Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR)
communications have recently emerged as a reliable and effective solution. Among various …

It is widely believed that the advances of networking technologies will reshape the future of
telecommunication system. The continuous growth in data traffic originated by mobile users …

We propose a high-resolution imaging radar system to enable high-fidelity four-dimensional
(4D) sensing for autonomous driving, ie, range, Doppler, azimuth, and elevation, through a …

Sparse representation can efficiently model signals in different applications to facilitate
processing. In this article, we will discuss various applications of sparse representation in …

The immense progress in physiological signal acquisition and processing in health
monitoring allowed a better understanding of patient disease detection and diagnosis. With …

Conventionalliterature on array signal processing (ASP) is based on the “capture first,
process later” philosophy and to this end, signal processing algorithms are typically …

Periodic nonuniform sampling (PNS) is widely used in sub-Nyquist sampling schemes of
multitone signals. However, what sampling pattern of PNS enables the signal to recover …

In this paper, we study the problem of wideband direction of arrival (DoA) estimation with
sparse linear arrays (SLAs), where a number of uncorrelated wideband signals impinge on …

The Multiple Measurement Vector (MMV) problem is central to sparse signal processing,
where the goal is to recover the common support of a set of unknown sparse vectors of size …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …