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DOE OSTI2024

FY2024 Q4 L2 Milestone 8237 for Opacity-on-NIF

This document addresses parts 2 and 3 of the original milestone request. In the following writeup, the development of OpSpecTR at LLNL by LLNL and NNSS personnel will be detailed first, followed by a write-up of the development of Vme-resolved simulaVons of the opacity-on-NIF experiment. Capsule backlight simulaVons, hohlraum simulaVons, and sample simulaVons from CASSIO are combined to produce the line of sight from the capsule to an effecVve OpSpec posiVon. Spectra are generated by post-processing these simulaVons from either Spect3D, a commercial software by Prism ComputaVonal Sciences, or FESTR (Finite Element Spectroscopic Transport of RadiaVon), a LANL code. While the simulaVons do not at this stage proceed late enough to include the worst of the backgrounds generated from the hohlraum experimentally and each post-processing simulaVon includes only one ray for each spectrometer channel, these efforts represent proof of concept for a new capability to complete Vme-resolved and Vmegated simulaVons for the complete line of sight of the complex opacity-on-NIF geometry. Future work will be discussed at the end of the report for both efforts.

42 ENGINEERING↗

DOE OSTI2023

Geolocation tracking for human identification and activity recognition using radar deep transfer learning

Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.

Alkasimi, Ahmad↗