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A notion of band limited functions is considered in the case of the hyperbolic plane in its Poincare upper half-plane $mathbb{H}$ realization. The concept of band-limitedness is based on the existence of the Helgason-Fourier transform on $mathbb{H}$. An iterative algorithm is presented, which allows to reconstruct band-limited functions from some countable sets of their values. It is shown that for sufficiently dense metric lattices a geometric rate of convergence can be guaranteed as long as the sampling density is high enough compared to the band-width of the sampled function.
Reconstructing a band-limited function from its finite sample data is a fundamental task in signal analysis. A simple Gaussian or hyper-Gaussian regularized Shannon sampling series has been proved to be able to achieve exponential convergence for uni
Phaseless reconstruction from space-time samples is a nonlinear problem of recovering a function $x$ in a Hilbert space $mathcal{H}$ from the modulus of linear measurements ${lvert langle x, phi_irangle rvert$, $ ldots$, $lvert langle A^{L_i}x, phi_i
We consider the problem of recovering a set of correlated signals (e.g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measureme
Correlation coefficient is usually used to measure the correlation degree between two time signals. However, its performance will drop or even fail if the signals are noised. Based on the time-frequency phase spectrum (TFPS) provided by normal time-f
We prove a counterpart of the log-convex density conjecture in the hyperbolic plane.