This letter focuses on the estimation of low-complexity signals when they are observed
through M uniformly quantized compressive observations. Among such signals, we consider …

This paper addresses lossy distributed source coding for acquiring correlated sparse
sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements …

Sparsity is an attribute present in a myriad of natural signals and systems, occurring either
inherently or after a suitable projection. Such signals with lots of zeros possess minimal …

Sensor technology developments provide a basis for effective fault diagnosis in
manufacturing systems. However, the limited number of sensors due to physical constraints …

We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements
using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes …

We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a
sensor observes a sparse information source indirectly. The compressed noisy …

Compressed sensing is a thriving research field covering a class of problems where a large
sparse signal is reconstructed from a few random measurements. In the presence of several …

Radio Frequency Identification (RFID) technology is considered to be the next step in the
revolution of supplychain management, retail, and beyond. To derive real benefit from RFID …

Medium bit rate hybrid speech coding schemes have gained much interest in the recent
years and many of them have been standardized for various applications. This work …

This paper addresses distributed finite-rate quantized compressed sensing (QCS)
acquisition of correlated sparse sources in wireless sensor networks. We propose a …