Researchers in Stanford University's EXtreme Environment Microsystems Laboratory (XLab) working in collaboration with the University of Arkansas' Mixed-Signal Computer-Aided Design (MSCAD) Laboratory developed a Hall-effect sensor design that detects ultra fast changes in the
Brief Description: Inventors at Stanford have developed a novel fiber-optic technology to achieve unprecedented sensitivity and immunity to motion artifacts that can be used in freely moving animals.
Inventors at Stanford have developed a novel strategy to perform concurrent fluorescence measurements of multiple biological parameters in freely moving and head-restrained animals.
Researchers in the Noh Lab have developed a gait based, emotion recognition system using geophone sensors that are attached to the floor. People's gait changes under various emotions creating distinct structural vibration patterns.
Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Researchers at Stanford have developed a new type of light source for spectroscopy applications, making it smaller and more energy efficient. Furthermore, this application allows a broad range of wavelengths without the interference from a pump laser.
Stanford inventors have developed a mobile thermoelectric device designed to preserve organs during transit by maintaining 10°C (+/-1°C) for over 6 hours.
Researchers at Stanford University have developed a multilayered immiscible polymer system that can autonomously realign its layers to facilitate the healing process following damage.
Stanford University researchers have developed aptamer-antibody chimeras that achieve dynamic, sensitive, and specific biomolecule sensing beyond the capacity of antibodies or aptamers alone.
Stanford researchers have developed strain-sensitive, stretchable, and self-healable semiconducting film. The researchers have created a multiplexed sensory transistor array using this material which can detect strain distribution by surface deformation.
Stanford researchers have developed a patient classification method (healthy, idiopathic, diabetic, etc.) based on a quantitative assessment score derived from autonomic and gastric electrocardiogram (ECG) and electrogastrogram (EGG) data.
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.
Stanford researchers in Zhenan Bao's Group have developed a nanomesh sensor printed directly on the hand that uses an AI-trained model to detect multiple movement types from a single sensor.