A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge

1 code implementation • 5 Jun 2023 • Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun

This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input.

SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation

1 code implementation • 4 Jun 2023 • Dor H. Shmuel, Julian P. Merkofer, Guy Revach, Ruud J. G. van Sloun, Nir Shlezinger

Direction of arrival (DoA) estimation is a fundamental task in array processing.

DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

2 code implementations • 22 Sep 2021 • Julian P. Merkofer, Guy Revach, Nir Shlezinger, Tirza Routtenberg, Ruud J. G. van Sloun

A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.

Super-Resolution