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Yayın A novel image compression method based on classified energy and pattern building blocks(Springer International Publishing AG, 2011) Güz, ÜmitIn this paper, a novel image compression method based on generation of the so-called classified energy and pattern blocks (CEPB) is introduced and evaluation results are presented. The CEPB is constructed using the training images and then located at both the transmitter and receiver sides of the communication system. Then the energy and pattern blocks of input images to be reconstructed are determined by the same way in the construction of the CEPB. This process is also associated with a matching procedure to determine the index numbers of the classified energy and pattern blocks in the CEPB which best represents (matches) the energy and pattern blocks of the input images. Encoding parameters are block scaling coefficient and index numbers of energy and pattern blocks determined for each block of the input images. These parameters are sent from the transmitter part to the receiver part and the classified energy and pattern blocks associated with the index numbers are pulled from the CEPB. Then the input image is reconstructed block by block in the receiver part using a mathematical model that is proposed. Evaluation results show that the method provides considerable image compression ratios and image quality even at low bit rates.Yayın A new method to represent speech signals via predefined signature and envelope sequences(Hindawi Publishing Corporation, 2007) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaA novel systematic procedure referred to as "SYMPES" to model speech signals is introduced. The structure of SYMPES is based on the creation of the so-called predefined "signature S = {S(R)(n)} and envelope E = {E(K) (n)}" sets. These sets are speaker and language independent. Once the speech signals are divided into frames with selected lengths, then each frame sequence X(i)( n) is reconstructed by means of the mathematical form X(i)( n) = C(i)E(K) (n) S(R)(n). In this representation, C(i) is called the gain factor, S(R)(n) and E(K) (n) are properly assigned from the predefined signature and envelope sets, respectively. Examples are given to exhibit the implementation of SYMPES. It is shown that for the same compression ratio or better, SYMPES yields considerably better speech quality over the commercially available coders such as G. 726 (ADPCM) at 16 kbps and voice excited LPC-10E (FS1015) at 2.4 kbps.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 new coding method for speech and audio signals(IEEE, 2005) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this paper a new representation or modeling method of speech signals is introduced. The proposed method is based on the generation of the so-called Predefined Signature S={S R } and Envelope vector E={E K } Sets (PSEVS). These vector sets are speaker and language independent. In this method, once the speech signals are divided into frames with selected lengths, then each frame signal piece X i is reconstructed by means of the mathematical form of X i =C i E K S R . In this representation, C i is called the frame coefficient, S R and E K are the vectors properly assigned from the PSEVS respectively. It is shown that the proposed method provides fast reconstruction and substantial compression ratio with 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 image compression method based on classified energy and pattern blocks: initial results(Springer, 2010) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this work, a new method for the compression of the images based on the generation of the so called classified energy and pattern blocks is introduced and the initial results are presented. The initials results proved that the new method provides considerable image compression ratios and image quality even at low bit rates.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 A novel representation method for electromyogram (EMG) signal with predefined signature and envelope functional bank(IEEE, 2004) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a new method to model EMG signals by means of "Predefined Signature and Envelope Functional Banks (PSEB)" is presented. Since EMG signals present quasi-stationary behavior, any EMG signal Xi is modeled by the form of Xi ? Ci?K?R on a frame bases in this work. In this model, ?R is defined as the Predefined Signature Vector (PSV); ?K is referred to as Predefined Envelope Vector (PEV) and Ci is called the Frame-Scaling Coefficient (FSC). EMG signal for each frame is described in terms of the two indices "R" and "K" of PSEB and the frame -scaling coefficient Ci. Furthermore, It has been shown that the new method of modeling provides significant data compression while preserving the clinical information in the reconstructed signal.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.
