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Machine Learning

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Cellular neural network. In computer science and machine learning, cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing non-visual problems to geometric maps, modelling biological vision and other sensory-motor organs.

CNN architecture[edit] Due to their number and variety of architectures, it is difficult to give a precise definition for a CNN processor. From an architecture standpoint, CNN processors are a system of a finite, fixed-number, fixed-location, fixed-topology, locally interconnected, multiple-input, single-output, nonlinear processing units. The nonlinear processing units are often referred to as neurons or cells. Cells are defined in a normed space, commonly a two-dimensional Euclidean geometry, like a grid. Literature review[edit] Model of computation[edit] Revisiting P2P content sharing in wireless adhoc networks. Localization inSensor Networks. Localized Algorithms In Wireless Ad-Hoc Networks:Location Discovery And Sensor Exposure. Ad-Hoc Localization Using Ranging and Sectoring. Robust Distributed Network Localizationwith Noisy Range Measurements.

Localization in Ad-Hoc Sensor Network: AMachine Learning Based Approach. Machine learning.

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