Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1027108

Integrated optic vector-matrix multiplier

Watts, Michael R
View original source ↗Read source document ↗Export metadata ↓

Abstract

A vector-matrix multiplier is disclosed which uses N different wavelengths of light that are modulated with amplitudes representing elements of an N.times.1 vector and combined to form an input wavelength-division multiplexed (WDM) light stream. The input WDM light stream is split into N streamlets from which each wavelength of the light is individually coupled out and modulated for a second time using an input signal representing elements of an M.times.N matrix, and is then coupled into an output waveguide for each streamlet to form an output WDM light stream which is detected to generate a product of the vector and matrix. The vector-matrix multiplier can be formed as an integrated optical circuit using either waveguide amplitude modulators or ring resonator amplitude modulators.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Watts, Michael R. 2011-09-27. Integrated optic vector-matrix multiplier. https://www.osti.gov/biblio/1027108

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗