Researchers in Dr. Leonore Herzenberg's lab at Stanford University have developed a portfolio of data management, storage, and analysis technologies that may be used for large data sets.
This portfolio of inventions provides the tools for an advanced navigational system and panoramic virtual tours – technology that is incorporated in Google Street View.
Although tracking has been studied for decades, real-time tracking algorithms often suffer from low accuracy and poor robustness when confronted with difficult, real-world data.
Researchers in Prof. Vijay Pande's laboratory developed a novel computational technique (“SCISSORS”) that affords several orders of magnitude acceleration in chemical library screening.
Stanford Researchers have patented an improved technique for capturing and processing dynamic and high speed scenes using a collection of precisely timed video cameras. This system uses multiple synchronized image sensors with precise time delays to capture high-speed video.
A team of researchers from the Stanford Artificial Intelligence Laboratory have patented a portfolio of innovations that harness depth sensing technology to analyze human motion for touch-free control of devices and motion capture.
A team of researchers from the Stanford Artificial Intelligence Laboratory have developed a portfolio of patented innovations that harness depth sensing technology to analyze human motion for touch-free control of devices and motion capture.
Researchers from Stanford University and the Max Planck Institute have patented a new marker-less approach to capturing human performances from multi-view video.
Stanford researchers have patented a method for detecting malicious bots, programs that are installed as viruses on a computer and then proceed to execute malicious commands from another remote computer.
Researchers in Dr. Juan Rivas-Davila's lab have developed 3D printing methods to make aircore inductors and capacitors with more complex geometries and functionality than components using printed circuit boards.
FragFEATURE is a data-driven computational method for fragment binding prediction. It predicts small molecule fragments preferred by a protein structure using a knowledge base of all previously observed protein-fragment interactions.
Druggability of a protein is its potential to be modulated by drug-like molecules. It is important in the target selection phase. We developed DrugFEATURE to quantify druggability by assessing the microenvironments in potential small-molecule binding sites.
Stanford researchers have invented a system for identifying head impacts and rejecting spurious motion events. The system has been implemented in an instrumented mouthguard which measures head kinematics on the sports field.
Disclosed is x-ray cone beam scan data reconstruction of an imaged object with a reconstruction algorithm using shift invariant filtering and backprojection with the maximum tomographic capability of a circular scan larger than p plus cone angle, when CB data is not truncated