Abstract: Compiler systems and methods that solve the DAE transformation problem by simulating DAEs using general-purpose compilers are disclosed herein. The compiler systems and methods extend an existing general-purpose intermediate representation (IR) with a minimal set of intrinsics to represent the model function, which allows leveraging existing compiler technology. A first compiler input is a model of a physical system from source modeling languages. A second compiler input is a first IR for each subroutine specific to the model. The model is compiled to a second IR that is universal across multiple source modeling languages. The IR maps each primitive of the source modeling language to a subroutine call specific to the source modeling language from which the primitive is derived. The first and second IRs are combined into a third IR that describes the model in terms of intrinsics of the third IR.

Abstract: Disclosed herein are systems and methods for blind adaptive beamforming narrowband signals with an uncalibrated antenna array using machine learning. A machine-learning model in the spatial domain enhances the signal of interest and mitigates interfering signals. The systems and methods described herein do not require a calibrated antenna array RF chain. They are based on a neural network that learns the structure of the signal of interest and separates it from other signals in the time, frequency, and spatial domains, without the need of any additional information.

Abstract: A head-mounted display (HMD) tracks a user's hand positions, orientations, and gestures using an ultrasound sensor coupled to the HMD. The ultrasound sensor emits ultrasound signals that reflect off the hands of the user, even if a hand of the user is obstructed by the other hand. The ultrasound sensor identifies features used to train a machine learning model based on detecting reflected ultrasound signals. For example, one of the features is the time delay between consecutive reflected ultrasound signals detected by the ultrasound sensor. The machine learning model learns to determine poses and gestures of the user's hands. The HMD optionally includes a camera that generates image data of the user's hands. The image data can also be used to train the machine learning model. The HMD may perform a calibration process to avoid detecting other objects and surfaces such as a wall next to the user.

Abstract: A head-mounted display (HMD) tracks a user's hand positions, orientations, and gestures using an ultrasound sensor coupled to the HMD. The ultrasound sensor emits ultrasound signals that reflect off the hands of the user, even if a hand of the user is obstructed by the other hand. The ultrasound sensor identifies features used to train a machine learning model based on detecting reflected ultrasound signals. For example, one of the features is the time delay between consecutive reflected ultrasound signals detected by the ultrasound sensor. The machine learning model learns to determine poses and gestures of the user's hands. The HMD optionally includes a camera that generates image data of the user's hands. The image data can also be used to train the machine learning model. The HMD may perform a calibration process to avoid detecting other objects and surfaces such as a wall next to the user.