Abstract: The Analyst Cueing method addresses the issues of locating desired targets of interest from among very large datasets in a timely and efficient manner. The combination of computer aided methods for classifying targets and cueing a prioritized list for an analyst produces a robust system for generalized human-guided data mining. Incorporating analyst feedback adaptively trains the computerized portion of the system in the identification and labeling of targets and regions of interest. This system dramatically improves analyst efficiency and effectiveness in processing data captured from a wide range of deployed sensor types.

Abstract: Adaptive methods and systems for applying a channel correction factor to received signals to correct errors caused by channel distortion. The channel correction factor is dynamically calculated and updated based on the received signal or signals. A receiver continuously calculates an error factor representing a deviation of the received signal from the signal being transmitted, and generates the channel correction factor based on the error factor. Since the error factor includes information related to channel distortions, the channel correction factor so calculated adapts to the change of channel characteristics. If preferred, an advanced algorithm can be used to predict the channel response for the next incoming signal such that the receiver can correct channel errors on a substantially real-time basis.