Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system's frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

Abstract: Methods and systems are described that secure application data being maintained in transient data buffers that are located in a memory that is freely accessible to other components, regardless as to whether those components have permission to access the application data. The system includes an application processor, a memory having a portion configured as a transient data buffer, a hardware unit, and a secure processor. The hardware unit accesses the transient data buffer during execution of an application at the application processor. The secure processor is configured to manage encryption of the transient data buffer as part of giving the hardware unit access to the transient data buffer.

Abstract: This document describes techniques and systems that enable automatic exposure and gain control for face authentication. The techniques and systems include a user device initializing a gain for a near-infrared camera system using a default gain. The user device ascertains patch-mean statistics of one or more regions-of-interest of a most-recently captured image that was captured by the near-infrared camera system. The user device computes an update in the initialized gain to provide an updated gain that is usable to scale the one or more regions-of-interest toward a target mean-luminance value. The user device dampens the updated gain by using hysteresis. Then, the user device sets the initialized gain for the near-infrared camera system to the dampened updated gain.