We are pleased to announce the winners of the Best Student Paper Award SSP2016:
Best Student Paper Award
“Network Topology Identification from Spectral Templates”
Santiago Segarra (University of Pennsylvania, USA)
with Antonio G. Marques (Universidad Rey Juan Carlos, Spain), Gonzalo Mateos (University of Rochester, USA) and Alejandro Ribeiro (University of Pennsylvania, USA)
Network topology inference is a cornerstone problem in statistical analyses of complex systems. In this context, the fresh look advocated here permeates benefits from convex optimization and graph signal processing, to identify the so-termed graph shift operator (encoding the network topology) given only the eigenvectors of the shift. These spectral templates can be ob- tained, for example, from principal component analysis of a set of graph signals defined on the particular network. The novel idea is to find a graph shift that while being consistent with the provided spectral information; it endows the network structure with certain desired properties such as sparsity. The focus is on developing efficient recovery algorithms along with identifiability conditions for two particular shifts, the adjacency matrix and the normalized graph Laplacian. Application domains include network topology identification from steady-state signals generated by a diffusion process, and design of a graph filter that facilitates the distributed implementation of a prescribed linear network operator. Numerical tests showcase the effectiveness of the proposed algorithms in recovering synthetic and structural brain networks.
Second Best Student Paper Award
“On the Statistical Performance of MUSIC for Distributed Sources”
Ouiame Najim (University of Bordeaux, France)
with Pascal Vallet (Bordeaux INP & IMS, France), Guillaume Ferré (University of Bordeaux, France) and Xavier Mestre (CTTC, Spain)
This paper addresses the statistical behaviour of the MUSIC method for DoA estimation, in a scenario where each source signal direct path is disturbed by a clutter spreading in an angular neighborhood around the source DoA. In this scenario, it is well-known that subspace methods performance suffers from an additional clutter subspace, which breaks the orthogonality between the source steering vectors and noise subspace. To perform a statistical analysis of the MUSIC DoA estimates, we consider an asymptotic regime in which both the number of sensors and the sample size tend to infinity at the same rate, and rely on classical random matrix theory results. We establish the consistency of the MUSIC estimates and provide numerical results illustrating their performance in this nons- tandard scenario.