Browsing by Subject "Position data"
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Item Open Access Chemical concentration map building through bacterial foraging optimization based search algorithm by mobile robots(IEEE, 2010) Turduev, M.; Kırtay, Murat; Sousa P.; Gazi V.; Marques L.In this article we present implementation of Bacterial Foraging Optimization algorithm inspired search by multiple robots in an unknown area in order to find the region with highest chemical gas concentration as well as to build the chemical gas concentration map. The searching and map building tasks are accomplished by using mobile robots equipped with smart transducers for gas sensing called "KheNose". Robots perform the search autonomously via bacterial chemotactic behavior. Moreover, simultaneously the robots send their sensor readings of the chemical concentration and their position data to a remote computer (a base station), where the data is combined, interpolated, and filtered to form an real-time map of the chemical gas concentration in the environment. ©2010 IEEE.Item Open Access Detection of compound structures using clustering of statistical and structural features(IEEE, 2012) Akçay, H. Gökhan; Aksoy, SelimWe describe a new method for detecting compound structures in images by combining the statistical and structural characteristics of simple primitive objects. A graph is constructed by assigning the primitive objects to its vertices, and connecting potentially related objects using edges. Statistical information that is modeled using spectral, shape, and position data of individual objects as well as the structural information that is modeled in terms of spatial alignments of neighboring object groups are also encoded in this graph. Experiments using WorldView-2 data show that hierarchical clustering of the graph vertices can discover high-level compound structures that cannot be obtained using traditional techniques. © 2012 IEEE.Item Open Access Detection of compound structures using hierarchical clustering of statistical and structural features(IEEE, 2011) Akçay, H. Gokhan; Aksoy, SelimWe describe a new procedure that combines statistical and structural characteristics of simple primitive objects to discover compound structures in images. The statistical information that is modeled using spectral, shape, and position data of individual objects, and structural information that is modeled in terms of spatial alignments of neighboring object groups are encoded in a graph structure that contains the primitive objects at its vertices, and the edges connect the potentially related objects. Experiments using WorldView-2 data show that hierarchical clustering of these vertices can find high-level compound structures that cannot be obtained using traditional techniques. © 2011 IEEE.