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.
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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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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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The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…