Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Audio Restoration: A Statistical Model Based Approach
Digital Audio Restoration: A Statistical Model Based Approach
Estimating Signals With Finite Rate of Innovation From Noisy Samples: A Stochastic Algorithm
IEEE Transactions on Signal Processing - Part II
Sampling and reconstruction of signals with finite rate of innovation in the presence of noise
IEEE Transactions on Signal Processing - Part I
Sampling signals with finite rate of innovation
IEEE Transactions on Signal Processing
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Works in the last decades have shown that a large class of parametric non-bandlimited signals can be exactly reconstructed from samples of their filtered versions. In particular, signals x(t) that are linear combinations of a finite number of Diracs per unit of time can be acquired by linear filtering followed by uniform sampling. Nevertheless, when the samples are distorted by noise, many of the early proposed schemes can become ill-conditioned. Recently, a stochastic algorithm that recovers the filtered signal z(t) of x(t), but which fails in the reconstruction of x(t) has been presented. In the present paper, a novel stochastic algorithm which blends together concepts of evolutionary algorithms with those of Gibbs sampling and which successes in recovering x(t) is proposed. This algorithm is adapted to the case where the samples are distorted by quantization noise.