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 designed and successfully tested two prototype dynamic surface grasping devices. These devices use opposed pairs of gecko-inspired directional adhesives to attach to any smooth surface.
Stanford researchers at the Vuckovic Lab have created a computational nanophotonic design library for gradient-based optimization called the Stanford Photonic INverse design Software (Spins).
Stanford researchers have patented a novel concept for a position sensitive high-energy photon sensor device for high resolution radiation imaging that can enhance capabilities of Positron Emission Tomography (PET).
A Stanford researcher has developed two advanced approaches for the positron sensitive high-energy photon sensor technology for Positron Emission Tomography (PET).
Stanford Researchers have developed a method for a high-resolution photon imaging device with high fill factor (the ratio of the area of the active imaging elements vs. the dead area occupied by non-imaging elements).
Stanford researchers have patented a fabrication process for monolithic integration of different epitaxial materials on the same substrate for improved coupling of optoelectronic devices.
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.
Researchers in the Collaborative Haptics and Robotics in Medicine Lab at Stanford University have patented a haptic device that simulates a stroking sensation.
Stanford researchers patented a method to design, computationally optimize and fabricate efficient optical devices using semiconducting and dielectric nanostructures.
Stanford researchers have developed a method of assigning a “glucotype” to patients based on their temporal glycemic patterns. This algorithm classifies people with glycemic dysregulation through constant monitoring.
This invention, the “Charge Cloud Tracker” is a fast, low-cost, strip geometry x-ray detector that is predicted to provide limiting resolution on the order of 5 microns, with very high x-ray detection efficiency.
Researchers in Prof. Sylvia Plevritis' laboratory have developed an algorithm designed to optimize cancer combination therapy for individual patients by analyzing distinct single-cell responses from heterogeneous tumors.
Stanford engineers have developed a patented algorithm that improves search results from ranking the objects of a database when viewed as a graph (e.g. a web graph).
Engineers in Prof. Anthony Kovscek's laboratory have developed a patented, dual-function core holder apparatus that can be used in enhanced oil recovery (EOR) experiments to both saturate the core and perform spontaneous imbibition analysis.