Teknik Bilimler Meslek Yüksekokulu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/2671
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Article Hücresel Sinir Ağları için Gerilim Kaynaklı Hücre Modelleri(AVES YAYINCILIK, 2001) Tander, Baran; Ün, Mahmut; Computer Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has UniversityBu makalede, bağımsız ve bağımlı gerilim kaynağı tabanlı yeni bir Hücresel Sinir Ağı hücre devresi önerilmiştir. Bu modelde akım kaynaklı Chua ve Yang ‘ın klasik hücre devresinin aksine hücreler için denge noktaları dinamik birimdeki Rx ve Cx’ den bağımsızdırlar. Tam bir hücre devresi tasarlanıp kararlı ve kararsız durumlar için benzetimleri yapılmıstır. Önerilen modelin avantaj ve dezavantajları sonuçlar bölümünde tartışılmıştır.Conference Object Citation - WoS: 1Support Vector Machines Based Target Tracking Techniques(IEEE, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat; 01. Kadir Has UniversityThis 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.Conference Object Citation - Scopus: 2Support Vector Machines Based Target Tracking Techniques [destek Vektör Makineleri Tabanlı Hedef Takip Yöntemleri](2006) Özer, Sedat; Cirpan, Hakan Ali; Kabaoğlu, Nihat; 01. Kadir Has UniversityThis 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.Article Citation - WoS: 1Citation - Scopus: 1Support vector regression for surveillance purposes(Springer-Verlag Berlin, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat; 01. Kadir Has UniversityThis 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.Conference Object Citation - WoS: 9Citation - Scopus: 9Unconditional Maximum Likelihood Approach for Localization of Near-Field Sources in 3-D Space(IEEE, 2004) Kabaoğlu, Nihat; Çırpan, Hakan Ali; Paker, Selçuk; 01. Kadir Has UniversitySince maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios we propose unconditional ML (UML) method for estimating azimuth elevation and range parameters of near-field sources in 3-D space in this paper Besides these superiorities stability asymptotic unbiasedness asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages ML estimator has computational complexity. Fortunately this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.
