scholarly journals Fast X-Ray Diffraction (XRD) Tomography for Enhanced Identification of Materials

2021 ◽  
Author(s):  
Airidas Korolkovas ◽  
Alexander Katsevich ◽  
Michael Frenkel ◽  
William Thompson ◽  
Edward Morton
Keyword(s):  
Graphics Processing Unit ◽  
Low Cost ◽  
Photon Counting ◽  
Finite Size ◽  
Processing Unit ◽  
X Ray Diffraction ◽  
X Ray ◽  
Specific Material ◽  
Xrd Patterns ◽  
Graphics Processing

X-ray computed tomography (CT) can provide 3D images of density, and possibly the atomic number, for large objects like passenger luggage. This information, while generally very useful, is often insufficient to identify threats like explosives and narcotics, which can have a similar average composition as benign everyday materials such as plastics, glass, light metals, etc. A much more specific material signature can be measured with X-ray diffraction (XRD). Unfortunately, XRD signal is very faint compared to the transmitted one, and also challenging to reconstruct for objects larger than a small laboratory sample. In this article we analyze a novel low-cost scanner design which captures CT and XRD signals simultaneously, and uses the least possible collimation to maximize the flux. To simulate a realistic instrument, we derive a formula for the resolution of any diffraction pathway, taking into account the polychromatic spectrum, and the finite size of the source, detector, and each voxel. We then show how to reconstruct XRD patterns from a large phantom with multiple diffracting objects. Our approach includes a reasonable amount of photon counting noise (Poisson statistics), as well as measurement bias, in particular incoherent Compton scattering. The resolution of our reconstruction is sufficient to provide significantly more information than standard CT, thus increasing the accuracy of threat detection. Our theoretical model is implemented in GPU (Graphics Processing Unit) accelerated software which can be used to assess and further optimize scanner designs for specific applications in security, healthcare, and manufacturing quality control.

scholarly journals Fast X-Ray Diffraction (XRD) Tomography for Enhanced Identification of Materials

2021 ◽  
Author(s):  
Airidas Korolkovas ◽  
Alexander Katsevich ◽  
Michael Frenkel ◽  
William Thompson ◽  
Edward Morton
Keyword(s):  
Graphics Processing Unit ◽  
Low Cost ◽  
Photon Counting ◽  
Finite Size ◽  
Processing Unit ◽  
X Ray Diffraction ◽  
X Ray ◽  
Specific Material ◽  
Xrd Patterns ◽  
Graphics Processing

X-ray computed tomography (CT) can provide 3D images of density, and possibly the atomic number, for large objects like passenger luggage. This information, while generally very useful, is often insufficient to identify threats like explosives and narcotics, which can have a similar average composition as benign everyday materials such as plastics, glass, light metals, etc. A much more specific material signature can be measured with X-ray diffraction (XRD). Unfortunately, XRD signal is very faint compared to the transmitted one, and also challenging to reconstruct for objects larger than a small laboratory sample. In this article we analyze a novel low-cost scanner design which captures CT and XRD signals simultaneously, and uses the least possible collimation to maximize the flux. To simulate a realistic instrument, we derive a formula for the resolution of any diffraction pathway, taking into account the polychromatic spectrum, and the finite size of the source, detector, and each voxel. We then show how to reconstruct XRD patterns from a large phantom with multiple diffracting objects. Our approach includes a reasonable amount of photon counting noise (Poisson statistics), as well as measurement bias, in particular incoherent Compton scattering. The resolution of our reconstruction is sufficient to provide significantly more information than standard CT, thus increasing the accuracy of threat detection. Our theoretical model is implemented in GPU (Graphics Processing Unit) accelerated software which can be used to assess and further optimize scanner designs for specific applications in security, healthcare, and manufacturing quality control.

AN EVALUATION OF MULTIPLE FEED-FORWARD NETWORKS ON GPUs

2011 ◽  
Vol 21 (01) ◽  
pp. 31-47 ◽  
Author(s):  
NOEL LOPES ◽  
BERNARDETE RIBEIRO
Keyword(s):  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Low Cost ◽  
Back Propagation ◽  
General Purpose ◽  
Training System ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Data Parallel ◽  
Graphics Processing

The Graphics Processing Unit (GPU) originally designed for rendering graphics and which is difficult to program for other tasks, has since evolved into a device suitable for general-purpose computations. As a result graphics hardware has become progressively more attractive yielding unprecedented performance at a relatively low cost. Thus, it is the ideal candidate to accelerate a wide variety of data parallel tasks in many fields such as in Machine Learning (ML). As problems become more and more demanding, parallel implementations of learning algorithms are crucial for a useful application. In particular, the implementation of Neural Networks (NNs) in GPUs can significantly reduce the long training times during the learning process. In this paper we present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementation in traditional hardware, due to maximized floating-point throughput and memory bandwidth. Moreover, a preliminary GPU based Autonomous Training System (ATS) is developed which aims at automatically finding high-quality NNs-based solutions for a given problem.

scholarly journals High throughput transmission optical projection tomography using low cost graphics processing unit

2009 ◽  
Vol 17 (25) ◽  
pp. 22320 ◽  
Author(s):  
Claudio Vinegoni ◽  
Lyuba Fexon ◽  
Paolo Fumene Feruglio ◽  
Misha Pivovarov ◽  
Jose-Luiz Figueiredo ◽  
...  
Keyword(s):  
High Throughput ◽  
Graphics Processing Unit ◽  
Low Cost ◽  
Processing Unit ◽  
Optical Projection Tomography ◽  
Optical Projection ◽  
Graphics Processing

scholarly journals Real-time Visualisation and Analysis of Tera-scale Datasets

2012 ◽  
Vol 10 (H16) ◽  
pp. 679-680
Author(s):  
Christopher J. Fluke
Keyword(s):  
Real Time ◽  
High Performance ◽  
Graphics Processing Unit ◽  
Low Cost ◽  
Processing Unit ◽  
Computing Environments ◽  
Graphics Processing ◽  
Interactive Visualisation ◽  
Performance Computing ◽  
Scale Data

AbstractAs we move ever closer to the Square Kilometre Array era, support for real-time, interactive visualisation and analysis of tera-scale (and beyond) data cubes will be crucial for on-going knowledge discovery. However, the data-on-the-desktop approach to analysis and visualisation that most astronomers are comfortable with will no longer be feasible: tera-scale data volumes exceed the memory and processing capabilities of standard desktop computing environments. Instead, there will be an increasing need for astronomers to utilise remote high performance computing (HPC) resources. In recent years, the graphics processing unit (GPU) has emerged as a credible, low cost option for HPC. A growing number of supercomputing centres are now investing heavily in GPU technologies to provide O(100) Teraflop/s processing. I describe how a GPU-powered computing cluster allows us to overcome the analysis and visualisation challenges of tera-scale data. With a GPU-based architecture, we have moved the bottleneck from processing-limited to bandwidth-limited, achieving exceptional real-time performance for common visualisation and data analysis tasks.

GAPD: a GPU-accelerated atom-based polychromatic diffraction simulation code

2018 ◽  
Vol 25 (2) ◽  
pp. 604-611 ◽  
Author(s):  
J. C. E ◽  
L. Wang ◽  
S. Chen ◽  
Y. Y. Zhang ◽  
S. N. Luo
Keyword(s):  
Large Scale ◽  
Graphics Processing Unit ◽  
Reciprocal Lattice ◽  
Processing Unit ◽  
Simulation Code ◽  
X Ray ◽  
Plane Detector ◽  
Arbitrary Plane ◽  
Diffraction Patterns ◽  
Graphics Processing

GAPD, a graphics-processing-unit (GPU)-accelerated atom-based polychromatic diffraction simulation code for direct, kinematics-based, simulations of X-ray/electron diffraction of large-scale atomic systems with mono-/polychromatic beams and arbitrary plane detector geometries, is presented. This code implements GPU parallel computationviaboth real- and reciprocal-space decompositions. WithGAPD, direct simulations are performed of the reciprocal lattice node of ultralarge systems (∼5 billion atoms) and diffraction patterns of single-crystal and polycrystalline configurations with mono- and polychromatic X-ray beams (including synchrotron undulator sources), and validation, benchmark and application cases are presented.

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