Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - Scopus: 1Cost Effective Localization in Distributed Sensory Networks(Elsevier Ltd., 2011) Coşkun, Anıl; Sevil, Hakkı Erhan; Özdemir, SerhanThe most important mechanism to occur in biological distributed sensory networks (DSNs) is called lateral inhibition, (LI). LI relies on one simple principle. Each sensor strives to suppress its neighbors in proportion to its own excitation. In this study, LI mechanism is exploited to localize the unknown position of a light source that illuminated the photosensitive sensory network containing high and low quality sensors. Each photosensitive sensor was then calibrated to accurately read the distance to the light source. A series of experiments were conducted employing both quality sensors. Low quality array was allowed to take advantage of LI, whereas the high quality one was not. Results showed that the lateral inhibition mechanism increased the sensitivity of inferior quality sensors, giving the ability to make the localization as sensitive as high quality sensors do. This suggests that the networks with multitude of sensors could be made cost-effective, were these sensory networks equipped with LI.Article Citation - WoS: 2Citation - Scopus: 2Trait-based heterogeneous populations plus (TbHP+) genetic algorithm(Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Gezgin, Erkin; Özdemir, SerhanThis study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.Book Part Citation - Scopus: 6Swarm and Entropic Modeling for Landmine Detection Robots(Springer Verlag, 2008) Bayram, Çağdaş; Sevil, Hakkı Erhan; Özdemir, SerhanEven at the dawn of the 21st century, landmines still pose a global threat. Buried just inches below the surface, combatants and noncombatants alike are all at risk of stepping on a mine. Their very nature is such that these furtive weapons do not discriminate, making it an urgent task to tackle the problem. According to the U.S.State Department [1], based on an estimate reported just a few years ago, there are well over 100 million anti-personnel mines around the world. The existence of these passive weapons causes a disruption in the development of already impoverished regions, as well as maiming or killing countless innocent passers-by. Since the ratification of the anti-personnel mine total ban treaty in 1997, their detection, removal, and elimination have become a top priority. Nevertheless, at the current rate, given the manpower and the man-hours that could be dedicated to the removal of these sleeping arms, it would take centuries. The concerns regarding the speed of removal and safety of the disposers eventually bring us to the discussion of the proposed method. Nature already provided good solutions to manage groups of less able beings: fish schools, ant swarms, animal packs, bird flocks, and so on.With the growing desire of humans to create intelligent systems, these biosystems are being thoroughly inspected [3-10] and implemented [11-14] in various studies. In this study a robotic agent is referred to as a drone, the group of robotic agents is referred to as a swarm, and the agent with mapping abilities is referred to as the alpha drone.