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Publication Metadata only Denoising embolic Doppler ultrasound signals using Dual Tree Complex Discrete Wavelet Transform(2010) Serbes, Görkem; Aydın, Nizamettin; Serbes, Görkem, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyEarly and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB). © 2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only INTERSPEECH 2009 emotion recognition challenge evaluation, INTERSPEECH 2009 duygu tanima yarişmasi deǧerlendirmesi(2010) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, TurkeyIn this paper we evaluate INTERSPEECH 2009 Emotion Recognition Challenge results. The challenge presents the problem of accurate classification of natural and emotionally rich FAU Aibo recordings into five and two emotion classes. We evaluate prosody related, spectral and HMM-based features with Gaussian mixture model (GMM) classifiers to attack this problem. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of pitch, first derivative of pitch and intensity. We employ unsupervised training of HMM structures with prosody related temporal features to define HMM-based features. We also investigate data fusion of different features and decision fusion of different classifiers to improve emotion recognition results. Our two-stage decision fusion method achieves 41.59 % and 67.90 % recall rate for the five and two-class problems, respectively and takes second and fourth place among the overall challenge results. ©2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only MIMO systems with non-exact CSI, Eksik kanal bilgisi Altinda MIMO sistemler(2010) Soysal, Alkan; Soysal, Alkan, Bahçeşehir Üniversitesi, Istanbul, TurkeyMultiple antenna systems are known to provide very large data rates, when the perfect channel state information is available at the receiver. However, this requires the receiver to perform a noise-free, multi-dimensional channel estimation, without using communication resources. In practice, any channel estimation is noisy and uses system resources. We shall examine the trade-off between improving channel estimation and increasing the achievable data rate. Lower and upper bounds for the capacity of the system will be derived. ©2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Air drums: A computer vision based drums simulator, Bilgisayarla görü tabanli davul benzetimcisi(2010) Fidan, Kaan Can; Kehribar, İhsan; Şahin, M. Tuǧçe; Cosar, Serhan; Ünay, Devrim; Fidan, Kaan Can, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Kehribar, İhsan, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Şahin, M. Tuǧçe, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Cosar, Serhan, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe aim of this paper is to present a novel system which tracks the motion of a drummer and generates the corresponding drum sounds. The input video sequence from a camera is processed in real-time by using local and adaptive color segmentation and Kalman filter based tracking. The Kalman filter is used to predict the hits so that we can overcome the processing delays and provide a more-realistic drumming experience. We use a local and adaptive search to detect the effective points of the drum sticks, which ensures robustness to background clutter and reduces the computational burden. We have developed a working demo and evaluated its performance by comparing with the output signal of an electronic drum pad. We observed that the timing errors have an average of -8.4 ms and a standard deviation of 5.4 ms in a real drumming experiment consisting of 121 hits. ©2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Modified dual tree complex wavelet transform for processing quadrature signals(Elsevier Ltd, 2011) Serbes, Görkem; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, Turkey, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyDual-tree complex wavelet transform (DTCWT) is a shift invariant transform with limited redundancy. Complex quadrature signals are dual channel signals obtained from the systems employing quadrature demodulation. An example of such signals is quadrature Doppler signal obtained from blood flow analysis systems. Prior to processing Doppler signals using the DTCWT, directional flow signals must be obtained and then two separate DTCWT applied, increasing the computational complexity. In order to decrease computational complexity, a modified DTCWT algorithm is proposed. A comparison between the new transform and the phasing-filter technique is presented. The results show that the proposed method gives the same output as the phasing-filter method and the computational complexity for processing quadrature signals using DTCWT is greatly reduced. © 2010 Elsevier Ltd. All rights reserved. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Comparison of LTE 800 MHz and LTE 2600 MHz frequency bands in terms of cell coverage, LTE 800 MHz ve LTE 2600 MHz frekans bantlarinin hücre kapsama bakimindan karşilaştirilmasi(2011) Tura, Ömer; Yüksel, Güray; Soysal, Alkan; Tura, Ömer, Bahçeşehir Üniversitesi, Istanbul, Turkey; Yüksel, Güray,; Soysal, Alkan, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this study, we compared 800 MHz and 2600 MHz bands in terms of cell coverage which will be used in 3GPP (Third generation Partnership Project) LTE (Long term Evolution) release 8. In RF planning, 800 MHz frequency band is used in the rural areas since coverage is crucial in such areas, and 2600 MHz frequency band is used in the urban areas in which capacity is more necessary than coverage. We used Atoll as an RF planning tool to simulate the two frequency bands for comparison in terms of coverage. The results are evaluated with respect to the furthest distance from the base station where the signal level threshold is obtained. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Face recognition-based IMDB plug-in for movies, Filmler için yüz tanima tabanli IMDB eklentisi(2011) Ulukaya, Sezer; Kayim, Güney; Ekenel, Hazim Kemal; Ulukaya, Sezer, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kayim, Güney, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ekenel, Hazim Kemal, Boğaziçi Üniversitesi, Bebek, TurkeyIn this paper, we present an initial study on an IMDB plug-in for cast identification in movies. In the system, training face images are collected by using Google image search. While watching a movie, the user clicks on the face of the person he is interested to acquire information. Afterwards, the system first tries to detect close to frontal faces, if it cannot find any, then it runs a profile face detector. The found face are then tracked backwards and forwards in the shot and this way a face sequence is obtained. Matching is performed between the extracted face sequence from the movie and the face image sets collected from the web. IMDB page links of the closest three persons resulted from the matching process is then presented to the user. In this study, we addressed the following three interesting points: matching between face sequence and face image sets, the effect of automatically collected noisy training images from the web on the performance, and finally, the performance effect of utilizing prior information of cast list and performing the classification within a limited number of classes. Experiments have shown that matching between face sequence and face image sets is a difficult problem. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only A comparison of geometrical facial features for affect recognition, Duygu tanima i̇çi̇n geometri̇k yüz özni̇teli̇kleri̇ni̇n karşilaştirilmasi(2011) Ulukaya, Sezer; Erdem, Cigdem Eroglu; Ulukaya, Sezer, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this work, we compare two different geometric feature extraction methods derived from coordinates of facial points tracked by Active Appearance Models. The compared feature extraction methods differ in their use of coordinates or distances between facial points and whether they use the information of a neutral facial expression. Experiments on the extended Cohn-Kanade database show that the coordinate-based features using the neutral frame information gives the best emotion recognition results (%94) using a SVC classifier with a polynomial kernel. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Symmetrical modified dual tree complex wavelet transform for processing quadrature Doppler ultrasound signals(2011) Serbes, Görkem; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyDual-tree complex wavelet transform (DTCWT), which is a shift invariant transform with limited redundancy, is an improved version of discrete wavelet transform. Complex quadrature signals are dual channel signals obtained from the systems employing quadrature demodulation. An example of such signals is quadrature Doppler signal obtained from blood flow analysis systems. Prior to processing Doppler signals using the DTCWT, directional flow signals must be obtained and then two separate DTCWT applied, increasing the computational complexity. In this study, in order to decrease computational complexity, a symmetrical modified DTCWT algorithm is proposed (SMDTCWT). A comparison between the new transform and the symmetrical phasing-filter technique is presented. Additionally denoising performance of SMDTCWT is compared with the DWT and the DTCWT using simulated signals. The results show that the proposed method gives the same output as the symmetrical phasing-filter method, the computational complexity for processing quadrature signals using DTCWT is greatly reduced and finally the SMDTCWT based denoising outperforms conventional DWT with same computational complexity. © 2011 IEEE. © 2012 Elsevier B.V., All rights reserved.Publication Metadata only Feature extraction using time-frequency/scale analysis and ensemble of feature sets for crackle detection(2011) Serbes, Görkem; Sakar, C. Okan; Kahya, Yasemin Palanduz; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kahya, Yasemin Palanduz, Department of Electrical Engineering, Boğaziçi Üniversitesi, Bebek, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyPulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data are proposed. © 2011 IEEE. © 2012 Elsevier B.V., All rights reserved.