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Yayın An efficient ECG data compression technique based on predefined signature and envelope vector banks(IEEE, 2005) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a new method to compress ElectroCardioGram (ECG) Signal by means of "Predefined Signature and Envelope Vector Banks-PSEVB" is presented. In this work, on a frame basis, any ECG signal is modeled by multiplying three parameters as called the Predefined Signature Vector, Predefined Envelope Vector, and Frame-Scaling Coefficient. It has been demonstrated that the predefined signature vectors and predefined envelope vectors constitute a "PSEVB" to describe any measured ECG signal. In this case, ECG signal for each frame is described in terms of the two indices "R" and "K" of PSEVB and the frame-scaling coefficient. The new compression method achieve good compression ratios with low level reconstruction error while preserving diagnostic information in the reconstructed ECG signal. Furthermore, once PSEVB are stored on each communication node, transmission of ECG signals reduces to the transmission of indexes "R" and "K" of PSEVB and the frame-scaling coefficient which also result in considerable saving in the transmission band.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 A novel noise robust and low bit rate speech coding algorithm(IEEE, 2009) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this work, a new noise robust and variable length frame based speech modeling method is introduced. This method consists of three major steps which includes noise removal algorithm, coding and encoding algorithms, respectively. Coding and encoding parts are developed based on SYMPES (SYsteMatic Procedure for Predefined Envelope and Signature sequence sets). These sets have been developed in two types which represent voiced and unvoiced parts of the speech signals separately in order to obtain more efficient coding strategy and higher compression ratio while preserving the perceptual quality of the speech signals. As an extension of our previous works our new framework is not only consider the coding of the clean speech signals but also noisy speech signals. The new noise robust module suppresses the noise and delivers the clean speech signal to the newly designed modeling part. The modeling part promises higher compression ratios by switching to the more appropriate type of predefined sets take into account the voiced and unvoiced frames.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 method to represent the speech signals by using language and speaker independent predefined functions sets(IEEE, 2004) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this paper a new modeling method of speech signals is introduced. The proposed method is based on the generation of the so-called Predefined Signature S={s(R)(t)} and Envelope Function E = {e(K)(t)} Sets (PSEFS). These function sets are independent of any speaker and any language. Once the speech signals are divided into frames with selected lengths, then each frame signal piece X-i(t) is synthesized by means of the mathematical form of x(i)(t)=C(i)e(K)(t)s(R)(t). In this representation, C-i is called the frame coefficient, s(R)(t) and e(K)(t) are properly assigned from the PSEFS respectively. It is shown that the proposed method provides fast reconstruction and substantial compression with acceptable hearing quality.Yayın Modeling of electrocardiogram signals using predefined signature and envelope vector sets(Hindawi Publishing Corporation, 2007) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaA novel method is proposed to model ECG signals by means of "predefined signature and envelope vector sets (PSEVS)." On a frame basis, an ECG signal is reconstructed by multiplying three model parameters, namely, predefined signature vector (PSV)(R)," "predefined envelope vector (PEV)(K)," and frame-scaling coefficient (FSC). All the PSVs and PEVs are labeled and stored in their respective sets to describe the signal in the reconstruction process. In this case, an ECG signal frame is modeled by means of the members of these sets labeled with indices R and K and the frame-scaling coefficient, in the least mean square sense. The proposed method is assessed through the use of percentage root-mean-square difference (PRD) and visual inspection measures. Assessment results reveal that the proposed method provides significant data compression ratio (CR) with low-level PRD values while preserving diagnostic information. This fact significantly reduces the bandwidth of communication in telediagnosis operations. Copyright (c) 2007 Hakan Gurkan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.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.
