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ERCAN, Ali Özer

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Ali Özer

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    ArticlePublicationOpen Access
    Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time
    (IEEE, 2018) Khodabakhsh, Athar; Arı, İsmail; Bakır, M.; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ARI, Ismail; ERCAN, Ali Özer; Khodabakhsh, Athar
    Large-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.
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    Object tracking in the presence of occlusions using multiple cameras: a sensor network approach
    ( Association for Computing Machinery, 2013) Ercan, Ali Özer; El Gamal, A.; Guibas, L. J.; Electrical & Electronics Engineering; ERCAN, Ali Özer
    This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset selection. In the task-driven approach, each camera first performs simple local processing to detect the horizontal position of the object in the image. This information is then sent to a cluster head to track the object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is introduced, where occlusions are captured through occlusion indicator functions. An auxiliary particle filter that incorporates the occluder information is used to track the object. The camera subset selection algorithm uses the minimum mean square error of the best linear estimate of the object position as a metric, and tracking is performed using only the selected subset of cameras.Using simulations and preselected subsets of cameras, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the trade-off between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and (iii) the importance of having occluder priors to the tracker performance as the number of occluders increases. We find that computing moving occluder priors may not be worthwhile, unless it can be obtained cheaply and to high accuracy. We also investigate the effect of dynamically selecting the subset of camera nodes used in tracking on the tracking performance. We show through simulations that a greedy selection algorithm performs close to the brute-force method and outperforms other heuristics, and the performance achieved by greedily selecting a small fraction of the cameras is close to that of using all the cameras.
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    Bispectrum estimation using a MISO autoregressive model
    (Springer International Publishing, 2016) Erdem, Tanju; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ERDEM, Arif Tanju; ERCAN, Ali Özer
    Bispectra are third-order statistics that have been used extensively in analyzing nonlinear and non-Gaussian data. Bispectrum of a process can be computed as the Fourier transform of its bicumulant sequence. It is in general hard to obtain reliable bicumulant samples at high lags since they suffer from large estimation variance. This paper proposes a novel approach for estimating bispectrum from a small set of given low lag bicumulant samples. The proposed approach employs an underlying MISO system composed of stable and causal autoregressive components. We provide an algorithm to compute the parameters of such a system from the given bicumulant samples. Experimental results show that our approach is capable of representing non-polynomial spectra with a stable underlying system model, which results in better bispectrum estimation than the leading algorithm in the literature.
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    Fault masking as a service
    (Wiley, 2014-07) Gülcü, Koray; Sözer, Hasan; Aktemur, Tankut Barış; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; SÖZER, Hasan; AKTEMUR, Tankut Bariş; ERCAN, Ali Özer; Gülcü, Koray
    In SOA, composite services depend on a set of partner services to perform their tasks. These partner services may become unavailable because of system and/or network faults, leading to an increased error rate for the composite service. In this paper, we propose an approach to prevent the occurrence of errors that result from the unavailability of partner services. We introduce an external Web service, dubbed Fault Avoidance Service (FAS), to which composite services can register at will. After registration, FAS periodically checks the partner links, detects unavailable partner services, and updates the composite service with available alternatives. Thus, in case of a partner service error, the composite service will have been updated before attempting an ill-destined request. We provide mathematical analysis regarding the error rate and the false positive rate with respect to the monitoring frequency of FAS for two models. We obtained empirical results by conducting several tests on the Amazon Elastic Compute Cloud to evaluate our mathematical analyses. We also introduce an industrial case study for improving the quality of a service-oriented system from the broadcasting and content delivery domain.
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    Girişim bulunan IEEE 802.11ac aǧlarda TCP sıkışıklık denetim algoritmalarinin başarımları
    (IEEE, 2016) Çakmak, Kıvanç; Sunay, Mehmet Oğuz; Ercan, Ali Özer; Karaca, M.; Electrical & Electronics Engineering; Computer Science; SUNAY, Mehmet Oğuz; ERCAN, Ali Özer; Çakmak, Kıvanç
    In order to meet the incremental requirements from clients, fifth generation Wireless Local Area Network(WLAN) protocol IEEE 802.11ac provides a lot more throughput than his predecessors. However, in all distributed channel access 802.11 protocols, probability of packet loss increases with respect to number of available stations; consecutive packet losses in link layer activates congestion control mechanism of TCP which decreases throughput. In this work, we investigated the performance of congestion control algorithms of TCP protocol when interference available wireless medium is used in between the IEEE 802.11ac capable devices. As a benchmark, we repeated the experiments with UDP protocol which does not have reliable connection and congestion control mechanism. According to our experiments, we observed that UDP provides more throughput than all other TCP congestion control algorithms. When we compare TCP congestion control algorithms by themselves, we observed that the algorithms which does not decrease congestion window size too much during interference and have more aggressive increment of congestion window -such as Cubic and Illinois, Scalable have better performance in terms of throughput and standard deviation.
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    Occlusion-aware 3D multiple object tracker with two cameras for visual surveillance
    (IEEE, 2014) Topçu, O.; Alatan, A. A.; Ercan, Ali Özer; Electrical & Electronics Engineering; ERCAN, Ali Özer
    An occlusion-aware multiple deformable object tracker for visual surveillance from two cameras is presented. Each object is tracked by a separate particle filter tracker, which is initiated upon detection of a new person and terminated when s/he leaves the scene. Objects are considered as 3D points at their centre of masses as if their mass density is uniform. Point objects and corresponding silhouette centroids in two views together with the epipolar geometry they satisfy resulted in a practical tracking methodology. An occlusion filter is described, that provides the tracker filters conditional occlusion probabilities of the objects, given their estimated positions. Advances over the previous work; in the computation of conditional occlusion probabilities, in incorporation of these probabilities in the particle filter, and in maintaining tracking of separating objects after long periods of moving close-by, are presented on PETS 2006, PETS 2009 and EPFL datasets.
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    Color filters: When “optimal” is not optimal
    (IEEE, 2016) Trussell, H. J.; Ercan, Ali Özer; Kingsbury, N. G.; Electrical & Electronics Engineering; ERCAN, Ali Özer
    It is well known that many more than three or four spectral measurements are required for accurate measurement of color. Previous work has shown seven to ten measurements can yield accurate results on average, but with significant numbers of errors above the threshold of obvious visual detection. Furthermore, the filters used for these measurements are very difficult to fabricate. We show that such filters are not needed and, in fact, have much poorer performance, in perceptual quality measured in ΔEab, than simple narrow-band filters. This is especially true in the presence of Poisson noise at a level common in current digital cameras. In realistic Poisson noise, our filter sets of up to 12 filters allow average ΔEab values around 0.5, with maximum errors below 3.
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    Rao-Blackwell parçacık süzgeci ile 3 boyutlu insan takibi
    (IEEE, 2014) Topçu, O.; Orguner, U.; Alatan, A. A.; Ercan, Ali Özer; Electrical & Electronics Engineering; ERCAN, Ali Özer
    Visual tracking has an important place among computer vision applications. Visual tracking with particle filters is a well-known methodology. The performance of particle filters is dependent on efficient sampling of the state space, which in turn, is dependent on number of particles. In this paper, Rao-Blackwell technique is applied to particle filters to improve sampling efficiency. Both algorithms are applied to people tracking problem. Under the same circumstances, the resulting algorithm is demonstrated to perform better than the original algorithm via experiments on the PETS2009 benchmark dataset.
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    Connectivity brokerage: from coexistence to collaboration
    (IEEE, 2010) Parsa, A.; Ercan, Ali Özer; Malagon, P.; Burghardt, F.; Rabaey, J. M.; Wolisz, A.; Electrical & Electronics Engineering; ERCAN, Ali Özer
    The explosive growth in the density of wirelessly connected devices and their traffic load is creating interference and gradually leading to a severe spectrum shortage. Approaches to address this challenge include dynamic spectrum allocation (cognitive radio) and pro-active interference mitigation strategies requiring coordination between heterogeneous networking technologies. This paper describes a modular and scalable methodology and architecture, called Connectivity Brokerage, that enables proactive co-existence and collaboration between diverse technologies, making joint optimization of the scarce spectrum resources possible.
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    A multi-sensor integrated head-mounted display setup for augmented reality applications
    (IEEE, 2015) Kermen, Ahmet; Aydın, T.; Ercan, Ali Özer; Erdem, Tanju; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; ERDEM, Arif Tanju; Kermen, Ahmet
    We present an HMD based AR system comprising visual and inertial sensors. The visual sensor is a camera pair and the inertial sensors consist of an accelerometer and a gyroscope. We discuss the temporal and spatial calibration issues that relate to such a system. We introduce simple yet effective methods for estimating the time lag between the camera and the inertial sensors and for estimating the relative pose between the camera and the inertial sensors. These methods do not require a complicated setup for data collection and involve simple equations to solve. Sample results are presented to demonstrate the visual performance of the system.