To attack the challenge of object identification and annotation across diverse families of image data, Prometheus and Raytheon will implement a software toolkit based on our new mathematicallybased AI tool. The AI input will be matrix coefficients resulting from the unique Prometheus-developed energy-spreading transform, PONS, the Prometheus Orthonormal Set. PONS is currently in use by both the US military, in the form of novel radar waveforms, and commercially by Cisco in their Intelligent Proximity technology. PONS applicability here and elsewhere results because the transform expresses high-resolution signals as sums of special low-resolution signals, where each low-resolution component is both a highly compressed version of the original signal and contains roughly the same amount of information as any other such component. This enables employment of these components as image snippets, thereby greatly reducing the raw data required by the AI engine. Existing real-world applications of PONS transforms, to wireless communications, optical communications, robust transmission of digital data, watermarking, and radar, all rely upon one-dimensional PONS transforms. We have extended the mathematical foundation by creating multidimensional PONS bases, deriving a new tensor decomposition theorem, and showing that the crucial energy spreading property carries over to multidimensional PONS. In the initial portion of this project we will extend the suite of one-dimensional PONS algorithms and FFT-like codes in order to utilize the mathematically proven and published two-dimensional PONS expansions. We will then apply our proven Artificial Intelligence and Machine Learning technologies to identify the same or similar objects in massive image data sets by employing one, or at most two, two-dimensional PONS matrix coefficients for each image.
