Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Vision and Deep Learning for Classification of Radio SETI
  Signals   We apply classical machine vision and machine deep learning methods to
prototype signal classifiers for the search for extraterrestrial intelligence.
Our novel approach uses two-dimensional spectrograms of measured and simulated
radio signals bearing the imprint of a technological origin. The studies are
performed using archived narrow-band signal data captured from real-time SETI
observations with the Allen Telescope Array and a set of digitally simulated
signals designed to mimic real observed signals. By treating the 2D spectrogram
as an image, we show that high quality parametric and non-parametric
classifiers based on automated visual analysis can achieve high levels of
discrimination and accuracy, as well as low false-positive rates. The (real)
archived data were subjected to numerous feature-extraction algorithms based on
the vertical and horizontal image moments and Huff transforms to simulate
feature rotation. The most successful algorithm used a two-step process where
the image was first filtered with a rotation, scale and shift-invariant affine
transform followed by a simple correlation with a previously defined set of
labeled prototype examples. The real data often contained multiple signals and
signal ghosts, so we performed our non-parametric evaluation using a simpler
and more controlled dataset produced by simulation of complex-valued voltage
data with properties similar to the observed prototypes. The most successful
non-parametric classifier employed a wide residual (convolutional) neural
network based on pre-existing classifiers in current use for object detection
in ordinary photographs. These results are relevant to a wide variety of
research domains that already employ spectrogram analysis from time-domain
astronomy to observations of earthquakes to animal vocalization analysis.

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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph