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: 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: 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.

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: 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, determine acoustic events based upon the covariance matrices and a machine learning network, and generate an acoustic event report from the acoustic events.

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: 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.

Abstract: Systems and methods for operating a receiver. The methods comprise: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform. The demodulation operations comprise: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.

Abstract: Systems and methods for operating a receiver. The methods comprise: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform. The demodulation operations comprise: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.

Abstract: An article delivery assembly for unattended, trackable and secure delivery of articles includes a box that defines an internal space. A door is hingedly coupled to the box and is positioned to cover an open face of the box. A locking mechanism, a power module, a microprocessor and a reader are coupled to a back of the door. A keypad and a first antenna are coupled to a front of the door. The locking mechanism is selectively couplable to the box. The microprocessor is operationally coupled to the power module. The reader and the keypad are operationally coupled to the microprocessor. The first antenna is operationally coupled to the reader. A fastener, coupled to the box, is configured to attach the box to a substrate. Each of a plurality of tags, readable by the reader, is configured to couple to an article to be positioned in the internal space.