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most citedStructured Compressed Sensing: From Theory to Applications

1.2k citations · 2.3k across the 71 of their papers we have counts for

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Showing 2018 · eess.SPShow all

8 papers · 2 filters

eess.SP2018

Deep Signal Recovery with One-Bit Quantization

Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian +1

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of th…

eess.SP2018

Asymptotic Task-Based Quantization with Application to Massive MIMO

Nir Shlezinger, Yonina C. Eldar, Miguel R. D. Rodrigues

Quantizers take part in nearly every digital signal processing system which operates on physical signals. They are commonly designed to accurately represent the underlying signal,…

eess.SP2018

TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

Siqi Na, Kumar Vijay Mishra, Yimin Liu +2

We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the tar…

eess.SP2018

Sub-Nyquist Radar Systems: Temporal, Spectral and Spatial Compression

Deborah Cohen, Yonina C. Eldar

Conventional radar transmits electromagnetic waves towards the targets of interest. In between the outgoing pulses, the radar measures the signal reflected from the targets to dete…

eess.SP2018

A Cognitive Sub-Nyquist MIMO Radar Prototype

Kumar Vijay Mishra, Yonina C. Eldar, Eli Shoshan +2

We present a cognitive prototype that demonstrates a colocated, frequency-division-multiplexed, multiple-input multiple-output (MIMO) radar which implements both temporal and spati…

eess.SP2018

A Markov Variation Approach to Smooth Graph Signal Interpolation

Ayelet Heimowitz, Yonina C. Eldar

In this paper we present the Markov variation, a smoothness measure which offers a probabilistic interpretation of graph signal smoothness. This measure is then used to develop an…