Stanford researchers have developed a simple optical device for low-power, active light tuning. The device tunes the color of light across the visible spectrum and at select wavelengths by electrical biasing an array of micron sized pixels or nanowires.
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.
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.
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
Stanford researchers have developed a high throughput, low energy consumption, optical method for real-time, image differentiation (image sharpening) using a photonic crystal slab.
Stanford researchers at the Vuckovic Lab have developed a full chip-scale integration of a Ti:Sapphire laser system which dramatically reduces the size, cost, and power consumption by many orders of magnitude, compared to today's state-of-the-art systems which are bulky and ex
Researchers at Stanford have developed a device capable of delivering ultrasonic neuromodulation to defined areas of the brain while simultaneously recording neuronal activity with cell-type specificity.
Stanford researchers developed a method to make large phase shifts with little or no power dissipation in integrated optics. The approach uses a directional coupler moved by a MEMS actuator to achieve a path delay, i.e. an effective change in refractive index.
Stanford researchers have developed an optical coating that steers infrared and visual light in different paths while suppressing the typical undesired rainbow effect.
Stanford researchers have developed a new machine learning method for extracting gait parameters, such as cadence, step length, peak knee flexion, and Gait Deviation Index (GDI), from a single video.
Dr. Guillem Pratx and colleagues have developed a high-throughput single cell scintillation counting system that can sort cells on the basis of uptake of a small radiolabeled molecule.
Stanford researchers in the McNab lab have developed a marker-less neuro-navigation device that only needs to be setup once during the first transcranial magnetic stimulation (TMS) session and by tracking the subjects head, automatically achieves the same accurate coil locatio