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ERCAN, Ali Özer

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Ali Özer

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ERCAN
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Now showing 1 - 10 of 30
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    Recovery of temporal synchronization error through online 3D tracking with two cameras
    (ACM, 2014) Topçu, O.; Ercan, Ali Özer; Alatan, A. A.; Electrical & Electronics Engineering; ERCAN, Ali Özer
    Multiple object tracking within a network of cameras with overlapping fields of views has gained interest. The acquisition of images in an asynchronous manner hinders the practical implementation of such systems. Most of the previous work reported tests over short intervals, leaving the performance degradation due to asynchronous image acquisition unknown. In this work, we propose an online method to recover the synchronization error while tracking objects. The recovered error is fed back to trackers so as to restore their performance. The time synchronization error is measured by the mismatch in the epipolar constraint between the two cameras. We show that successful recovery of the synchronization error is possible when its product with the object motion speeds are within some limits.
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    Dağıtık bir openflow kontrol birimi mimarisi
    (IEEE, 2012) Yazıcı, Volkan; Sunay, Mehmet Oğuz; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; SUNAY, Mehmet Oğuz; YAZICI, Volkan
    Considering the modern internet traffic rates, the network architecture is of particular importance as the running services itself. On the other hand, due to the increasing complexity and black-box structure of the available networking hardware (switches, routers, etc.), the necessary network innovation imposed by the running services becomes infeasible in practice. The software-defined networking notion introduced to solve this problem and one of its emerging and powerful implementations, the OpenFlow protocol, advocate the idea of providing the control and data paths in separate planes. A network operating system running on this control plane, is anticipated to provide necessary measures for scalability and reliability in order to stand against the gigantic traffic pumped by the network. In this paper, we propose a distributed OpenFlow network operating system built with necessary scalability and reliability qualifications without requiring any changes to the existing OpenFlow protocol and networking equipment.
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    IEEE 802.11ac aǧların çok kullanıcılı ÇGÇÇ özelliǧi üzerine incelemeler
    (IEEE, 2016) Çakmak, Kıvanç; Sunay, Mehmet Oğuz; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; SUNAY, Mehmet Oğuz; ERCAN, Ali Özer; Çakmak, Kıvanç
    This paper studies the multi-user MIMO feature of IEEE 802.11ac networks that serve, along with IEEE 802.11ac nodes, also legacy IEEE 802.11n nodes. For this purpose, we develop a simulator that models the IEEE 802.11ac and IEEE 802.11n networks. Then, using a setup, we first study the tradeoff between the amount of overhead used in channel sounding and the corresponding rate of information, concluding that in this setting, channel sounding with all clients lead to better throughput. Secondly, we observe the negative impact of IEEE 802.11n nodes on the IEEE 802.11ac traffic due to the deafness problem, and analyze the performance of the usage of RTS/CTS handshake and cts2self mechanisms to mitigate this effect. We show that the regular RTS/CTS handshake mitigates the deafness problem to a certain degree. However, the cts2self mechanism achieves a better performance since no airtime is wasted to collisions with the RTS frames.
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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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    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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    Energy sensing strategy optimization for opportunistic spectrum access
    (IEEE, 2012-06) Ercan, Ali Özer; Sunay, Mehmet Oğuz; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; SUNAY, Mehmet Oğuz
    This paper introduces a correlator-based energy sensing strategy for opportunistic spectrum access in a slow, flat-fading channel. The correlator provides weighted energy accumulation in time. We assume that the noise variance is known and the primary user (PU) traffic follows a two state Markov chain with known idle and busy rates. Using Chebyshev bounds on missed detection and false alarm probabilities, we find that the optimal weighting function is an increasing function of time and its shape is dependent on the PU traffic characteristics and SNR. We show that the traditional flat-integration based energy collection method is suboptimal both in the error probability and channel utilization sense.
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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.
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    Analysis of energy harvesting for green cognitive radio networks
    (IEEE, 2014) Ercan, Ali Özer; Sunay, Mehmet Oğuz; Pollin, S.; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; SUNAY, Mehmet Oğuz
    This paper develops a Markov Chain analysis framework for an RF energy harvesting cognitive radio network where the secondary system users are furnished with finite-sized batteries. We consider a network where the only source of energy for the secondary system is the RF signals of the primary system resulting in an inter-play between energy harvesting and transmission for the secondary network. We show that an RF energy harvesting cognitive radio using todays technology is suitable for a delay-tolerant sensor network where the nodes need to transmit sensor output values very sporadically. We observe that the interplay between primary user traffic arrival and departure rates as well as probabilities of missed detection and false alarm for the secondary user detector and available battery size is of importance and that a percentile channel occupation of the primary network on its own is not a sufficient metric.
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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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    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.