The design of conventional sensors is based primarily on the Shannon? Nyquist sampling
theorem, which states that a signal of bandwidth W Hz is fully determined by its discrete time …

In-memory computing is a promising non-von Neumann approach where certain
computational tasks are performed within resistive memory units by exploiting their physical …

In this paper, we present a novel architecture based on field-programmable gate arrays
(FPGAs) for the reconstruction of compressively sensed signal using the orthogonal …

Compressed sensing allows to reconstruct sparse signals sampled at sub-Nyquist rates.
However, reconstruction of the original signal requires high computational effort, even for …

Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-
arrival, from wideband radio-frequency (RF) signals finds use in a growing number of …

Current medical practice of long-term chronic respiratory diseases treatment lacks a
convenient method of empowering the patients and caregivers to continuously quantitatively …

This paper develops new theory and algorithms to recover signals that are approximately
sparse in some general dictionary (ie, a basis, frame, or over-/incomplete matrix) but …

Sparse signals can be reconstructed from a reduced set of signal samples using
compressive sensing (CS) methods. The discrete cosine transform (DCT) can provide highly …

One of the key tasks in cognitive radio and communications intelligence is to detect active
bands in the radio-frequency (RF) spectrum. In order to perform spectral activity detection in …

Compressive sensing (CS) is a novel signal processing technology to reconstruct the sparse
signal at sub-Nyquist rate. Orthogonal matching pursuit (OMP) is one of the most widely …