Researchers at Stanford have developed a cloud-based behind-the-meter (BTM) system that can cut energy costs and reduce reliance on the grid close to 93% respectively.
The Foundational QED embodies a set of source code files for performing the basic EDDI, CFCSS, and CFTSS QED transformations for creating tests with extremely short error detection latencies and high error detection coverage.
During post-silicon validation and debug, manufactured integrated circuits (ICs) are tested in actual system environments to detect and fix design flaws (bugs). Existing techniques are costly due to ad hoc, manual methods.
Stanford researchers have developed new Fast Quick Error Detection (Fast QED) tests that are four orders of magnitude faster than standard QED tests while also preserving quick error detection properties.
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.
Image sensors are used across the board in high-resolution image sensing technologies, and critically rely on their ability to separate colors of light.
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.
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.