ﻻ يوجد ملخص باللغة العربية
Optical wave-based computing has enabled the realization of real-time information processing in both space and time domains. In the past few years, analog computing has experienced rapid development but mostly for a single function. Motivated by parallel space-time computing and miniaturization, we show that reconfigurable graphene-based metasurfaces offer a promising path towards spatiotemporal computing with integrated functionalities by properly engineering both spatial- and temporal-frequency responses. This paper employs a tunable graphene-based metasurface to enable analog signal and image processing in both space and time by tuning the electrostatic bias. In the first part of the paper, we propose a switchable analog computing paradigm in which the proposed metasurface can switch among defined performances by selecting a proper external voltage for graphene monolayers. Spatial isotropic differentiation and edge detection in the spatial channel and first-order temporal differentiation and metasurface-based phaser with linear group-delay response in the temporal channel are demonstrated. In the second section of the paper, simultaneous and parallel spatiotemporal analog computing is demonstrated. The proposed metasurface processor has almost no static power consumption due to its floating-gate configuration. The spatial- and temporal-frequency transfer functions (TFs) are engineered by using a transmission line (TL) model, and the obtained results are validated with full-wave simulations. Our proposal will enable real-time parallel spatiotemporal analog signal and image processing.
We report the fabrication and electron transport properties of nanoparticles self-assembled networks (NPSAN) of molecular switches (azobenzene derivatives) interconnected by Au nanoparticles, and we demonstrate optically-driven switchable logical ope
We introduce chiral gradient metasurfaces that allow perfect transmission of all the incident wave into a desired direction and simultaneous perfect rotation of the polarization of the refracted wave with respect to the incident one. Besides using gr
This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture. It describes the second-generation BrainScales-2 (BSS-2) version and its most recent in-silico realization, the HICANN-X Application
A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is dispersed among the workers. Lagrange coded computing (LCC), proposed by Yu et
Optical computing has emerged as a promising candidate for real-time and parallel continuous data processing. Motivated by recent progresses in metamaterial-based analog computing [Science 343, 160 (2014)], we theoretically investigate realization of