Browsing by Author "Özdoğan, C."

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Hydrogen hosting of nanoscale boron cluster
(Editura Academiei Romane, 2008) Böyükata, Mustafa; Özdoğan, C.; Güvenç, Ziya B.; 40569
In this paper the Density Functional Results of hydrogen bonded boron micro clusters with B3LYP/6-311++G(d,p) level of computations will be presented. Energetics and structural stability with their possible stable geometries of various selected micro complexes of B(m)H(n) (for m and n <= 11 )boron hydrides have been analysed, and their binding energies with HOMO-LUMO energy gaps have been determined. Mainly, erects of the number of hydrogen atoms on the structures of the boranes are assessed. Moreover, for the cage configurations their distortions have been investigated for the neutral, anionic and cationic cases. It has been observed that there have been two opposing factors for the cage configurations. One of which is the "peeling" of the cage structures by the hydrogen atoms, and the other one is reforming a smaller cage form simultaneously with there remaining boron atoms inside. This is observed mostly for the odd values of m. From our studies it has been also observed that with the bare boronclusters alone, it is difficult to increase the capacity of the hydrogen storage. Therefore, further studies are necessary with the boron complexes
Citation - Scopus: 19
Parallel data reduction techniques for big datasets
(IGI Global, 2013) Yildirim, A.A.; Özdoğan, Cem; Özdoğan, C.; Watson, D.; 40569; Ortak Dersler Bölümü
Data reduction is perhaps the most critical component in retrieving information from big data (i.e., petascale-sized data) in many data-mining processes. The central issue of these data reduction techniques is to save time and bandwidth in enabling the user to deal with larger datasets even in minimal resource environments, such as in desktop or small cluster systems. In this chapter, the authors examine the motivations behind why these reduction techniques are important in the analysis of big datasets. Then they present several basic reduction techniques in detail, stressing the advantages and disadvantages of each. The authors also consider signal processing techniques for mining big data by the use of discrete wavelet transformation and server-side data reduction techniques. Lastly, they include a general discussion on parallel algorithms for data reduction, with special emphasis given to parallel waveletbased multi-resolution data reduction techniques on distributed memory systems using MPI and shared memory architectures on GPUs along with a demonstration of the improvement of performance and scalability for one case study. © 2014, IGI Global. All right reserved.