Sparsely Observed Functional Time Series: Theory and Applications
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Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several i ...
Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several i ...
Recently, we proposed a noniterative cepstral technique for exact signal recovery in frequency-domain optical-coherence tomography. In this paper, we address the influence of measurement noise on the performance of the method. We derive analytical expressi ...
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