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Related Experiment Video

Updated: Jan 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line.

Ben J Wolf1, Steven Warmelink, Sietse M van Netten

  • 1Author to whom correspondence should be addressed.

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An artificial lateral line (ALL) system uses neural networks to locate moving objects. Recurrent neural networks, specifically LSTM, achieved higher accuracy and noise resistance than feed-forward networks for hydrodynamic sensing.

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Area of Science:

  • Robotics and Biomimicry
  • Hydrodynamics and Sensor Technology
  • Artificial Intelligence and Machine Learning

Background:

  • The lateral line system in aquatic animals is crucial for sensing hydrodynamic stimuli.
  • Developing artificial systems to mimic this biological capability is key for underwater navigation and object detection.
  • Existing methods for artificial lateral line (ALL) source localization often use feed-forward neural networks.

Purpose of the Study:

  • To develop and evaluate a 2D-sensitive artificial lateral line (ALL) system for object localization.
  • To compare the performance of recurrent neural networks (LSTM) against feed-forward neural networks (OS-ELM) for hydrodynamic sensing.
  • To assess the impact of noise on localization accuracy.

Main Methods:

  • An artificial lateral line (ALL) with eight all-optical flow sensors was constructed.
  • Hydrodynamic velocity profiles were measured in response to a moving 6 cm sphere.
  • Object location was reconstructed using both Long Short-Term Memory (LSTM) and Online-Sequential Extreme Learning Machine (OS-ELM) neural networks.

Main Results:

  • The LSTM achieved a significantly lower average localization error (0.72 cm) compared to the OS-ELM (4.27 cm) within a 62 cm x 9.5 cm area.
  • The recurrent neural network (LSTM) demonstrated greater resilience to noise in the measurements.
  • The study successfully reconstructed the location of a moving object using hydrodynamic data.

Conclusions:

  • Recurrent neural networks, particularly LSTM, are more effective for hydrodynamic object localization using ALL systems.
  • The ALL system provides a promising approach for near-field hydrodynamic sensing and object tracking.
  • The findings suggest that recurrent connections offer advantages in noisy environments for underwater sensing applications.