This invention works by imaging an ultrafast pulse diffracted from a large grating onto a spatial light modulator (SLM) thereby directly transcribing an arbitrary record on a pulse front tilted (PFT) ultrafast pulse. The grating generates PFT of the input pulse, and the SLM provides temporal control of the pulse through the space-to-time mapping of the tilted pulse. Coupling this patterned…
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This invention exploits the non-linearities of optical Mach-Zehnder (MZ) electrooptic modulators to enhance small signal dynamic range at higher bandwidths. A linear photodiode (PD) converts the amplified optical signal output from the MZ back to an electrical signal completing an Electrical-Optical-Electrical (EOE) conversion cycle. The dynamic range can be further enhanced by daisy chaining…
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To address the need for realistic and high-fidelity first responder training, a multidisciplinary team at LLNL has worked to establish the new gold standard simulator called TARANTULA (Tactical Augmented Reality Applications for Nuclear Emergency Support Team (NEST) Training using Livermore Analytics). TARANTULA is a scientifically accurate, fully functional, field-deployable simulator that…
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LLNL researchers in the NIF Directorate DoD Technologies RF Photonics Group explored phase modulation solutions to this signal processing challenge. Optical frequency combs offer phase noise characteristics that are orders of magnitude lower than available from commercial microwave references. The Photonics Group researchers recognized that by converting the intensity information into phase,…
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Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.
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Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…
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Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…
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MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…
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There are three main components to the RaFTS system: 1) the radiation detector, which can be of any type and from any manufacturer; 2) the RaFTS electronics, which produce the electronic pulses that are injected into the electronics of the radiation detector through a (to be) standardized port interface; and 3) the exercise scenario, which defines the synthetic radiation field and time-varying…
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LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…