Speaker "Sam Siewert" Details Back
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Name
Sam Siewert
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Company
Embry Riddle Aeronautical University And University Of Colorado Boulder
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Designation
Software Engineer
Topic
Drone Net - Multi-modal sensor network for sUAS shared air space safety and security
Abstract
The challenge and opportunity presented by use of UAS "drones" in the national airspace has historic significance. The FAA estimates that by 2020 the drone market will be $98 billion with 7 million drones added annually. How drones ranging from professional service to hobby will safely share airspace is unclear. Preliminary research at Embry Riddle to develop a drone detector, which can be placed on rooftops and networked with other detectors and information services, has shown that multi-spectral electro-optical/infrared detection is quite effective. Our team is using Big Data with machine vision and machine learning systems in an EO/IR, acoustic and active RADAR sensor fusion system. The Big Data and machine vision and learning architecture provides real-time object detection for aircraft and drones using salient object detection algorithms both on embedded instruments and networked to Cloud based high performance computing. We'll present the intelligence, power efficiency and real-time processing advantages our architecture provides compared to traditional systems.