Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 12Lifetime Analysis of Energy Harvesting Underwater Wireless Sensor Nodes(Institute of Electrical and Electronics Engineers Inc., 2017-05) Erdem, Huseyin Emre; Güngör, Vehbi ÇağrıThe application of Wireless Sensor Networks (WSNs) in underwater environments poses various challenges. One of the most important problems is the limited lifetime of underwater sensor nodes. Considering how challenging and costly it is to change the batteries of sensor nodes in underwater environments, energy harvesting methods are rendered as a promising solution. In this study, the contributions of energy harvesting via turbine and hydrophone harvesters as well as schedule and trigger driven energy management methods on node lifetime have been analyzed. Performance evaluations have been conducted considering real-life conditions, e.g. flow rates, of Istanbul Bosphorus Strait. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3LTE Ağları için Servis Kalitesi Farkında Aşağı Yönlü Çizelgeleme Algoritması: Kenar Kullanıcıları Üzerine İnceleme(Institute of Electrical and Electronics Engineers Inc., 2017-05) Güngör, Vehbi Çağrı; Uyan, Osman Gokhan4G/LTE (Long Term Evolution) is the state of the art wireless mobile broadband technology. It makes use of the OFDM technology to offer high speed and provides the system resources both in time and frequency domain. A scheduling algorithm running on the base station holds the allocation of these resources. In this paper, the performance of existing downlink scheduling algorithms has been investigated in two ways. First, the performance of the algorithms has been investigated in terms of throughput and fairness metrics. Second, a new quality of service-aware (QoS-aware) fairness criterion, which accepts that the system is fair if it can provide the users with the network traffic speeds that they demand, has been proposed and evaluated. In addition, a novel QoS-aware downlink-scheduling algorithm, which increases the QoS-aware fairness and overall throughput of the edge users, has been proposed. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2Akıllı Şebeke Uygulamaları için Pille Çalışan Duyarga Düğümlerinin Yaşam Süresi Analizi(Institute of Electrical and Electronics Engineers Inc., 2016-05) Eris, Cigdem; Güngör, Vehbi Çağrı; Boluk, Pinar SarisarayWireless Sensor Networks (WSNs) enable smart grids where sensor nodes monitor and control the important parameters of power grid components. However, energy-aware communication protocols should be developed to extend network lifetime of WSNs in smart grid environments. In this study, the lifetime of wireless sensor nodes has been analyzed for various smart grid environments, such as 500 kV substation, main power control room, and underground network transformer vaults. In addition, the effects of different operation modes of sensor nodes on node lifetime have been reviewed. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Koroner Arter Hastalığı Tanısı İçin Alan Bilgisi İçeren Topluluk Öznitelik Seçim Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Kolukisa, Burak; Güngör, Vehbi Çağrı; Bakir-Güngör, Burcu; Gungor, Burcu BakirCoronary Artery Disease (CAD) is the condition where, the heart is not fed enough as a result of the accumulation of fatty matter called atheroma in the walls of the arteries. In 2016, CAD accounts for 31% (17.9 million) of the world's total deaths and its diagnosis is difficult. It is estimated that approximately 23.6 million people will die from this disease in 2030. With the development of machine learning and data mining techniques, it might be possible to diagnose CAD inexpensively and easily via examining some physical and biochemical values. In this study, for the CAD classification problem, a novel ensemble feature selection methodology that incorporates domain knowledge is proposed. Via applying the proposed methodology on the UCI Cleveland CAD dataset and using different classification algorithms, performance metrics are compared. It is shown that in our experiments, when Multilayer Perceptron classifier is used with 9 selected features, our proposed solution reached 85.47% accuracy, 82.96% accuracy and 0.839 F-Measure. As a future work, we aim to generate a machine learning model that can quickly diagnose CAD on real-time data in hospitals. © 2021 Elsevier B.V., All rights reserved.