Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves.
According to a report by the Phys.org website, the new method 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.
The new machine-learning approach skips the step of creating an image for analysis by a human and instead analyzes the pure data directly.
It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data requires to recognize objects.
The Phys.org website cited Roarke Horstmeyer, assistant professor of biomedical engineering at Duke, as saying: "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate. But that's inefficient because the computer doesn't need to look at an image at all."
"This approach circumvents that step and allows the program to capture the important details while ignoring other details that it doesn't need.
"We're basically trying to see the object directly from the eyes of the machine," he explained.
