Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Event Boundary Detection with Unreliable Sensors: Analysis of the
  Majority Vote Scheme   In this paper we study the identification of an event region $X$ within a
larger region $Y$, in which the sensors are distributed by a Poisson process of
density $\lambda$ to detect this event region, i.e., its boundary. The model of
sensor is a 0-1 sensor that decides whether it lies in $X$ or not, and which
might be incorrect with probability $p$. It also collects information on the
0-1 values of the neighbors within some distance $r$ and revises its decision
by the majority vote of these neighbors. In the most general setting, we
analyze this simple majority vote scheme and derive some upper and lower bounds
on the expected number of misclassified sensors. These bounds depend on several
sensing parameters of $p$, $r$, and some geometric parameters of the event
region $X$. By making some assumptions on the shape of $X$, we prove a
significantly improved upper bound on the expected number of misclassified
sensors; especially for convex regions with sufficiently round boundary, and we
find that the majority vote scheme performs well in the simulation rather than
its theoretical upper bound.

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The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga