Abstract: Methods and systems detect a random state change in a subject in real time. Eye state changes may be identified in encephalogram brain signals, or honeybee dance patterns may be classified. Multivariate signals including state change information are received via a plurality of channels. The signals are sampled and may be filtered to remove DC components. Statistical characteristics of the signals are monitored. When the statistical characteristics exceed a threshold during a critical time interval, a potential change of state is detected. The critical time segment of the signals may be filtered to generate respective state change artifact signals. The state change artifact signals are decomposed by MEMD, and intrinsic mode functions are generated. Features are extracted from the intrinsic mode functions. These steps may be repeated while the extracted features are provided to a logistic regression classifier that is used to predict a state of the subject.

Abstract: A stochastic model is described for cybersecurity using a host access attack graph to determine network security risk. The model uses Markov chains in conjunction with vulnerability metrics to analyze risks associated with a number of different types of computing devices in various types of networks. The model can be used to identify critical nodes in a host access attack graph where attackers may be most likely to focus. Based on that information, a network administrator can make appropriate, prioritized decisions for system patching. Further, a flexible risk ranking technique is described, where the decisions made by an attacker can be adjusted using a bias factor. The model can be generalized for use with complicated network environments.

Abstract: In one embodiment, a method for detecting buried objects includes receiving ground penetrating radar (GPR) signals captured at discrete locations along a surface of a medium in which an object may be buried, computing parameters based on the received GPR signals that are indicative of the proximity of a buried object, plotting the computed parameters as a function of location, determining an apex of a hyperbola that results from the plotting of the computed parameters, and designating a location at which the apex occurs as an estimated location of the buried object.

Abstract: In one embodiment, a method for detecting buried objects includes receiving ground penetrating radar (GPR) signals captured at discrete locations along a surface of a medium in which an object may be buried, computing parameters based on the received GPR signals that are indicative of the proximity of a buried object, plotting the computed parameters as a function of location, determining an apex of a hyperbola that results from the plotting of the computed parameters, and designating a location at which the apex occurs as an estimated location of the buried object.