Wireless sensor networks produce a large amount of data that needs to be processed,
delivered, and assessed according to the application objectives. The way these data are …

Sensing of the application environment is the main purpose of a wireless sensor network.
Most existing energy management strategies and compression techniques assume that the …

We introduce a new approach to radar imaging based on the concept of compressive
sensing (CS). In CS, a low-dimensional, nonadaptive, linear projection is used to acquire an …

Compressive sensing is a signal acquisition framework based on the revelation that a small
collection of linear projections of a sparse signal contains enough information for stable …

Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate
conventional MRI by reducing the number of acquired data. The combination of parallel MRI …

In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for
enabling dynamic spectrum sharing, but entails several major technical challenges: very …

Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply,
memory capacity, communication bandwidth, and processor performance. Compression of …

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

We address the problem of compressing large and distributed signals monitored by a
Wireless Sensor Network (WSN) and recovering them through the collection of a small …

Recent developments in low-cost CMOS cameras have created the opportunity of bringing
imaging capabilities to sensor networks. Various visual sensor platforms have been …