Abstract The maxentropic reconstruction is a technique for finding an unknown probability distribution from some known information. In this paper we obtain the maxentropic reconstruction of some probability distributions from the knowledge of a prior distribution and of some lower and upper bounds for the mean values of some random variables. For this we use the Csiszár’s I-projection theorems ...

The problem of reconstruction of band-limited signals from sampled and noisy observations is studied. It is proposed to sample a signal at quasi-random points, that form a deterministic sequence with properties resembling a random variable being uniformly distributed. Such quasi-random points can be easily and efficiently generated yielding signal reconstruction algorithms with the improved acc...

Random equivalent sampling (RES) can composite a waveform with high equivalent sampling rate from multiple low speed sampling sequences. In practical application, the performance of RES signal reconstruction would be degraded by the non-uniform distribution of sampling time. Compressed sensing (CS) theory is adopted to reconstruct RES samples, which could mitigate the inherent coherence of samp...

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown t...

This paper represents the reconstruction of sampled signal in CS by using OMP algorithm. We have used the concept of compressive sensing for sub Nyquist sampling of sparse signal. Compressive sensing reconstruction methods have complex algorithms of l1 optimisation to reconstruct a signal sampled at sub nyquist rate. But out of those algorithm OMP algorithm is fast and computationally efficient...

Two-dimensional phase retrieval by use of a window function is considered. First the uniqueness and the reconstruction of a two-dimensional signal from the Fourier intensities of the three signals, an original signal, the signal windowed by a window w(m, n) , and the signal windowed by its complementary window w(c)(m, n) = 1 - w(m, n) , are addressed. Then phase retrieval without a complementar...

Existing convex relaxation-based approaches to reconstruction in compressed sensing assume that noise in the measurements is independent of the signal of interest. We consider the case of noise being linearly correlated with the signal and introduce a simple technique for improving compressed sensing reconstruction from such measurements. The technique is based on a linear model of the correlat...

This paper introduces a new reconstruction formula for reconstructing a periodic bandlimited signal from its nonuniform samples, as well as filterbank interpretations of various sampling strategies, which lead to efficient interpolation and reconstruction methods. As an example of a potential application of these results, we apply it to the problem of reconstructing a two-dimensional bandlimite...