Ricerc@Sapienza

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided.

In a classical distributed framework, we present a novel distributed observer for genuinely nonlinear
continuous-time plants. A network of sensors monitors a multiple-outputs plant. Each sensor measures
only a portion of the plant’s outputs, and the sensing capability is different from sensor to sensor.
The assumption of strongly connected digraph on the underlying sensor network ensures robustness
and direct communication paths between nodes. Moreover, incremental homogeneity assumptions