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

Abstract

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.