Capability enhancement of the X-ray micro-tomography system via ML-assisted approaches

Ring artifacts in X-ray micro-CT images are one of the primary causes of concern in their accurate visual interpretation and quantitative analysis. The geometry of X-ray micro-CT scanners is similar to the medical CT machines, except the sample is rotated with a stationary source and detector. The ring artifacts are caused by a defect or non-linear responses in detector pixels during the MicroCT data acquisition. Artifacts in MicroCT images can often be so severe that the images are no longer useful for further analysis. Therefore, it is essential to comprehend the causes of artifacts and potential solutions to maximize image quality. This article presents a convolution neural network (CNN)-based Deep Learning (DL) model inspired by UNet with a series of encoder and decoder units with skip connections for removal of ring artifacts. The proposed architecture has been evaluated using the Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE). Additionally, the results are compared with conventional filter-based non-ML techniques and are found to be better than the latter.

Deep Learning assisted microwave-plasma interaction based technique for plasma density estimation

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) is based on plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. To address such practical concerns, the article proposes a novel machine learning (ML) assisted microwave-plasma interaction based strategy which is capable enough to determine the electron density profile within the plasma. The electric field pattern due to microwave scattering is measured to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of Gaussian-shaped density profiles, in the range $10^{16}-10^{19}m^{-3}$, addressing a range of experimental configurations have been considered in our study. The results obtained show promising performance in estimating the 2D radial profile of the density for the given linear plasma device. The performance of the proposed deep learning based approach has been evaluated using three metrics- SSIM, RMSLE and MAPE. The favourable performance affirms the potential of the proposed ML based approach in plasma diagnostics.