A team of American and French scientists have developed a new machine-learning approach that lets computers identify objects with the help of microwaves.
According to the Duke University and Institut de Physique de Nice team, identifying objects with microwaves improve accuracy while reducing the associated computing time and power requirements.
It also provides a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing.
In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required.
“Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate,” says Duke assistant professor Roarke Horstmeyer.
“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 details that an image-forming process might miss while ignoring other details of the scene that it doesn’t need,” adds Aaron Diebold, a research assistant in distinguished professor David R. Smith’s lab.
“We’re basically trying to see the object directly from the eyes of the machine.”
In the study, the researchers used a metamaterial antenna that can sculpt a microwave wave front into many different shapes.
The metamaterial is an 8×8 grid of squares, each of which contains electronic structures that allow it to be dynamically tuned to either block or transmit microwaves.
For each measurement, the intelligent sensor selects a handful of squares to let microwaves pass through. This creates a unique microwave pattern, which bounces off the object to be recognized and returns to another similar metamaterial antenna.
The sensing antenna also uses a pattern of active squares to add further options to shape the reflected waves. The computer then analyzes the incoming signal and attempts to identify the object.
By repeating this process thousands of times for different variations, the machine learning algorithm eventually discovers which pieces of information are the most important as well as which settings on both the sending and receiving antennas are the best at gathering them.
Image and content: Duke University
