Browsing by Subject "Signal processing"
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Item Open Access 3-Boyutlu orman yangını yayılımı sistemi(IEEE, 2008) Köse, Kıvanç; Yılmaz, E.; Grammalidis, N.; Aktuğ, B.; Çetin, A. Enis; Aydın, İ.In the last few years, due to the global warming and draught related to it, there is an increase in the number of forest fires. Forest fire detection is mainly done by people but there exists some automated systems in this field too. Besides the detection of the forest fires, effective fire extinhguising has an important role in fire fighting. If the spread of the fire can be predicted from the starting, early intervene can be achieved and fire can be extinguished swiftly. Using the Fire Propagation Simulator explained here it is aimed, to predict the fire development beforehand and to visulalize this predictions on a 3D-GIS environment. ©2008 IEEE.Item Open Access 3D electron density estimation in the ionosphere(IEEE, 2014) Tuna, Hakan; Arıkan, Orhan; Arıkan, F.Ionosphere has ion distribution which is variable in space and time. There have been physical and empirical studies for modeling the ionosphere. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) is the most recent model developed for this purpose. However, IRI-Plas presents a model about the ionosphere and its compliance with the instantaneous state of the ionosphere does not provide the accuracy needed for engineering purposes. One of the important information sources about the instantaneous state of the ionosphere is GPS signals. In this study, constructing the ionosphere which is compatible with both the instantaneous ionosphere measurements and the physical structure of the ionosphere is presented as an optimization problem, and solved by using the particle swarm optimization technique. The ionosphere over Turkey is investigated by using the proposed optimization method and the importance of the instantaneous ionosphere measurements obtained from GPS signals is demonstrated.Item Open Access Adaptive decision fusion based cooperative spectrum sensing for cognitive radio systems(IEEE, 2011) Töreyin, B. U.; Yarkan, S.; Qaraqe, K. A.; Çetin, A. EnisIn this paper, an online Adaptive Decision Fusion (ADF) framework is proposed for the central spectrum awareness engine of a spectrum sensor network in Cognitive Radio (CR) systems. Online learning approaches are powerful tools for problems where drifts in concepts take place. Cooperative spectrum sensing in cognitive radio networks is such a problem where channel characteristics and utilization patterns change frequently. The importance of this problem stems from the requirement that secondary users must adjust their frequency utilization strategies in such a way that the communication performance of the primary users would not be degraded by any means. In the proposed framework, sensing values from several sensor nodes are fused together by weighted linear combination at the central spectrum awareness engine. The weights are updated on-line according to an active fusion method based on performing orthogonal projections onto convex sets describing power reading values from each sensor. The proposed adaptive fusion strategy for cooperative spectrum sensing can operate independent from the channel type between the primary user and secondary users. Results of simulations and experiments for the proposed method conducted in laboratory are also presented. © 2011 IEEE.Item Open Access Adaptive filtering approaches for non-Gaussian stable processes(IEEE, 1995-05) Arıkan, Orhan; Belge, Murat; Çetin, A. Enis; Erzin, EnginA large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this paper, α-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such kind of noise is a requirement of many practical problems. Since, direct application of commonly used adaptation techniques fail in these applications, new approaches for adaptive filtering for α-stable random processes are introduced.Item Open Access Adaptive filtering for non-gaussian stable processes(IEEE, 1994) Arıkan, Orhan; Çetin, A. Enis; Erzin, E.A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this letter, a-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such a noise is a requirement of many practical problems. Since direct application of commonly used adaptation techniques fail in these applications, new algorithms for adaptive filtering for α-stable random processes are introduced.Item Open Access Adaptive hierarchical space partitioning for online classification(IEEE, 2016) Kılıç, O. Fatih; Vanlı, N. D.; Özkan, H.; Delibalta, İ.; Kozat, Süleyman SerdarWe propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.Item Open Access Adaptive tracking of narrowband HF channel response(Wiley-Blackwell Publishing, 2003) Arikan, F.; Arıkan, OrhanEstimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.Item Open Access ADMM based mainlobe power constrained phase-only sidelobe supression(IEEE, 2014) Alp, Yaşar Kemal; Arıkan, OrhanA novel sidelobe suppression technique is proposed for phased arrays, where only the phases of the array elements are adjusted to suppress the gain in the direction of interest while keeping the mainlobe power at a certain level. Mainlobe power constrained sidelobe suppression is formulated as a convex RSDP (Relaxed Semidefinite Program). Solution to resultant RSDP is obtained by ADMM (Alternating Direction Method of Multipliers) technique, which can handle designs for arrays with number of elements is significantly larger than that can be handled by other convex solvers such as CVX. In addition, although the available convex solvers can not provide a rank-1 solution matrix, a rank-1 solution matrix is obtained by modifying the ADMM iterations. In the conducted experiments, it is observed that proposed ADMM based method can achieve more than 10dB improvement in sidelobe levels compared to alternative techniques.Item Open Access Algorithms and basis functions in tomographic reconstruction of ionospheric electron density(IEEE, 2005) Yavuz, E.; Arıkan, F.; Arıkan, Orhan; Erol, C. B.Computerized ionospheric tomography (CIT) is a method to investigate ionosphere electron density in two or three dimensions. This method provides a flexible tool for studying ionosphere. Earth based receivers record signals transmitted from the GPS satellites and the computed pseudorange and phase values are used to calculate Total Electron Content (TEC). Computed TEC data and the tomographic reconstruction algorithms are used together to obtain tomographic images of electron density. In this study, a set of basis functions and reconstruction algorithms are used to investigate best fitting two dimensional tomographic images of ionosphere electron density in height and latitude for one satellite and one receiver pair. Results are compared to IRI-95 ionosphere model and both receiver and model errors are neglected.Item Open Access Application of signal-processing techniques to dipole excitations in the finite-difference time-domain method(Taylor & Francis, 2002) Oğuz, U.; Gürel, LeventThe applications of discrete-time signal-processing techniques, such as windowing and filtering for the purpose of implementing accurate excitation schemes in the finite-difference time-domain (FDTD) method are demonstrated. The effects of smoothing windows of various lengths and digital lowpass filters of various bandwidths and characteristics are investigated on finite-source excitations of the FDTD computational domain. Both single-frequency sinusoidal signals and multifrequency arbitrary signals are considered.Item Open Access Application of signal-processing techniques to reduce the errors related to the FDTD excitations(IEEE, 2001) Gürel, Levent; Oğuz, UğurA study on the reduction of the errors related to the finite-difference time-domain (FDTD) excitations was performed by employing signal-processing techniques. Plane-wave scattering problems were simulated. The improvements in both plane-wave and finite-source excitation schemes were demonstrated. The result showed that a visible DC offset value was exhibited even after five periods of the incident wave.Item Open Access Applications of the fractional Fourier transform in optics and signal processing-a review(SPIE, 1996) Özaktaş, Haldun M.; Mendlovic, D.The fractional Fourier transform The fractional Fourier transform is a generalization of the common Fourier transform with an order parameter a. Mathematically, the ath order fractional Fourier transform is the ath power of the fractional Fourier transform operator. The a = 1st order fractional transform is the common Fourier transform. The a = 0th transform is the function itself. With the development of the fractional Fourier transform and related concepts, we see that the common frequency domain is merely a special case of a continuum of fractional domains, and arrive at a richer and more general theory of alternate signal representations, all of which are elegantly related to the notion of space-frequency distributions. Every property and application of the common Fourier transform becomes a special case of that for the fractional transform. In every area in which Fourier transforms and frequency domain concepts are used, there exists the potential for generalization and improvement by using the fractional transform.Item Open Access Approximate computation of DFT without performing any multiplications: application to radar signal processing(IEEE, 2014) Arslan, Musa Tunç; Bozkurt, Alican; Sevimli, Rasim Akın; Akbaş, Cem Emre; Çetin, A. EnisIn many radar problems it is not necessary to compute the ambiguity function in a perfect manner. In this article a new multiplication free algorithm for approximate computation of the ambiguity function is introduced. All multiplications (a × b) in the ambiguity function are replaced by an operator which computes sign(a × b)(a + b). The new transform is especially useful when the signal processing algorithm requires correlations. Ambiguity function in radar signal processing requires high number of correlations and DFT computations. This new additive operator enables an approximate computation of the ambiguity function without requiring any multiplications. Simulation examples involving passive radars are presented.Item Open Access Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19(2023-10) Terzi, Merve BegümCoronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.Item Open Access Assessment of information redundancy in ECG signals(IEEE, 1997-09) Acar, Burak; Özçakır, Lütfü; Köymen, HayrettinIn this paper, the morphological information redundancy in standard 12 lead ECG channels is studied. Study is based on decomposing the ECG channels into orthogonal channels by an SVD based algorithm and then reconstructing them. Then 7 of 8 independently recorded ECG channels are decomposed and the missing channel is reconstructed from these orthogonal channels. Thus the unique morphological information content of each ECG channel is assessed through the loss of clinical information in the reconstructed signal. A comparison of the clinical parameters measured from the reconstructed and original ECG is reported.Item Open Access Atatürk'ün el yazmalarının işlenmesi(IEEE, 2010-04) Soysal, Talha; Adıgüzel Hande; Öktem, Alp; Haman, Alican; Can, Ethem Fatih; Duygulu, Pınar; Kalpaklı, MehmetBu çalımada Atatürk'ün el yazmalarının etkin ve kolay eriimini salayabilecek kelime tabanlı bir arama sisteminin ilk aaması olarak sayısallatırılmı belgelerin ön ilemesi ve satır ve kelimelere bölütlenmesi konusunda çalımalar yapılmıtır. Tarihi el yazması belgeler çeitli zorluklar getirmekte, basılı belgelerde kullanılan yöntemlerin uygulanması baarılı sonuçlar üretememektedir. Bu nedenle daha gelimi çözümler üzerine younlaarak satır bölütlemede Hough dönüümü [1] tabanlı bir yöntem uyarlanmı, kelime bölütlemede ise yazıların eiklii göz önüne alınmıtır. Afet nan tarafından salanan belgelerin [4] 30 sayfası üzerinde yapılan çalımalarda elde edilen sonuçlar gelecek çalımalar açısından umut vericidir. In this paper, as a first step to an easy and convenient way to access the manuscripts of Atatürk with a word based search engine, the preprocessing of digitalized documents and their line and word segmentation is studied. The techniques that are applied on printed documents may not yield satisfactory results. Due to this fact, more developed techniques are decided to be applied consisting of a technique based on Hough transform [1] for line segmentation and a technique that is based on dealing with skewness of lines for word segmentation. The results, which are acquired through studies that are conducted on the documents provided by Afet İnan and consisting of 30 pages [2], prove to be highly accurate and promising for future researches. ©2010 IEEE.Item Open Access Audio-visual perception of outpatients in an oncology polyclinic(2022-06) Uğurlu, M. ZeynepThis study aims to analyze the effects of auditory and visual perception in a hospital environment to provide comfort for the outpatients. The research focused on the waiting area of an oncology polyclinic. The binaural audio recordings and 360° photographs were taken from three different locations (reception area, courtyard area, and corridor) on the site. Audio recordings were visualized through signal processing, and the photographs were evaluated through image analysis via MATLAB to show the auditory and visual differences among the locations. These three locations differed from each other in their auditory and visual environments. Regarding the hospital auditory environment, sound sources are human activity-related and technology-based. The reception area has an indoor opening, the courtyard area has indoor and outdoor openings, and the corridor area has none. Voluntary oncology outpatients in three locations in the polyclinic were given questionnaires (n=66) and interviewed (n=20). Questionnaires were analyzed in IBM SPSS Statistics, and interviews were analyzed with the Grounded Theory method in ATLAS.ti software. Equivalent Continuous A-Weighted Sound Level (LAeq) measurements were taken within the interview hours and at one-hour intervals from three locations. The questionnaire revealed that the courtyard area is calmer and more pleasant than the other areas. The conceptual framework created with semi-structured interviews showed how the auditory and visual environments affect the outpatients' perception. The conceptual framework revealed audio-visual perceptions in the hospital environment with the existing condition and outpatients' preferences and proposed suggestions for a hospital environment.Item Open Access Autofocus method in thermal cameras based on image histogram(IEEE, 2011) Turgay, E.; Teke, OğuzhanIn this paper, a new histogram based auto-focusing method for thermal cameras is proposed. This proposed method is realized by FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) working together and simultaneously. HF (High Frequency) component, obtained from real-time image flow by FPGA and DSP is used for auto-focusing process. Proposed method is able to determine the focus direction from the HF component produced in the process of histogram equalization by FPGA, unlike Fourier transform and pixel differenve based methods in the literature. With this superiority, proposed method requires no extra calculation for thermal cameras for which histogram equalization is necessary. Analysis show that proposed method is successful on the simulations and scanning thermal cameras.Item Open Access Average fisher information optimization for quantized measurements using additive independent noise(IEEE, 2010) Balkan, Gokce Osman; Gezici, SinanAdding noise to nonlinear systems can enhance their performance. Additive noise benefits are observed also in parameter estimation problems based on quantized observations. In this study, the purpose is to find the optimal probability density function of additive noise, which is applied to observations before quantization, in those problems. First, optimal probability density function of noise is formulated in terms of an average Fisher information maximization problem. Then, it is proven that optimal additive "noise" can be represented by a constant signal level. This result, which means that randomization of additive signal levels is not needed for average Fisher information maximization, is supported with two numerical examples. ©2010 IEEE.Item Open Access Background subtraction with a moving camera(IEEE, 2013) Topçu O.; Kalem, Aslıhan; Esen, E.Moving object segmentation with a nonstationary camera is a difficult problem due to the motion of both camera and the object. A moving object segmentation method is proposed in this work to be used in pan-tilt-zoom (PTZ) cameras. The method is based on composing scene mosaic and applying Gaussian mixture background subtraction algorithm after constructing a background model using the mosaic. Background subtraction is performed by mapping the frames captured during camera's course of motion to the background mosaic. The proposed mosaic building method requires less number of picture correspondences when compared to known methods. The success of the proposed segmentation method is demonstrated by the conducted experiments. © 2013 IEEE.