Artificial lateral-line system for imaging dipole sources using beamforming techniques

Dagamseh, A.M.K.; Wiegerink, Remco J.; Lammerink, Theodorus S.J.; Krijnen, Gijs

doi:10.1016/j.proeng.2011.12.191

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…However, such approach is typically applicable only to inviscid scenarios and simple objects, such as spheres or dipole sources. In recent years, more advanced pattern recognition and matching techniques, such as beamforming technique [6] and template matching [5,19], have been successfully employed. The recent advancements in machine learning, particularly in deep neural networks (DNNs), have facilitated the intricate capture of complex patterns and the acquisition of substantial non-linear mappings from extensive, high-dimensional datasets.…”

Section: Introductionmentioning

confidence: 99%

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system

Liu,

Ding,

Xie

2024

Bioinspir. Biomim.

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This study aims to investigate the feasibility of using an Artificial Lateral Line system for predicting the real-time position and pose of an undulating swimmer with Carangiform swimming patterns. We established a 3D Computational Fluid Dynamics simulation to replicate the swimming dynamics of a freely swimming mackerel under various motion parameters, calculating the corresponding pressure fields. Using the simulated lateral line data, we trained an artificial neural network to predict the centroid coordinates and orientation of the swimmer. A comprehensive analysis was further conducted to explore the impact of sensor quantity, distribution, noise amplitude and sampling intervals of the Artificial Lateral Line array on predicting performance. Additionally, to quantitatively assess the reliability of the localization network, we trained another neural network to evaluate error magnitudes for different input signals. These findings provide valuable insights for guiding future research on mutual sensing and schooling in underwater robotic fish.

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Section: Introductionmentioning

confidence: 99%

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system

Liu,

Ding,

Xie

2024

Bioinspir. Biomim.

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“…Several signal processing methods have been put forth to use measured velocity profiles to recover the relative location of the object; thereby partly solving the inverse problem (Abdulsadda and Tan 2013a). These methods include Capon's beamforming (Nguyen et al 2008, Dagamseh et al 2011 and template matching (Ćurić-Blake andvan Netten 2006, Pandya et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

Wolf

Warmelink²,

Netten³

2019

Bioinspir. Biomim.

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The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the object’s location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the long short-term memory, LSTM) is compared to that of a feed-forward neural network (the online-sequential extreme learning machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm s−1. Results show that, in a 62 cm 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm, respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation.

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“…Several signal processing methods have been put forth to use measured velocity profiles to recover the relative location of the object; thereby partly solving the inverse problem [5]. These methods include Capon's beamforming [34,95] and template matching [31,96].…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic Imaging with Artificial Intelligence: detecting submerged objects at a distance using a 2D-sensitive flow sensor array and neural networks

Wolf

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Background 2 1.1.1 The biological lateral line 2 1.1.2 What is hydrodynamic imaging? 4 1.1.3 Artificial neural networks in practice 8 1.2 Research motivation 10 1.2.1 Scalability 10 1.2.2 Neural-network based signal processing 11 1.2.3 2D-sensitive sensing 11 1.3 Thesis outline 11 Graphical abstract 13 i sensing 2 design and validation of an all-optical 2d flow sensor 17 2.1 Introduction 18 2.2 Sensor and fluid model 20 2.2.1 Sensor design 20 2.2.2 FBG sensing 20 2.2.3 Isotropic sensor mechanics 21 2.2.4 Dynamic properties 24 2.2.5 Design optimization 25 2.3 Methods 26 2.4 Results 29 2.4.1 Linearity 29 2.4.2 Dynamic response 29 2.5 Discussion 31 2.5.1 Sensitivity and dynamic range 31 2.5.2 Two-dimensional fluid flow sensing 32 2.6 Conclusion 34 ii simulation studies 3 simulated 1d-sensitive localization 37 3.1 Introduction 38 3.2 Methods 40 v vi contents 3.2.1 Data generation 40 3.2.2 Neural network algorithms 44 3.3 Results 48 3.3.1 Parameter settings 48 3.3.2 Overall performance on high signal to noise input 49 3.3.3 MLP overfitting 50 3.3.4 Noise robustness 50 3.4 Discussion 53 3.4.1 Practical implementation neural networks 53 3.4.2 Overall performance on x and y coordinates 55 3.4.3 Noise robustness 55 3.5 Conclusion 56 3.5.1 Practical implementation neural networks 56 3.5.2 Further research on single source localization 56 4 simulated 1d-sensitive multi-source localization 59 4.1 Introduction 60 4.2 Background 61 4.2.1 Source location encoding by the lateral line organ 61 4.2.2 Object localization using artificial lateral lines 63 4.2.3 Convolutional neural networks 64 4.3 Methods 66 4.3.1 Location encoding in 3D 66 4.3.2 Data synthesis 69 4.3.3 CNN implementation 70 4.3.4 CNN optimization 73 4.3.5 Location decoding in 3D 75 4.4 Results 77 4.4.1 Probability grid reconstruction 77 4.4.2 3D position reconstruction 79 4.5 Discussion 79 4.5.1 The processing pipeline 80 4.5.2 CNN design variations 80 4.5.3 Iterative source detection 82 4.5.4 Detection limits and future research 83 4.6 Conclusion 84 Supplementary information 84 5 simulated 2d-sensitive localization 87 5.1 Introduction 88 5.1.1 State of the art 88 5.1.2 Aims of this study 89 contents vii 5.

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Artificial lateral-line system for imaging dipole sources using beamforming techniques

Cited by 4 publications

References 8 publications

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

Hydrodynamic Imaging with Artificial Intelligence: detecting submerged objects at a distance using a 2D-sensitive flow sensor array and neural networks

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