Active manipulation of light beams is required for a range of emerging optical technologies, including sensing, optical computing, virtual/augmented reality, dynamic holography, and computational imaging.
Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
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 Murmann lab has developed a method for an extraction information from acoustic signals that utilizes low power consumption. N-path filters are used to decompose the original acoustic signals' waveform before downconverting to lower their Nyquist-rate bandwidth.
The Dionne lab has developed ultrathin and compact devices for electrically driven beamsteering that fit on a semiconductor chip. These devices rely on resonant dielectric nanostructured surfaces known as "high quality factor" (high-Q) metasurfaces.
Researchers in the Arbabian Lab have developed a system that uses a combination of radio frequency (RF) electromagnetic and ultrasound (US) waves to detect, localize, and identify multiple battery-free tags.
Stanford researchers at the Bao Research Group have patented a body area sensor network (bodyNET) that can be used to monitor human physiological signals for next-generation personalized healthcare.
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.
Stanford researchers at the Bao Research Group have developed a second-generation stretchable multi-sensor tag technology for detecting physiological signals.
Engineers at Stanford have invented a smart toilet platform that will autonomously monitor excreted waste from humans. We describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health.
Machine learning models currently require extensive computational resources and this demand is growing rapidly with new models and applications being introduced.
Engineers in the Zhenan Bao Research Group have developed a highly versatile electronics platform with individual modular building blocks that can be easily configured and reconfigured for a variety of applications.
Stanford researchers have developed a method to fabricate highly efficient Si/TMDs tandem solar cells which aims to break the 30% efficiency barrier with low cost and increased reliability.
Researchers in Prof. Amin Arbabian's laboratory have developed a modular RF-Ultrasound architecture to download data, upload data or wirelessly charge devices implanted deep in the body.