Abstract: Described herein are methods and systems implementing physically unclonable functions (PUFs). An example method includes: inputting a first challenge bit string into a first PUF; capturing a first response bit string from the first PUF; inputting a second challenge bit string into a second PUF; capturing a second response bit string from the second PUF; performing a hash operation on the first response bit string and the second response bit string, the hash operation generating a hashed-response bit string; and storing the first and second challenge bit strings and the hashed-response bit string in a first database.

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: 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.