Note: Intro -- Preface -- Contents -- Part I RQA Theory -- Chapter 1 Mathematical and Computational Foundations of Recurrence Quantifications -- 1.1 Phase Space Trajectories -- 1.2 Recurrence Plots -- 1.2.1 Definition of Recurrence Plots -- 1.2.2 Structures in Recurrence Plots -- 1.3 Recurrence Quantifications -- 1.3.1 Classical Recurrence Quantification Analysis -- 1.3.2 Extended Recurrence Quantification Analysis -- 1.3.3 Recurrence Time Based Measures -- 1.3.4 Complex Network Based Quantification -- 1.3.5 Advanced Quantification -- 1.3.6 Windowing Techniques -- 1.3.7 Remark on Significance -- 1.3.8 Example: Rössler System with Regime Transitions -- 1.4 Bivariate Extensions of Recurrence Analysis -- 1.4.1 Cross Recurrence Plot -- 1.4.2 Joint Recurrence Plot -- 1.4.3 Comparison Between CRPs and JRPs -- 1.5 Computational Foundations of Recurrence Quantification Analysis -- 1.5.1 Brief Historical Background -- 1.5.2 Computational Strategies -- 1.5.3 Example Program Runs -- 1.5.3.1 Programs RQD, KRQD and JRQD -- 1.5.3.2 Programs RQS, KRQS and JRQS -- 1.5.3.3 Programs RQE, KRQE and JRQE -- 1.5.4 Advanced Topics -- 1.6 Summary -- Appendix: Mathematical Models -- Auto-Regressive Process of 1st Order -- Lorenz System lorenz63 -- Rössler System roessler1976 -- Mutually Coupled Rössler Systems -- References -- Chapter 2 Estimating Kolmogorov Entropy from Recurrence Plots -- 2.1 Introduction -- 2.1.1 Entropy -- 2.1.2 Recurrence Plots -- 2.2 Discrete-State Signals -- 2.2.1 Messages, Symbolic Sequences and Discrete Sources -- 2.2.2 Entropy Rate of Symbolic Sequences -- 2.2.3 Shannon-McMillan-Breiman Theorem -- 2.2.4 RP-Based Estimation of the Entropy (Per Unit Time) -- 2.3 Continuous-State Dynamics -- 2.3.1 Kolmogorov Entropy for a Continuous State System -- 2.3.2 Grassberger and Procaccia Method for Computing K2 -- 2.3.3 RP-Based Method for Computing K2. , 2.4 Some Factors Influencing Computation of Entropy -- 2.4.1 The Notion of ε-Entropy for Analyzing Noisy Dynamics -- 2.4.2 Non-stationarity -- 2.5 Discussion -- References -- Chapter 3 Identifying Coupling Directions by Recurrences -- 3.1 Part I: Estimation of the Direction of the Coupling by Conditional Probabilities of Recurrence -- 3.1.1 Introduction: Part I -- 3.1.2 Detection of the Coupling Direction by Recurrences -- 3.1.3 Numerical Examples -- 3.1.3.1 Strongly Coupled Systems -- 3.1.3.2 Weakly Coupled Systems -- 3.1.3.3 Structurally Different Systems -- 3.1.4 Choice of the Parameters -- 3.1.5 Influence of Noise -- 3.1.6 Passive Experiments -- 3.1.7 Comparison with Other Methods -- 3.1.8 Conclusions: Part I -- 3.2 Part II: Inferring Indirect Coupling -- 3.2.1 Introduction: Part II -- 3.2.2 First Step: Univariate Analysis -- 3.2.3 Second Step: Pairwise Analysis -- 3.2.4 Third Step: Partial MCR -- 3.2.5 Decision Tree -- 3.2.6 Partial MCR for All Couplings of Fig.3.10 -- 3.2.7 Conclusions: Part II -- References -- Chapter 4 Complex Network Analysis of Recurrences -- 4.1 Introduction -- 4.2 From Recurrence Plots to Recurrence Networks -- 4.2.1 Recurrence Networks from Single Dynamical Systems -- 4.2.1.1 Basic Idea -- 4.2.1.2 Complex Network Characteristics -- 4.2.1.3 Shortest Paths in Recurrence Networks -- 4.2.1.4 Local (Vertex-Based) Measures -- 4.2.1.5 Pairwise Vertex and Edge Measures -- 4.2.1.6 Global Network Measures -- 4.2.2 Inter-System Recurrence Networks -- 4.2.2.1 Cross-Recurrences and Cross-Recurrence Networks -- 4.2.2.2 Combining Single-System and Cross-Recurrence Networks -- 4.2.2.3 Interacting Network Characteristics -- 4.2.2.4 Local Measures -- 4.2.2.5 Global Measures -- 4.2.3 Joint Recurrence Networks -- 4.2.3.1 Basic Idea -- 4.2.3.2 α-Joint Recurrence Networks -- 4.3 Analytical Description of Recurrence Networks. , 4.3.1 Random Geometric Graphs -- 4.3.2 Single-System Recurrence Network Characteristics -- 4.3.2.1 General Setting -- 4.3.2.2 Shortest Paths and Geodesics -- 4.3.2.3 Local (Vertex-Based) Measures -- 4.3.2.4 Pairwise Vertex and Edge Measures -- 4.3.2.5 Global Network Measures -- 4.3.2.6 Further Characteristics -- 4.3.3 Inter-System Recurrence Network Characteristics -- 4.3.3.1 Local Measures -- 4.3.3.2 Global Measures -- 4.4 Recurrence Networks: General Properties and Applications -- 4.4.1 Generic Network Characteristics -- 4.4.1.1 Absence of Small-World Effects -- 4.4.1.2 Emergence of Scale-Free Distributions -- 4.4.1.3 Assortative vs. Disassortative Mixing -- 4.4.2 Characterization of Dynamical Complexity -- 4.4.2.1 Average Path Length -- 4.4.2.2 Network Transitivity -- 4.4.2.3 Other Network Characteristics -- 4.4.2.4 Example: Tracing Bifurcations in the Rössler System -- 4.4.3 Characterization of Local Dimensionality -- 4.4.4 Cross-Transitivity Properties and Coupling Asymmetry -- 4.4.5 Joint Transitivity Properties and Synchronization -- 4.4.6 Real-World Applications -- 4.4.6.1 Applications in Climatology -- 4.4.6.2 Applications in Fluid Dynamics -- 4.4.6.3 Applications in Electrochemistry -- 4.4.6.4 Applications in Medicine -- 4.5 Related Approaches -- 4.6 Summary -- References -- Part II RQA Best Practices -- Chapter 5 From Time to Space Recurrences in Biopolymers -- 5.1 Introduction -- 5.1.1 Generalities -- 5.1.2 Recurrence Plots as Distance Matrices -- 5.2 Results -- 5.2.1 Folding Metrics -- 5.2.2 Chemical Metrics -- 5.2.2.1 Functional Hot-Spots in the P53 Protein -- 5.2.2.2 Thermophylic and Mesophylic Rubredoxins -- 5.3 Mixed Metrics for Complex Behavior -- 5.4 Shifting from Protein to DNA Analysis: A Cryptography Tale -- 5.4.1 Coding and Noncoding Regions in DNA -- 5.4.2 Specific Repeated Words in DNA. , 5.5 Analyzing the Information Content of Biopolymers -- 5.6 Conclusion -- Appendix 1: Cryptography -- Appendix 2: Strings from Human Languages -- (A) Dante Alighieri - Inferno - I Canto (tercets 1-3) - FILTERED -- (B) Dante Alighieri - Inferno - I Canto (tercets 1-3) - NONFILTERED (English Translation by Henry Wadsworth Longfellow)) -- (C) Dr. Suess Poem - NONFILTERED -- Appendix 3: Nucleotidic Strings -- Satellite DNA 1 - GenBank: BI067039.1 Homo Sapiens Genomic Region Containing Hypervariable Minisatellites, mRNA Sequence -- Satellite DNA 2 - GenBank: BM439581.1 Homo Sapiens Genomic Region Containing Hypervariable Minisatellites, mRNA Sequence -- References -- Chapter 6 Dynamic Coupling Between Respiratory and Cardiovascular System -- 6.1 Introduction -- 6.2 Recurrence Plot Analysis for the Interaction Between Two Series -- 6.2.1 Application to Test Data Set -- 6.2.2 Quantification -- 6.3 Quantification of Recurrences of the Van Der Pol Model -- 6.4 Cardiorespiratory Synchronization Experiment -- 6.5 Discussion -- References -- Chapter 7 Analysis of Brain Recurrence -- Abbreviations -- 7.1 Introduction -- 7.2 The Brain -- 7.2.1 Physiological Role -- 7.2.2 The Baseline EEG -- 7.2.3 The Stimulated EEG -- 7.2.4 Uses of EEG -- 7.2.5 Complexity Conjecture -- 7.3 Recurrence Analysis -- 7.3.1 Historical Development -- 7.3.2 General Properties of Recurrence Analysis -- 7.3.3 Recurrence Analysis of Model Systems -- 7.3.3.1 Model Systems -- 7.3.3.2 Detection of Nonlinear Determinism -- 7.3.4 Overview of Recurrence Analysis of Brain Electrical Activity -- 7.4 Application of Recurrence Analysis -- 7.4.1 New Paradigm for Studying the Brain -- 7.4.2 Statistical Basis of Brain Recurrence Analysis -- 7.4.3 Discovery of Human Magnetic Sense -- 7.4.4 Rationalizing Inconsistency -- 7.4.5 Canonical Conditions and ABR Variables -- 7.4.6 Inferring Mechanisms. , 7.4.7 Cell-Phone Effects on the Brain -- 7.4.8 Detecting the Presence Effect -- 7.4.9 Diagnosing Multiple Sclerosis -- 7.4.10 Applications in Sleep Medicine -- 7.5 Summary -- References -- Chapter 8 Recurrence Analysis of Otoacoustic Emissions -- 8.1 Introduction -- 8.2 Ear Morphology and Physiology -- 8.3 Otoacoustic Emissions (OAE) -- 8.4 Hearing Losses -- 8.4.1 Age-Related Hearing Loss (ARHL) -- 8.4.2 Noise Induced Hearing Loss (NIHL) -- 8.5 Clinical Practice -- 8.5.1 Pure Tone Audiometry -- 8.5.2 ILO Test -- 8.6 RQA to Study the Characteristics of Otoacoustic Emissions -- 8.7 RQA and Models of the Ear to Study the Origin of Otoacoustic Emissions -- 8.8 RQA in the Diagnosis of Hearing Losses -- 8.8.1 RQA Applied to the Study of CHL and SHL -- 8.8.2 RQA Applied to the Study of ARHL and NIHL -- 8.9 Conclusions and Future Directions -- References -- Chapter 9 Vibration Analysis in Cutting Materials -- 9.1 Introduction and the Dynamical Model -- 9.2 Simulation Results -- 9.3 Recurrence Plot Analysis -- 9.4 Conclusions -- References -- Chapter 10 Dynamical Patterns in Seismology -- 10.1 Introduction -- 10.2 Elements of Seismology -- 10.2.1 Physics of Earthquakes -- 10.2.2 Stick-Slip as a Physical Model of the Seismic Process -- 10.2.3 Seismic Time Series -- 10.2.4 Scaling Relations -- 10.2.5 Patterns of Seismicity -- 10.2.6 Earthquake Prediction -- 10.2.7 Earthquake "Control" -- 10.3 Methods of Revealing Dynamic Patterns in Acoustic/Seismic Time Series -- 10.4 Triggering and Synchronization of Stick-Slip: Laboratory Model of Seismic Process -- 10.4.1 Electromagnetic Triggering of Slip -- 10.4.2 Electromagnetic Stick-Slip Synchronization -- 10.4.3 Measuring EM Synchronization Strength of Stick-Slip -- 10.4.3.1 High Order EM Synchronization of Stick-Slip -- 10.4.4 Dynamic Mechanical Synchronization of Stick-Slip -- 10.5 Dynamical Patterns in Seismicity. , 10.5.1 Recurrence Patterns in Seismic Catalogs.