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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 Spectral coding of mesh geometry with a hierarchical set partitioning algorithm(Spie-Int Soc Optical Engineering, 2008) Konur, Umut; Bayazıt, Uluğ; Ateş, Hasan Fehmi; Gürgen, Sadık FikretThis work proposes a progressive mesh geometry coder, which expresses geometry information in terms of spectral coefficients obtained through a transformation and codes these coefficients using a hierarchical set partitioning algorithm that assigns right priorities to those coefficients at all bit planes. The spectral transformation used is the one proposed in [8] where the spectral coefficients are obtained by projecting the mesh geometry on an orthonormal basis determined by mesh topology. The set partitioning method used in coding, treats spectral coefficients belonging to the three spatial coordinates with the right priority at all bit planes and realizes a truly embedded system by achieving implicit bit allocation via joint coding the zeroes of coefficients at the bit planes. The experiments performed on common irregular meshes reveal that the rate-distortion performance of the coder is significantly superior to the coding system proposed in [8].Yayın A novel method to represent ECG signals via predefined personalized signature and envelope functions(IEEE, 2001) Yarman, Bekir Sıddık Binboğa; Gürkan, Hakan; Güz, Ümit; Aygün, B.In this paper, a new method to model ECG signals by means of "Predefined Personalized Signature and Envelope Functions" is presented. ECG signals are somewhat unique to a person. Moreover, it presents quasi-stationary behavior. Therefore in this work, on a frame basis, personal ECG signals X-i(t) is modeled by the form of X-i(t) approximate to C(i)phi(i)(t) alpha(i)(t). In this model, phi(i)(t) is defined as the Personalized Signature Function (PSF); alpha(i)(t) is referred to as Personalized Envelope Function (PEF) and C-i is called the Frame-Scaling Coefficient (FSC). It has been demonstrated that for each person, the sets Phi = {phi(k)(t)} and A = {alpha(r)(t)} constitute a "Predefined Personalized Functional Bases or Banks (PPFB)" to describe any measured ECG signal. Almost optimum forms of (PPFB), namely {alpha(r)(t)}, {phi(k)(t)} pairs are generated in the Least Mean Square (LMS) sense. Thus, ECG signal for each frame is described in terms of the two indices "R" and "K" of PPFB and the frame-scaling coefficient Ci. It has been shown that the new method of modeling provides significant data compression. Furthermore, once PPFB are stored on each communication node, transmission of ECG signals reduces to the transmission of indexes "R" and "K" of pairs and the coefficients C-i, which also result in considerable saving in the transmission band.
