Abstract: Mipping systems and methods are disclosed. For example, a mipping system can include processing circuitry configured to receive combinations of a plurality of pixels N at a time, each pixel having material codes directed to respective materials of the pixels, where the material codes relate to infrared properties of the respective materials, and N is a positive integer greater than 1; and train an artificial neural network having a classification space by providing respective neurons for each unique combination of material codes, and condition the artificial neural network so that the respective neurons activate when presented with their unique of material code combinations in order to create a combined set of material code parameters for accurate rendering of the mipped pixels.

Abstract: Mechanisms for incorporating a human into an automated multiple-entity scenario simulation are disclosed. A Petri-net message processor function (PMPF) receives a first Petri-net message from a source activity node function (ANF). The Petri-net message includes a simulation activity identifier that identifies a first simulation activity. The PMPF routes the first Petri-net message to a first ANF based on the simulation activity identifier. The first ANF receives the first Petri-net message and provides a message to a user based on the first Petri-net message. A response is received from the user. A second Petri-net message is generated based on the response and is communicated to a destination ANF to trigger a second simulation activity by the destination ANF.

Abstract: Mipping systems and methods are disclosed. For example, a mipping system can include processing circuitry configured to receive combinations of a plurality of pixels N at a time, each pixel having material codes directed to respective materials of the pixels, where the material codes relate to infrared properties of the respective materials, and N is a positive integer greater than 1; and train an artificial neural network having a classification space by providing respective neurons for each unique combination of material codes, and condition the artificial neural network so that the respective neurons activate when presented with their unique of material code combinations in order to create a combined set of material code parameters for accurate rendering of the mipped pixels.

Abstract: Mechanisms for incorporating a human into an automated multiple-entity scenario simulation are disclosed. A Petri-net message processor function (PMPF) receives a first Petri-net message from a source activity node function (ANF). The Petri-net message includes a simulation activity identifier that identifies a first simulation activity. The PMPF routes the first Petri-net message to a first ANF based on the simulation activity identifier. The first ANF receives the first Petri-net message and provides a message to a user based on the first Petri-net message. A response is received from the user. A second Petri-net message is generated based on the response and is communicated to a destination ANF to trigger a second simulation activity by the destination ANF.