11,801 research outputs found
A Phase Field Model for Continuous Clustering on Vector Fields
A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hilliard model, which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns-the actual clustering-during which the underlying simulation data specifies preferable pattern boundaries. We introduce specific physical quantities in the simulation to control the shape, orientation and distribution of the clusters as a function of the underlying flow field. In addition, the model is expanded, involving elastic effects. In the early stages of the evolution shear layer type representation of the flow field can thereby be generated, whereas, for later stages, the distribution of clusters can be influenced. Furthermore, we incorporate upwind ideas to give the clusters an oriented drop-shaped appearance. Here, we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries. However, the method also carries provisions for other fields as well. The clusters can be displayed directly as a flow texture. Alternatively, the clusters can be visualized by iconic representations, which are positioned by using a skeletonization algorithm.
Clustering in Highest Energy Cosmic Rays: Physics or Statistics?
Directional clustering can be expected in cosmic ray observations due to
purely statistical fluctuations for sources distributed randomly in the sky. We
develop an analytic approach to estimate the probability of random cluster
configurations, and use these results to study the strong potential of the
HiRes, Auger, Telescope Array and EUSO/OWL/AirWatch facilities for deciding
whether any observed clustering is most likely due to non-random sources.Comment: 19 pages, LaTeX, 3 figure
Cortical spatio-temporal dimensionality reduction for visual grouping
The visual systems of many mammals, including humans, is able to integrate
the geometric information of visual stimuli and to perform cognitive tasks
already at the first stages of the cortical processing. This is thought to be
the result of a combination of mechanisms, which include feature extraction at
single cell level and geometric processing by means of cells connectivity. We
present a geometric model of such connectivities in the space of detected
features associated to spatio-temporal visual stimuli, and show how they can be
used to obtain low-level object segmentation. The main idea is that of defining
a spectral clustering procedure with anisotropic affinities over datasets
consisting of embeddings of the visual stimuli into higher dimensional spaces.
Neural plausibility of the proposed arguments will be discussed
Pairwise Check Decoding for LDPC Coded Two-Way Relay Block Fading Channels
Partial decoding has the potential to achieve a larger capacity region than
full decoding in two-way relay (TWR) channels. Existing partial decoding
realizations are however designed for Gaussian channels and with a static
physical layer network coding (PLNC). In this paper, we propose a new solution
for joint network coding and channel decoding at the relay, called pairwise
check decoding (PCD), for low-density parity-check (LDPC) coded TWR system over
block fading channels. The main idea is to form a check relationship table
(check-relation-tab) for the superimposed LDPC coded packet pair in the
multiple access (MA) phase in conjunction with an adaptive PLNC mapping in the
broadcast (BC) phase. Using PCD, we then present a partial decoding method,
two-stage closest-neighbor clustering with PCD (TS-CNC-PCD), with the aim of
minimizing the worst pairwise error probability. Moreover, we propose the
minimum correlation optimization (MCO) for selecting the better
check-relation-tabs. Simulation results confirm that the proposed TS-CNC-PCD
offers a sizable gain over the conventional XOR with belief propagation (BP) in
fading channels.Comment: to appear in IEEE Trans. on Communications, 201
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