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Yayın Parçacık süzgeçleme ile hedef izleme uygulamasında topak çizelgeleme(IEEE, 2007) Özfidan, Özgür; Bayazıt, Uluğ; Çırpan, Hakan AliBu çalışmada, uzaklık ölçer algılayıcılarla hedef takibi uygulamasında algılayıcı çizelgeleme problemi ele alınmıştır. Çok algılayıcılı uygulamalarda algılayıcıların yönetimi ürettikleri verilerin sınıflandırılması için olduğu kadar algılayıcıların verimli kullanımı için de gereklidir. Algılayıcı yönetimindeki önemli hususlardan biri algılayıcı çizelgelemesidir. Algılayıcıları çizelgeleyerek bant genişliği, güç, ve hesaplamada ciddi ölçüde kazanımlar sağlanabilir.Yayın Cluster based sensor scheduling in a target tracking application with particle filtering(IEEE, 2007) Özfidan, Özgür; Bayazıt, Uluğ; Çırpan, Hakan AliIn multi-sensor applications management of sensors is necessary for the classification of data they produce and for the efficient use of sensors as well. One of the important aspects in sensor management is the sensor scheduling. By scheduling the sensors, serious reductions can be achieved in the cost of bandwidth, power, and computation. In this work a simple solution for the problem of sensor scheduling in a multi-sensor target tracking application is presented. Due to non-linearity of the problem itself, proposed solution is presented in the framework of non-linear Bayesian estimation.Yayın An incremental model selection algorithm based on cross-validation for finding the architecture of a Hidden Markov model on hand gesture data sets(IEEE, 2009-12-13) Ulaş, Aydın; Yıldız, Olcay TanerIn a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this paper, we define the problem of optimizing the architecture of a Hidden Markov Model (HMM) as a state space search and propose the MSUMO (Model Selection Using Multiple Operators) framework that incrementally modifies the structure and checks for improvement using cross-validation. There are five variants that use forward/backward search, single/multiple operators, and depth-first/breadth-first search. On four hand gesture data sets, we compare the performance of MSUMO with the optimal parameter set found by exhaustive search in terms of expected error and computational complexity.Yayın Pilot-aided bayesian MMSE channel estimation for OFDM systems: Algorithm and performance analysis(IEEE, 2004) Şenol, Habib; Çırpan, Hakan Ali; Panayırcı, ErdalThis paper proposes a computationally efficient, pilot-aided minimum mean square error (MMSE) channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover, optimal rank reduction is achieved in the proposed approach by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first consider the stochastic Cramer-Rao bound and derive the closed-form expression for the random KL coefficients. We then exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE.
