Stanford researchers have developed two related inventions which advance the state-of-the-art of CMUT's (capacitive micromachined ultrasonic transducers).
Researchers in Prof. Shanhui Fan's laboratory have invented a thermal extraction device that is designed to enhance power emission from thermal radiators up to 10x compared to conventional structures.
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.
A team of Stanford engineers has developed an efficient battery that can convert salinity gradient power (a.k.a. “blue energy”) into electricity using low-cost, non-toxic electrode materials.
Engineers in Prof. Fritz Prinz's laboratory have developed a low cost, scalable method to fabricate anti-reflective, highly conductive metal silicide nanowires electrodes for photovoltaic cells.
Researchers in Prof. W.E. Moerner's laboratory have developed a compact point spread function (PSF) that enables optical imaging in three dimensions with nanoscale precision using a limited number of photons.
Engineers in Prof. Krishna Saraswat's laboratory have developed a patented heterostructure channel transistor based on III-V semiconductor materials and designed for optimum hole transport.
Stanford researchers have designed a tunable wedge-based phase mask for 3D super-resolution imaging that can simultaneously determine both the position and rotational mobility of individual light-emitting molecules from a single camera image.
Stanford researchers at the Dahl Lab have developed a method to reduce artifacts in ultrasound image reconstruction using a trained convolutional neural network (CNN).