The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.

With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.

The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.

By synchronizing media streams transmitted from the cloud to two devices, researchers could improve cloud gaming and AR/VR applications.

Developed by MIT researchers, BrightMarkers are invisible fluorescent tags embedded in physical objects to enhance motion tracking, virtual reality, and object detection.

A Lincoln Laboratory team visited Hill Air Force Base in Utah to determine how susceptible the latest-generation mobile network is to detection, geolocation, and jamming.

The receiver chip efficiently blocks signal interference that slows device performance and drains batteries.

A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.

Researchers demonstrate two security methods that efficiently protect analog-to-digital converters from powerful attacks that aim to steal user data.

Engineers build a lower-energy chip that can prevent hackers from extracting hidden information from a smart device.