Quantization plays a critical role in digital signal processing systems. Quantizers are
typically designed to obtain an accurate digital representation of the input signal, operating …

We present an end-to-end image compression system based on compressive sensing. The
presented system integrates the conventional scheme of compressive sampling (on the …

In this paper, the BATS project is presented, which aims to track the behavior of bats via an
ultra-low power wireless sensor network. An overview about the whole project and its parts …

Processing, storing, and communicating information that originates as an analog signal
involves converting this information to bits. This conversion can be described by the …

This paper addresses lossy distributed source coding for acquiring correlated sparse
sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements …

The significance of the electroencephalography (EEG) signal is used to read the brain
activity in the form of electrical patterns. EEG signals help to diagnose anomalies in the brain …

Sparsity is an attribute present in a myriad of natural signals and systems, occurring either
inherently or after a suitable projection. Such signals with lots of zeros possess minimal …

Data originating from devices and sensors in Internet of Things scenarios can often be
modeled as sparse signals. In this paper, we provide new source compression schemes for …

One of the key issues in the acquisition of sparse data by means of compressed sensing is
the design of the measurement matrix. Gaussian matrices have been proven to be …

Representing a continuous-time signal by a set of samples is a classical problem in signal
processing. We study this problem under the additional constraint that the samples are …