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One reason for using a common digital layer, according to Lewis, is the hope that it will enable a community developing “apps” in software to accelerate the innovation process and unlock new applications for software-reconfigurable imagers.
In follow-on phases of the program, performers will need to demonstrate portability of the developing technology in outdoor testing and, in Lewis’s words, “develop learning algorithms that guide the sensor, through real-time adaptation of sensor control parameters, to collecting the data with the highest content of useful information.” That adaption might translate, in response to visual cues, into toggling into a thermal detection mode to characterize a swarm of UAVs or into hyper-slow-motion (high-frame rate) video to help tease out how a mechanical device is working.
“Even as fast as machine learning and artificial intelligence are moving today, the software still generally does not have control over the sensors that give these tools access to the physical world,” Lewis said. “With ReImagine, we would be giving machine-learning and image processing algorithms the ability to change or decide what type of sensor data to collect.”
Importantly, he added, as with eyes and brains, the information would flow both ways: the sensors would inform the algorithms and the algorithms would affect the sensors. Although defense applications are foremost on his mind, Lewis also envisions commercial spinoffs. Smart phones of the future could have camera sensors that do far more than merely take pictures and video footage, their functions limited only by the imaginations of a new generation of app developers, he suggested.
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