Agenda

Network topology inference from graph stationary signals

Network topology inference from graph stationary signals

Antonio Garcia Marques (King Juan Carlos University)

We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end we develop efficient inference algorithms stemming from provably-tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the adjacency matrix and the normalized Laplacian. Algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known, but also when the eigenvectors are noisy or when only a subset of them are given. Numerical tests showcase the effectiveness of the proposed algorithms in recovering social, brain, and amino-acid networks.

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Agenda

Microelectronics Colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Tenure track colloquium

Daniele Cavallo (TS group); wideband phased arrays for future wireless communication terminals, Massimo Mastrangeli (ECTM Group); Towards smart organs-on-chip, Sten Vollebregt (ECTM group) Emerging electronic materials: from lab to fab

Signal Processing Seminar

Krishnaprasad Nambur Ramamohan

Signal processing algorithms for acoustic vector sensors

Symposium MRI for Low-Resource Setting

Steven Schiff, Johnes Obungoloch

Sustainable Low-Field MRI Technology for Point of Care Diagnostics in Low-Income Countries

Kick-off meeting of the project "A sustainable MRI system to diagnose hydrocephalus in Uganda"

Signal Processing Seminar

Peter Gerstoft

Machine learning in physical sciences

Machine learning (ML) is booming thanks to efforts promoted by Google. However, ML also has use in physical sciences. I start with a general overview of ML for supervised/unsupervised learning. Then I will focus on my applications of ML in array processing in seismology and ocean acoustics. This will include source localization using neural networks or graph processing. Final example is using ML-based tomography to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a 5200-element array. This method exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data.

Signal Processing Seminar

Aydin Rajabzadeh

manufacturing defect detection