| Preface | p. v |
| Natural Computing paradigms and emergent computation | p. 1 |
| Principles of natural Computing | p. 1 |
| Natural computing structures as hierarchies of interconnected cells | p. 1 |
| The principle of optimal number of entities (Ockham's razor) | p. 2 |
| Natural Computing Systems are dissipative Systems | p. 3 |
| Transient nature of the behavioral complexity of natural Systems | p. 3 |
| Natural Systems and recurrence | p. 3 |
| Emergence, complexity, and local activity of cells | p. 3 |
| Open problems and book description | p. 4 |
| Cellular nonlinear networks: State of the art and applications | p. 7 |
| Introduction | p. 7 |
| Typical applications of cellular Computers | p. 9 |
| Hardware platforms for implementing cellular Computers | p. 12 |
| Cellular and natural Computing models and Software Simulation | p. 15 |
| Cellular Systems: cells, neighborhoods, states and dynamics | p. 15 |
| Genes | p. 17 |
| Discrete and continuous states and Outputs | p. 17 |
| Boundary conditions | p. 17 |
| Major cellular Systems paradigms | p. 18 |
| The Cellular Neural Network (CNN) model | p. 18 |
| The Generalized Cellular Automata | p. 19 |
| Reaction-Diffusion Cellular Nonlinear Networks | p. 20 |
| Matlab Simulation of Generalized cellular automata and cellular neural networks | p. 21 |
| Uncoupled GCAs | p. 21 |
| Coupled GCAs | p. 23 |
| Simulation of Standard cellular neural networks | p. 25 |
| Nonlinear representation of cells | p. 25 |
| Piecewise-linear representation and implementation | p. 26 |
| Extended families of cells | p. 36 |
| Structured universes of cells | p. 37 |
| Modeling and Simulation of semitotalistic cellular automata | p. 38 |
| Modeling and Simulation of "Small-Worlds" Systems | p. 40 |
| A wider variety of cells, taxonomy and family labels | p. 41 |
| A CA Simulator for all kind of cells | p. 43 |
| Emergence, locating and measuring it | p. 47 |
| Emergence: the Software engineering of cellular Computing Systems | p. 47 |
| Visual Interpretation of emergent phenomena - classes of behaviors | p. 48 |
| Semitotalistic cells: a first Step in narrowing the search space | p. 57 |
| Clustering and transients and as measures of emergence | p. 60 |
| Visualizing complexity for an entire family of cellular Systems - Wolfram classes revisited | p. 66 |
| Simulation examples, properties, more accurate complexity measures | p. 69 |
| Properties of complexity measures | p. 71 |
| A composite complexity measure | p. 74 |
| Exponents of growth | p. 77 |
| Exponents of growth and their relationship to various emergent behaviors | p. 77 |
| Mutations for continuous State cellular Systems | p. 85 |
| Distribution of exponents of growth among families of genes | p. 87 |
| Influences of the "Small World" model | p. 89 |
| The "small worlds" model allows fine tuning of the "edge of chaos" | p. 90 |
| On the independence between various measures of complexities | p. 92 |
| Sieves for selection of genes according to desired behaviors | p. 95 |
| Introduction | p. 95 |
| Defining sieves | p. 96 |
| Examples and applications of the double sieve method | p. 99 |
| Intelligent life behaviors and uncertainty in using sieves | p. 99 |
| Classic "Life": using sieves to locate similar behaviors | p. 101 |
| Other interesting emergent behaviors | p. 104 |
| Sieves to locate feature extractors | p. 107 |
| Pink-noise generators | p. 109 |
| Sieving and intelligence - evolving a list of interesting genes | p. 110 |
| Predicting emergence from cell's structure | p. 113 |
| Introduction | p. 113 |
| Relationships between CA behavior and the Boolean description of the cell | p. 114 |
| Parametrizations as tools to locate similar behaviors | p. 116 |
| The theory of probabilistic exponents of growth | p. 118 |
| Uncertainty index of a cell, active and expansion areas | p. 120 |
| Minimal set of cells to compute the probabilistic exponent of growth | p. 121 |
| Computing the cells function of probability | p. 123 |
| Computing the probabilistic exponent of growth | p. 124 |
| Exponents of growth and their significance | p. 126 |
| Predicting behaviors from ID, an example | p. 127 |
| Other properties of the probabilistic exponents of growth | p. 127 |
| Comparison with the experimental exponent of growth | p. 129 |
| Conclusions | p. 130 |
| Applications of emergent phenomena | p. 133 |
| Introduction | p. 133 |
| Smart sensor for character recognition | p. 134 |
| Motivation and general description | p. 134 |
| Architecture and functionality of the CA-based sensor | p. 136 |
| Feature extraction and Classification | p. 138 |
| Experimental results | p. 140 |
| Excitable membranes, for temporal sequence Classification | p. 141 |
| Mapping variable-length Signals into terminal states | p. 142 |
| Detecting genes to build EMTMs | p. 144 |
| Experimental results in sound Classification | p. 145 |
| Image compression using CA-based vector quantization | p. 147 |
| Coding and decoding principle | p. 147 |
| Detecting the useful genes | p. 151 |
| Results and comparison with other compression Standards | p. 153 |
| Aspects on hardware implementation | p. 156 |
| References | p. 159 |
| Index | p. 165 |
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