Abstract: A method and a system for pattern recognition utilizes an ensemble of reference patterns to represent the possible instances of the models to be recognized; constructs a hierarchy of estimators to simplify and enhance the recognition of the models of interest; approximates complex reference patterns with linear compositions of simpler patterns; fragments complex patterns into local patterns so that interference between the local patterns is sufficiently small for linearization methods to be applicable; constructs estimators during an offline stage to offload calculations from the online signal processing stage; designs model estimators based on optimization principles to enhance performance and to provide performance metrics for the estimated model instances; generates a hierarchy of reference descriptors during the offline stage, which are used for the design and construction of the model estimators. Specific examples are provided for the recognition of image features such as edges and junctions.

Abstract: A system to control a dynamic system, comprising actuators, sensors, a control module and a plurality of observers to monitor the state of the actuators is disclosed. Certain embodiments of the invention comprise a global failure detection and identification module, a baseline controller, and a retrofit controller. Methods to control the dynamic system are disclosed. Some of the methods utilize a second-order mathematical model of the dynamics of the actuators. In some embodiments, this mathematical model is parameterized by a loss-of-effectiveness (LOE) parameter and a lock-in-place (LIP) parameter. The method may further comprise the step of detecting disturbance conditions and structural damage conditions.