Ricerc@Sapienza

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed operating level.

Network recovery after large-scale failures has tremendous cost implications. While numerous approaches have been proposed to restore critical services after large-scale failures, they mostly assume having full knowledge of failure location, which cannot be achieved in real failure scenarios. Making restoration decisions under uncertainty is often further complicated in a large-scale failure. This paper addresses progressive network recovery under the uncertain knowledge of damages. We formulate the problem as a mixed integer linear programming (MILP) and show that it is NP-Hard.

In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods. The proposed approach is based on a novel stochastic optimization approach known as the Adaptive Moment Estimation (Adam) algorithm. The proposed BSS solution can benefit from the excellent properties of the Adam approach. In order to derive the new learning rule, the Adam algorithm is introduced in the derivation of the cost function maximization in the standard InfoMax algorithm. The natural gradient adaptation is also considered.