In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchronizing chaotic signal achieving an order of magnitude higher accuracy. Noise consideration and optimal parameter setting of the model are discussed. Taking the CSTDR computing as the core, a novel scheme of secure communication is further designed, in which the “smart” receiver is different from the traditional in that it can synchronize to the chaotic signal used for encryption in an adaptive manner. The scheme can solve the issues such as design constrains for identical dynamical systems and couplings between transmitter and receiver in conventional settings. To further manifest the practical significance of the scheme, the digital implementation using field-programmable gate array is conducted and tested experimentally with real-world examples including image and video transmission. The work sheds light on developing machine learning-based signal processing and communication applications.

In this work, a novel realization of the well known machine learning approach-reservoir computing (RC) using nanoelectromechanical system (NEMS) GaAs resonator is proposed. The specially designed resonator can interact with laser directly without cavity. Thanks to the richness of nonlinearity embedded in the light-mediated dynamical interacting process, the RC platform performs outstandingly even by eliminating the time delay feedback, and the conduction of nonlinear autoregressive moving-average (NARMA) task shows that the normalized mean square error (NMSE) can achieve 2.1 × 10 −3 that is one order of magnitude lower than the recently reported result. Detailed theoretical modeling as well as numerical simulations on device's nonlinear dynamics and RC's hyperparameters are given. The interplay between the transient dynamics of the resonator and RC performance is particularly investigated.