735,033 research outputs found
Few-molecule reservoir computing experimentally demonstrated with surface enhanced Raman scattering and ion-gating stimulation
Reservoir computing (RC) is a promising solution for achieving low power
consumption neuromorphic computing, although the large volume of the physical
reservoirs reported to date has been a serious drawback in their practical
application. Here, we report the development of a few-molecule RC that employs
the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA)
detected by surface enhanced Raman scattering (SERS) with tungsten oxide
nanorod/silver nanoparticles (WOx@Ag-NPs). The Raman signals of the pMBA
molecules, adsorbed at the SERS active site of WOx@Ag-NPs, were reversibly
perturbated by the application of voltage-induced local pH changes in the
vicinity of the molecules, and then used to perform RC of pattern recognition
and prediction tasks. In spite of the small number of molecules employed, our
system achieved good performance, including 95.1% to 97.7% accuracy in various
nonlinear waveform transformations and 94.3% accuracy in solving a second-order
nonlinear dynamic equation task. Our work provides a new concept of molecular
computing with practical computation capabilities.Comment: 22 pages, 4 figure
STV-based Video Feature Processing for Action Recognition
In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
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