Yüksekokullar

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Browsing Yüksekokullar by Department "Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü"

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    Mobile Application Development for the Estimation of Recurrence in Post-Operative Kidney Cancer Cases
    (IEEE, 2018) Tander, Baran; Özmen, Atilla; Ozden, Ender
    In this paper a post-operative recurrence estimation tool called Sorbellini's nomogram for the kidney cancer patients showing no metastates is introduced and a novel application for mobile devices based on this model is developed for the physician's follow up procedures. The TNM stage tumor size nuclear (Fuhrman) grade the existance of necrosis and vascular invasion are employed as the input parameters for this software to predict the recurrence probability in mentioned patients. Finaly the performance analyses are carried out to verify the reliability of the application.
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    Near field parameter estimation of moving sources with recursive expectation maximization algorithm
    (IEEE, 2006) Cekli, Serap; Cekli, Erdinc; Kabaoğlu, Nihat; Cirpan, Hakan Ali
    In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented.
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    Near field parameter estimation of moving sources with recursive expectation maximization algorithm [Yinelemeli beklenti/en büyükleme algoritması ile hareketli kaynakların yakın-alan parametrelerinin kestirimi]
    (2006) Çekli, Serap; Çekli, Erdinç; Kabaoğlu, Nihat; Cirpan, Hakan Ali
    In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented. © 2006 IEEE.
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    Support vector machines based target tracking techniques
    (IEEE, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor network scenario with a moving target in a polynomial route.
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    Citation Count: 1
    Support vector machines based target tracking techniques [Destek vektör makineleri tabanlı hedef takip yöntemleri]
    (2006) Özer, Sedat; Cirpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor network scenario with a moving target in a polynomial route. © 2006 IEEE.
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    Article
    Citation Count: 1
    Support vector regression for surveillance purposes
    (Springer-Verlag Berlin, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers to use dynamic model together as feature vectors and makes the byperplane and the support vectors follow the changes in these features. The performance of the tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose.