Stanford researchers in the Xing Lab have developed GPT-RadPlan, a large language model (LLM) and vision-language model (VLM) based radiation therapy treatment planning automation tool that reduces treatment planning time and lowers costs.
The Satellite Hardware-In-the-loop Rendezvous Trajectory (SHIRT) dataset consists of images and pose labels associated with two rendezvous trajectory scenarios (ROE1 and ROE2) in Low Earth Orbit (LEO) created from two different sources.
SPEED+ is an advanced dataset for vision-based spacecraft pose estimation with specific emphasis on evaluating the robustness of Machine Learning (ML) models across the domain gap.
Stanford engineers have developed biophilic illusions, which are technologies that augment building interiors using elements from ambient nature such as shifting sunlight, swaying tree shadows, and wildlife sounds.
Stanford researchers have created a system that enables efficient fabrication of complex three-dimensional (3D) nanostructures via triplet-triplet-annihilation upconversion (TTA-UC).
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.
Stanford scientists have created software, referred to as Symbolica, for automating model development for multiscale systems that can accelerate the generation of multi-physical models by 10^5 times what can be completed by hand.
The Fan Lab at Stanford University has developed an ultra-fast, physics-augmented, deep learning enhanced surrogate field solver for high-speed electromagnetic simulation and optimization. Denoising WaveY-Net uses a two-stage approach to target different field error sources.
Stanford inventors have created an audio-visual system with a radiotransparent screen provides a means for communication and visual distractions during procedures such as radiation therapy and radiation imaging.
This software is a transformative technology in the fields of AI and digital image processing, offering a breakthrough approach to convolution, particularly for large-scale images.
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 University have established a deep learning segmentation algorithm for non-contrast CT images to aid clinicians in decision making and improve the speed of symptom to treatment in acute ischemic stroke
Researchers from Stanford University have developed an algorithm for electromagnetic device prototyping which optimizes geometric shape based on physical functionality.