Stanford engineers have prototyped and tested a flexible, soft growing robot that can deploy sensor networks for investigation in constrained spaces (see video below). Existing sensors for growing robots have focused on moving with the tip of the robot.
Researchers at Stanford have developed an approach to dramatically improve the efficiency of microwave-to-optical quantum transduction – a significant step towards realizing efficient communication between distant superconducting quantum systems.
Image sensors are used across the board in high-resolution image sensing technologies, and critically rely on their ability to separate colors of light.
Near-infrared (NIR) imaging is a valuable research tool that produces quality images with high spatial and temporal resolution through millimeter tissue depths.
Researchers at the Stanford Robotics Lab have developed new methods for modeling multi-contact collisions and steady physical interactions between multiple rigid bodies.
Researchers at Stanford are developing a device that uses quantum engineered states and interactions to detect electromagnetic waves with a sensitivity and bandwidth beyond that possible with existing technology.
Stanford researchers have developed a method to use conditional generative adversarial networks (C-GANs) for solving highly complex optimization problems, e.g., with 1050 to 10 80 dimensions.
Stanford researchers at the Poon Lab have developed a method for battery-less, short range transmission of data with very low power and very high data rates. It can potentially replace current near field communications (NFC) systems due to these advantages.
Stanford researchers in the Vuckovic group have developed an optical phased array (OPA) for solid-state beam-steering in optical systems such as LIDAR, projectors, and microscopy.
Researchers at Stanford have developed technology to bring new dimensions to wearable haptic devices and better reflect the breadth of haptic interactions in our lives.
Researchers at Stanford have developed an ultracompact, high-quality-factor (high-Q) metasurface that enables more convenient phase contrast imaging. Phase contrast imaging is a critical technique in biology and medicine to image essentially transparent objects such as cells.
Researchers in Roger Kornberg's lab have developed a deep convolutional neural network algorithm that predicts the location and strength of transcription factor activation domains (ADs) in eukaryotes.
This invention facilitates the realization of optical elements with spatially multiplexed/interleaved phase profiles to achieve a high packing density of distinct optical elements on a surface.