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 …

Compressive sensing (CS) has received much attention in several fields such as digital
image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) …

Ground surface roughness is regarded as one of the most crucial indicators of machining
quality and is hard to be predicted due to the random distribution of abrasive grits and …

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

Statistical inference and information processing of high-dimensional data often require an
efficient and accurate estimation of their second-order statistics. With rapidly changing data …

This paper presents two new algorithms for wideband spectrum sensing at sub-Nyquist
sampling rates, for both single nodes and cooperative multiple nodes. In single-node …

In cognitive radio, one of the main challenges is wideband spectrum sensing. Existing
spectrum sensing techniques are based on a set of observations sampled by an …

Compressed sensing deals with the reconstruction of signals from sub-Nyquist samples by
exploiting the sparsity of their projections onto known subspaces. In contrast, this article is …

A new framework for the problem of sparse support recovery is proposed, which exploits
statistical information about the unknown sparse signal in the form of its correlation. A key …

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 …