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Introducing direction and history into artificial lateral line source localization using neural networks

Steenstra, S (2017) Introducing direction and history into artificial lateral line source localization using neural networks. Bachelor's Thesis, Artificial Intelligence.

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Fish are able to detect alterations in water flow velocities with their mechanoreceptive lateral line organ. This organ consists of an array of receptors distributed along the body of the fish. The excitation profiles of such an array can be used to localize nearby moving objects. This organ can be simulated along with its environment. Previous research has shown that using neural networks an artificial lateral line is capable of source localization with high accuracy in a 2-dimensional environment. This research aims to accurately detect the direction of movement of a source. Another aim was to improve localization accuracy by combining angular and temporal information. This research shows that the direction of a source can be predicted accurately and is reliable, with a mean error of 0.13 degrees and a standard deviation of 9.5 degrees. However, when excitation patterns are concatenated over time, utilizing temporal information does not improve localization accuracy. Transformations including angular data of earlier excitation patterns can be used instead of these excitation patterns themselves. When these transformations are concatenated with the current excitation profile the improvement of the localization of a moving source is significant and relevant. The improvement is a factor 1.5.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 08:29
Last Modified: 15 Feb 2018 08:29

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