Abstract: Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal.

Abstract: Systems and methods for classifying baseband signals with respect to modulation type include receiving, at a consolidated neural network whose objective is modulation classification performance, a complex quadrature vector of interest including multiple samples of a baseband signal derived from a radio frequency signal of unknown modulation type, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving a classification result for the baseband signal based on combined outputs of the parallel neural networks.

Abstract: Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal.

Abstract: Systems and methods for classifying baseband signals with respect to modulation type include receiving, at a consolidated neural network whose objective is modulation classification performance, a complex quadrature vector of interest including multiple samples of a baseband signal derived from a radio frequency signal of unknown modulation type, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving a classification result for the baseband signal based on combined outputs of the parallel neural networks.

Abstract: Compositions that include Toll-like Receptor 5 agonists and at least a portion of at least one viral antigen can be employed in methods that stimulate an immune response in a subject, in particular, a protective immune response in a subject. Compositions can be associated with particles and employed in the methods in relatively low doses to provide protective immunity to viral infection.

Abstract: A system (160) and method (500) is provided for suppressing an echo during a handover between two networks. During a handover from a first network (110) to a second network (140), a microphone to the second network can be muted (506) to suppress an echo. The handover to the second network can be conducted while maintaining a connection with the first network. Upon connecting to the second network, the microphone to the first network can be muted (510), and a network switch (512) can be performed while the microphone is muted on the first network and also muted on the second network. The microphone on the second network can then be un-muted (514) and the first call on the first network can be terminated.