Research Projects

Physics-Based Radiography Data Simulator Powered by GPU for Non-Destructive Evaluation Applications

A CAD component with artificial defects inserted is placed in between the virtual flat panel X-ray detector (cyan color) and point X-ray source (red color on the right most side of image). The X-rays are projected from the source to the detector.

  • Developed a physics based radiography data simulator for NDE applications powered by GPU.

  • The simulator models the experimental scenario to generate radiograph using any CAD component and is capable of inserting desired defects in the component.

  • It can automatically produce large amount of data, which can replace the experimental data for the training of Deep Learning models.

  • We proposed a unique noise modelling technique which significantly improves the image texture and the histograms.

  • A part of this work has been published in the Journal of Nondestructive Evaluation 2021 (JNDE), DOI: 10.1007/s10921-021-00750-4

GPU Based Raycasting Operation To Calculate Distance Travelled by Rays Inside the Surface Triangulated CAD Component

  • GPU powered ray cast operation to calculate the distance traveled by the rays inside the CAD geometry with triangulated surfaces.

  • Implemented in both C++ and python.

  • Upto 16x speedup compared to multi-threaded CPU implementation.

  • The time in the above curve is for the processing of 2048*2048 rays.

High-throughput and real-time feature extraction of crystal microstructures from high resolution TEM data.

  • We extended a graph based crystal detection framework focusing on performance optimization using efficient tree based data structures for near real time crystal characterization in TEM data.

  • The approach extracts a large corpus of crystal features in high throughput manner using HPC nodes.

  • We also propose a Wasserstein distance based data sufficiency determination method.