TY - GEN
T1 - Noise robustness condition for chaotic maps with piecewise constant invariant density
AU - Pareschi, Fabio
AU - Setti, Gianluca
AU - Rovatti, Riccardo
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2004/9/6
Y1 - 2004/9/6
N2 - Chaotic maps represent an effective method for generating random-like sequences, that combines the benefits of relying on simple, causal models with good unpredictability. Regrettably such positive features are counterbalanced by the fact that statistics of true-implemented chaotic maps are generally strongly dependent on implementation errors and external perturbations. Here we study the effect of an external, additive, map-independent noise perturbation in the map model, and present a technique to guarantee, for a quite large class of maps, independence of the first-order statistics of the noise features.
AB - Chaotic maps represent an effective method for generating random-like sequences, that combines the benefits of relying on simple, causal models with good unpredictability. Regrettably such positive features are counterbalanced by the fact that statistics of true-implemented chaotic maps are generally strongly dependent on implementation errors and external perturbations. Here we study the effect of an external, additive, map-independent noise perturbation in the map model, and present a technique to guarantee, for a quite large class of maps, independence of the first-order statistics of the noise features.
UR - http://www.scopus.com/inward/record.url?scp=4344641142&partnerID=8YFLogxK
M3 - Conference contribution
BT - Proceedings - IEEE International Symposium on Circuits and Systems
ER -