Hrl Laboratories, LLC

Bryan H Fong

Peter Petre

Shankar R Rao

Sanaz Adl

David M Pepper

Hans W Bruesselbach

Adour Vahe Kabakian

Gabriel L Virbila

Daniel Frederic Sievenpiper

Charles E Martin

Joseph S Colburn

John Jacob Ottusch

John L Visher

Daniel S Matic

Austin F Garrido

Described is a Neuromorphic Adaptive Core (NeurACore) cognitive signal processor (CSP). The NeurACore CSP includes a NeurACore local learning layer block that is operable for receiving as an input a mixture of in-phase and quadrature (I/Q) signals and mapping the I/Q signals onto a neural network to determine complex-valued output weights of neural states of the neural network. A global learning layer is included that is operable for adapting the complex-valued output weights to predict a most likely next value of the input I/Q signal. Further, a neural combiner is included that operable for combining a set of delayed neural state vectors with weights of the global learning layer to compute an output signal, the output signal being separate in-phase and quadrature signals.

Complex neuromorphic adaptive core (neuracore) and physics enhanced neuromorphic adaptive controller

Described is a system for Neuromorphic Adaptive Core (NeurACore) signal processor for ultra-wide instantaneous bandwidth denoising of a noisy signal. The NeurACore signal processor includes a digital signal pre-processing unit for performing cascaded decomposition of a wideband complex valued In-phase and Quadrature-phase (I/Q) input signal in real time. The wideband complex valued I/Q input signal is decomposed into I and Q sub-channels. The NeurACore signal processor further includes a NeurACore and local learning layers for performing high-dimensional projection of the wideband complex valued I/Q input signal into a high-dimensional state space; a global learning layer for performing a gradient descent online learning algorithm; and a neural combiner for combining outputs of the global learning layer to compute signal predictions corresponding to the wideband complex valued I/Q input signal.

Ultra-wide instantaneous bandwidth complex neuromorphic adaptive core processor

Described is a Neuromorphic Adaptive Core (NeurACore) cognitive signal processor (CSP) for wide instantaneous bandwidth denoising of noisy signals. The NeurACore CSP includes a NeurACore block, a globally learning layer, and a neural combiner. The NeurACore block is operable for receiving as an input a mixture of in-phase and quadrature (I/Q) signals and mapping the I/Q signals onto a neural network to determine complex-valued output weights of neural states of the neural network. The global learning layer is operable for adapting the complex-valued output weights to predict a most likely next value of the input I/Q signal. Further, the neural combiner is operable for combining a set of delayed neural state vectors with the weights of the global learning layer to compute an output signal, the output signal being separate in-phase and quadrature signals.

Low size, weight and power (swap) efficient hardware implementation of a wide instantaneous bandwidth neuromorphic adaptive core (NeurACore)

Described is a cognitive signal processor that is implemented in a field programmable gate array (FPGA). During operation, the FGPA receives a continuous noisy signal. The continuous noisy signal is a time-series of data points from a mixture signal of waveforms having both noise and a desired waveform signal. The continuous noisy signal is linearly mapped to reservoir states of a dynamical reservoir. A high-dimensional state-space representation of the continuous noisy signal is generated by digitally combining the continuous noisy signal with the reservoir states. Notably, the continuous noisy signal is approximated over a time interval based on a linear basis function. One or more delay-embedded state signals are then generated based on the reservoir states. The continuous noisy signal is then denoised by removing the noise from the desired waveform signal, resulting in a denoised waveform signal.

Cognitive signal processor

Described is a system for signal denoising using a cognitive signal processor having a time-varying reservoir. The system receives a noisy input signal of a time-series of data points from a mixture of waveform signals. The noisy input signal is linearly mapped into the time-varying reservoir. A high-dimensional state-space representation of the mixture of waveform signals is generated by combining the noisy input signal with a plurality of reservoir states. The system then generates a denoised signal corresponding to the noisy input signal.

Cognitive denoising of nonstationary signals using time varying reservoir computer

Described is a cognitive signal processor (CSP) for signal denoising. In operation, the CSP receives a noisy signal as a time-series of data points from a mixture of both noise and one or more desired waveform signals. The noisy signal is linearly mapped to reservoir states of a dynamical reservoir. A high-dimensional state-space representation is then generated of the noisy signal by combining the noisy signal with the reservoir states. A delay-embedded state signal is generated from the reservoir states. The reservoir states are denoised by removing noise from each reservoir state signal, resulting in a real-time denoised spectrogram of the noisy signal. A denoised waveform signal is generated combining the denoised reservoir states. Additionally, the signal denoising process is implemented in software or digital hardware by converting the state-space representation of the dynamical reservoir to a system of delay difference equations and then applying a linear basis approximation.

System for parallelized cognitive signal denoising

Described is a cognitive signal processor for signal denoising and blind source separation. During operation, the cognitive signal processor receives a mixture signal that comprises a plurality of source signals. A denoised reservoir state signal is generated by mapping the mixture signal to a dynamic reservoir to perform signal denoising. At least one separated source signal is identified by adaptively filtering the denoised reservoir state signal.

Cognitive signal processor for simultaneous denoising and blind source separation

An IR laser source providing light in the IR spectrum, the laser source comprising a pump laser operating at a frequency equivalent to wavelength shorter than 2 &#x3bc;m and at a predetermined power, and an optic fiber coupled to the pump laser. The optic fiber has at least a section of a hollow core photonic crystal fiber, the at least a section of hollow core photonic crystal fiber being designed to have at least a passband in the IR spectrum and being filled with a molecular gas for triggering at least one Stoke's shift in the light entering the at least a section of hollow core photonic crystal fiber for the particular power of the pump laser, the at least one Stoke's shift be selected to cause the light entering the at least a section of hollow core photonic crystal fiber to shift in frequency into the passband in the IR spectrum of the hollow core photonic crystal fiber.

Molecular gas-filled hollow core photonic crystal fiber laser at infra-red wavelengths

A method and system for determining an optimized artificial impedance surface is disclosed. An artificial impedance pattern is calculated on an impedance surface using an optical holographic technique given an assumed surface wave profile and a desired far field radiation pattern. Then, an actual surface wave profile produced on the impedance surface from the artificial impedance pattern, and an actual far field radiation pattern produced by the actual surface wave profile are calculated. An optimized artificial impedance pattern is then calculated by iteratively re-calculating the artificial impedance pattern from the actual surface wave profile and the desired far field radiation pattern. An artificial impedance surface is determined by mapping the optimized artificial impedance pattern onto a representation of a physical surface.

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Los Angeles, CA, United States of America

Bryan H Fong