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
Digital Signal Processing (4th Edition)
Digital Signal Processing (4th Edition)
On SVD for estimating generalized eigenvalues of singular matrixpencil in noise
IEEE Transactions on Signal Processing
Estimating Signals With Finite Rate of Innovation From Noisy Samples: A Stochastic Algorithm
IEEE Transactions on Signal Processing - Part II
Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
IEEE Transactions on Signal Processing
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
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analog-to-digital converter survey and analysis
IEEE Journal on Selected Areas in Communications
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In early 2000, it was shown that it is possible to develop exact sampling schemes for a large class of parametric non-bandlimited noiseless signals, namely certain signals of finite rate of innovation. 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. However, when noise is present, many of the early proposed schemes can become ill-conditioned. Recently, a novel stochastic algorithm based on Gibbs sampling was proposed by Tan &Goyal [IEEE Trans. Sign. Proc., 56 (10) 5135] to recover the filtered signal z(t) of x(t) by observing noisy samples of z(t). In the present paper, by blending together concepts of evolutionary algorithms with those of Gibbs sampling, a novel stochastic algorithm which substantially improves the results in the cited reference is proposed.