LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects.  DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications. 

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.

LLNL’s novel technology automates the inspection process by using a scanning system that captures data within the walnut shell without having to open the shell. The system output gives a visual image inside the walnut shell sufficient to evaluate and rate the quality of the walnut.  The system uses a camara and radar that can capture data at a rapid rate. This improves speed and…

The approach is to develop a solid-state X-ray imager based on the architecture of the Silicon Drift Detector (SDD) which uses a series of cathode strips on both sides of a silicon wafer to achieve bulk depletion and electron drift.  The invention leverages this SDD functionality to achieve signal stretching of liberated charge carriers from X-Ray photons that converts the time domain…

LLNL’s novel approach is to use a continuous moving camera with a scan speed of >1 mm/sec and a frame rate of 100 frames per second.  The key is to have a light source that flashes with a duration of one nanosecond, thus essentially freezing the image with no blur.  Clear images of high resolution can then be captured through a high-magnification objective lens (reflection mode)…

CSP-POST provides the capability to inspect all incoming and outgoing emails while providing after-the-fact forensic capabilities. Using commercially available lightweight and serverless technologies, CSP-POST easily collects all email and parses it into easily searchable metadata, enriched and ready for analysis. The web-based application is deployed in a repeatable, testable, and auditable…

LLNL has invented a new system that uses public key cryptography to differentiate between human-generated text and AI-generated text. This invention can be used to validate that text is likely to be human generated for the purposes of sorting or gatekeeping on the internet, can detect cheating on essay assignments, and can be used as an automatic captcha that does away with the hassle of…

LLNL’s approach to the development of a wide-field, three-dimensional quantum (3DQ) microscope is to harness quantum entangled photons to form simultaneous 3D optical images, which could be a new paradigm for 3D volumetric imaging of biological specimens.  The 3DQ microscope is comprised of a novel optical system with highly sensitive detectors and an on-demand light source of entangled…

LLNL has developed a method of extending device lifetimes by imprinting into the device a shape that excludes specific vibrational modes, otherwise known as a phononic bandgap. Eliminating these modes prevents one of the primary energy loss pathways in these devices. LLNL’s new method enhances the coherence of superconducting circuits by introducing a phononic bandgap around the system’s…

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…

LLNL's 3D X-ray imager combines two different hardware pieces. The first is an x-ray optic with a depth-of-field that is small compared to the object under investigation. Reflective Wolter type x-ray optics are one such design. These hollow optics have a relatively large collection efficiency and can be designed with a large field of view. The depth of focus, which is the distance over which a…

LLNL has developed a new method for securely processing protected data on HPC systems with minimal impact on the existing HPC operations and execution environment. It can be used with no alterations to traditional HPC operations and can be managed locally. It is fully compatible with traditional (unencrypted) processing and can run other jobs, unencrypted or not, on the cluster simultaneously…

LLNL's NeMS system enables network mapping operations by using two LLNL-developed software systems: LLNL's NeMS tool and the Everest visualization system. Each software system can be also used separately for their specific applications. When the two systems are used together as an iterative analysis platform, LLNL's NeMS system provides network security managers and information technology…

LLNL's high fidelity hydrocode is capable of predicting blast loads and directly coupling those loads to structures to predict a mechanical response. By combining this code and our expertise in modeling blast-structure interaction and damage, along with our access to experimental data and testing facilities, we can contribute to the design of protective equipment that can better mitigate the…