Using microwaves for machine vision

An example of a wave pattern (right) and its intensity levels (left) developed by the machine learning algorithm to best illuminate the most important features of an object being identified.

Article Highlights

Hardware and software tweak microwave patterns to discover the most efficient way to identify objects
Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements
The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing
“We’re basically trying to see the object directly from the eyes of the machine.”
“The transmitter and receiver act together and are designed together by the machine learning algorithm,”
Whether or not this level of improvement would scale up to more complicated sensing applications is an open question
But the researchers are already trying to use their new concept to optimize hand-motion and gesture recognition for next-generation computer interfaces
“Microwaves are ideal for applications like concealed threat detection, identifying objects on the road for driverless cars or monitoring for emergencies in assisted-living facilities,” said del Hougne. “When you think about all of these applications, you need the sensing to be as quick as possible, so we hope our approach will prove useful in making these ideas reliable realities.”

The Complete Story: Machine Learning Shapes Microwaves for a Computer’s Eyes

Duke University | Institut de Physique de Nice

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