Yazar "Çekli, S." seçeneğine göre listele

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    Classification of chaotic circuit output patterns with probabilistic neural networks
    (2011) Çekli, S.; Uzuno?lu, C.P.
    This study focused on the classification of chaotic circuit behaviors with probabilistic neural network (PNN). Although, chaotic circuit outputs track similar traces for the defined parameters, still the circuit outputs preserve their own random characteristics at each trial. PNN is an effective tool for classification of pattern recognition problems. Inherited features of PNN are very compatible with the chaotic circuit output classification problem and it provides satisfying performance. The selection of the proper features in the feature extraction step defines the performance of the classification significantly. In order to, compare classification performance of the PNN, different feature vectors are employed in the training process. Moreover, the spread parameter is a considerably vital factor for the performance of the network. The simulation results and the corresponding illustrations for the performance analysis are also given. © 2011 IEEE.
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    Kinect brain waves with controlled smart automation system for als patients
    (Institute of Electrical and Electronics Engineers Inc., 2016) Kantekin, U.; Aytekin, U.; Alaybeyo?lu, B.; Çekli, S.
    Nowadays, smart automation systems which directly affect people's life styles are being studied and designed frequently. These studies are carried out in a home environment, hospital or wheelchair to satisfy user's needs effectively. But these studies mainly focused on the healthy people. In this study a smart home automation system is proposed for ALS patients, orthopedic disabilities. In this work, Emotive device, Kinect One and Arduino Yún microcontroller were used to establish automation system which will render especially disabled people's daily life more safely and practically while they are at home. © 2015 IEEE.
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    Position detection with spherical interpolation least squares based on time difference of arrivals using separated acoustic signals by independent component analysis
    (2012) Çekli, S.
    In this study, locations of two sources have been detected using source signals separated with independent component analysis (ICA) method. The spherical interpolation least squares method which is based on the time difference of arrivals (TDOA) between the microphones is used for the solution of position detection. Although, there are different approaches and methods based on the TDOA for position detection, these methods presents a solution for a single source in general. In addition, the efficient signal separation is another problem when there are more than one target object in the sensor field. Therefore, the emitted signals which are received by the randomly distributed sensors (microphones) in the field are separated by ICA method. The source locations are found using the spherical interpolation least squares method regarding the signal groups which have the relative time differences between each other. The mean square error graphics and results are presented in the study for different signal to noise ratio values. © 2012 IEEE.