Abstract: Exemplary embodiments of the present disclosure relate to systems and methods for structural health monitoring in which a sensor network includes motes distributed with respect to a structure. The sensor network can utilize dynamic pattern matching to monitor and localize damage in the structure without modeling or solving equations of the engineered structure, and without ascertaining or separately accounting for extraneous and often-difficult-to-recognize or evaluate factors, such as of the environmental and stimuli-related variability type.

Abstract: Exemplary embodiments of the present disclosure relate to systems and methods for structural health monitoring in which a sensor network includes motes distributed with respect to a structure. The sensor network can utilize dynamic pattern matching to monitor and localize damage in the structure without modeling or solving equations of the engineered structure, and without ascertaining or separately accounting for extraneous and often-difficult-to-recognize or evaluate factors, such as of the environmental and stimuli-related variability type.

Abstract: In any situation where an individual's personal attributes are at risk to be revealed or otherwise inferred by a third, there is a chance that such attributes may be linked back to the individual. Examples, of such situations include publishing user profile micro-data or information about social ties, sharing profile information on social networking sites or revealing personal information in computer-mediated communication. Measuring user anonymity is the first step to ensure that a users identity cannot be inferred. The systems and methods of the present disclosure, embrace an information-entropy-based estimation of the user anonymity level which may be used to predict identity inference risk. One important aspect of the present disclosure is complexity reduction with respect to the anonymity calculations.