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Section: Our Story
University of Ioannina
Ioannina, Greece
Section: Our Story
University of Hawaii at Manoa
Honolulu, HI, USA
Section: Our Story
Laboratoire Lagrange - UMR
Nice, France
Section: Our Story
Sarvajanik College of Engineering & Technology
Surat, India
Section: Publications & Resources
Expander recovery is an iterative algorithm designed to recover sparse signals measured with binary matrices with linear complexity. In the paper, we study the expander recovery performance of the bipartite graph with girth greater than 4, which can be associated with a binary matrix with column correlations equal to either 0 or 1.
Section: Publications & Resources
We study the problem of distributed filtering for state space models over networks, which aims to collaboratively estimate the states by a network of nodes. Most of existing works on this problem assume that both process and measurement noises are Gaussian and their covariances are known in advance. In some cases, this assumption breaks down and no longer holds.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This paper studies resilient distributed estimation under measurement attacks. A set of agents each makes successive local, linear, noisy measurements of an unknown vector field collected in a vector parameter. The local measurement models are heterogeneous across agents and may be locally unobservable for the unknown parameter.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
We consider the problem of decentralized consensus optimization, where the sum of n smooth and strongly convex functions are minimized over n distributed agents that form a connected network. In particular, we consider the case that the communicated local decision variables among nodes are quantized in order to alleviate the communication bottleneck in distributed optimization.
Section: Events
November 12-14, 2019
Early Registration Deadline: TBA
Location: Bangalore, India
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Section: Publications & Resources
New enhancements to the display of Section and Chapter data are now available in the Geographic Map within OU Analytics. Boundaries have been expanded to incorporate additional Sections/Subsections not previously included.
