The rapid proliferation of Wireless Sensor Networks (WSN) and other linked devices has
given rise to several notions that blend the virtual and real worlds. A vision in which billions …

Sparsity of representations of signals has been shown to be a key concept of fundamental
importance in fields such as blind source separation, compression, sampling and signal …

A number of techniques for the compressed sensing of imagery are surveyed. Various
imaging media are considered, including still images, motion video, as well as multiview …

In this paper, we propose a lensless compressive imaging architecture. The architecture
consists of two components, an aperture assembly and a sensor. No lens is used. The …

Low-complexity video encoding has been applicable to several emerging applications.
Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity …

The paper considers the problem of detecting the sparsity pattern of a k-sparse vector in
\BBR^n from m random noisy measurements. A new necessary condition on the number of …

In this paper, we study the problem of recovering sparse or compressible signals from
uniformly quantized measurements. We present a new class of convex optimization …

The robust reversible watermarking (RRW) requires high robustness and capacity on the
condition of reversibility and imperceptibility, which still remains a big challenge nowadays …

Principal component analysis (PCA) is often central to dimensionality reduction and
compression in many applications, yet its data-dependent nature as a transform computed …

In this letter, we apply a new sampling theory named compressed sensing (CS) for
aerospace remote sensing to reduce data acquisition and imaging cost. We can only record …