Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

Compressive sensing (CS) has become a popular signal processing technique and has
extensive applications in numerous fields such as wireless communications, image …

Results in compressed sensing describe the feasibility of reconstructing sparse signals
using a small number of linear measurements. In addition to compressing the signal, do …

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the
implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive …

Data gathering in sensor networks is required to be efficient, adaptable and robust.
Recently, compressive sensing (CS) based data gathering shows promise in meeting these …

A sparse principal component analysis (PCA) seeks a sparse linear combination of input
features (variables), so that the derived features still explain most of the variations in the …

Sensor node energy constraint is considered as an impediment in the further development
of the Internet of Things (IoT) technology. One of the most efficient solution is to combine …

Abstract Internet of Things (IoT) integrates diverse types of sensors, mobiles and other
technologies to physical world and IoT technology is used in a wide range of applications …

Path expressions are ubiquitous in XML processing languages. Existing approaches
evaluate a path expression by selecting nodes that satisfies the tag-name and value …

Distributed Compressed Sensing (DCS) is an important research area of compressed
sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) …