P. van der Meulen, B.J. Wolf, P. Pirih, S.M. van Netten
University of Groningen
PPerformance of Neural Networks in Source Localization
using Artificial Lateral Line Sensor Configurat
The optimal configuration improved performance for both sources,
compared to other configurations. Theref
ore, the main research
question can be answered positively in that using an optimal
configuration can imp
rove source localization performance using
With regard to the secondary research question, both neur
networks are capable of detecting two sources in a 3D environment,
if sources are an equal distance r
emoved from the ALL. If not, only
the closest source to the array is accurately reconstructed.
l configuration also improved ELM results for all source
generation conditions; the use of an ELM leads
to a higher
performance of the worst estimated source, for the majority of
conditions, compared to using a
Artificial lateral lines (ALLs) are used to detect the movement and
locations of sources underwater,
and are based on the neuromasts
(fig.1) located in the lateral line organ found in fish and
amphibians. ALLs con
sists of a set of biaxial sensors (fig. 2)
Görner, P. (1963). Untersuchungen zur morphologie und elektrophysiologie des
seitenlinienorgans vom krallenfros
ch (xenopus laevis daudin). Journal of Comparative
Physiology A: Neuroethology, Sensory, Neural, and Beh
avioral Physiology, 47 (3), 316–
Wolf, B. J., Morton, J. A., MacPherson, W. B. N., & van Netten, S. M. (2
018). Bioinspired all-
optical artificial neuromast for 2d flow sensing. Bioinspiration & biomimetics.
3D matrix containing 1331 density
probability points for source locations
convolutional neural network
extreme learning machine
Source prediction process:
Can the placement of artificial lateral line sensors be beneficial for
improving the accuracy of source
localization through the use of
convolutional neural networks?
Are convolutional neural networks and ex
treme learning machines
capable of predicting the locations of multiple sources in three-
Fig. 1: Superficial neuromasts of a
clawed frog. From Görner (1963).
Fig. 2: Biaxial ALL sensor. From
et al. (2018).
A Cramér-Rao lower bound analysis was performed on a subset of
sensor configurations (16 sensor
basin) to estimate their
likely performances and indicate the best and worst configurations.
The best and worst configurations were used to generate simulated
datasets to train and test extreme lear
ning machines (ELMs) and
convolutional neural networks (CNNs) on their location accuracy.
s consisted of 2 sources in a 3D basin (1m3
the sensor readings of 16 ALL sensors.
Source localization pipeline
Calculate sensor readings
SOURCE LOCALIZATION PIPELINE
Fig. 4: Bar plots for the normalized worst predicted source location and
ions versus the distance between both source
locations (horizontal ALL, using
). (a): CNN; (b): ELM. Colors
indicate distributions. With CNNs, the secondary source performance
shows a lin
ear relationship with the distance between sources, which is
not the case with ELMs.
Fig. 5: Estimated prob
ability curves for the distribution of total source-
prediction distances for the best (a) and worst (b
) predicted sources.
ALLs were placed horizontally at z:0. Curves were averaged over 5
4 dataset conditions.
Fig. 3: 2D sensor configurations for 16 sensors. Configurations were
izontally (at z:0) and vertically (at y:0). The Cramér-Rao
lower bound analysis indicates that
(blue) performed best horizontally and vertically,
(red) is indicated as the worst performing
Poster presented at the ICT.OPEN2018 conference, 19-20 March 2018, Amersfoort, Netherlands