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Multidimensional signal reconstruction and crowd-sensing: Sampling theory with energy and capacity constraints

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Abstract: Multidimensional reconstruction of signals is a key enabler for emerging applications in various sectors including array signal processing, magnetic resonance imaging, seismology, digital communication and control, software defined radio and networks, vehicular networks, and environmental monitoring. Big data and crowd-sensing applications can also be associated with multidimensional random sampling and reconstruction (e.g., reconstructing spatial distribution of data). This talk focuses on the generalisations of the reconstruction of a deterministic signal and a stationary random process, which was first addressed in one dimension by Whittaker, Kotelnikov, and Shannon (WKS).

Speaker Bio: Flavio Zabini received the Laurea (summa cum laude) in telecommunications engineering and the Ph.D. in electronic engineering and computer science from the University of Bologna, Italy, in 2004 and 2010, respectively. He is a Researcher at the University of Bologna, Italy. Prior to joining the University of Bologna, he was with the National Research Council (CNR). In 2008, he worked as a visiting student at the DoCoMo Eurolabs of Munich, Germany. In 2013-2014 he was post-doctoral Fellow at German Aerospace Center (DLR), Cologne, Germany. His research interests include the theory and experimentation of wireless systems and networks, including channel coding for optical links, echo cancellation for on-channel repeaters, random sampling for sensor networks, and performance-fairness trade-off in communications. He is an editor of the KSII Transactions on Internet and Information Systems.

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