It is shown that 8 of all 256 elementary cellular automata have the property of chaos synchronization using a binary driving sequence. The binary synchronization signal ensures good noise robustness. A simple measure for characterizing long periodic cycles as being "chaotic" was introduced. It is also shown that certain relations between the profile of attractors shaped by the local rule in the finite state space and the property of chaos synchronization exists. A simple modulation–demodulation scheme is proposed allowing to build spread spectrum communication links at low implementation costs while the cellular automata state space (number of cells) can be taken as large as required by cryptographic reasons.

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A novel convolution neural network model, abbreviated NL-CNN is proposed, where nonlinear convolution is emulated in a cascade of convolution + nonlinearity layers. The code for its implementation and some trained models are made publicly available. Performance evaluation for several widely known datasets is provided, showing several relevant features: i) for small / medium input image sizes the proposed network gives very good testing accuracy, given a low implementation complexity and model size; ii) compares favorably with other widely known resources-constrained models, for instance in comparison to MobileNetv2 provides better accuracy with several times less training times and up to ten times less parameters (memory occupied by the model); iii) has a relevant set of hyper-parameters which can be easily and rapidly tuned due to the fast training specific to it. All these features make NL-CNN suitable for IoT, smart sensing, biomedical portable instrumentation and other applications where artificial intelligence must be deployed in energy-constrained environments.