32,910 research outputs found
Finite-Difference Time-Domain Study of Guided Modes in Nano-plasmonic Waveguides
A conformal dispersive finite-difference time-domain (FDTD) method is
developed for the study of one-dimensional (1-D) plasmonic waveguides formed by
an array of periodic infinite-long silver cylinders at optical frequencies. The
curved surfaces of circular and elliptical inclusions are modelled in
orthogonal FDTD grid using effective permittivities (EPs) and the material
frequency dispersion is taken into account using an auxiliary differential
equation (ADE) method. The proposed FDTD method does not introduce numerical
instability but it requires a fourth-order discretisation procedure. To the
authors' knowledge, it is the first time that the modelling of curved
structures using a conformal scheme is combined with the dispersive FDTD
method. The dispersion diagrams obtained using EPs and staircase approximations
are compared with those from the frequency domain embedding method. It is shown
that the dispersion diagram can be modified by adding additional elements or
changing geometry of inclusions. Numerical simulations of plasmonic waveguides
formed by seven elements show that row(s) of silver nanoscale cylinders can
guide the propagation of light due to the coupling of surface plasmons.Comment: 6 pages, 10 figures, accepted for publication, IEEE Trans. Antennas
Propaga
A Radial-Dependent Dispersive Finite-Difference Time-Domain Method for the Evaluation of Electromagnetic Cloaks
A radial-dependent dispersive finite-difference time-domain (FDTD) method is
proposed to simulate electromagnetic cloaking devices. The Drude dispersion
model is applied to model the electromagnetic characteristics of the cloaking
medium. Both lossless and lossy cloaking materials are examined and their
operating bandwidth is also investigated. It is demonstrated that the perfect
"invisibility" from electromagnetic cloaks is only available for lossless
metamaterials and within an extremely narrow frequency band.Comment: 18 pages, 10 figure
Fast k-means based on KNN Graph
In the era of big data, k-means clustering has been widely adopted as a basic
processing tool in various contexts. However, its computational cost could be
prohibitively high as the data size and the cluster number are large. It is
well known that the processing bottleneck of k-means lies in the operation of
seeking closest centroid in each iteration. In this paper, a novel solution
towards the scalability issue of k-means is presented. In the proposal, k-means
is supported by an approximate k-nearest neighbors graph. In the k-means
iteration, each data sample is only compared to clusters that its nearest
neighbors reside. Since the number of nearest neighbors we consider is much
less than k, the processing cost in this step becomes minor and irrelevant to
k. The processing bottleneck is therefore overcome. The most interesting thing
is that k-nearest neighbor graph is constructed by iteratively calling the fast
-means itself. Comparing with existing fast k-means variants, the proposed
algorithm achieves hundreds to thousands times speed-up while maintaining high
clustering quality. As it is tested on 10 million 512-dimensional data, it
takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the
same scale of clustering, it would take 3 years for traditional k-means
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