A new device promises to differentiate two electronic devices of the same make and model by analyzing their unique radio frequency emissions. Photo by Disney Research

ORLANDO, Fla., May 4 (UPI) -- Almost all electronic devices -- laptops, tablets, smartphones -- emit radio frequencies.

The output may seem random, but scientists at Disney Research recently proved a device's so-called system noise can be used to identify it -- like a fingerprint.


"The idea that these electronic devices have such distinctive RF emissions is astounding," Jessica Hodgins, vice president at Disney Research, said in a news release. "Our researchers were able not only to discover this phenomenon, but to develop a means of using it to identify devices right out of the box."

Researchers devised a method called EM-ID to analyze, recognize and differentiate between RF emissions.

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"Electromagnetic emissions are highly structured and a direct manifestation of the circuits that generate them," Disney Research's Chouchang Yang and said. "But variations in the manufacturing of all components and in final assembly create differences in the EM signal that enable us to differentiate, for example, a laptop computer from another laptop of the same make and model."

Yang and research partner Alanson P. Sample demonstrated the EM-ID process at the IEEE International Conference held this week in Orlando, Fla.

The method employs a low-cost software-defined radio to pick up and record the system noise. The frequencies are are digitized and fed into a computer where an algorithm parses the patterns. Low-frequency background noise is stripped away, leaving frequency peaks consisting of 1,000 to 2,000 elements.

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The algorithm uses a two-tier analysis strategy. Focusing first on frequency distribution to place the new device within a general category of electronic devices -- power tools, computers, household appliances, automobiles. To differentiate devices within a category, the software analyzes frequency and magnitude.

Testing proved the novel method can identify individual electronic devices with an accuracy rate of 95 percent.

"But even though we can't ensure that EM-IDs are always unique, we have a reliable algorithm for predicting the identification success rate," Sample added. "So when a new device is registered and entered into an inventory system, it can alert the user whether the device's EM-ID is unique enough to be read or if an alternative strategy is needed."