Researchers in Professor Zhenan Bao's group at Stanford University have developed a biomimetic soft electronic skin (e-skin) with multiple levels of biologically inspired patterning that can detect the direction of applied forces.
Engineers in Prof. Khuri-Yakub's laboratory have developed ultrasonic methods for non-invasive flow meters to accurately measure flow rate, pressure, velocity and other parameters of gas or liquid traveling through a pipe.
Engineers in Prof. Khuri-Yakub's laboratory have developed ultrasonic methods for non-invasive flow meters to accurately measure flow rate, pressure, velocity profile and other parameters of gas or liquid traveling through a pipe.
This nanoparticle platform for electric field detection is the first inorganic platform to use both intensity and spectro-ratiometric (relative color change) readout for the determination of local electric fields in vitro, in vivo, and in situ.
Researchers at Stanford have developed a device to monitor environmental exposure in personal (wearable) or public (stationary) settings. Human health can be viewed as the interactive outcome between inherited traits and environmental risks.
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 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).
Researchers in Prof. Ronald K. Hanson's laboratory have developed a non-intrusive gas sensor designed for high performance temperature and species concentration measurements in high pressure, particulate laden environments.
This patented ultrasound imaging system reduces the hardware complexity for coherent array image formation and restoration. This technology is especially useful when there are fewer front-end electronic channels than the number of transducer elements in an array.
Researchers in the Khuri-Yakub laboratory have developed patented two dimensional (2D) capacitive micromachined ultrasonic transducer (CMUT) arrays and methods for fabricating them with direct wafer bonding.
Stanford researchers have developed two related inventions which advance the state-of-the-art of CMUT's (capacitive micromachined ultrasonic transducers).
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).
Summary: Stanford researchers at the Melosh Lab have proposed a non-invasive, high electrode density, high resolution (100 micrometers to 10 nanometers) neural device implantation for electrical stimulation of neural/biological tissues.