3,387 research outputs found
Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks
Accurate localization for mobile nodes has been an important and fundamental
problem in underwater acoustic sensor networks (UASNs). The detection
information returned from a mobile node is meaningful only if its location is
known. In this paper, we propose two localization algorithms based on color
filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively
accomplishing accurate localization of underwater mobile nodes with minimum
energy expenditure. They both adopt the overlapping signal region of task
anchors which can communicate with the mobile node directly as the current
sampling area. PCFL employs the projected distances between each of the task
projections and the mobile node, while ACFL adopts the direct distance between
each of the task anchors and the mobile node. Also the proportion factor of
distance is proposed to weight the RGB values. By comparing the nearness
degrees of the RGB sequences between the samples and the mobile node, samples
can be filtered out. And the normalized nearness degrees are considered as the
weighted standards to calculate coordinates of the mobile nodes. The simulation
results show that the proposed methods have excellent localization performance
and can timely localize the mobile node. The average localization error of PCFL
can decline by about 30.4% than the AFLA method.Comment: 18 pages, 11 figures, 2 table
Discovering Organizational Correlations from Twitter
Organizational relationships are usually very complex in real life. It is
difficult or impossible to directly measure such correlations among different
organizations, because important information is usually not publicly available
(e.g., the correlations of terrorist organizations). Nowadays, an increasing
amount of organizational information can be posted online by individuals and
spread instantly through Twitter. Such information can be crucial for detecting
organizational correlations. In this paper, we study the problem of discovering
correlations among organizations from Twitter. Mining organizational
correlations is a very challenging task due to the following reasons: a) Data
in Twitter occurs as large volumes of mixed information. The most relevant
information about organizations is often buried. Thus, the organizational
correlations can be scattered in multiple places, represented by different
forms; b) Making use of information from Twitter collectively and judiciously
is difficult because of the multiple representations of organizational
correlations that are extracted. In order to address these issues, we propose
multi-CG (multiple Correlation Graphs based model), an unsupervised framework
that can learn a consensus of correlations among organizations based on
multiple representations extracted from Twitter, which is more accurate and
robust than correlations based on a single representation. Empirical study
shows that the consensus graph extracted from Twitter can capture the
organizational correlations effectively.Comment: 11 pages, 4 figure
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