Surface Light Barriers

Theo Gabloffsky; Britta Kruse; Ralf Salomon

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Paper

Surface Light Barriers

Topics: Artificial intelligence and machine learning; Embedded Systems; Machine Learning Algorithms and Tools

In Proceedings of the 11th International Conference on Sensor Networks SENSORNETS - Volume 1, 97-104, 2022

Authors: Theo Gabloffsky ; Britta Kruse and Ralf Salomon

Affiliation: University of Rostock, 18051, Germany

Keyword(s): Light Barrier, Neural Network, Backpropagation Network, Radial Basis Function, Speed Measuring, Curling.

Abstract: It should be known to almost all readers that light barriers are commonly used for measuring the speed of various objects. These devices are easy to use, quite robust, and of low cost. Despite their advantages, light barriers exhibit certain limitations that occur when the objects of interest move in more than one spatial dimension. This paper discusses a physical setup in which light barriers can also be used in case of two-dimensional trajectories. However, this setup requires rather complicated calculations. Therefore, this paper performs these calculations by means of different neural network models. The results show that backpropagation networks as well as radial basis functions are able to achieve a residual error less than 0.21 %, which is more than sufficient for most sports and everyday applications.

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Paper citation in several formats:

Gabloffsky, T.; Kruse, B. and Salomon, R. (2022). Surface Light Barriers. In Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS; ISBN 978-989-758-551-7; ISSN 2184-4380, SciTePress, pages 97-104. DOI: 10.5220/0010777500003118

@conference{sensornets22,
author={Theo Gabloffsky. and Britta Kruse. and Ralf Salomon.},
title={Surface Light Barriers},
booktitle={Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS},
year={2022},
pages={97-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010777500003118},
isbn={978-989-758-551-7},
issn={2184-4380},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS
TI - Surface Light Barriers
SN - 978-989-758-551-7
IS - 2184-4380
AU - Gabloffsky, T.
AU - Kruse, B.
AU - Salomon, R.
PY - 2022
SP - 97
EP - 104
DO - 10.5220/0010777500003118
PB - SciTePress