Search NASA⌕ Search

DOE OSTI · 1576333

Long-pulse-width variable-wavelength chirped pulse generator and method

Abstract

A device for creating an optic pulse with different wavelengths separated by time. A pump laser is configured to output energy to a dye cell which, responsive to the energy, outputs an optic pulse. Mirrors direct the optic pulse away from the dye cell towards a spectrograph. The spectrograph has an input and two or more outputs. The spectrograph receives and converts the optic pulse to a wavelength separated optic signal presented on the two or more outputs. A first optic cable has an input end and an output end. The input end receives a first output from the spectrograph. A second optic cable has an input end and an output end. The input end receives a second output from the spectrograph. The second optic cable is a different length than the first optic cable to establish a time shift between the signals exiting the first and second cable.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Capelle, Gene A., Frayer, Daniel K.. 2019-09-10. Long-pulse-width variable-wavelength chirped pulse generator and method. https://www.osti.gov/biblio/1576333

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗