Yazar "Ucan, Osman N." seçeneğine göre listele

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    Cellular Nerural Network Approach For Enhancement Of Dental Image With Low Dosed X-Ray
    (Beykent Üniversitesi, 2007) Kilic, Niyazi; Ucan, Osman N.; Ozkarslı, Fatih; Yilmaz, Bulent; Dindar, Seckin
    Despite radiography's great importance in dental treatment, they caused serious health problems when were used repeatedly with high amount of radiation. In this paper, we have presented an algorithm for image enhancement in dental image with low dosed x-ray (radiation). We aimed to get high quality images (like images with high dosed x-ray) while we have reduced the amount of radiation in Radio VisioGraphy (RVG), which were used for those patients, by use of Cellular Neural Network (CNN). When compared with input images and CNN output images, CNN outputs better clear and quality than inputs from various angle have been seen.
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    Joint Multilevel Turbo Equalization and Continuous Phase Frequency Shift Keying
    (Springer, 2008) Bayat, Oguz; Odabasioglu, Niyazi; Osman, Onur; Ucan, Osman N.; Salehi, Masoud; Shafai, Bahram
    A novel type of turbo coded modulation scheme, called multilevel turbo coded-continuous phase frequency shift keying (MLTC-CPFSK), is designed to improve the overall bit error rate (BER) and bandwidth efficiency. Then, this scheme is combined with a new double decision feedback equalizer (DDFE) to remove the interference and to enhance BER performance for the intersymbol interference (ISI) channels. The entire communication scheme is called multilevel turbo equalization-continuous phase frequency shift keying (MLTEQ-CPFSK). In these schemes, parallel input data sequences are encoded using the multilevel scheme and mapped to CPFSK signals to obtain a powerful code with phase continuity over the air. The performances of both MLTC-CPFSK and MLTEQ-CPFSK systems were simulated over nonfrequency and frequency-selective channels, respectively. The superiority of the two level turbo codes with 4CPFSK modulation is shown against the trellis-coded 4CPFSK, multilevel convolutional coded 4CPFSK, and TTCM schemes. Finally, the bit error rate curve of MLTEQ-CPFSK system over Proakis B channel is depicted and ISI cancellation performance of DDFE equalizer is shown against linear and decision feedback equalizers Copyright (C) 2008 Oguz Bayat et al.
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    SHIP WASTE FORECASTING AT THE BOTAS LNG PORT USING ARTIFICIAL NEURAL NETWORKS
    (Parlar Scientific Publications (P S P), 2008) Satir, Tanzer; Demir, Hasan; Alkan, Gueler B.; Ucan, Osman N.; Bayat, Cuma
    Cargo and passenger vessels are required to give their waste to reception facilities when at port, and due to new regulations Turkish ports need to establish or reconstruct these facilities. It is thus very important for ports to be able to predict the quantity of waste. In this study, the authors use Artificial Neural Networks (ANNs) to model four years of data on the reception of ship's waste at the Botas LNG Port in Marmara Ereglisi, Turkey. Satisfactory results are obtained by the ANN outputs. and confirmed by classical approaches. This ANN forecasting model can be used by waste management companies to plan new ports.
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    Signalling enhancement on multilevel turbo codes
    (John Wiley & Sons Ltd, 2008) Bayat, Oguz; Shafai, Bahram; Salehi, Masoud; Ucan, Osman N.; Osman, Onur
    The design of multilevel turbo codes using M-PSK is optimized to achieve a low bit error rate (BER). Unequal error protection is employed via group set partitioning in multi-stage decoding to minimize the error propagation and BER. Simulation results are performed under Gaussian and Rayleigh fading channels to depict the superiority of the new scheme. Copyright (C) 2008 John Wiley & Sons, Ltd.