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.
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 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.