| Preface | 5 |
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| Contents | 7 |
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| Development of a Fault Detection Model- Based Controller | 12 |
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| 1 Introduction | 12 |
| 2 Fault Modeling | 13 |
| 3 Fault Detection and Diagnosis (FDD) | 15 |
| 4 Controller Reconfiguration (CR) | 19 |
| 5 Conclusion | 22 |
| References | 23 |
| Sensitivity Generation in an Adaptive BDF- Method | 26 |
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| 1 Introduction | 26 |
| 2 Efficient Solution of the Initial Value Problems | 27 |
| 3 Sensitivity Generation | 28 |
| 4 Numerical Examples | 33 |
| 5 Conclusion and Outlook | 34 |
| References | 35 |
| The gVERSE RF Pulse: An Optimal Approach to MRI Pulse Design | 36 |
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| 1 Introduction to the Problem | 36 |
| 2 MRI Background | 37 |
| 3 The gVERSE Model | 40 |
| 4 Results | 46 |
| 5 Image Reconstruction | 53 |
| 6 Conclusions and Future Work | 56 |
| Future Work | 58 |
| References | 58 |
| Modelling the Performance of the Gaussian Chemistry Code on x86 Architectures | 60 |
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| 1 Introduction | 60 |
| 2 Background | 62 |
| 3 Observed Timings and Hardware Counter Data | 65 |
| 4 Previous Work | 68 |
| 5 Conclusions and Future Work | 68 |
| Acknowledgments | 69 |
| References | 69 |
| Numerical Simulation of the December 26, 2004: Indian Ocean Tsunami | 70 |
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| 1 Introduction | 70 |
| 2 Source and Propagation Models | 71 |
| 3 Tsunami Simulations | 73 |
| 4 Discussion of Results | 74 |
| 5 Final Remarks | 78 |
| 6 Acknowledgments | 78 |
| References | 79 |
| Approximate Dynamic Programming for Generation of Robustly Stable Feedback Controllers | 80 |
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| 1 Introduction | 80 |
| 2 Robust Dynamic Programming with Constraints | 82 |
| 3 Polyhedral Dynamic Programming | 84 |
| 4 Approximate Robust Dynamic Programming | 86 |
| 5 Stable Epigraphs and the Uroborus | 89 |
| 6 Stability of a Tutorial Example | 92 |
| 7 Conclusions | 94 |
| References | 95 |
| Integer Programming Approaches to Access and Backbone IP Network Planning. | 98 |
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| 1 Introduction | 98 |
| 2 Problem Description | 100 |
| 3 Access Network Planning | 103 |
| 4 Backbone Network Planning | 105 |
| 5 Computation | 111 |
| 6 Results | 115 |
| Acknowledgments | 119 |
| References | 119 |
| An Adaptive Fictitious-Domain Method for Quantitative Studies of Particulate Flows | 122 |
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| 1 Introduction | 122 |
| 2 Adaptive Fictitious-Domain Method | 123 |
| 3 Numerical Results | 127 |
| 4 Conclusion | 131 |
| References | 131 |
| Adaptive Sparse Grid Techniques for Data Mining | 132 |
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| 1 Introduction | 132 |
| 2 Solving the System of Linear Equations | 135 |
| 3 Adaptivity | 137 |
| 4 Boundary Considerations | 139 |
| 5 Summary | 141 |
| References | 141 |
| On the Stochastic Geometry of Birth- and- Growth Processes. Application to Material Science, Biology and Medicine | 142 |
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| 1 Introduction | 143 |
| 2 Birth-and-Growth Processes | 144 |
| 3 A Volume Growth Model | 151 |
| 4 Closed Sets as Distributions – The Deterministic Case | 154 |
| 5 Stochastic Geometry | 154 |
| 6 The Hazard Function | 156 |
| 7 Mean Densities of Stochastic Tessellations | 162 |
| 8 Interaction with an Underlying Field | 164 |
| 9 Numerical Simulations | 167 |
| Acknowledgments | 170 |
| References | 170 |
| Inverse Problem of Lindenmayer Systems on Branching Structures | 174 |
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| 1 Introduct
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