Browsing by Subject "Least squares methods"

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    Classifying human leg motions with uniaxial piezoelectric gyroscopes
    (2009) Tunçel O.; Altun, K.; Barshan, B.
    This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost. © 2009 by the authors.
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    Human activity recognition using inertial/magnetic sensor units
    (Springer, Berlin, Heidelberg, 2010) Altun, Kerem; Barshan, Billur
    This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.
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    KD haberleşme için iyonkürenin plazma frekansı -yükseklik profilinin matematiksel modellenmesi
    (IEEE, 2014-04) Toker, C.; Arıkan F.; Arıkan, Orhan
    İyonkürenin plazma frekansı - yükseklik profili özellikle gökdalgası ile yapılan KD haberleşmesini önemli derecede etkilemektedir. Bu çalışmada, belirli bir coğrafik bölge üzerinden bulunan bu profilin, en küçük kareler yöntemi ile doğrusal ve küresel düzlem modellerine oturtulmakta incelenmiştir. IRI-PlasG ile gerçeğe yakın profiller elde edilerek, bahsi geçen modellerin Ankara çevresindeki bir bölge için bu profillere uygunluğu gösterilmektedir
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    Range based sensor node localization in the presence of unknown clock skews
    (IEEE, 2013) Gholami, M.R.; Gezici, Sinan; Strom, E.G.
    We deal with the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. To avoid the drawbacks in solving the optimal estimator, we use approximations and derive linear models, which facilitate efficient solutions. In particular, we employ the least squares method and solve a general trust region subproblem to find a coarse estimate. To further refine the estimate, we linearize the measurements and obtain a linear model which can be solved using regularized least squares. Simulation results illustrate that the proposed approaches asymptotically attain the Cramér-Rao lower bound. © 2013 IEEE.