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Yayın EEG signal compression based on classified signature and envelope vector sets(Wiley, 2009-03) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a novel method to compress electroencephalogram (EEG) signal is proposed. The proposed method is based on the generation process of the classified signature and envelope vector sets (CSEVS), which employs an effective k-means clustering algorithm. It is assumed that both the transmitter and the receiver units have the same CSEVS. In this work, on a frame basis, EEG signals are modeled by multiplying only three factors called as classified signature vector, classified envelope vector, and gain coefficient (GC), respectively. In other words, every frame of an EEG signal is represented by two indices R and K of CSEVS and the GC. EEG signals are reconstructed frame by frame using these numbers in the receiver unit by employing the CSEVS. The proposed method is evaluated by using some evaluation metrics that are commonly used in this area such as root-mean-square error, percentage root-mean-square difference, and measuring with visual inspection. The performance of the proposed method is also compared with the other methods. It is observed that the proposed method achieves high compression ratios with low-level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.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 A new algorithm for high speed speech and audio coding(IEEE, 2007) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this work, a new mathematical modeling approach is proposed for the representation of the speech and audio signals. This approach is based on the generation of the so called Predefined Signature Sequence (PSS) and Predefined Envelope Sequence (PES) Sets. After the generation process of the PSS and PES sets, they are clustered by effective k-means clustering algorithm and the PSS and PES are redefined by using the centroids of the clusters. By using this approach, the drawbacks such as the size of the sets, speed of the reconstruction process (computational complexity) which arise in our proposed methods previously are highly eliminated. In spite of these improvements, the initial results proved that, the quality of the reconstructed signals remains within the limitations of the acceptable hearing quality.Yayın Elektroensefalogram (EEG) işaretlerinin sıkıştırılmasında özgün bir yaklaşım(IEEE, 2008) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaBu çalışmada, Elektroensefalogram(EEG) işaretlerinin yeniden oluşturulmasına yönelik olarak yeni bir yöntem sunulmaktadır. Sunulan yöntem, etkin bir k-ortalamalı sınıflandırma algoritması kullanılarak Sınıflandırılmış Temel Tanım ve Zarf Vektör Setlerinin oluşturulmasına dayanmaktadır. Bu çalışmada, EEG işaretleri eşit uzunluklu çerçevelere bölünerek analiz edilmiş ve herbir çerçeve Sınıflandırılmış Temel Tanım vektörü, Sınıflandırılmış Zarf vektörü ve Çerçeve Ölçekleme Katsayısı olarak adlandırılan üç parametrenin çarpımı biçiminde modellenmiştir. Bu durumda, EEG işaretinin herbir çerçevesi sınıflandırılmış temel tanım ve zarf vektör setlerine ilişkin iki sıra numarası R ve K ile çerçeve ölçekleme katsayısı cinsinden tanımlanabilir. Önerilen yöntemin başarımı ortalama karesel hata tanımı ve görsel inceleme ölçütü yoluyla değerlendirilmiştir. Önerilen yöntem, EEG işaretlerinin tanı açısından önemli kısımları korunarak, düşük yeniden oluşturma hataları ve yüksek sıkıştırma oranları ile yeniden oluşturulmasını sağlamaktadır.Yayın A novel fast algorithm for speech and audio coding(IEEE, 2007) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this work a new mathematical modeling approach is proposed for the representation of the speech and audio signals. This approach is based on the generation of the so called Predefined Signature Sequence (PSS) and Predefined Envelope Sequence (PES) Sets. After the generation process of the PSS and PES sets, they are clustered by effective k-means clustering algorithm and the PSS and PES are redefined by using these centroids. By using this approach, the drawbacks by means of the size of the sets, speed of the reconstruction process (computational complexity) which arise in the proposed methods previously are highly eliminated. In spite of these improvements, the initial results proved that, the quality of the reconstructed signals remains within the limitations of the acceptable hearing quality.Yayın EEG signal compression based on classified signature and envelope vector sets(IEEE Computer Society, 2007) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a novel method to compress ElectroEncephaloGram (EEG) Signal is proposed. The proposed method is based on the generation Classified Signature and Envelope Vector Sets (CSEVS) by using an effective k-means clustering algorithm. In this work on a frame basis, any EEG signal is modeled by multiplying three parameters as called the Classified Signature Vector, Classified Envelope Vector, and Frame-Scaling Coefficient. In this case, EEG signal for each frame is described in terms of the two indices R and K of CSEVS and the frame-scaling coefficient. The proposed method is assessed through the use of root-mean-square error (RMSE) and visual inspection measures. The proposed method achieves good compression ratios with low level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.
