Abstract: Described are computer-based methods and apparatuses, including computer program products, for radar data processing. In some examples, the method includes determining a plurality of substantially equally spaced frequency intervals within radar data based on an interval size; transforming parts of the radar data within each of the plurality of substantially equally spaced frequency intervals; determining a magnitude of each of the transformed parts of the radar data; and summing the magnitude for each of the transformed parts of the radar data to form adaptive radar data. The adaptive radar data can have a higher signal-to-noise ratio than the radar data.

Abstract: The technology described herein includes a system and/or a method of risk management for object identification. The method includes selecting at least two fusion functions from a plurality of fusion functions. Each of the fusion functions is associated with a different risk bias. The method further includes fusing a received set of data based on each fusion function to form at least two sets of fused data. The method further includes generating a probability of identification of the classification of the object based on each set of the fused data. The method further includes determining a risk bias difference between the probability of identification of each set of the fused data.

Abstract: The technology described herein includes a system and/or a method for determining confidence of object identification. The technology includes selecting at least one confidence rule from a plurality of confidence rules based on a first discrimination identification and a second discrimination identification. The technology further includes generating an object confidence weight based on one or more first identification parameters, one or more second identification parameters, and the selected at least one confidence rule. The technology further includes fusing the received set of data based on an alpha parameter, a beta parameter, and the object confidence weight. The technology further includes generating a probability of identification of a classification of the object based on the fused data.

Abstract: The technology described herein includes a system and/or a method for identifying an object. The technology includes determining an alpha parameter that is associated with a fusion function. The technology includes determining a beta parameter that is associated with a degree of expected independence of a received set of data and the received set of data including information associated with a classification of the object. The technology includes fusing the received set of data based on the alpha parameter and the beta parameter. The technology includes generating a probability of identification of the classification of the object based on the fused data.

Abstract: An optical data storage medium includes a superstructure of micro-lenses defining first and second repeating periods. One of the periods is longer than the other, and the presence of the longer repeating period allows the medium to be used in an optical drive that is configured to use light diffracted by the longer period for tracking purposes. The micro-lenses may be configured such that adjacent lenses have different heights, widths, or shapes. In some embodiments, the spacing between adjacent data tracks is varied to provide the first and second repeating periods.