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Dogaru R. Systematic Design for Emergence in Cellular Nonlinear Networks. With Applications in Natural Computing and Signal Processing

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Dogaru R. Systematic Design for Emergence in Cellular Nonlinear Networks. With Applications in Natural Computing and Signal Processing
Springer, 2008. — 174 p.
The main problem addressed in this book came out during a Fulbright research fellowship stage at U.C. Berkeley (California, USA, 1996-1998). Then, I had the opportunity to work in the research group of Leon Chua on a subject called CNN (cellular neural/nonlinear network). The CNN, developed in the end of the 1980s was an important step ahead in getting cellular computing closer to practical applications. The CNN is actually a cellular array made of many identical elements interconnected in a neighborhood. But unlike the cellular automata (CA) the CNN was designed as a circuit-oriented architecture being developed up to the point where it serves as a low-power smart sensor or visual microprocessor capable to acquire and process images or multi-dimensional signals in general with processing speeds of the order of 1012 (Tera) operations per second. Today, there are several academic and industrial groups providing CNN-based solutions for various practical problems, particularly when high speed processing at low power is needed. Also, researchers in the area of nano-technology recognize cellular computing as a suitable computing paradigm to the specific of these technologies where many identical active elements are available on a mass proportion. Researchers in biology also recognize the cellular paradigm as a well-suited paradigm for models of natural systems. Indeed there is a similarity, as biological systems essentially are functionally meaningful aggregates of mostly local interconnected cells.
Natural computing paradigms and emergent computation
Cellular nonlinear networks: state of the art and applications
Cellular and natural computing models and software simulation
Emergence, locating and measuring it
Exponents of growth
Sieves for selection of genes according to desired behaviors
Predicting emergence from cell’s structure
Applications of emergent phenomena
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