Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Detection of Urban Change Using Remote Sensing and Gis: Izmir Case(Taylor and Francis Ltd., 2008) Tarhan, Çiğdem; Arkon, Cemal; Çelik, M.; Gümüştekin, Şevket; Tecim, V.This study is an example of how land use changes could be detected via high resolution remotely sensed data. In order to perform "change detection" IKONOS satellite images, belonging to 2001 and 2004, have been used. An automated Graphical User Interface (GUI) has been created for detection of environment. Different image enhancement techniques and a fuzzy inference system have been combined in the GUI. The detection results are classified according to some basic levels such as 20-50% and 70%. Additionally, four different change detection algorithms have been applied which are pixel-based, object based, feature based. These algorithms have been examined according to change detection levels with different image enhancement techniques. At the end of the study, the results have been compared.Book Part Nucleic acid biochemistry: Food applications(Taylor and Francis Ltd., 2010) Kurnaz, Işıl A.; Ceylan, Çağatay[No abstract available]Book Part Teaching a Regional Landscape Project Studio in the Interdisciplinary Setting(Taylor and Francis Ltd., 2019) Kaplan, Adnan; Velibeyoğlu, KorayRegional and urban landscapes in the age of the Anthropocene need to support recognition of complex and dynamic ecosystems. Water-based regional context and its transformative power at regional and urban scales have been themed on landscape studios of some scholarly works such as G. M. Kondolf et al. and S. Nijhuis and D. Jauslin. The interdisciplinary ‘regional landscape project studio’ follows a didactic approach that combines regional planning and specific mode of regional and urban transformation thinking. The whole idea of the graduate studio is, therefore, to apply landscape infrastructure and the fourth nature into ‘the regional landscape-urban transformation’ equilibrium, as a novel way to healing our living environments. Landscape infrastructure is being explored in urban studies as a concept/reality that expands the traditional set of spatial planning and design strategies towards the multifunctional system. The association of hydrological pattern with natural and urban landscapes calls for site-specific design interventions in some critical cross-section.Article Taylor Series Approximation of Semi-Blind Blue Channel Estimates With Applications To Dtv(Taylor and Francis Ltd., 2008) Pladdy, Christopher; Özen, Serdar; Nerayanuru, Sreenivasa M.; Ding, Peilu; Fimoff, Mark J.; Zoltowski, MichaelWe present a low-complexity method for approximating the semi-blind best linear unbiased estimate (BLUE) of a channel impulse response (CIR) vector for a communication system, which utilizes a periodically transmitted training sequence. The BLUE, for h, for the general linear model, y = Ah + w + n, where w is correlated noise (dependent on the CIR, h) and the vector n is an Additive White Gaussian Noise (AWGN) process, which is uncorrelated with w is given by h = (ATC(h)-1A)-1ATC(h)-1y. In the present work, we propose a Taylor series approximation for the function F(h) = (ATC(h)-1A)-1ATC(h)-1y. We describe the full Taylor formula for this function and describe algorithms using, first-, second-, and third-order approximations, respectively. The algorithms give better performance than correlation channel estimates and previous approximations used, at only a slight increase in complexity. Our algorithm is derived and works within the framework imposed by the ATSC 8-VSB DTV transmission system, but will generalize to any communication system utilizing a training sequence embedded within data.Conference Object Citation - WoS: 1Citation - Scopus: 6Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues(Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, GökmenA multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.Article Citation - WoS: 29Citation - Scopus: 33Areally-Averaged Overland Flow Equations at Hillslope Scale(Taylor and Francis Ltd., 1998) Tayfur, Gökmen; Kavvas, M. LeventMicroscale-averaged inter-rill area sheet flow and rill flow equations (Tayfur and Kavvas, 1994) are averaged along the inter-rill area length and rill length to obtain local areally-averaged inter-rill area sheet flow and rill flow equations (local-scale areal averaging). In this averaging, the local areally-averaged flow depths are related to the microscale-averaged flow depths at the outlet sections (downstream ends) of a rill and an inter-rill area by the assumption that the flow in these sections has the profile of a sine function. The resulting local areally-averaged flow equations become time dependent only. To minimize computational efforts and economize on the number of model parameters, local areally-averaged flow equations are then averaged over a whole hillslope section (hillslope-scale areal averaging). The expectations of the terms containing more than one variable are obtained by the method of regular perturbation. Comparison of model results with observed data is satisfactory. The comparison of the model results with those of previously developed models which use point-scale and large-scale (transectionally) averaged technology indicates the superiority of this model over them. Microscale-averaged inter-rill area sheet flow and rill flow equations (Tayfur & Kavvas, 1994) are averaged along the inter-rill area length and rill length to obtain local areally-averaged inter-rill area sheet flow and rill flow equations (local-scale areal averaging). In this averaging, the local areally-averaged flow depths are related to the microscale-averaged flow depths at the outlet sections (downstream ends) of a rill and an inter-rill area by the assumption that the flow in these sections has the profile of a sine function. The resulting local areally-averaged flow equations become time dependent only. To minimize computational efforts and economize on the number of model parameters, local areally-averaged flow equations are then averaged over a whole hillslope section (hillslope-scale areal averaging). The expectations of the terms containing more than one variable are obtained by the method of regular perturbation. Comparison of model results with observed data is satisfactory. The comparison of the model results with those of previously developed models which use point-scale and large-scale (transectionally) averaged technology indicates the superiority of this model over them