@inproceedings{cdc4f63771c440d09dfc02d806086d44,
title = "Sampling and reconstruction of time-varying atmospheric emissions",
abstract = "We study the spatio-temporal sampling of physical fields representing the dispersion of a substance in the atmosphere. We consider the following setup: N sensors are deployed at ground level and measure the concentration of a particular substance, while M smokestacks are located in the same area and emit a time-varying amount of the substance. To recover the emission rates of the smokestacks with a limited number of spatio-temporal samples, we consider time varying emissions rates lying in two specific low-dimensional subspaces. We propose efficient algorithms and sufficient conditions to recover the emission rates of the smokestacks from the local measurements collected by the sensor network.",
keywords = "Atmospheric dispersion, inverse problems, sensors networks, source estimation, spatio-temporal sampling",
author = "Juri Ranieri and Ivan Dokmani{\'c} and Amina Chebira and Martin Vetterli",
year = "2012",
doi = "10.1109/ICASSP.2012.6288713",
language = "English (US)",
isbn = "9781467300469",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "3673--3676",
booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",
note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",
}