Synchronization and chaotic masking scheme based on occasional coupling

We present a synchronization and a related chaotic masking scheme for discrete-time systems. This method is based on occasional coupling of transmitter and receiver systems. We show that the synchronization may be achieved and the message can be recovered with acceptable error under certain conditions. Then we show that the proposed schemes are robust with respect to noise and parameter mismatch. We also present some simulation results.

On the design of dynamic associative neural memories.

We consider the design problem for a class of discrete-time and continuous-time neural networks. We obtain a characterization of all connection weights that store a given set of vectors into the network, that is, each given vector becomes an equilibrium point of the network. We also give sufficient conditions that guarantee the asymptotic stability of these equilibrium points.