Preface
Acknowledgements
1 Chaos and Communications
1.1 Historical Account
1.2 Chaos
1.3 Quantifying Chaotic Behavior
1.4 Properties of Chaos
1.5 Chaos-Based Communications
1.6 Communcations Using Chaos as Carriers
1.7 Remarks on Chaos-Based Communications
2 Reconstruction of Signals
2.1 Reconstruction of System Dynamics
2.2 Differentable Embeddings
2.3 Phase Space Reconstruction-Example
2.4 Problems and Research Approaches
3 Fundamentals of Neural Networks
3.1 Motivation
3.2 Benefits of Neural Networks
3.3 Radial Basis Function Neural Networks
3.4 Recurrent Neural Networks
4 Signal Reconstrucion in Noisefree and Distortionless Channels
4.1 Reconstruction of Attractor for Continuous Time-Varying Systems
4.2 Reconstruction and Observability
4.3 Communication Based on Reconstruction Approach
4.4 Reconstruction of Attractor of r Discrete Time-Varying Systems
4.5 Summary
5 Signal Reconsturcation from a Filtering Viewpoint:Theory
5.1 The Kalman Filter and Extended Kalman Filter
5.2 The Unscented Kalman Filter
5.3 Summary
6 Signal Reconstruction from a Filtering Viewpoint:Application
6.1 Interoduction
6.2 Filtering of Noisy Chaotic Signals
6.3 Blind Equalization for Fading Channels
6.4 Summary
7 Signal Reconstruction in Noisy Channels
8 Signal Reconstruction in Noisy Distorted Channels
9 Chaotic Network Synchronization and Its Applications in Communications
10 Conclusions
Bibliography
Index