Paper Info
Title
Compressive sampling with chaotic dynamical systems
Abstract
We investigate the possibility of using different chaotic sequences to construct measurement matrices in compressive sampling. In particular, we consider sequences generated by Chua, Lorenz and Rossler dynamical systems and investigate the accuracy of reconstruction when using each of them to construct measurement matrices. Chua and Lorenz sequences appear to be suitable to construct measurement matrices. We compare the recovery rate of the original sequence with that obtained by using Gaussian, Bernoulli and uniformly distributed random measurement matrices. We also investigate the impact of correlation on the recovery rate. It appears that correlation does not influence the probability of exact reconstruction significantly.
Year
DOI
Venue
2012
10.1109/TELFOR.2011.6143641
telecommunications forum
Keywords
Field
DocType
gaussian processes,compressive sampling,dynamic system,random measure,sparse matrices,vectors,signal reconstruction,probability,compressed sensing,logistics,probability density function,correlation
Discrete mathematics,Chaotic dynamical systems,Matrix (mathematics),Gaussian,Dynamical systems theory,Chaotic,Mathematics,Compressed sensing,Bernoulli's principle
Journal
Volume
Citations 
PageRank 
abs/1201.0362
4
0.41
References 
Authors
5
2
Name
Order
Citations
PageRank
Venceslav Kafedziski1224.85
Toni Stojanovski283.46