Tactile Internet with Human-in-the-Loop describes the change from the current Internet,
which focuses on the democratization of information independent of location or time, to the …

Computing in Communication Networks: From Theory to Practice provides comprehensive
details and practical implementation tactics on the novel concepts and enabling …

Compressed sensing aims to retrieve sparse signals from very few samples. It relies on
dedicated reconstruction algorithms and well-chosen measurement matrices. In combination …

In this chapter, we describe the transformation of current communication networks to future
communication systems. Communication networks are always prone to transformation due …

The demand for bandwidth in cognitive radio networks (CRNs) is growing as applications
become increasingly data intensive. Techniques such as compressed spectrum sensing …

Traditional signal recognition methods generally use biosensors for signal acquisition. With
senior citizens, sensor signal acquisition will be affected by their movements. These signal …

Compressed sensing promises a drastic reduction in data traffic, especially in wireless
sensor network (WSN) applications, by exploiting data correlation. Although its original …

Abstract Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything
on a never-before-seen scale. Such networks, however, have to tackle the inherent issues …

Abstract In Compressive Sensing, the incoherence of a measurement matrix during
subsampling is a crucial requirement for the accurate reconstruction of a signal. However …

The incoherence of a measurement matrix is a crucial requirement for the reconstruction of
an unaliased signal in Compressive Sensing. The subsampling process therefore needs all …