Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.