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Publication Metadata only A multi-window fractional evolutionary spectral analysis(Institute of Electrical and Electronics Engineers Inc., 2003) Cekic, Yalcin; Akan, Aydin I.; Cekic, Yalcin, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Akan, Aydin I., Department of Electrical Engineering, Istanbul Üniversitesi, Istanbul, TurkeyIn this work, we present a multiple window Evolutionary Spectral analysis on a non-rectangular time-frequency lattice based on a discrete fractional Gabor expansion. The traditional Gabor expansion uses a fixed, and rectangular time-frequency plane tiling. Many of the practical signals such as speech, music, etc., require a more flexible, non-rectangular time-frequency lattice for a compact representation. The proposed method uses a set of basis functions that are related to the fractional Fourier basis and generate a parallelogram-shaped tiling. Simulation results are given to illustrate the performance of our algorithm. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only A fractional Gabor expansion(2003) Akan, Aydin I.; Cekic, Yalcin; Akan, Aydin I., Department of Electrical Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyWe present a fractional Gabor expansion on a non-rectangular time-frequency lattice. Sinusoidal analysis used in the traditional Gabor expansion is not appropriate for a compact representation for chirp-type signals. Basis functions of the proposed expansion are obtained via fractional Fourier basis. Completeness and biorthogonality conditions of the new expansion are derived. © 2003 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only Kaleidomap visualizations of cardiovascular function in critical care medicine(2006) Bale, Kim; Chapman, Paul; Purdy, Jon H.; Aydın, Nizamettin; Dark, Paul Michael; Bale, Kim, Department of Computer Science, University of Hull, Hull, United Kingdom; Chapman, Paul, Department of Computer Science, University of Hull, Hull, United Kingdom; Purdy, Jon H., Department of Computer Science, University of Hull, Hull, United Kingdom; Aydın, Nizamettin, Department of Genetics and Bioinformatics, Bahçeşehir Üniversitesi, Istanbul, Turkey; Dark, Paul Michael, Intensive Care Research Group, The University of Manchester, Manchester, United KingdomIn this paper we consider how the use of Kaleidomaps can facilitate our understanding and interpretation of large complex multivariate medical datasets relating to cardiovascular function in critical care medicine. Kaleidomaps are a new technique for the visualization of multivariate time-series data. They build upon the classic cascade plot and use the curvature of a line to enhance the detection of periodic patterns within multivariate dual-periodicity datasets. Kaleidomaps keep user interaction to a minimum, facilitating the rapid identification of periodic patterns not only within their own variants but also across many different sets of the variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide medical experts with a set of visualization tools that permit the combination of medical datasets in their raw form and also with the results of mathematical analysis. © 2006 IEEE. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only Kaleidomaps: A new technique for the visualization of multivariate time-series data(2007) Bale, Kim; Chapman, Paul; Barraclough, Nick E.; Purdy, Jon H.; Aydın, Nizamettin; Dark, Paul Michael; Bale, Kim, Department of Computer Science, University of Hull, Hull, United Kingdom; Chapman, Paul, Department of Computer Science, University of Hull, Hull, United Kingdom; Barraclough, Nick E., Department of Psychology, University of Hull, Hull, United Kingdom; Purdy, Jon H., Department of Computer Science, University of Hull, Hull, United Kingdom; Aydın, Nizamettin, Faculty of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Dark, Paul Michael, Intensive Care Research Group, The University of Manchester, Manchester, United KingdomIn this paper, we describe a new visualization technique that can facilitate our understanding and interpretation of large complex multivariate time-series data sets. Kaleidomaps have been carefully developed taking into account research into how we perceive form and structure within Glass patterns. We have enhanced the classic cascade plot using the curvature of a line to alter the detection of possible periodic patterns within multivariate dual periodicity data sets. Similar to Glass patterns, the concentric nature of the Kaleidomap may induce a motion signal within the brain of the observer facilitating the perception of patterns within the data. Kaleidomaps and our associated visualization tools alter the rapid identification of periodic patterns not only within their own variants but also across many different sets of variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide the user with a set of visualization tools that permit the combination of multivariate time-series data sets in their raw form and also with the results of mathematical analysis. In this paper, we provide two case study examples of how Kaleidomaps can be used to improve our understanding of large complex multivariate time dependent data. © 2007 Palgrave Macmillan Ltd. All rights reserved. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only Data mining usage in emboli detection(2007) Karahoca, Adem; Kucur, Turkalp; Aydın, Nizamettin; Karahoca, Adem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kucur, Turkalp, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyAsymptomatic circulating cerebral emboli, which are particles bigger than blood cells, can be detected by transcranial Doppler ultrasound. In certain conditions asymptomatic embolic signals (ES) appear to be markers of increased stroke risk. ES, reflected by an embolus, have usually larger amplitude than the signals from normal blood flow and show a transient characteristic. A number of methods to detect cerebral emboli have been studied in the literature. In this study, data mining techniques have been used in order to increase sensitivity and specificity of an embolic signal detection system. The classification results of different methods have been compared by using a data set including 100 ES, 100 speckle and 100 artifact. The ROC analysis results show that adaptive neuro fuzzy inference (ANFIS) system method appears to give better results. © 2007 IEEE. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only A robust image watermarking based on time-frequency, Zaman-siklik tabanli gürbüz görüntu damgalama(2007) Ozturk, Mahmut; Akan, Aydin I.; Cekic, Yalcin; Ozturk, Mahmut, Istanbul Üniversitesi, Istanbul, Turkey; Akan, Aydin I., Istanbul Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, Bahçeşehir Üniversitesi, Istanbul, TurkeyWatermarking techniques are proposed as a solution to copyright protection of digital media files. In this work, a new and robust watermarking method that is based on time-frequency (TF) representions is presented. We use the discrete evolutionary transform to represent an image in the TF domain. A watermark is embedded onto selected cells in the joint TF domain. Hence by combining the advantages of spatial and spectral domain watermarking methods, a robust and perceptual watermarking algorithm is presented. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only DWT based adaptive threshold determination in embolic signal detection(2007) Aydın, Nizamettin; Aydın, Nizamettin, Engineering Faculty, Bahçeşehir Üniversitesi, Istanbul, TurkeyEarly and accurate detection of emboli is important for monitoring of preventive therapy in stroke-prone patients. An embolic signal caused by emboli can be detected by Doppler ultrasound. Embolic signals are highly nonstationary and last only for a short time. Advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells and artifacts. Any detection method involving change detection must rely on determination of an appropriate threshold. In this paper, an adaptive threshold determination method based on discrete wavelet transform (DWT) and statistical properties of the data under investigation is described. The results reveal that the proposed adaptive threshold results in a robust embolic signal detection system. © 2007 IEEE. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only A novel information-theoretic clustering algorithm for robust, unsupervised classification(2007) Temel, Turgay; Aydın, Nizamettin; Temel, Turgay, Department of Electrical Engineering, Fatih Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Engineering Faculty, Bahçeşehir Üniversitesi, Istanbul, TurkeyA new information-theoretic, unsupervised, subtractive clustering algorithm is proposed. The algorithm eliminates threshold constraint to detect possible cluster members. Cluster centers are formed with minimum entropy. Instead of using a fixed-threshold, a decision region is formed with the use of maximum mutual information. Cluster members are chosen with a relative-cost assigned in partitions of data set. The algorithm yields more reliably distributed cluster numbers in statistical sense, hence reducing further computation for validation, which is justified for a set of synthetic data. ©2007 IEEE. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only Vision based behavior analysis of laboratory animals: A 3D gradient based approach, Laboratuar hayvanlarinin davranişlarinin görü tabanli çozümlenmesi: 3 Boyutlu gradyan tabanli bir yaklaşim(2009) Sandikci, Selçuk; Şahin, Pinar Duygulu; Özgüler, Bülent Bülent; Sandikci, Selçuk, ASELSAN A.Ş., Yenimahalle, Turkey; Şahin, Pinar Duygulu, Bilgisayar Müh. Bölümü, Bilkent Üniversitesi, Ankara, Turkey; Özgüler, Bülent Bülent, Mühendislik Fakültesi, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn pharmacological experiments behavior pattern of laboratory mice, which are under the influence of psychotherapeutic drugs, reveals important clues about effects of the drug. Behavior analysis of laboratory mice by video processing saves both time and labor. In this work a method which was previously used to recognize human behaviors is adapted to laboratory mice case. Method is based on fitting histograms of spatio-temporal gradients extracted from 3D space-time volumes to multidimensional statistical distributions and class(lj'ing according to distances between the distributions. In this work the method is tested on a common mice video dataset, compared to other methods in the literature and found to be successful. ©2009 IEEE. © 2009 Elsevier B.V., All rights reserved.Publication Metadata only High capacity image watermarking in the joint spatio-frequency domain, Birleşik uzay-frekans bölgesinde yüksek kapasiteli görüntü damgalama(2009) Ozturk, Mahmut; Akan, Aydin I.; Cekic, Yalcin; Ozturk, Mahmut, Istanbul Üniversitesi, Istanbul, Turkey; Akan, Aydin I., Istanbul Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, Bahçeşehir Üniversitesi, Istanbul, TurkeyWatermarking techniques are proposed as a solution to copyright protection of digital media files. Watermarking algorithms are mainly concentrated on spatial or spectral domains. In this work, a robust and high capacity watermarking method that is based ou spatio-frequeucy (SF) representations is presented. We use the Discrete Evolutionary Transform (DEl) calculated by Gabor expansion to represent an image in the SF domain. A watermark is embedded onto selected coefficients in the joint SF domain. Hence by combining the advantages of spatial and spectral domain watermarking methods, a robust, invisible, secure and high capacity watermarking method is presented. ©2009 IEEE. © 2009 Elsevier B.V., All rights reserved.