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 …

In recent years, non-contact radar detection technology has been able to achieve long-term
and long-range detection for the breathing and heartbeat signals. Compared with contact …

Compressive sensing (CS) is a new technology in digital signal processing capable of high-
resolution capture of physical signals from few measurements, which promises impressive …

Improving the lifetime of wireless sensor networks (WSNs) is directly related to the energy
efficiency of computation and communication operations in the sensor nodes. Compressive …

The sampling rate of the sensors in wireless sensor networks (WSNs) determines the rate of
its energy consumption, since most of the energy is used in sampling and transmission. To …

Reconstructing complex networks from observed data is a fundamental problem in network
science. Compressive sensing, widely used for recovery of sparse signals, has also been …

The IoT (Internet-of-Things) concept has been introduced as a strategic innovation aspect
that will benefit society in many ways. Environmental monitoring, smart traffic control …

Compressive Sensing (CS) can use fewer samples to recover a great number of original
data, which have a sparse representation in a proper basis. For energy-constrained …

Multiple-prespecified-dictionary sparse representation (MSR) has shown powerful potential
in compressive sensing (CS) image reconstruction, which can exploit more sparse structure …

As a means of detecting abnormal events in Wireless Sensor Networks (WSNs), this paper
presents a Compressive Sensing (CS)-based algorithm, called Minimum Spanning Tree and …