@conference {170916,
	title = {Finite Dimensional FRI},
	booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2014},
	month = {May},
	address = {Florence},
	abstract = {Traditional Finite Rate of Innovation (FRI) theory has considered\&nbsp;the problem of sampling continuous-time signals. This framework can be naturally extended to the case where the\&nbsp;input is a discrete-time signal. Here we present a novel approach\&nbsp;which uses both the traditional FRI sampling scheme,\&nbsp;based on the annihilating filter method, and the fact that in\&nbsp;this new setup the null space of the problem to be solved is\&nbsp;finite dimensional.

In the noiseless scenario, we show that this new approach\&nbsp;is able to perfectly recover the original signal at the critical\&nbsp;sampling rate. We also present simulation results in the noisy\&nbsp;scenario where this new approach improves performances in\&nbsp;terms of the mean squared error (MSE) of the reconstructed\&nbsp;signal when compared to the canonical FRI algorithms and\&nbsp;compressed sensing (CS).},
	author = {Jon O{\~n}ativia and Yue M. Lu and Pier Luigi Dragotti}
}
