Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups

• URL: http://arxiv.org/abs/2410.14988v1
• Date: Sat, 19 Oct 2024 05:37:44 GMT
• Title: Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups
• Authors: Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon,
• Abstract summary: We propose a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses a deep neural network (DNN) model that approximates the water surface slopes from polarized light intensities. Once trained on simple wave trains, the WPLL is capable of producing high-resolution and accurate 2D reconstruction of the water surface and elevation in a variety of wave fields.
• Score: 2.3599126081503177
• License:
• Abstract: Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are crucial for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limitations in wavenumber/frequency response. To address these challenges, we propose Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and accurate 2D reconstruction of the water surface slopes and elevation in a variety of irregular wave fields. The method's robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating at arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting.

Related papers

• Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array [2.3599126081503177]
  Existing techniques are often cumbersome and generally suffer from limited wave/frequency response. To address these challenges a novel method was developed, using polarization filter as camera equipped the main sensor and Machine Learning (number) algorithms for data processing. The developed method training and evaluation was based on in-house made supervised dataset.
  arXiv  Detail & Related papers  (2024-10-30T09:35:27Z)
• A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning [4.193522044994739]
  The optical properties of indoor water surfaces pose challenges for image and stereo reconstruction. The optical imaging properties of water in the long-wave infrared spectrum were found to be suitable for stereo matching. A reconstruction strategy involving deep learning techniques was proposed to improve stereo matching performance.
  arXiv  Detail & Related papers  (2024-08-20T09:13:12Z)
• RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation [50.10282876199739]
  This paper introduces RFTrans, an RGB-D-based method for surface normal estimation and manipulation of transparent objects. It integrates the RFNet, which predicts refractive flow, object mask, and boundaries, followed by the F2Net, which estimates surface normal from the refractive flow. A real-world robot grasping task witnesses an 83% success rate, proving that refractive flow can help enable direct sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-11-21T07:19:47Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Scalable Surface Water Mapping up to Fine-scale using Geometric Features of Water from Topographic Airborne LiDAR Data [0.0]
  We propose a unique method that focuses on the geometric characteristics of water instead of its variable reflectance properties. By harnessing this natural law in conjunction with connectivity, our method can accurately and scalably identify small water bodies.
  arXiv  Detail & Related papers  (2023-01-16T19:04:23Z)
• Quantitative optical imaging method for surface acoustic waves using optical path modulation [0.0]
  A precise measurement of the surface wave amplitude is often necessary to discuss the coupling strengths. Here we develop and demonstrate a straightforward measurement technique that can quantitatively characterize the SAW.
  arXiv  Detail & Related papers  (2022-12-14T17:46:43Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data [137.47124933818066]
  We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
  arXiv  Detail & Related papers  (2022-02-22T15:39:00Z)
• Augmented Sliced Wasserstein Distances [55.028065567756066]
  We propose a new family of distance metrics, called augmented sliced Wasserstein distances (ASWDs) ASWDs are constructed by first mapping samples to higher-dimensional hypersurfaces parameterized by neural networks. Numerical results demonstrate that the ASWD significantly outperforms other Wasserstein variants for both synthetic and real-world problems.
  arXiv  Detail & Related papers  (2020-06-15T23:00:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.