Stanford researchers have developed a compact, low-cost complete sensor solution (sensor plus reader) which can interpret fully-passive sensors through a simple handheld external reader. The readout mechanism can take measurements independent of the readout distance (i.e.
Using a novel convolutional neural network architecture, PlexusNet can be used for histologic image analysis with smaller parameter and training sets than current state-of-the-art models.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Researchers at Stanford have demonstrated a new type of energy-efficient and ultrathin memory. This low-energy cost memory is based on stacking orders in the atomically thin limit, associated with tiny changes in the position of one atomic layer with respect to another.
This invention is a set of structures and associated processes to integrate GaN with Diamond to develop a full complementary CMOS device capable of operation in high power and high temperature applications.
Artificial intelligence can be leveraged to evaluate how facial expressions will be perceived by others. A deep learning neural network is used to generate facial vectors for each image of a person.
Stanford researchers have demonstrated a self healing electrode that can dramatically enhance the cycle lifetime of lithium ion batteries by applying Si microparticles with a thin layer of self-healing conductive composite.
Stanford researchers have developed deep learning methods which can more precisely localize the position and orientation of a camera in the lung anatomy in real-time.
Researchers at Stanford have developed a non-destructive method for generating and patterning optical color centers with nanoscale resolution without the need for high energy radiation. Color centers, which are optically active defects within the lattice structur
Despite widespread adoption of stationary wireless charging, dynamic wireless power transfer suffers from a sensitivity to relative movement of the device with respect to the power source.
Stanford engineers at Zhenan Bao's laboratory have designed a compliance sensor which can identify softness (compliance) of touched objects and provide human-like sensation to robots and prosthetics.
Stanford researchers have developed a damage free method for activating buried p-type or Mg-doped epitaxial layers in III-nitride devices that improves performance and can reduce device cost when used as edge termination.
Researchers from Stanford and UC Santa Barbara have created a novel robot that blends traditional and soft robotics. This human-scale pneumatic robot can change shape and move independently once inflated, without needing a constant power or air source.
An interdisciplinary team of Stanford engineers have developed a low-cost, patented, in situ method to efficiently produce electricity from organic matter such as wastewater.