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Unrolled Optimization with Deep Learning-based Priors for Phaseless Inverse Scattering Problems
Such inverse problems can be highly non-linear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size.
Physics Assisted Deep Learning for Indoor Imaging using Phaseless Wi-Fi Measurements
Thus, our proposed method is the first inverse scattering-based deep learning framework which can image large scatterers with high permittivity and achieve accurate indoor RF imaging using phaseless Wi-Fi measurements.
Improved Covariance Matrix Estimator using Shrinkage Transformation and Random Matrix Theory
One of the major challenges in multivariate analysis is the estimation of population covariance matrix from sample covariance matrix (SCM).
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