Duke University researchers “developing USD1,000 app to detect drones near prisons”

Engineers at Duke University  in the USA are teaming up with the North Carolina Department of Public Safety to address a growing concern for correctional facilities worldwide—drones delivering contraband over prison walls.

“Mary “Missy” Cummings, professor of electrical and computer engineering at Duke and a team of students, have been developing an alerting system that uses microphones and thermal cameras to detect unwanted drones and the people flying them, says a Duke University news report. “Their colleagues at Clemson, meanwhile, built a synthetic “bird’s nest” the size of a hawk’s nest to camouflage the associated equipment. The team will be beta testing the system with the Town of Cary later this summer, and eventually with Duke Gardens. If the initial results are promising, Sutton hopes the emerging system might be able to extend to monitoring the walls of North Carolina’s prisons.”

In related research, according to university news report. “Chunge Wang, a Duke undergraduate student majoring in computer science, created a new app interface for the equipment that is tailored to the needs of prisons, dubbed Prison Reconnaissance Information System (PRIS). In its current form, the hardware consists of a microphone connected to a Raspberry Pi—a simple, inexpensive computer board originally developed to teach basic programming—a data server and a smart phone, costing less than USD1,000 total.

The Raspberry Pi is loaded with a machine learning algorithm that constantly processes the data collected from the microphone to isolate the sounds a drone makes from background noise. When it detects the buzzing whir of a drone’s propellers, it sends a notification to an app loaded on smart phones carried by the prison’s security personnel.”

“The app displays the information in a visually dynamic way in real-time, using different symbols overlaid on a map view of the prison,” explained Wang. “The goal is for the users to be able to quickly understand where and how far away the potential threat is, how confident the system is that it’s actually a drone, and what’s most likely to happen next.”

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