Recovering Signals From Lowpass Data

Higher order sampling and recovering of lowpass signals

A higher order sampling function applied to lowpass signals is described. To recover the original signal from its periodic nonuniform samples, a formulation of the sampling function is introduced, based on the occurrence of local gaps in the sampled signal spectrum. As a result, the interpolant is greatly simplified.

Blind Source Separation: Recovering Signals from Mixtures

Recovering Information from Summary Data

We formulate this task as an inverse problem, specifying a well-defined cost function that has to be optimized under constraints. We show that our formulation includes the uniformity and independence assumptions as a special case, and that it can achieve better reconstruction results if we maximize the smoothness as opposed to the uniformity. In our experiments on real and synthetic datasets, t...

recovering 1d conductivity from aem data using occam inversion

over the past three decades helicopter-borne electromagnetic (hem) measurements have been used to reveal the resistivity distribution of the earth's subsurface for a variety of applications. hem systems include a “bird” or sensor containing one or more pairs of transmitting and receiving coils. the separation between the rigidly mounted transmitting and receiving coils of a coil-pair typic...

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconst...