Tree-Hidden Markov Models;460
1.42.2.1;40.2.1 EDT-HMM Overview;460
1.42.3;40.3 Classification Scheme;462
1.42.3.1;40.3.1 Image Unsupervised Segmentation;463
1.42.3.2;40.3.2 Window Size Computation;464
1.42.3.3;40.3.3 Image Pre-classification;465
1.42.3.4;40.3.4 Classification Correction;466
1.42.4;40.4 Experimentation;467
1.42.4.1;40.4.1 Data Overview;467
1.42.4.2;40.4.2 Learning Database;467
1.42.4.3;40.4.3 Mono-class Images Generation;467
1.42.4.4;40.4.4 Experimental Results;467
1.42.5;40.5 Conclusion;469
1.42.6;References;469
1.43;41 Applying View Models in SOA: A Case Study;470
1.43.1;41.1 Introduction;470
1.43.2;41.2 View Models;471
1.43.3;41.3 Case Study for LD-Cast;473
1.43.3.1;41.3.1 Applying the ``4+1'' View Model;473
1.43.3.2;41.3.2 Applying SOA Specific Models;476
1.43.3.2.1;41.3.2.1 Service Views;476
1.43.3.2.2;41.3.2.2 The BDC View Model;477
1.43.3.3;41.3.3 Useful Elements from Other View Models;478
1.43.3.3.1;41.3.3.1 RM-ODP, the Perspectives for Distributed Processing;478
1.43.3.3.2;41.3.3.2 The SEI Viewpoint Model: ``Views and Beyond'';479
1.43.4;References;479
1.44;42 Optimal Sample Number for Autonomous and Central Wireless Sensor Actuator Network;481
1.44.1;42.1 Introduction;482
1.44.2;42.2 Sample Frequency Calculation;484
1.44.3;42.3 Sample Number Selection;488
1.44.3.1;42.3.1 Central Network;489
1.44.3.2;42.3.2 Autonomous Network;490
1.44.4;References;491
1.45;43 WI-FI Point-to-Point Links: Performance Aspects of IEEE 802.11a, b, g Laboratory Links;492
1.45.1;43.1 Introduction;492
1.45.2;43.2 Experimental Details;493
1.45.3;43.3 Results and Discussion;495
1.45.4;43.4 Conclusions;498
1.45.5;References;499
1.46;44 A Subnet Handover Scheme Based Communication System of Subway;500
1.46.1;44.1 Introduction;500
1.46.2;44.2 Related Work;501
1.46.3;44.3 Proposed Subnet Handover Scheme;502
1.46.3.1;44.3.1 Principle of Subnet Handover Scheme;502
1.46.3.2;44.3.2 Gateway Model on L3 STA;504
1.46.3.3;44.3.3 Dynamic NAP Scanning and Finding Model;504
1.46.3.4;44.3.4 The Reused Tunnel Model;505
1.46.4;44.4 The Subnet Based Communication System;506
1.46.4.1;44.4.1 The Vehicle Subsystem;506
1.46.4.2;44.4.2 The Trackside Subsystem;506
1.46.4.3;44.4.3 The Central Control Subsystem;507
1.46.4.4;44.4.4 The Roaming Subsystem;508
1.46.5;44.5 Performance Analysis;509
1.46.6;44.6 Conclusion;510
1.46.7;References;510
1.47;45 PROMESPAR: A High Performance Computing Implementation of the Regional Atmospheric Model PROMES;512
1.47.1;45.1 Introduction;512
1.47.2;45.2 The Regional Atmospheric Model PROMES;513
1.47.3;45.3 PROMESPAR: A Distributed Memory Implementation of PROMES;515
1.47.4;45.4 Experimental Results;517
1.47.5;45.5 Conclusion;520
1.47.6;References;521
1.48;46 Transparent Integration of a Low-Latency Linux Driver for Dolphin SCI and DX;524
1.48.1;46.1 Introduction;524
1.48.2;46.2 Dolphin's High-Speed Interconnects;526
1.48.2.1;46.2.1 Scalable Coherent Interface (SCI);526
1.48.2.2;46.2.2 Dolphin DX;526
1.48.2.3;46.2.3 Dolphin Software Stack;526
1.48.3;46.3 Architecture of ETHOM;527
1.48.3.1;46.3.1 Configuration;527
1.48.3.2;46.3.2 Connection Establishment;528
1.48.3.3;46.3.3 Communication Phase;529
1.48.4;46.4 Performance Evaluation;531
1.48.4.1;46.4.0.1 Latency;531
1.48.4.2;46.4.0.2 Bandwidth;531
1.48.5;46.5 Conclusions;533
1.48.6;References;534
1.49;47 Effect of Dyslipidemia on a Simple Morphological Feature Extracted from Photoplethysmography Flow Mediated Dilation;535
1.49.1;47.1 Introduction;535
1.49.1.1;47.1.1 Vascular Endothelial Dysfunction;536
1.49.1.2;47.1.2 Flow Mediated Dilation;536
1.49.2;47.2 Methods;537
1.49.2.1;47.2.1 Data Acquisition;537
1.49.2.2;47.2.2 Signal Processing;538
1.49.2.3;47.2.3 Feature Definition;539
1.49.3;47.3 Results;540
1.49.4;47.4 Conclusion;544
1.49.5;References;544
1.50;48 Study of the General Solution for the Two-Dimensional Electrical Impedance Equation;546
1.50.1;48.1 Introduction;546
1.50.2;48.2 Preliminaries;547
1.50.2.1;48.2.1 Elements of Applied Quaternionic Analysis;547
1.50.2.2;48.2.2 Elements of Applied Pseudoanalytic Function Theory;548
1.50.3;48.3 Quaternionic Reformulation of the Electrical Impedance Equation, and Its Relation with the Vekua Equation;552
1.50.3.1;48.3.1 The Two-Dimensional Case;552
1.50.3.2;48.3.2 Explicit Generating Sequence for the Case When Is a Separable-Variables Function;554
1.50.4;48.4 Conclusions;555
1.50.5;References;556
1.51;49 Biological Application of Widefield Surface Plasmon Resonance Microscope to Study Cell/Surface Interactions and the Effect of TGF-3, HCL and BSA/HCL on Cell Detachment Assay of Bone Cells Monolayer;558
1.51.1;49.1 Introduction;559
1.51.2;49.2 Aims and Objectives;560
1.51.3;49.3 Materials and Methods;560
1.51.4;49.4 Results and Discussion;561
1.51.4.1;49.4.1 WSPR Image Analyses;563
1.51.5;49.5 Statistical Analysis;565
1.51.6;49.6 Conclusions;565
1.51.7;References;565
1.52;50 Application of a Novel Widefield Surface Plasmon Resonance Microscope in Cell Imaging and Wound Closure Properties of TGF-3, BSA/HCl and HCl in Cultured Human Bone Cell Monolayer;567
1.52.1;50.1 Introduction;568
1.52.2;50.2 Aims and Objectives;569
1.52.3;50.3 Materials and Methods;569
1.52.3.1;50.3.1 Cell Culture;569
1.52.3.2;50.3.2 SP Substrate Preparation;569
1.52.3.3;50.3.3 Wound Healing;570
1.52.4;50.4 Results and Discussion;571
1.52.4.1;50.4.1 Cell Culture;571
1.52.4.2;50.4.2 Cell Behaviour and Morphology;574
1.52.4.3;50.4.3 WSPR Image Analyses;575
1.52.5;50.5 Statistical Analysis;575
1.52.6;50.6 Conclusions;575
1.52.7;References;577
1.53;51 Speech Rehabilitation Methods for Laryngectomised Patients;578
1.53.1;51.1 Introduction;579
1.53.2;51.2 Current Methods of Speech Rehabilitation;580
1.53.2.1;51.2.1 Oesophageal Speech;580
1.53.2.2;51.2.2 Tracheoesophageal Puncture (TEP);580
1.53.2.3;51.2.3 Electrolarynx;581
1.53.3;51.3 Whispered and Phonated Speech;581
1.53.4;51.4 Speech Regeneration;583
1.53.5;51.5 Summary;587
1.53.6;References;587
1.54;52 Study of the Tip Surface Morphology of GlassMicropipettes and Its Effects on Giga-Seal Formation;589
1.54.1;52.1 Introduction;590
1.54.2;52.2 3D Reconstruction of Micropipette Tip;591
1.54.3;52.3 Focused Ion Beam Polishing;593
1.54.4;52.4 Patch Clamping Experiments;596
1.54.5;52.5 Discussions and Conclusions;598
1.54.6;References;598
1.55;53 Effect of Canned Cycles on Drilled Hole Quality;600
1.55.1;53.1 Introduction;600
1.55.2;53.2 Drilling Canned Cycle;601
1.55.3;53.3 Scope;602
1.55.4;53.4 Experimental Work;602
1.55.5;53.5 Results and Analysis;603
1.55.5.1;53.5.1 Diameter Error;603
1.55.5.2;53.5.2 Circularity;606
1.55.5.3;53.5.3 Surface Roughness;609
1.55.6;53.6 Concluding Remarks;611
1.55.7;References;611
1.56;54 Micro Machine Parts Fabricated from Aqueous Based Stainless Steel Slurry;613
1.56.1;54.1 Introduction;613
1.56.2;54.2 SU-8 Master Moulds and Their Negative Replicas;614
1.56.3;54.3 Preparing Stainless Steel Slurry;615
1.56.4;54.4 De-binding and Sintering;616
1.56.5;54.5 Results and Discussions;616
1.56.5.1;54.5.1 Optimization of the Slurry Properties;616
1.56.5.2;54.5.2 Green Micro Parts;618
1.56.5.3;54.5.3 Sintered Micro Components;618
1.56.5.4;54.5.4 Density of the Sintered Parts;618
1.56.6;54.6 Conclusions;620
1.56.7;References;621
1.57;55 Voxel-Based Component Description for Functional Graded Parts;622
1.57.1;55.1 Introduction;622
1.57.2;55.2 Concept of the Work;625
1.57.3;55.3 Procedure Model;625
1.57.3.1;55.3.1 Component Construction;625
1.57.3.2;55.3.2 Determination of the Requirements;626
1.57.3.3;55.3.3 Voxelisation of the Component Model;627
1.57.3.4;55.3.4 Allocation of the Component Properties;628
1.57.3.5;55.3.5 Interpolation of the Properties;628
1.57.4;55.4 Prototype of the Voxelisation Tool;629
1.57.5;55.5 Prospect;631
1.57.6;55.6 Conclusion;631
1.57.7;References;632
1.58;56 A Multi-Parametric Analysis of Drift Flux Models to Pipeline Applications;633
1.58.1;56.1 Introduction;633
1.58.2;56.2 Application to a Vertical Pipe Considering the Buoyancy Effect;634
1.58.3;56.3 Drift Flux Models as Applied to Wear Rate in Horizontal Pipelines;638
1.58.3.1;56.3.1 The Continuous Model;639
1.58.3.2;56.3.2 Particle Equation of Motion;640
1.58.3.3;56.3.3 The Erosion Prediction Equation;642
1.58.4;56.4 Conclusion;642
1.58.5;References;642
1.59;57 Influence of Preventive Maintenance Frequency on Manufacturing Systems Performances;644
1.59.1;57.1 Introduction;644
1.59.2;57.2 Economic Optimization of Preventive Maintenance Schedule;645
1.59.3;57.3 Queueing Models for Unreliable Machines;646
1.59.3.1;57.3.1 Model I;647
1.59.3.1.1;57.3.1.1 Model Ia;648
1.59.3.1.2;57.3.1.2 Model Ib;649
1.59.3.2;57.3.2 Model II;649
1.59.4;57.4 Analysis Results;650
1.59.5;57.5 Conclusion;654
1.59.6;References;654
1.60;58 On the Numerical Prediction of Stability in Thin Wall Machining;656
1.60.1;58.1 Introduction;656
1.60.2;58.2 Application of the Nyquist Criterion;659
1.60.3;58.3 System's Transfer Function;660
1.60.3.1;58.3.1 Damping Ratio Prediction;661
1.60.3.2;58.3.2 Damping Matrix;663
1.60.3.3;58.3.3 Examples;663
1.60.4;References;665
1.61;59 Risk Analysis of ERP Projects in the Manufacturing SMES: Case Study;666
1.61.1;59.1 Introduction;666
1.61.2;59.2 Risks in ERP Systems;668
1.61.2.1;59.2.1 Main Characteristics of ERP Projects;668
1.61.2.2;59.2.2 Risk Factors;668
1.61.3;59.3 Risk Management Tools;670
1.61.3.1;59.3.1 Risk Analysis Method;670
1.61.3.2;59.3.2 Characteristics Analysis Method;670
1.61.4;59.4 Case Study;671
1.61.4.1;59.4.1 Company A;672
1.61.4.2;59.4.2 Company B;673
1.61.5;59.5 Conclusion;674
1.61.6;References;674
1.62;60 Sleeping in Sitting Posture Analysis of Economy Class Aircraft Passenger;677
1.62.1;60.1 Introduction;677
1.62.2;60.2 Aircraft Seat;678
1.62.3;60.3 Relationship of Subjective Method to Comfort and Discomfort;679
1.62.4;60.4 Relationship of Objective Method to Comfort and Discomfort;680
1.62.5;60.5 Sleeping Posture Analysis;680
1.62.5.1;60.5.1 Observation on Sleeping Posture;681
1.62.5.2;60.5.2 Objective Analysis on Sitting Posture While Sleeping;681
1.62.6;60.6 Conclusion;684
1.62.7;References;686
1.63;61 A Scheduling Method for Cranes in a Container Yard with Inter-Crane Interference;688
1.63.1;61.1 Introduction;688
1.63.2;61.2 Model Development;691
1.63.3;61.3 Hybrid Genetic Algorithm and Tabu Search;693
1.63.3.1;61.3.1 Representation;694
1.63.3.2;61.3.2 Fitness Evaluation and Selection Operation;694
1.63.3.3;61.3.3 Tabu Search Crossover (TSC) Operation;695
1.63.3.4;61.3.4 Tabu Search Mutation (TSM) operation;695
1.63.4;61.4 Computation Results;696
1.63.4.1;61.4.1 Comparison with Branch and Bound Algorithm;696
1.63.4.2;61.4.2 Comparison with Genetic Algorithm;697
1.63.5;61.5 Conclusion;698
1.63.6;References;698

Leseproben

"Chapter 14 Software Fault Tolerance: An Aspect Oriented Approach (p. 153-154)

Kashif Hameed, RobWilliams, and Jim Smith

Abstract Software fault tolerance demands additional tasks like error detection and recovery through executable assertions, exception handling, diversity and redundancy based mechanisms. These mechanisms do not come for free; rather they introduce additional complexity to the core functionality. This paper presents light weight error detection and recovery mechanisms based on the rate of change in signal or data values. Maximum instantaneous and mean rates are used as plausibility checks to detect erroneous states and recover. These plausibility checks are exercised in a novel aspect oriented software fault tolerant design framework that reduces the additional logical complexity. A Lego NXT Robot based case study has been completed to demonstrate the effectiveness of the proposed design framework.

Keywords Aspect oriented design and programming - executable assertions - exception handling - fault tolerance - plausibility checks

14.1 Introduction

Adding fault tolerance measures to safety critical and mission critical applications introduces additional complexity to the core application. By incorporating handler code, for error detection, checkpointing, exception handling, and redundancy/ diversity management, the additional complexity may adversely affect the dependability of a safety critical or mission critical system. One of the solutions to reduce this complexity is to separate and modularize the extra, cross-cutting concerns from the true functionality. At the level of design and programming, several approaches have been utilized that aim at separating functional and non-functional aspects.

Component level approach like IFTC [1], computational reflection and meta-object protocol based MOP [2] have shown that dependability issues can be implemented independently of functional requirements. The evolving area of Aspect-Oriented Programming & Design (AOP&D) presents the same level of independence by supporting the modularized implementation of crosscutting concerns. Aspect-oriented language extensions, like AspectJ [3] and AspectCCC [4] provide mechanisms like Advice (behavioural and structural changes) that may be applied by a pre-processor at specific locations in the program called join point.

These are designated by pointcut expressions. In addition to that, static and dynamic modifications to a program are incorporated by slices which can affect the static structure of classes and functions. In the context of fault tolerance, an induced fault can activate an error that changes the behaviour of the program and may lead to system failure. In order to tolerate a fault, abnormal behaviour must be detected and transformed back by introducing additional behaviour changes (Exception Handler) or alternate structure adoption (Recovery Blocks, N-Version Programming) strategies."