In this paper, we introduce a computational framework for recovering high-resolution approximation of an unknown function from its low-resolution indirect measurements as well training observations by merging the frameworks generalized sampling and functional principal component analysis. particular, increase signal resolution via data-driven approach, which models interest realization random f...

Compressed sensing technique is a recent framework for signal sampling and recovery. It allows signal acquisition with less sampling than required by Nyquist-Shannon theorem and reduces data acquisition time in MRI. When the sampling rate is low, prior knowledge is essential to reconstruct the missing features. In this paper, a different reconstruction method is proposed by using the principal ...

This paper considers the processing of radar signals in digital radar rangefinders. Special attention is focused on the case where limited performance of digital signal processors cannot provide the input signal sampling rate corresponding to the Nyquist criterion. The considered method of stroboscopic sampling allows overcoming this limitation. A suggested pseudo random modification of the str...

In this paper, it has been shown that the spectrum aliasing and folding effects occur only in case of non-ideal signal sampling. When duration sampling is equal to zero, these do not at all. other words, absolutely necessary condition for their occurrence just a nonzero value time. Periodicity process plays secondary role.

The noise performance of digital correlated double sampling (DCDS) for readout of charge coupled devices (CCD) with dominant white noise is presented. The trade-offs between analogue and digital signal filtering and the impact on the sampling rate are investigated and numerically simulated for realistic systems. The results can be used to select the signal bandwidth, the settling accuracy and t...