scholarly journals Dense U-Net for Limited Angle Tomography of Sound Pressure Fields

2021 ◽  
Vol 11 (10) ◽  
pp. 4570
Author(s):  
Oliver Rothkamm ◽  
Johannes Gürtler ◽  
Jürgen Czarske ◽  
Robert Kuschmierz
Keyword(s):  
Measurement Errors ◽  
Sound Pressure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Projection Data ◽  
Quantitative Measurements ◽  
Pressure Fields ◽  
The Neural Network ◽  
Bias Flow ◽  
3D Information

Tomographic reconstruction allows for the recovery of 3D information from 2D projection data. This commonly requires a full angular scan of the specimen. Angular restrictions that exist, especially in technical processes, result in reconstruction artifacts and unknown systematic measurement errors. We investigate the use of neural networks for extrapolating the missing projection data from holographic sound pressure measurements. A bias flow liner was studied for active sound dampening in aviation. We employed a dense U-Net trained on synthetic data and compared reconstructions of simulated and measured data with and without extrapolation. In both cases, the neural network based approach decreases the mean and maximum measurement deviations by a factor of two. These findings can enable quantitative measurements in other applications suffering from limited angular access as well.

scholarly journals Measurement of the Local Sound Pressure on a Bias-Flow Liner Using High-Speed Holography and Tomographic Reconstruction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 153466-153474 ◽  
Author(s):  
Andres E. Ramos Ruiz ◽  
Johannes Gurtler ◽  
Robert Kuschmierz ◽  
Jurgen W. Czarske
Keyword(s):  
High Speed ◽  
Sound Pressure ◽  
Tomographic Reconstruction ◽  
Bias Flow ◽  
Local Sound

scholarly journals High-resolution limited-angle phase tomography of dense layered objects using deep neural networks

2019 ◽  
Vol 116 (40) ◽  
pp. 19848-19856 ◽  
Author(s):  
Alexandre Goy ◽  
Girish Rughoobur ◽  
Shuai Li ◽  
Kwabena Arthur ◽  
Akintunde I. Akinwande ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Single Step ◽  
Training Set ◽  
Step Method ◽  
3 Dimensional ◽  
Dimensional Reconstruction ◽  
Optical Domain

We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

Experimental synthesis of sound pressure fields for active structural acoustic control testing

2013 ◽  
Vol 25 (7) ◽  
pp. 881-889 ◽  
Author(s):  
Stephan Algermissen ◽  
Sebastian Meyer ◽  
Christina Appel ◽  
Hans P Monner
Keyword(s):  
Sound Pressure ◽  
Pressure Fields ◽  
Acoustic Control ◽  
Structural Acoustic Control

scholarly journals Shearlet-based regularized reconstruction in region-of-interest computed tomography

2018 ◽  
Vol 13 (4) ◽  
pp. 34
Author(s):  
T.A. Bubba ◽  
D. Labate ◽  
G. Zanghirati ◽  
S. Bonettini
Keyword(s):  
Optimization Problem ◽  
Ad Hoc ◽  
Iterative Scheme ◽  
Tomographic Reconstruction ◽  
Region Of Interest ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Convex Optimization Problem ◽  
Scanning Time ◽  
Novel Approach

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, bothad hocanalytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

scholarly journals Prediction of Mining Conditions in Geotechnically Complex Sites

Mining ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 279-296
Author(s):  
Marc Elmouttie ◽  
Jane Hodgkinson ◽  
Peter Dean
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
Synthetic Data ◽  
Mining Operations ◽  
Body Of Knowledge ◽  
Future Production ◽  
The Neural Network ◽  
Mining Conditions ◽  
Parameter Interactions ◽  
High Level

Geotechnical complexity in mining often leads to geotechnical uncertainty which impacts both safety and productivity. However, as mining progresses, particularly for strip mining operations, a body of knowledge is acquired which reduces this uncertainty and can potentially be used by mining engineers to improve the prediction of future mining conditions. In this paper, we describe a new method to support this approach based on modelling and neural networks. A high-level causal model of the mining operations based on historical data for a number of parameters was constructed which accounted for parameter interactions, including hydrogeological conditions, weather, and prior operations. An artificial neural network was then trained on this historical data, including production data. The network can then be used to predict future production based on presently observed mining conditions as mining proceeds and compared with the model predictions. Agreement with the predictions indicates confidence that the neural network predictions are properly supported by the newly available data. The efficacy of this approach is demonstrated using semi-synthetic data based on an actual mine.

Recent Development of Cone-Beam X-Ray Microtomography at Sunyab

1997 ◽  
Vol 3 (S2) ◽  
pp. 1125-1126
Author(s):  
S.J. Pan ◽  
A. Shih ◽  
W.S. Liou ◽  
M.S. Park ◽  
G. Wang ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Imaging System ◽  
Tomographic Reconstruction ◽  
Ccd Camera ◽  
Test Sample ◽  
Glass Beads ◽  
Cone Beam ◽  
Projection Data ◽  
X Ray ◽  
Cooled Ccd

An experimental X-ray cone-beam microtomographic imaging system utilizing a generalized Feldkamp reconstruction algorithm has been developed in our laboratory. This microtomographic imaging system consists of a conventional dental X-ray source (Aztech 65, Boulder, CO), a sample position and rotation stage, an X-ray scintillation phosphor screen, and a high resolution slow scan cooled CCD camera (Kodak KAF 1400). A generalized Feldkamp cone-beam algorithm was used to perform tomographic reconstruction from cone-beam projection data. This algorithm was developed for various hardware configuration to perform reconstruction of spherical, rod-shaped and plate-like specimen.A test sample consists of 8 glass beads (approx. 800μm in diameter) dispersed in an epoxy-filled #0 gelatin capsule. One hundred X-ray projection images were captured equal angularly (at 3.6 degree spacing) by the cooled CCD camera at a of 1317×967 (17×17mm2) pixels with 12-bit dynamic range. Figure 1 shows a 3D isosurface rendering of the test sample. The eight glass beads and trapped air bubbles (arrows) in the epoxy resin (e) are clearly visible.

scholarly journals Bed topography and lubrication inferred from surface measurements on fast-flowing ice streams

2003 ◽  
Vol 49 (167) ◽  
pp. 481-490 ◽  
Author(s):  
Throstur Thorsteinsson ◽  
Charles F. Raymond ◽  
G. Hilmar Gudmundsson ◽  
Robert A. Bindschadler ◽  
Paul Vornberger ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Synthetic Data ◽  
Ice Thickness ◽  
West Antarctica ◽  
Horizontal Scale ◽  
Ice Streams ◽  
Bed Topography ◽  
Ice Stream ◽  
Flow Disturbances ◽  
Surface Measurements

AbstractObservations of surface elevation (s) and horizontal velocity components (u and v) are inverted to infer the topography (b) and lubrication (c) at the bed of an ice stream, based on a linearized perturbation theory of the transmission of flow disturbances through the ice thickness. Synthetic data are used to illustrate non-uniqueness in the inversion, but also demonstrate that effects of b and c can be separated when s, u and v are specified, even with added noise to simulate measurement errors. We have analyzed prominent short-horizontal-scale (∼2 km) features in topography and velocity pattern in a local 64 km by 32 km area of the surface of Ice Stream E,West Antarctica. Our preferred interpretation of bed conditions beneath the most prominent features on the surface identifies a deep trough in the basal topography with low lubrication in the base of the trough.

Migration moveout analysis and depth focusing

Geophysics ◽  
1993 ◽  
Vol 58 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Claude F. Lafond ◽  
Alan R. Levander
Keyword(s):  
Heterogeneous Media ◽  
A Priori ◽  
Complex Structure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Real Data ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Data Set ◽  
Velocity Models

Prestack depth migration still suffers from the problems associated with building appropriate velocity models. The two main after‐migration, before‐stack velocity analysis techniques currently used, depth focusing and residual moveout correction, have found good use in many applications but have also shown their limitations in the case of very complex structures. To address this issue, we have extended the residual moveout analysis technique to the general case of heterogeneous velocity fields and steep dips, while keeping the algorithm robust enough to be of practical use on real data. Our method is not based on analytic expressions for the moveouts and requires no a priori knowledge of the model, but instead uses geometrical ray tracing in heterogeneous media, layer‐stripping migration, and local wavefront analysis to compute residual velocity corrections. These corrections are back projected into the velocity model along raypaths in a way that is similar to tomographic reconstruction. While this approach is more general than existing migration velocity analysis implementations, it is also much more computer intensive and is best used locally around a particularly complex structure. We demonstrate the technique using synthetic data from a model with strong velocity gradients and then apply it to a marine data set to improve the positioning of a major fault.

scholarly journals 3D Imaging of Buried Dielectric Targets with a Tomographic Microwave Approach Applied to GPR Synthetic Data

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Alessandro Galli ◽  
Davide Comite ◽  
Ilaria Catapano ◽  
Gianluca Gennarelli ◽  
Francesco Soldovieri ◽  
...  
Keyword(s):  
Near Field ◽  
Scattering Problem ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Numerical Data ◽  
Shallow Subsurface ◽  
Different Shapes ◽  
3D Information ◽  
Ground Penetrating

Effective diagnostics with ground penetrating radar (GPR) is strongly dependent on the amount and quality of available data as well as on the efficiency of the adopted imaging procedure. In this frame, the aim of the present work is to investigate the capability of a typical GPR system placed at a ground interface to derive three-dimensional (3D) information on the features of buried dielectric targets (location, dimension, and shape). The scatterers can have size comparable to the resolution limits and can be placed in the shallow subsurface in the antenna near field. Referring to canonical multimonostatic configurations, the forward scattering problem is analyzed first, obtaining a variety of synthetic GPR traces and radargrams by means of a customized implementation of an electromagnetic CAD tool. By employing these numerical data, a full 3D frequency-domain microwave tomographic approach, specifically designed for the inversion problem at hand, is applied to tackle the imaging process. The method is tested here by considering various scatterers, with different shapes and dielectric contrasts. The selected tomographic results illustrate the aptitude of the proposed approach to recover the fundamental features of the targets even with critical GPR settings.

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