5 accepted papers
We study Sparse Signal Recovery (SSR) methods for multichannel imaging with compressed {forward and backward} operators that preserve reconstruction accuracy. We propose a Compressed Block-Convolutional (C-BC) measurement model based on a low-rank Convolutional Neural Network (CNN) decomposition tha…
This paper introduces a deep unfolding-based approach for Full Waveform Inversion (FWI) in quantitative ultrasound imaging. Our technique leverages trained deep neural networks to perform an optimized gradient step that achieves superior results and significantly reduces the number of iterations req…
Sampling signals with a high dynamic range (DR) poses significant challenges in analog-to-digital conversion (ADC), where the DR of the ADC must exceed that of the input signal to prevent information loss. Modulo sampling offers a promising solution to this problem by reducing the DR of the signal p…
The rising demand for multi-person non-contact vital signs monitoring (NCVSM) in healthcare highlights the potential of radar technology, especially in cluttered environments. Single-input multiple-output frequency-modulated continuous- wave (FMCW) radars enable multi-object localization, which is c…
The recovery of high dynamic range shift-invariant (SI) signals presents a significant challenge in the realm of analog-to-digital converters (ADCs). A critical aspect in this context is that the dynamic range of the ADC, needs to surpass that of the input analog signal in order to prevent any loss…