Ricerc@Sapienza

Sensor networks are one of the largest data generators on the Internet. A wide range of areas corresponding to even very diverse scenarios are monitored by thousands of sensors capable of acquiring signals of various natures. Considering the usual sampling rates determined by the Shannon/Nyquist theorem, even using the usual information compression techniques, the enormous amount of information flow poses problems in the management of transmission resources, processing, and storage of the data itself.

In this scenario Compressive Sensing (CS), based on the possibility of reconstructing a signal from a few measurements, represents an emerging paradigm able to natively acquire the information directly in the non-redundant form, overcoming the limits of the sampling theorem.

Very recent studies, in part developed by the proponents of this project, have shown how hyper-complex tensor algebra, in particular quaternion algebra, represents the most natural way for the representation and processing of spatially and temporally correlated data such as those coming from arrays of audio and video sensors.

DYCOS project has the following objectives: 1) the study of a theoretical framework, and the basis for the technological development, related to CS defined in quaternion domain able to directly acquire high definition multi-dimensional signals with unique measurements; 2) the determination of a reconstruction solution, computationally parsimonious, based on end-to-end generative data-driven methods implemented with deep quaternion neural networks (DQNN) with dense or convolutional layers; 3) the implementation of a virtual testbed scenario; 4) the study and development of a theoretical/experimental framework for the performance analysis of the proposed methods.

DYCOS represents a significant improvement over the CS state of the art, greatly improving the calculation efficiency and thus extending possible application scenarios.