Ao / Gelman Advances in Electrical Engineering and Computational Science

1. Auflage 2009
ISBN: 978-90-481-2311-7
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 726 Seiten

Advances in Electrical Engineering and Computational Science 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. Advances in Electrical Engineering and Computational Science will offer the state of art of tremendous advances in electrical engineering and computational science and also serve as an excellent reference work for researchers and graduate students working with/on electrical engineering and computational science.

Autoren/Hrsg.

Ao, Sio-Iong

Gelman, Len

Inhaltsverzeichnis

1;Preface;6
2;Contents;7
3;Chapter 1 Acoustic Behavior of Squirrel Cage Induction Motors;27
3.1;1.1 Introduction;27
3.2;1.2 Acoustic Noise Physics;28
3.3;1.3 Mutation of Stator and Rotor Parts;31
3.4;1.4 Direct Comparison of Sound Pressure Levels for Different Motor Designs;32
3.5;1.5 Conclusion;36
3.6;References;37
4;Chapter 2 Model Reduction of Weakly Nonlinear Systems;38
4.1;2.1 Introduction;38
4.2;2.2 Perturbative Approximation of Nonlinear Systems;39
4.3;2.3 KRYLOV-Based Model Reduction;41
4.4;2.4 Scale Invariance Property;42
4.5;2.5 KRYLOV Reduction of Perturbative Representation;44
4.6;2.6 Illustrative Example;44
4.7;2.7 Conclusions;46
4.8;References;47
5;Chapter 3 Investigation of a Multizone Drift Doping Based Lateral Bipolar Transistor on Buried Oxide Thick Step;48
5.1;3.1 Introduction;48
5.2;3.2 Ideal, Conventional and Proposed Devices;49
5.3;3.3 Simulation Results and Discussion;50
5.4;3.4 Conclusion;57
5.5;References;57
6;Chapter 4 Development of a Battery Charging Management Unit for Renewable Power Generation;58
6.1;4.1 Introduction;58
6.2;4.2 Energy Conversion Modes;60
6.3;4.3 Lead Acid Battery;60
6.4;4.4 Battery Charging and Discharging Strategies;62
6.5;4.5 System Implementation;68
6.6;4.6 Conclusions and Future Works;70
6.7;References;71
7;Chapter 5 Optical CDMA Transceiver Architecture: Polarization Modulation with Dual-Balanced Detection;72
7.1;5.1 Introduction;72
7.2;5.2 POLSK–OCDMA Transmitter;73
7.3;5.3 Analysis of POLSK–OCDMA Receiver;75
7.4;5.4 Discussion of Results;80
7.5;5.5 Conclusion;81
7.6;References;82
8;Chapter 6 Template Based: A Novel STG Based Logic Synthesis for Asynchronous Control Circuits;83
8.1;6.1 Introduction;83
8.2;6.2 Petri-Net and Signal Transition Graph;85
8.3;6.3 Asynchronous DMA Controller Specification;86
8.4;6.4 Synthesis Using State Based Method;88
8.5;6.5 Synthesis Using Structural Encoding Method;90
8.6;6.6 Synthesis Using State Based Method;91
8.7;6.7 Result;96
8.8;6.8 Concluding Remarks;97
8.9;References;98
9;Chapter 7 A Comparison of Induction Motor Speed Estimation Using Conventional MRAS and an AI-Based MRAS Parallel System;99
9.1;7.1 Introduction;99
9.2;7.2 Speed Estimation Using Conventional Model Reference Adaptive System;100
9.3;7.3 Artificial Intelligence-Based Model Reference Adaptive System;101
9.4;7.4 MRAS Based Two-Layer ANN Speed Estimator with Dynamic Reference Model;102
9.5;7.5 Simulation Results and Discussion;105
9.6;7.6 Conclusion;107
9.7;References;108
10;Chapter 8 A New Fuzzy-Based Additive Noise Removal Filter;110
10.1;8.1 Introduction;110
10.2;8.2 Conventional Noise Removal Techniques;112
10.3;8.3 Proposed Fuzzy Noise Removal Filter;113
10.4;8.4 Results and Discussion;116
10.5;8.5 Conclusion;119
10.6;References;119
11;Chapter 9 Enhancement of Weather Degraded Color Images and Video Sequences UsingWavelet Fusion;121
11.1;9.1 Introduction;121
11.2;9.2 Atmospheric Scattering Models;123
11.3;9.3 Contrast Correction;124
11.4;9.4 Intensity Based Enhancement;126
11.5;9.5 Wavelet Fusion;126
11.6;9.6 Video Enhancement;127
11.7;9.7 Performance Analysis;127
11.8;9.8 Results and Discussion;128
11.9;9.9 Conclusion;129
11.10;References;130
12;Chapter 10 A GA-Assisted Brain Fiber Tracking Algorithm for DT-MRI Data;132
12.1;10.1 Introduction;132
12.2;10.2 Brief Tracking Algorithm Description;134
12.3;10.3 Proposed GA for Parameter Estimation;137
12.4;10.4 Numerical Results;139
12.5;10.5 Conclusions;142
12.6;References;143
13;Chapter 11 A Bridge-Ship Collision Avoidance System Based on FLIR Image Sequences;144
13.1;11.1 Introduction;144
13.2;11.2 The FLIR Video Surveillance System;146
13.3;11.3 The Detection Algorithm for Moving Ships;147
13.4;11.4 Experimental Results and Discussion;151
13.5;11.5 Conclusion;151
13.6;References;154
14;Chapter 12 A Gray Level Feature Detector and Its Hardware Architecture;155
14.1;12.1 Introduction;155
14.2;12.2 Feature Point Detection Model;156
14.3;12.3 Hardware Implementation;159
14.4;12.4 Synthesis Results and Analysis;162
14.5;12.5 Conclusions;164
14.6;References;164
15;Chapter 13 SVD and DWT-SVD Domain Robust Watermarking using Differential Evolution Algorithm;166
15.1;13.1 Introduction;166
15.2;13.2 SVD Domain and DWT-SVD DomainWatermarking Techniques;168
15.3;13.3 OptimalWatermarkings using DE;170
15.4;13.4 Results;173
15.5;13.5 Conclusions;174
15.6;References;177
16;Chapter 14 Design and Performance Evaluation of a Prototype Large Ring PET Scanner;179
16.1;14.1 Introduction;179
16.2;14.2 The Macropet Design;180
16.3;14.3 Performance Evaluation;182
16.4;14.4 Conclusions;189
16.5;References;189
17;Chapter 15 RobustWavelet-Based VideoWatermarking Using Edge Detection;191
17.1;15.1 Introduction;191
17.2;15.2 The Watermark Embedding Process;192
17.3;15.3 The Watermark Detection Process;195
17.4;15.4 Experimental Results;195
17.5;15.5 Conclusion;199
17.6;References;200
18;Chapter 16 High Speed Soft Computing Based Circuit for Edges Detection in Images;201
18.1;16.1 Introduction;201
18.2;16.2 Edge Detection Algorithm;202
18.3;16.3 Hardware Implementation;206
18.4;16.4 Conclusion;211
18.5;References;211
19;Chapter 17 A Comparison Between 3D OSEM and FBP Image Reconstruction Algorithms in SPECT;213
19.1;17.1 Introduction;213
19.2;17.2 Background;214
19.3;17.3 A Comparison of 3D OSEM with FBP Image Reconstruction Algorithms;219
19.4;17.4 Summary;222
19.5;References;223
20;Chapter 18 Performance Improvement ofWireless MAC Using Non-Cooperative Games;225
20.1;18.1 Introduction;225
20.2;18.2 Background;227
20.3;18.3 Game-Theoretic Model of Medium Access Control;227
20.4;18.4 Distributed Mechanisms to Achieve Nash Equilibrium;230
20.5;18.5 Performance Evaluation;231
20.6;18.6 Discussion;235
20.7;18.7 Conclusion;235
20.8;References;236
21;Chapter 19 Performance Evaluation of Mobile Ad Hoc Networking Protocols;237
21.1;19.1 Introduction;237
21.2;19.2 Mobile Ad Hoc Network’s Routing Protocols;239
21.3;19.3 Simulation Setup;240
21.4;19.4 Simulation Environment;241
21.5;19.5 Simulation Results;242
21.6;19.6 Conclusion;246
21.7;References;247
22;Chapter 20 IEEE 802.11E Block Acknowledgement Policies;248
22.1;20.1 Introduction;248
22.2;20.2 IEEE 802.11e and Block Acknowledgement Description;249
22.3;20.3 State of the Art;251
22.4;20.4 System, Scenario and Assumptions;252
22.5;20.5 Results;254
22.6;20.6 Conclusions;258
22.7;References;258
23;Chapter 21 Routing in a Custom-Made IEEE 802.11E Simulator;260
23.1;21.1 Introduction;260
23.2;21.2 PreviousWork;261
23.3;21.3 Overview;262
23.4;21.4 Results;263
23.5;21.5 Conclusions;269
23.6;References;270
24;Chapter 22 Web-Based Management of Distributed Services;271
24.1;22.1 Introduction;271
24.2;22.2 RelatedWork;272
24.3;22.3 WebDMF: A Web-Based Management Framework for Distributed Services;273
24.4;22.4 Implementation and Performance Evaluation;278
24.5;22.5 Conclusions;280
24.6;References;281
25;Chapter 23 Image Index Based Digital Watermarking Technique for Ownership Claim and Buyer Fingerprinting;283
25.1;23.1 Introduction;283
25.2;23.2 Image Key and Buyer Fingerprint;285
25.3;23.3 Proposed Technique;286
25.4;23.4 Results and Discussion;288
25.5;23.5 Conclusion;292
25.6;References;292
26;Chapter 24 Reverse Engineering: EDOWAWorm Analysis and Classification;293
26.1;24.1 Introduction;293
26.2;24.2 Method of Testing;294
26.3;24.3 EDOWA Classification;297
26.4;24.4 Conclusion;303
26.5;References;304
27;Chapter 25 Reconfigurable Hardware Implementation of a GPS-Based Vehicle Tracking System;305
27.1;25.1 Introduction;305
27.2;25.2 Upgrading the Aram Locator GPS System;306
27.3;25.3 The FPGA-Based Aram System;307
27.4;25.4 Performance Analysis and Evaluation;312
27.5;25.5 Conclusion;314
27.6;References;314
28;Chapter 26 Unknown Malicious Identification;316
28.1;26.1 Introduction;316
28.2;26.2 RelatedWork;317
28.3;26.3 Na¨ive Bayesian and Application;318
28.4;26.4 Increment Na¨ive Bayes;320
28.5;26.5 Experimental Results;324
28.6;26.6 Conclusion;326
28.7;References;326
29;Chapter 27 Understanding Programming Language Semantics for the Real World;328
29.1;27.1 Introduction;328
29.2;27.2 Extension of the Applications to a Real World;330
29.3;27.3 Modularization and Communication in the ChaoticWorld;331
29.4;27.4 Parallel or Concurrency Is the Nature of theWorld;334
29.5;27.5 Accuracy and Efficiency Are Required by This Sophicificate World;336
29.6;27.6 Exception Handling Is the Safe Guard in the Dangerous World;339
29.7;27.7 Conclusion;341
29.8;References;342
30;Chapter 28 Analysis of Overhead Control Mechanisms in Mobile AD HOC Networks;344
30.1;28.1 Introduction;344
30.2;28.2 Theoretical Analysis of Overhead in Hierarchical Routing Scheme;345
30.3;28.3 Theoretical Analysis and Overhead Minimizing Techniques for AD HOC Networks Using Clustering Mechanisms;347
30.4;28.4 Minimizing Overhead in AD HOC Networks by Header Compression;351
30.5;28.5 Minimizing Overhead for AD HOC Networks Connected to Internet;352
30.6;References;354
31;Chapter 29 Context Aware In-Vehicle Infotainment Interfaces;357
31.1;29.1 In-Vehicle Infotainment;358
31.2;29.2 Context Aware Interaction;359
31.3;29.3 Interaction Architecture;360
31.4;29.4 Application Development;365
31.5;29.5 Summary;367
31.6;References;368
32;Chapter 30 Facial Expression Analysis Using PCA;369
32.1;30.1 Introduction;369
32.2;30.2 Background and Related Work;370
32.3;30.3 Method;371
32.4;30.4 Results;375
32.5;30.5 Conclusion;377
32.6;References;377
33;Chapter 31 The Design of a USART IP Core;379
33.1;31.1 Introduction;379
33.2;31.2 USART Theory of Operation;380
33.3;31.3 Specifications of the Developed USART;382
33.4;31.4 USART System Design Methodology;382
33.5;31.5 Testing and Verification Procedures;387
33.6;References;390
34;Chapter 32 Multilayer Perceptron Training Optimization for High Speed Impacts Classification;391
34.1;32.1 Introduction;391
34.2;32.2 Ballistic Impact;392
34.3;32.3 Use of ANN in Impacts Situations;394
34.4;32.4 Solution Proposed;397
34.5;32.5 Evaluation;399
34.6;32.6 Conclusions;401
34.7;References;402
35;Chapter 33 Developing Emotion-Based Pet Robots;403
35.1;33.1 Introduction;403
35.2;33.2 Building Emotion-Based Pet Robots;404
35.3;33.3 Experiments and Results;409
35.4;33.4 Conclusions and Future Work;412
35.5;References;414
36;Chapter 34 Designing Short Term Trading Systems with Artificial Neural Networks;415
36.1;34.1 Introduction;415
36.2;34.2 Review of Literature;416
36.3;34.3 Methodology;417
36.4;34.4 Results;419
36.5;34.5 Conclusions;422
36.6;References;423
37;Chapter 35 Reorganising Artificial Neural Network Topologies Complexifying Neural Networks by Reorganisation;424
37.1;35.1 Introduction;424
37.2;35.2 Background;425
37.3;35.3 Neuroscientific Foundations;426
37.4;35.4 Reorganising Neural Networks;426
37.5;35.5 Experiments and Results;429
37.6;35.6 Conclusion;433
37.7;References;434
38;Chapter 36 Design and Implementation of an E-Learning Model by Considering Learner’s Personality and Emotions;435
38.1;36.1 Introduction;435
38.2;36.2 PreviousWorks;436
38.3;36.3 Psychological Principles;436
38.4;36.4 Proposed Model;437
38.5;36.5 Virtual Classmate Model;439
38.6;36.6 Simulation of Proposed Model;442
38.7;36.7 Implementation;442
38.8;36.8 Results;444
38.9;36.9 Conclusion and Future Works;445
38.10;References;445
39;Chapter 37 A Self Progressing Fuzzy Rule-Based System for Optimizing and Predicting Machining Process;447
39.1;37.1 Introduction;447
39.2;37.2 System Configuration;449
39.3;37.3 The Self-Development Mode;450
39.4;37.4 Optimization and Prediction of Machining Process;454
39.5;37.5 Conclusion;458
39.6;References;458
40;Chapter 38 Selection of Ambient Light for Laser Digitizing of Quasi-Lambertian Surfaces;459
40.1;38.1 Introduction;459
40.2;38.2 Objectives;461
40.3;38.3 Configuration of the Tests;461
40.4;38.4 Experimental Procedure;463
40.5;38.5 Analysis Criteria;464
40.6;38.6 Results Discussion;465
40.7;38.7 Conclusions;469
40.8;References;469
41;Chapter 39 Ray-Tracing Techniques Applied to the Accessibility Analysis for the Automatic Contact and Non Contact Inspection;470
41.1;39.1 Introduction;470
41.2;39.2 AnalysisMethodology;471
41.3;39.3 Analysis Considering Inspection Devices as Infinite Half-Lines;471
41.4;39.4 Interference Analysis Considering Real Dimensions of the Inspection Device;473
41.5;39.5 Clustering;475
41.6;39.6 Application Results;476
41.7;39.7 Conclusions;479
41.8;References;480
42;Chapter 40 Detecting Session Boundaries to Personalize Search Using a Conceptual User Context;481
42.1;40.1 Introduction;481
42.2;40.2 RelatedWorks;482
42.3;40.3 Detecting Session Boundaries to Personalize Search;483
42.4;40.4 Experimental Evaluation;486
42.5;40.5 Conclusion and Outlook;491
42.6;References;491
43;Chapter 41 MiningWeather Information in Dengue Outbreak: Predicting Future Cases Based onWavelet, SVM and GA;493
43.1;41.1 Introduction;494
43.2;41.2 Model Construction;495
43.3;41.3 Results and Discussions;498
43.4;41.4 Conclusion;502
43.5;References;503
44;Chapter 42 PC Tree: Prime-Based and Compressed Tree for Maximal Frequent Patterns Mining;505
44.1;42.1 Problem of Maximal Frequent Patterns Mining;505
44.2;42.2 RelatedWork;506
44.3;42.3 Proposed Method;507
44.4;42.4 Experimental Results;511
44.5;42.5 Conclusion and Future Works;513
44.6;References;514
45;Chapter 43 Towards a New Generation of Conversational Agents Based on Sentence Similarity;515
45.1;43.1 Introduction;515
45.2;43.2 Sentence Similarity Measure;517
45.3;43.3 Traditional CA Scripting;519
45.4;43.4 CA Scripting Using Sentence Similarity;520
45.5;43.5 Experimental Methodology;521
45.6;43.6 Results and Discussion;522
45.7;43.7 Conclusions and Further Work;523
45.8;References;524
46;Chapter 44 Direction-of-Change Financial Time Series Forecasting Using Neural Networks: A Bayesian Approach;525
46.1;44.1 Introduction;525
46.2;44.2 MLPs for Financial Prediction;526
46.3;44.3 Bayesian Methods for MLPs;527
46.4;44.4 Empirical Results;529
46.5;44.5 Conclusions;533
46.6;References;534
47;Chapter 45 A Dynamic Modeling of Stock Prices and Optimal Decision Making Using MVP Theory;535
47.1;45.1 Introduction;536
47.2;45.2 Modeling and Prediction;537
47.3;45.3 Virtual Stock Market;539
47.4;45.4 The Market Simulation Results;542
47.5;45.5 Conclusion;544
47.6;Appendix;545
47.7;References;546
48;Chapter 46 A Regularized Unconstrained Optimization in the Bond Portfolio Valuation and Hedging;548
48.1;46.1 Introduction;548
48.2;46.2 Option-Adjusted Spread Analysis;550
48.3;46.3 The Spread Variance Minimization Approach;551
48.4;46.4 Numerical Method;552
48.5;46.5 Numerical Results;553
48.6;46.6 Conclusions;557
48.7;References;558
49;Chapter 47 Approximation of Pareto Set in Multi Objective Portfolio Optimization;559
49.1;47.1 Introduction;559
49.2;47.2 Multi-Objective Portfolio Optimization Problem;560
49.3;47.3 Multi-Objective Optimization by the Method of AdjustableWeights;561
49.4;47.4 Evolutionary Methods for Multi-Objective Optimization;564
49.5;47.5 Description of Experimental Investigation;566
49.6;47.6 Discussion on Experimental Results;567
49.7;47.7 Conclusions;569
49.8;References;569
50;Chapter 48 The Scaling Approach for Credit Limit Management and Its Application;571
50.1;48.1 Introduction;571
50.2;48.2 Analysis;573
50.3;48.3 Analysis Results;579
50.4;48.4 Conclusions;581
50.5;References;581
51;Chapter 49 Expected Tail Loss Efficient Frontiers for CDOS of Bespoke Portfolios Under One-Factor Copula Marginal Distributions;583
51.1;49.1 Introduction;583
51.2;49.2 Bespoke CDO Mechanics;584
51.3;49.3 Heavy-Tail Modelling and Copulas;585
51.4;49.4 Credit Risk Measures;586
51.5;49.5 Copula Marginal ETL Efficient Frontier for the CDO Collateral;589
51.6;49.6 Concluding Remarks;593
51.7;References;593
52;Chapter 50 Data Mining for Retail Inventory Management;595
52.1;50.1 Introduction;595
52.2;50.2 Literature Review;597
52.3;50.3 Purchase Dependencies in Retail Sale in the Context of Inventory Management;598
52.4;50.4 Model Development;599
52.5;50.5 Illustration with an Example;600
52.6;50.6 Case Discussion;602
52.7;50.7 Conclusions;604
52.8;References;605
53;Chapter 51 Economic Process Capability Index for Product Design and Process Planning Economic Process Capability Index;607
53.1;51.1 Introduction;607
53.2;51.2 Process Capability Indices (PCI);609
53.3;51.3 Proposed Process Capability Index;611
53.4;51.4 Summary;616
53.5;References;616
54;Chapter 52 Comparing Different Approaches for Design of Experiments (DoE);618
54.1;52.1 Introduction;618
54.2;52.2 Approaches to Design of Experiments;619
54.3;52.3 Limitations of the Different Approaches;623
54.4;52.4 Conclusions and Recommendations;625
54.5;References;626
55;Chapter 53 Prevention of Workpice Form Deviations in CNC Turning Based on Tolerance Specifications;629
55.1;53.1 Introduction;630
55.2;53.2 Form Errors in Turning;630
55.3;53.3 Calculation of Radial Deviations;631
55.4;53.4 Relationship Between Deviations and Tolerances;633
55.5;53.5 Deviations as Optimization Conditions;638
55.6;53.6 Conclusions;638
55.7;References;639
56;Chapter 54 Protein–Protein Interaction Prediction Using Homology and Inter-domain Linker Region Information;640
56.1;54.1 Introduction;640
56.2;54.2 Method;642
56.3;54.3 Experimental Work;646
56.4;54.4 Results and Discussion;647
56.5;54.5 Conclusion;649
56.6;References;649
57;Chapter 55 Cellular Computational Model of Biology;651
57.1;55.1 Introduction;651
57.2;55.2 List of Variables;654
57.3;55.3 Determining mRNA Polymerases, mR(n + 1);654
57.4;55.4 Determining;655
57.5;-Galactosidases,;655
57.6;55.5 Determining Lactoses with in and out the Cell, Lext(n C 1) and L(n C 1);657
57.7;55.6 Determining ATP Molecules, A(n);658
57.8;55.7 The System Structure;660
57.9;55.8 In Comparison to Traditional Biological Experiment;661
57.10;55.9 Conclusion;661
57.11;55.10 Discussion;661
57.12;References;662
58;Chapter 56 Patient Monitoring: Wearable Device for Patient Monitoring;663
58.1;56.1 Introduction;663
58.2;56.2 Wearable Monitoring Device;665
58.3;56.3 Functionality;666
58.4;56.4 Firmware Issue;669
58.5;56.5 Conclusion;672
58.6;References;672
59;Chapter 57 Face Recognition and Expression Classification;673
59.1;57.1 Introduction;673
59.2;57.2 Background and Related Work;674
59.3;57.3 Discrete Cosine Transform;674
59.4;57.4 Radial Basis Function;675
59.5;57.5 Method;675
59.6;57.6 Results;681
59.7;57.7 Conclusion;682
59.8;References;683
60;Chapter 58 Spiking Neurons and Synaptic Stimuli: Neural Response Comparison Using Coincidence-Factor;684
60.1;58.1 Introduction;684
60.2;58.2 Neuronal Model and Synapse;685
60.3;58.3 Comparison of Two Spike Trains;687
60.4;58.4 Conclusions;693
60.5;References;693
61;Chapter 59 Overcoming Neuro-Muscular Arm Impairment by Means of Passive Devices;696
61.1;59.1 Introduction;696
61.2;59.2 Background;697
61.3;59.3 Experimental Test Apparatus Set Up;698
61.4;59.4 Experimental Tests;700
61.5;59.5 Device Evolution;702
61.6;59.6 Conclusions;705
61.7;References;707
62;Chapter 60 EEG Classification of Mild and Severe Alzheimer’s Disease Using Parallel Factor Analysis Method PARAFAC Decomposition of Spectral-Spatial Characteristics of EEG Time Series;708
62.1;60.1 Introduction;708
62.2;60.2 Subjects and EEG Recordings;710
62.3;60.3 Method;710
62.4;60.4 Results;714
62.5;60.5 Discussion;716
62.6;References;717
63;Chapter 61 Feature Selection of Gene Expression Data Based on Fuzzy Attribute Clustering;719
63.1;61.1 Introduction;719
63.2;61.2 RelatedWork;720
63.3;61.3 The Proposed Fuzzy Approach;722
63.4;61.4 Experimental Results;724
63.5;61.5 Conclusion;727
63.6;References;728