Browsing by Author "Bozkurt, Alican"

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    Approximate computation of DFT without performing any multiplications: application to radar signal processing
    (IEEE, 2014) Arslan, Musa Tunç; Bozkurt, Alican; Sevimli, Rasim Akın; Akbaş, Cem Emre; Çetin, A. Enis
    In many radar problems it is not necessary to compute the ambiguity function in a perfect manner. In this article a new multiplication free algorithm for approximate computation of the ambiguity function is introduced. All multiplications (a × b) in the ambiguity function are replaced by an operator which computes sign(a × b)(a + b). The new transform is especially useful when the signal processing algorithm requires correlations. Ambiguity function in radar signal processing requires high number of correlations and DFT computations. This new additive operator enables an approximate computation of the ambiguity function without requiring any multiplications. Simulation examples involving passive radars are presented.
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    Çarpmasız yapay sinir ağı
    (IEEE, 2015-05) Akbaş, Cem Emre; Bozkurt, Alican; Çetin, A. Enis; Çetin-Atalay, R.; Üner, A.
    Bu bildiride çarpma işlemi kullanmadan oluşturulan bir Yapay Sinir Ağı (YSA) sunulmaktadır. Girdi vektörleri ve YSA katsayılarının iç çarpımları çarpmasız bir vektör işlemiyle hesaplanmıştır. Yapay sinir ağının eğitimi sign-LMS algoritması ile yapılmıştır. Önerilen YSA sistemi, hesap gücü kısıtlı olan veya düşük enerji tüketimine ihtiyaç duyulan mikroişlemcilerde kullanılabilir.
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    Comparison of multi-scale directional feature extraction methods for image processing
    (2013) Bozkurt, Alican
    Almost all images that are presented in classification problems regardless of area of application, have directional information embedded into its texture. Although there are many algorithms developed to extract this information, there is no ‘golden’ method that works the best every image. In order to evaluate performance of these developed algorithms, we consider 7 different multi-scale directional feature extraction algorithms along with our own multi-scale directional filtering framework. We perform tests on several problems from diverse areas of application such as font/style recognition on English, Arabic, Farsi, Chinese, and Ottoman texts, grading of follicular lymphoma images, and stratum corneum thickness calculation. We present performance metrics such as k-fold cross validation accuracies and times to extract feature from one sample, and compare with the respective state of art on each problem. Our multi-resolution computationally efficient directional approach provides results on a par with the state of the art directional feature extraction methods.
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    Deconvolution using projections onto the epigraph set of a convex cost function
    (IEEE, 2014) Tofighi, Mohammad; Bozkurt, Alican; Köse, K.; Çetin, A. Enis
    A new deconvolution algorithm based on making orthogonal projections onto the epigraph set of a convex cost function is presented. In this algorithm, the dimension of the minimization problem is lifted by one and sets corresponding to the cost function and observations are defined. If the utilized cost function is convex in RN, the corresponding epigraph set is also convex in RN+1. The deconvolution algorithm starts with an arbitrary initial estimate in RN+1. At each iteration cycle of the algorithm, first deconvolution projections are performed onto the hyperplanes representing observations, then an orthogonal projection is performed onto epigraph of the cost function. The method provides globally optimal solutions for total variation, l1, l2, and entropic cost functions.
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    L1 norm based multiplication-free cosine similarity measures for big data analysis
    (IEEE, 2014-11) Akbaş, Cem Emre; Bozkurt, Alican; Arslan, Musa Tunç; Aslanoğlu, Hüseyin; Çetin, A. Enis
    The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector 'product' using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm. As a result, new cosine measure-like similarity measures are normalized by the ℓ1-norms of the vectors. They can be computed using the MapReduce framework. Simulation examples are presented. © 2014 IEEE.
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    Projections onto convex sets (POCS) based optimization by lifting
    (IEEE, 2013) Çetin, A. Enis; Bozkurt, Alican; Günay, Osman; Habiboglu, Yusuf Hakan; Köse, K.; Onaran, İbrahim; Tofighi, Mohammad; Sevimli, Rasim Akın
    A new optimization technique based on the projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in RN the corresponding set which is the epigraph of the cost function is also a convex set in RN+1. The iterative optimization approach starts with an arbitrary initial estimate in R N+1 and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp; p < 1 may be handled by using the supporting hyperplane concept. The new POCS based method can be used in image deblurring, restoration and compressive sensing problems. © 2013 IEEE.