Compressed Sensing (CS) has been proposed as a low-complexity ECG data compression
scheme for wearable wireless bio-sensor devices. However, CS decoding is characterized …

Compressed sensing (CS) is a new approach to signal acquisition that can potentially allow
us to design very simple video encoders, which then develops as the compressed video …

We propose a two‐dimensional (2D) scattering‐center‐extraction (SCE) method using
sparse recovery based on the compressive‐sensing theory, even with data missing from the …

We address the problem of sparse signal recovery in compressed sensing when a fraction of
acquired measurements is invalid or damaged. This may occur due to some system …

The data rate between remote radio head (RRH) and baseband unit (BBU) in the distributed
base station architecture is steeply increasing due to the new technologies such as multiple …

The distributed base station architecture is one of the good solutions to reduce the system
complexity and manage the cellular mobile communication system more efficiently. Typical …

Texture classification of images is an important problem in texture analysis, with the goal to
assign an unknown sample image to one of a set of known texture classes. Many …

Compressed sensing (CS) demonstrates that a sparse, or compressible signal can be
acquired using a low rate acquisition process below the Nyquist rate, which projects the …

Some core ideas that lead to the definition of an environment for real-time systems are
discussed. It is explained how integration, flexibility, and validation support can be achieved …

A novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm utilizing a
sparse signal representation of higher-order power of covariance matrix is proposed …