Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling the Symmetric gray scale texture image and estimating by using proposed method, we have conclude that the 2-D Sinusoidal signal model can be effectively used to model symmetric gray- scale texture image and The efficiency of the proposed method to estimate model parameters.
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Sıfat - Fiillere Osmanlıcada fer’i fiil adı verilmektedir. Sıfat - fiiller için çeşitli kaynaklarda, kılın adı (kılın sanı[1]), ortaç[2], sıfat - fiil (sıfat - eylem[3]), partisip[4], isim - fiil[5] gibi terimler kullanılmıştır.
Sıfat - fiiller, isim- fiiler gibi fiilden türerler. Aldıkları belli başlı ekler vardır. Bu eklere sıfat - fiil ekleri denir.sıfat - fiiller, cümlede bir sıfat gibi görev yaparlar. Sıfatlar gibi isimlerin önün gelirler ve onları nitelerler tıpkı bir sıfat gibi ismin hal eklerini alab
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Bu tez Türkçe'de -yor ekinin kullanışları başlığını taşımaktadır.
Bilindiği gibi, bu ek Türkçe'de bir çok sayıda görev ve işlev yüklenebilmektedir. Bu ek Türkiye Türkçesinde en sık kullanılan şimdiki zaman ekidir. Bu ek hem şekil hem zaman ifade eder. Ayrıca da bu ekin çeşitli kullanışları verdır.
Bu ekle ilgili kaymalar çok açık olup örnekleri de son derece fazladır
The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
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Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras
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