Search NASA⌕ Search

DOE OSTI · 2470701

The Dynamic Tunability of Memristor-Based Active Filters

Abstract

When the memristor was fabricated for the first time, it launched an entirely new field of research. Many of the published papers regarding memristors are primarily theoretical and are based on computer simulations. Some recent papers analyze the memristor’s programming circuits, but to the best of the authors’ knowledge, no memristor has been embedded into a commercial analog circuit. This paper is practically oriented and it is based on the experimental results obtained by measurements on the circuit prototype. We present a solution for automated programming of a commercially available memristor and its implementation in tunable active bandpass filter design. The novelty of this paper is that the active bandpass filter’s central frequency could be programmed during the filter operation, so a pause for memristor state-switching is not required. The experimental results are promising, and open up possibilities for the memristor’s application in analog systems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marković, Ivo, Potrebić Ivaniš, Milka, Tošić, Dejan. 2023-11-05. The Dynamic Tunability of Memristor-Based Active Filters. https://doi.org/10.3390/mi14112064

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↗