Estimation of Physical Parameters of Waveforms With Neural Networks

• URL: http://arxiv.org/abs/2312.10068v1
• Date: Tue, 5 Dec 2023 22:54:32 GMT
• Title: Estimation of Physical Parameters of Waveforms With Neural Networks
• Authors: Saad Ahmed Jamal and Thomas Corpetti and Dirk Tiede and Mathilde Letard and Dimitri Lague
• Abstract summary: The potential of Full Waveform LiDAR is much greater than just height estimation and 3D reconstruction only. Existing techniques in the field of LiDAR data analysis include depth estimation through inverse modeling and regression of logarithmic intensity and depth for approximating the attenuation coefficient. This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis.
• Score: 0.8142555609235358
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Light Detection and Ranging (LiDAR) are fast emerging sensors in the field of Earth Observation. It is a remote sensing technology that utilizes laser beams to measure distances and create detailed three-dimensional representations of objects and environments. The potential of Full Waveform LiDAR is much greater than just height estimation and 3D reconstruction only. Overall shape of signal provides important information about properties of water body. However, the shape of FWL is unexplored as most LiDAR software work on point cloud by utilizing the maximum value within the waveform. Existing techniques in the field of LiDAR data analysis include depth estimation through inverse modeling and regression of logarithmic intensity and depth for approximating the attenuation coefficient. However, these methods suffer from limitations in accuracy. Depth estimation through inverse modeling provides only approximate values and does not account for variations in surface properties, while the regression approach for the attenuation coefficient is only able to generalize a value through several data points which lacks precision and may lead to significant errors in estimation. Additionally, there is currently no established modeling method available for predicting bottom reflectance. This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis. By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance. Performance of model was validated by testing it on real LiDAR data. In future, more data availability would enable more accuracy and reliability of such models.

Related papers

• HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores [1.3437002403398262]
  MagNet Challenge 2023 calls upon competitors to develop data-driven models for material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferrite cores. HardCORE approach shows that a residual convolutional neural network with physics-informed extensions can serve this task efficiently when trained on observational data beforehand. A model is trained from scratch for each material, while the topology remains the same.
  arXiv  Detail & Related papers  (2024-01-21T13:24:41Z)
• 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)
• Deep learning for full-field ultrasonic characterization [7.120879473925905]
  This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform. Two logics, namely the direct inversion and physics-informed neural networks (PINNs), are explored.
  arXiv  Detail & Related papers  (2023-01-06T05:01:05Z)
• Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [51.87740119160152]
  This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors.
  arXiv  Detail & Related papers  (2022-06-30T01:44:30Z)
• Probabilistic model-error assessment of deep learning proxies: an application to real-time inversion of borehole electromagnetic measurements [0.0]
  We study the effects of the approximate nature of the deep learned models and associated model errors during the inversion of extra-deep borehole electromagnetic (EM) measurements. Using a deep neural network (DNN) as a forward model allows us to perform thousands of model evaluations within seconds. We present numerical results highlighting the challenges associated with the inversion of EM measurements while neglecting model error.
  arXiv  Detail & Related papers  (2022-05-25T11:44:48Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Deep Learning to Estimate Permeability using Geophysical Data [0.7874708385247351]
  This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. Subsurface process models based on hydrogeophysics are used to generate synthetic data for deep learning analyses. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field.
  arXiv  Detail & Related papers  (2021-10-08T04:17:59Z)
• Probabilistic and Geometric Depth: Detecting Objects in Perspective [78.00922683083776]
  3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as an economical solution compared to conventional settings relying on binocular vision or LiDAR, has drawn increasing attention recently but still yields unsatisfactory results. This paper first presents a systematic study on this problem and observes that the current monocular 3D detection problem can be simplified as an instance depth estimation problem.
  arXiv  Detail & Related papers  (2021-07-29T16:30:33Z)
• Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
  Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid. Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori. We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
  arXiv  Detail & Related papers  (2021-07-16T12:56:09Z)
• Data-Driven Shadowgraph Simulation of a 3D Object [50.591267188664666]
  We are replacing the numerical code by a computationally cheaper projection based surrogate model. The model is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This model has shown a good quality reconstruction in a problem of perturbation of data within a narrow range of simulation parameters and can be used for input data of large size.
  arXiv  Detail & Related papers  (2021-06-01T08:46:04Z)
• 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.