Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Stanford engineers have developed an optical modulator to enable low-cost and high spatial-resolution time-of-flight imaging and LiDAR with low-cost standard image sensors.
Researchers at Stanford have developed, for the first time, a component analysis algorithm that does not require any assumption on the data structure or data generation process to find out the important components or trends in data.
Researchers at Stanford have developed a distributed digital "black box" audit trail design for connected and automated vehicle data and software assurance.
Stanford researchers have developed a method called KleinPAT, for creating sound models in seconds, making it cost effective to simulate sounds for many different objects in a virtual environment.
Engineers in the Solgaard lab have developed a high-speed, random access grating light valve (GLV) for phase modulation to steer and focus light in LIDAR and 3D imaging applications.
Stanford inventors have developed a deep learning framework that is able to label individual points from 3D Point Clouds that are acquired by various sensors (RGBD sensors, LIDAR sensors, etc.). This framework obtains a point-level fine-grained labeling of 3D Scenes.
This portfolio of inventions provides the tools for an advanced navigational system and panoramic virtual tours – technology that is incorporated in Google Street View.