Yazar "Ucan, Osman N." seçeneğine göre listele

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    ADAPTIVE WIENER-TURBO SYSTEM AND ADAPTIVE WIENER-TURBO SYSTEMS WITH JPEG & BIT PLANE COMPRESSIONS
    (Istanbul Univ, Fac Engineering, 2007) Buyukatak, Kenan; Ucan, Osman N.; Gose, Ersin; Osman, Onur; Kent, Sedef
    In order to improve unequal error protection and compression ratio of 2-D colored images over wireless environment, we propose two new schemes denoted as Adaptive Wiener-Turbo System (AWTS) and Adaptive Wiener-Turbo Systems with JPEG & Bit Plane Compressions (AW-TSwJBC). In AW-TS, there is a feedback link between Wiener filtering and Turbo decoder and process iteratively. The scheme employs a pixel-wise adaptive Wiener-based Turbo decoder and uses statistics (mean and standard deviation of local image) of estimated values of local neighborhood of each pixel. It has extra-ordinary satisfactory results of both bit error rate (BER) and image enhancement performance for less than 2 dB Signal-to-Noise Ratio (SNR) values, compared to separately application of traditional turbo coding scheme and 2-D filtering. In AW-TSwJBC scheme, 2-D colored image is passed through a color & bit planes' slicer block. In this block, each pixel of the input image is partitioned up to three main color planes as R, G, B and each pixel of the color planes is sliced up to N binary bit planes, which corresponds to binary representation of pixels. Thus depending on importance of information knowledge of the input image, pixels of each color plane can be represented by fewer number of bit planes. Then they are compressed by JPEG prior to turbo encoder. Hence, two consecutive compressions are achieved regarding the input image. In 2-D images, information is mainly carried by neighbors of pixels. Here, we benefit of neighborhood relation of pixels for each color plane by using a new iterative block, named as Adaptive Wiener-Turbo scheme, which employs Turbo decoder, JPEG encoder/decoders and Adaptive Wiener Filtering.
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    AUTOMATIC COLON SEGMENTATION USING CELLULAR NEURAL NETWORK FOR THE DETECTION OF COLORECTAL POLYPS
    (Istanbul Univ, Fac Engineering, 2007) Kilic, Niyazi; Osman, Onur; Ucan, Osman N.; Demirel, Kemal
    In this paper, an automatic colon segmentation method for Computed Tomography (CT) colonography is presented. Colon segmentation is considered in order to prevent the time consumption while searching polyps out of the colon region and reduce radiologists' interpretation time. The proposed method is the combination of pre-processing and Cellular Neural Networks (CNN). Also recurrent perceptron learning algorithm (RPLA) is used for CNN training. Original CT images are passed through a threshold and then CNN is used to erase unrelated small objects and smooth sharp corners. It is expected automatic colon segmentation will improve the radiologists' diagnostic performance.
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    COMPUTER NETWORK OPTIMIZATION USING GENETIC ALGORITHM
    (Istanbul Univ, Fac Engineering, 2006) Akbulut, Olcay; Osman, Onur; Ucan, Osman N.
    In this paper, Genetic Algorithm (GA) is proposed as optimization software to find the shortest path of various computer networks. It deals with different method concerning the placement routers, routes the packages. The genetic based algorithm defines an optimum way when a computer network system is constructed. Genetic Algorithm gives better results regarding other classical methods as the number of nodes of the network increases.