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.
This software tool takes clinical notes from veterinary electronic medical records and assigns SNOMED-CT VET extension diagnostic codes based on the content written on the notes.
Mobile devices often connect to the network via wireless channels. In general, the downlink of the wireless channel (e.g., the cellular access network) is limited in throughput.
Stanford researchers have developed a new grammar checking tool with an emphasis on improving translation. The technology is a browser integration and Google Docs plugin for querying and rendering edits provided by an endpoint that suggests edits to text.
Stanford researchers have created the first large-scale dataset of aerial videos from multiple classes of targets interacting in complex outdoor spaces.
Stanford researchers have developed a new machine learning method for extracting gait parameters, such as cadence, step length, peak knee flexion, and Gait Deviation Index (GDI), from a single video.
Stanford researchers have developed an intuitive, dynamic construction scheduling software tool called Loops. Loops is both Building Information Modeling (BIM) based and Lean-enabled.
Nonstationary image artifacts frequently arise in MRI from off-resonance and motion. Current methods to correct these nonstationary effects are computationally expensive. Stanford researchers have developed a new deep learning framework to improve image quality in minutes.
Engineers in Prof. Shanhui Fan's laboratory have developed an efficient, scalable, in-situ method to train, configure and tune complex photonic circuits for artificial intelligence and machine learning.
Stanford researchers have developed a statistical method to map tissue activity distribution and photon attenuation, correcting for attenuation in real time without a transmission scan, using Positron Emission Tomography.
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.
Stanford researchers at the Dahl Lab have developed a method to reduce artifacts in ultrasound image reconstruction using a trained convolutional neural network (CNN).
Stanford researchers have patented a hardware and software system designed for automated assisted steering that combines automated and human vehicle control within driving lanes.