Compressive sampling (CS) is a novel signal processing paradigm whereby the data
compression is performed simultaneously with the sampling, by measuring some linear …

Compressed sensing has been proposed to both yield low-cost compression and low-cost
encryption. This can be very useful in the design of sensor nodes with a limited resource …

The recovery of sparse signals given their linear map** on lower-dimensional spaces can
be partitioned into a support estimation phase and a coefficient estimation phase. We …

Recent enhancements in the field of wireless communication protocols have led to the
development of several wearable devices and sensor networks suitable for health …

Chaining, ie, the mode of operation in which each message is encrypted considering a
digital summary of previous ones, is here applied to block-cipher stages based on …

In recent years, compressed sensing (CS) has proved to be effective in lowering the power
consumption of sensing nodes in biomedical signal processing devices. This is due to the …

The amount of data generated by distributed monitoring systems that can be exploited for
anomaly detection, along with real time, bandwidth, and scalability requirements leads to the …

Despite the universal nature of the compressed sensing mechanism, additional information
on the class of sparse signals to acquire allows adjustments that yield substantial …

We investigate the advantage of a two-step approach in the recovery of Compressed
Sensing (CS) encoded signals in a realistic environment. First, the support of the signal is …

This paper presents a new compressive sensing (CS) measurement method for image
sensors, which limits pixel summation within neighbor pixels and follows regular summation …