Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reconstruction of Binary Functions and Shapes from Incomplete Frequency
  Information   The characterization of a binary function by partial frequency information is
considered. We show that it is possible to reconstruct binary signals from
incomplete frequency measurements via the solution of a simple linear
optimization problem. We further prove that if a binary function is spatially
structured (e.g. a general black-white image or an indicator function of a
shape), then it can be recovered from very few low frequency measurements in
general. These results would lead to efficient methods of sensing,
characterizing and recovering a binary signal or a shape as well as other
applications like deconvolution of binary functions blurred by a low-pass
filter. Numerical results are provided to demonstrate the theoretical
arguments.

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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys