The essence of this invention is a method that couples network architecture using neural implicit representations coupled with a novel parametric motion field to perform limited angle 4D-CT reconstruction of deforming scenes.
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![Livermore Tomography Tools LTT](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/LTT.jpg?itok=cQE9Kpef)
To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most…
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![graphic_of_simulation.png graphic_of_simulation](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/graphic_of_simulation.png?itok=eyhMWp8B)
Livermore researchers have developed a method for implementing closed-loop control in extrusion printing processes by means of novel sensing, machine learning, and optimal control algorithms for the optimization of printing parameters and controllability. The system includes a suite of sensors, including cameras, voltage and current meters, scales, etc., that provide in-situ process monitoring…
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![microencapsulation_manufacture.png microencapsulation_manufacture](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/microencapsulation_manufacture.png?itok=cLPUpaW-)
Livermore researchers have developed a method of fabricating functional polymer-based particles by crosslinking UV-curable polymer drops in mid-air and collecting crosslinked particles in a solid container, a liquid suspension, or an air flow. Particles could contain different phases in the form or layered structures that contain one to multiple cores, or structures that are blended with…
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![Machine Learning for Monitoring microfluidic microcapsules](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Machine%20Learning%20for%20Monitoring%20microfluidic%20microcapsules%20875_0.jpg?itok=cLdsZh03)
LLNL researchers have developed a system that relies on machine learning to monitor microfluidic devices. The system includes (at least) a microfluidic device, sensor(s), and a local network computer. The system could also include a camera that takes real-time images of channel(s) within an operating microfluidic device. A subset of these images can be used to train/teach a machine learning…