Abstract: A radio frequency (RF) signal classification device may include an RF receiver configured to receive RF signals, a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the RF signals using the selected deep learning model for RF signal classification.

Abstract: A system is for monitoring marine vessels that generate automatic identification system (AIS) message data. The system may include a memory and a processor cooperating therewith to train a maritime scenario topic model using training textual data having known corresponding maritime scenarios associated therewith. The training textual data may include AIS message data. The processor operate the trained maritime scenario topic model on new textual data having unknown corresponding maritime scenarios to determine probabilistic maritime scenarios corresponding to the new textual data, where the new textual data includes new AIS message data. The processor may further generate an alert based upon the probabilistic maritime scenarios.

Abstract: A quantum computing circuit may include a plurality of column subset qubit registers and a plurality of row subset qubit registers, a reward matrix register configured to receive a reward matrix, and an adder coupled to the column subset qubit registers, the row subset qubit registers, and the reward matrix register. The adder may be configured to perform subset summing to generate reward matrix sums from column and row subset qubits and the reward matrix. The quantum computing circuit may further include a plurality of row action qubit registers, and a comparator coupled to the adder and the row action qubit registers and configured to generate an output based thereon.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the ?-OTDR, and determine an acoustic event based upon comparing the series of covariance matrices with a corresponding Toeplitz matrix.

Abstract: A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF jammer having a selectable jamming frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF jammer to detect an RF transmission, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected RF transmission, process the QUBO inputs with a QUBO objective function to determine a new jamming frequency window, and operate the RF jammer at the new jamming frequency window.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to train a plurality of machine learning networks with DAS data from the ?-OTDR based upon different respective optimizers, select a trained machine learning network from among the plurality thereof based upon a game theoretic model, and generate an acoustic event report from the DAS data using the selected trained machine learning network.

Abstract: An object detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The object detection device may also include a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the new image data using the selected deep learning model for object detection.

Abstract: A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency decoy window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency decoy window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency decoy window, and operate the RF decoy transmitter at the new hopping frequency decoy window.

Abstract: An aviation anomaly detection system may include an interface configured to receive audio communications between an air traffic control station and a plurality of aircraft, a speech-to-text converter configured to convert the received audio communications from the interface to text data, and a processor. The processor may be configured to determine at least one aviation anomaly from the text data with a variational autoencoder (VAE) deep learning model, and generate an alert based upon the at least one aviation anomaly.

Abstract: A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency window, and operate the RF transmitter at the new hopping frequency window.

Abstract: A computational fluid dynamics system may include a quantum computing circuit and a processor. The processor may be configured to generate and store a union mesh table representing a plurality of point groups, with at least some points being shared among different groups, and each point having a same property. The processor may be further configured to cooperate with the quantum computing circuit to perform a fluid dynamic computation based on a streaming fluid velocity vector and the union mesh table.

Abstract: An anomaly detection device for an aircraft may include a memory and a processor configured to receive satellite position data collected by the aircraft and comprising a sequence of aircraft positions defining an aircraft trajectory, and process the satellite position data using a plurality of different deep learning models to determine respective aircraft trajectory anomalies in a runway approach flight path for the aircraft. The processor may be further configured to select a given deep learning model that most accurately determines the anomalies in the runway approach flight path based upon a game theoretic model, and generate an alert if the respective anomaly in the runway approach flight path determined by the given deep learning model exceeds a threshold.

Abstract: A cognitive radio system may include cognitive radio frequency (RF) radios and a controller configured to selectively change at least one operating parameter of the cognitive RF radios based upon a cognitive group hierarchy. The cognitive group hierarchy may include a first group based upon a signal modulation classification, a second group based upon a waveform requirement, a third group based upon an optimal cognitive RF radio path, a fourth group based upon a cognitive RF radio dynamic spectrum allocation, and a fifth group based upon frequency hopping.

Abstract: An Automatic Dependent Surveillance Broadcast (ADS-B) system may include a plurality of ADS-B terrestrial stations, with each ADS-B terrestrial station comprising an antenna and wireless circuitry associated therewith defining a station gain pattern. The system may further include a controller including a variational autoencoder (VAE) configured to compress station pattern data from the plurality of ADS-B terrestrial stations, create a normal distribution of the compressed data in a latent space of the VAE, and decompress the compressed station pattern data from the latent space. The controller may also include a processor coupled to the VAE and configured to process the decompressed station pattern data using a probabilistic model selected from among different probabilistic models based upon a game theoretic reward matrix, determine an anomaly from the processed decompressed station pattern data, and generate an alert (e.g., a station specific alert) based upon the determined anomaly.

Abstract: An image pixel classification device may include a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a pairwise game theory reward matrix for a plurality of different classes of an image pixel, with each class corresponding to a respective type of land feature from among a plurality of different types of land features, cooperate with the quantum computing circuit to perform quantum subset summing on the pairwise game theory reward matrix. The processor may further select a class for the image pixel based upon the quantum subset summing, and classify the image pixel as the corresponding type of land feature for the selected class.

Abstract: A change detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The change detection device may further include a controller configured to select a deep learning model from different deep learning models based upon the new image data and a game theory reward matrix, and process the new image data using the selected deep learning model to detect changes therein.

Abstract: Systems and methods for operating a quantum processor. The methods comprise: receiving a reward matrix at the quantum processor, the reward matrix comprising a plurality of values that are in a given format and arranged in a plurality of rows and a plurality of columns; converting, by the quantum processor, the given format of the plurality of values to a qubit format; performing, by the quantum processor, subset summing operations to make a plurality of row selections based on different combinations of the values in the qubit format; using, by the quantum processor, the plurality of row selections to determine a normalized quantum probability for a selection of each row of the plurality of rows; making, by the quantum processor, a decision based on the normalized quantum probabilities; and causing, by the quantum processor, operations of an electronic device to be controlled or changed based on the decision.

Abstract: Systems and methods for operating a quantum processor. The methods comprise: training one or more quantum neural networks using modulation class data to make decisions as to a modulation classification for a signal based on one or more feature inputs for the signal; obtaining, by the quantum processor, principle components of real and imaginary components of a signal received by a communication device; and performing first quantum neural network operations by the quantum processor using the principle components as inputs to the trained one or more quantum neural networks to generate a plurality of scores, wherein each said score represents a likelihood that the received signal was modulated using a given modulation type of a plurality of different modulation types.

Abstract: Systems and methods for operating a communication device. The methods comprise: receiving a signal at the communication device; performing, by the communication device, one or more machine learning algorithms using at least one feature of the signal as an input to generate a plurality of scores (each score representing a likelihood that the signal was modulated using a given modulation type of a plurality of different modulation types); assigning a modulation class to the signal based on the plurality of scores; determining whether a given wireless channel is available based at least on the modulation class assigned to the signal; and selectively using the given wireless channel for communicating signals based on results of the determining.

Abstract: A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.