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Juan Sebastián Botero Valencia

Luis Javier Morantes Guzmán

Abstract

Reflective optical sensors that measure distances are characterized by thenon-linearity of their output. Processing units in which these are used must allow for such conditions. This paper aims to reduce computational costs regarding storage capacity and processing times by linearizing sensor outputs to be used in low-budget embedded systems. Results from two different sensors show that the esteemed exponential curve can bead justed using neural networks with radial basis functions, with a reduction of over 50% in processing time. Regarding the design of the network,the paper focused on its efficiency in terms of the size of the network and the distribution of centroids, yielding important results. The achieve dreduction in time and space allows for the use of the system’s resources in other tasks, while it also allows foran increase of the sampling frequency in data acquisition.

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Keywords

Sensor linearization, distance optical sensors, neural networks with radial basis functions

References
How to Cite
Botero Valencia, J., & Morantes Guzmán, L. (2013). Distance Estimation with Reflective Optical Sensors Using Neural Networks with Radial Basis Functions for Embedded Applications. Ingenieria Y Universidad, 17(1), 27-40. https://doi.org/10.11144/Javeriana.iyu17-1.dewr
Section
Articles
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