Abstract: Systems, devices, and methods for generating a unique fingerprint are described herein. For example, an example integrated circuit (IC) chip includes a physically unclonable function (PUF) and an auxiliary circuit. The PUF is a hybrid Boolean network. Additionally, the auxiliary circuit is configured to receive a transient response enable signal.

Abstract: A technique is provided for control of a nonlinear dynamical system to an arbitrary trajectory. The technique does not require any knowledge of the dynamical system, and thus is completely model-free. When applied to a chaotic system, it is capable of stabilizing unstable periodic orbits (UPOs) and unstable steady states (USSs), controlling orbits that require non-vanishing control signal, synchronization to other chaotic systems, and so on. It is based on a type of recurrent neural network (RNN) known as a reservoir computer (RC), which, as shown, is capable of directly learning how to control an unknown system. Precise control to a desired trajectory is obtained by iteratively adding layers to the controller, forming a deep recurrent neural network.

Abstract: Reservoir computing systems and methods provide rapid processing speed by the reservoir and by the output layer. A hardware implementation of reservoir computing is based on an autonomous, time-delay, Boolean network realized on a readily-available platform known as a field-programmable gate array (FPGA). This approach allows for a seamless coupling of the reservoir to the output layer due to the spatially simple nature of the reservoir state and because matrix multiplication of a Boolean vector can be realized with compact Boolean logic. Embodiments may be used to predict the behavior of a chaotic dynamical system.