Publications

Google Scholar Profile

• P. Saladi and Y. Kalepu, "Electromagnetic Inverse Scattering Problem Solved by DConvNet and Adapted Attention U-Net," 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India, 2023, pp. 101-104, doi: 10.1109/CSNT57126.2023.10134734.Abstract: A two-stage Deep Learning strategy for resolving the electromagnetic inverse scattering problem (ISP) is proposed in this paper. The two steps involve utilizing Deep Convolutional Neural Network (DConvNet) to draw out features from the Scattered field data and refine the image reconstruction using adapted version of Attention U-Net. We observed that our proposed method delivered better results in terms of image quality and reconstruction precision. Moreover, we observed the outcomes of Attention U-Net, DConvNet, and DRCNN for ISP and found that Attention U-Net gave better image refinement.
  keywords: {Image quality;Deep learning;Image resolution;Inverse problems;Communication systems;Electromagnetic scattering;Convolutional neural networks;Inverse Scattering;Contrast;Attention U-Net;Deep Learning;DConvNet},URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10134734&isnumber=10134574

• Pravallika Saladi, Yaswanth Kalepu. Deep Learning Strategy for Resolving Electromagnetic Inverse Scattering Problem with Phase-less Data. Authorea. July 27, 2023. DOI: 10.22541/au.169045202.28967646/v1