Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical and Electronics Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11413/6818
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Browsing Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical and Electronics Engineering by Publisher "Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands"
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Publication Adaptive dual-mode OFDM with index modulation(Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands, 2018-10) Aldırmaz Çolak, Sultan; Başar, Ertuğrul; ACAR, YUSUF; 41633; 237377; 149116With the integration of the index modulation concept, the popular OFDM has gained an appealing new dimension. However, OFDM with index modulation (OFDM-IM) causes a decrease in data rate for higher order modulations due to its unused subcarriers. Recently, dual-mode OFDM with index modulation (DM-OFDM-IM) has been proposed to prevent this decrease in data rate. The spectral efficiency (SE) of both techniques depends on the selection of different parameters, such as modulation types and number of active subcarriers. However, the quality of the wireless channel has not been taken into account yet to increase the data rate. In this paper, we propose a new adaptive DM-OFDM-IM (A-DM-OFDM-IM) system to enhance the bit error probability (BER) performance considering the channel conditions and obtain a substantial increase in SE. Unlike the previous DM-OFDM-IM and the classical OFDM-IM schemes, our proposed system enables the use of different types of modulations for different subblocks in one OFDM signal. We demonstrate that the SE of the proposed system is comparable with that of its OFDM-IM and DM-OFDM-IM counterpart under frequency selective Rayleigh channels. Moreover, computer simulations corroborate that the derived theoretical results are considerably accurate for the SE of A-DM-OFDM-IM systems. (C) 2018 Elsevier B.V. All rights reserved.Publication Respiratory Parameter Estimation In Non-İnvasive Ventilation Based On Generalized Gaussian Noise Models(Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands, 2010-02) Akan, Aydın; SAATÇI, ESRA; TR112197; TR2918Modeling of respiratory system under non-invasive ventilation by using measured respiratory signals is of great interest in respiratory mechanics research area. Statistical processing techniques in the time-domain may be utilized as an alternative to the commonly used frequency-domain analysis to estimate model parameters. In this work, we propose using a generalized Gaussian distribution (GGD) to model the measurement noise in the respiratory system identification problem. The parameters of the GGD (i.e. the mean, the variance and the shape) are estimated by maximum likelihood (ML) and moment based estimators. However, the estimation error should also be taken into account which is in fact investigated as measurement innovations together with the measurement noise. Thus the Kalman iterations are applied with the help of the score function to compute the measurement innovations. Finally, the complete picture of the measurement noise and innovation analysis of the respiratory models is obtained which helped us to evaluate the non-Gaussian noise extension in the respiratory system analysis. (C) 2009 Elsevier B.V. All rights reserved.