Well-known compressed sensing (CS) is widely used in image acquisition and
reconstruction. However, accurately reconstructing images from measurements at low …

As a greedy algorithm to recover sparse signals from compressed measurements,
orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In …

This paper demonstrates theoretically and empirically that a greedy algorithm called
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in …

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 …

Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) are two well-known recovery
algorithms in compressed sensing. To recover a d-dimensional m-sparse signal with high …

Support recovery of sparse signals from noisy measurements with orthogonal matching
pursuit (OMP) has been extensively studied. In this paper, we show that for any K-sparse …

In recent years, the enormous demand for computing resources resulting from massive data
and complex network models has become the limitation of deep learning. In the large-scale …

Early fault feature extraction and fault diagnosis are of great importance for predictive
maintenance of rotating machinery. To accurately extract early fault features from original …

An innovative methodology for the design of modular and contiguously-clustered linear
arrays is proposed. The approach formulates the subarraying problem as a pattern matching …

In this paper, we consider a multiuser detection technique when the signal sparsity is
changing over time. The key ingredient of our method is a clever switching between the CS …