We propose an efficient open-loop channel estimator for a millimeter-wave (mm-wave)
hybrid multiple-input multiple-output (MIMO) system consisting of radio-frequency (RF) …

Computational meta-imagers synergize metamaterial hardware with advanced signal
processing approaches such as compressed sensing. Recent advances in artificial …

In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …

Spectrum scarcity and under-utilization of the apportioned spectral resources by the
licensed users have led to the introduction of a new communication paradigm called …

Applications of wireless body area networks (WBANs) are extended from remote health care
to military, sports, disaster relief, etc. With the network scale expanding, nodes increasing …

Compressive sensing involves 3 main processes: signal sparse representation, linear
encoding or measurement collection, and nonlinear decoding or sparse recovery. In the …

This paper develops schemes for block-sparse channel estimation in millimeter wave
(mmWave) multiple-input multiple-output (MIMO) systems that exploit the spatial sparsity …

There is much discussion in industry and academia about possible technical solutions to
address the growth in demand for wireless broadband. Massive multiple-input multiple …

Compressive sensing (CS)-based image coding has been widely studied in the field of
image processing. However, the CS-based image encoder has a significant gap in image …

This paper deals with alternating optimization of sensing matrix and sparsifying dictionary for
compressed sensing systems. Under the same framework proposed by JM Duarte …