Yazar "Kurnaz, Mehmet Nadir" seçeneğine göre listele

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    A Framework Towards Computational Discovery of Disease Sub-types and Associated (Sub-)Biomarkers
    (IEEE, 2013) Kurnaz, Mehmet Nadir; Seker, Huseyin
    Biomarker related patient data is generally assessed in order to determine relevant but generalized subset of the biomarkers. However, it fails to identify specific sub-groups of the patients or their corresponding (subset of) the biomarkers. This paper therefore proposes a novel framework that is capable of discovering disease sub-groups (types) and associated subset of biomarkers, which is expected to lead to enable the discovery of personalized biomarker set. The framework is based on the utilization of a histogram obtained by using the Euclidean distances between the samples in a given data set. The t-test method is used for the selection of sub-set(s) of the biomarkers whereas the classification is performed by means of k-nearest neighbor, support vector machines and naive Bayes (NBayes) classifiers. For the assessment of the methods, leave-out-out cross validation is employed. As a case study, the method is applied in the analysis of male hypertension microarray data that consists of 159 patients and 22184 gene expressions. The method has helped identify specific subgroups of the patients and their corresponding bio-marker sub-sets. The results therefore suggest that the generalized bio-marker sub-sets are not representative of the disease and therefore more focus should be on the sub-groups of the patients and their biomarker subsets identified through the proposed approach. It is particularly observed that the threshold values over the histogram are crucial to discover both sub-sets of the samples and biomarkers, and therefore can be used to determine complexity level of the study.
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    Segmentation of medical images by using k-NN classifier on field programmable logic array (FPGA)
    (2010) Çinar, Salim; Kurnaz, Mehmet Nadir
    Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristic of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computation complexity. Recently, Field Programmable Gate Array (FPGA) implementation capable of performing many complex computations in parallel has been applied in many areas needed for high computation time. In this study, it is proposed that neigbour-pixel-intensity based feature extraction methods for extraction of the textural features in medical images, and k-NN classifier for segmentation process.
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    Segmentation of MR Images by Using Grow and Learn Network on FPGAs
    (IEEE, 2013) Cinar, Salim; Kurnaz, Mehmet Nadir
    Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristics of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computational complexity. Recently, FPGA implementation has been applied in many areas due to its parallel processing capability. In this study, neighbor-pixel-intensity based method for feature extraction and Grow and Learn (GAL) network for segmentation process are proposed. The proposed method is comparatively examined on both PC and FPGA platforms.
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    Segmentation of MR images by using grow and learn network on FPGAs
    (2013) Cinar, Salim; Kurnaz, Mehmet Nadir
    Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristics of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computational complexity. Recently, FPGA implementation has been applied in many areas due to its parallel processing capability. In this study, neighbor-pixel-intensity based method for feature extraction and Grow and Learn (GAL) network for segmentation process are proposed. The proposed method is comparatively examined on both PC and FPGA platforms. © 2013 IEEE.
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    Transduser performans testleri
    (Niğde Üniversitesi, 1998) Kurnaz, Mehmet Nadir; Alkan, Mustafa
    Dönüştürücüler genellikle kontrol ve ölçme sistemlerinde hissetme cihazları olarak kullanılan endüstriyel bir cihazdır. Günümüzde dönüştürücüler kullanılmadan kontrol ve ölçme alanında araştırma yapmak ve ilerleme kaydetmek mümkün değildir. Dönüştürücülerin tasarımlan kolay, fakat yapım ve kalibrasyonları zordur. Bu tez çalışması, 6 bölümden oluşmaktadır. Bölüm 1' de dönüştürücü teknolojisindeki gelişmeler ve dönüştürücülerin elektronik sistemlerindeki önemi üzerinde duruldu. Bölüm 2" de enstrümantasyon sistemleri ve buna bağlı olarak da ölçme ve kontrol sistemleri kısaca izah edildi. Dönüştürücü esasları Bölüm 3' de açıklandı. Yine bu bölümde dönüşüm prensiplerinin kullanımı üzerinde duruldu ve bunlarla ilgili tanımlamalar yapıldı. Ayrıca herhangi bir katalogdan dönüştürücü seçerken faydalı olacak çeşitli dönüştürücü karakteristikleri ayrıntılı bir şekilde verildi. Bölüm 4' te, dönüştürücü seçim kriterleri tartışıldı. Bölüm 5' te, birçok dönüştürücü karakteristikleri için dönüştürücü performans testleri denendi. Bölüm 6' da dönüştürücü karakteristikleri için hangi testlerin ve ölçmelerin daha uygun olacağı tartışıldı. Anahtar Söcükler: Dönüştürücü, entrümantasyon sistemi, dönüşüm prensibi, dönüştürücü karakteristikleri, dönüştürücü performans testleri.