WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Publisher: search.filters.publisher.IEEE-Inst Electrical Electronics Engineers IncWoS Q: search.filters.wosQuartile.Q1

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Now showing 1 - 10 of 21
  • Article
    Citation - WoS: 19
    Citation - Scopus: 21
    Wireless Sensing in Complex Electromagnetic Media: Construction Materials and Structural Monitoring
    (IEEE-Inst Electrical Electronics Engineers Inc, 2015-10) Ozbey, Burak; Demir, Hilmi Volkan; Kurc, Ozgur; Erturk, Vakur B.; Altintas, Ayhan
    In this paper, wireless sensing in the presence of complex electromagnetic media created by combinations of reinforcing bars and concrete is investigated. The wireless displacement sensing system, primarily designed for use in structural health monitoring (SHM), is composed of a comb-like nested split-ring resonator (NSRR) probe and a transceiver antenna. Although each complex medium scenario is predicted to have a detrimental effect on sensing in principle, it is demonstrated that the proposed sensor geometry is able to operate fairly well in all scenarios except one. In these scenarios that mimic real-life SHM, it is shown that this sensor exhibits a high displacement resolution of 1 mu m, a good sensitivity of 7 MHz/mm in average, and a high dynamic range extending over 20 mm. For the most disruptive scenario of placing concrete immediately behind NSRR, a solution based on employing a separator behind the probe is proposed to overcome the handicaps introduced by the medium. In order to obtain a one-to-one mapping from the measured frequency shift to the displacement, a numerical fit is proposed and used. The effects of several complex medium scenarios on this fit are discussed. These results indicate that the proposed sensing scheme works well in real-life SHM applications.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 5
    Validation of Higher-Order Approximations and Boundary Conditions for Lossy Conducting Bodies
    (IEEE-Inst Electrical Electronics Engineers Inc, 2014-09) Sukharevsky, Ilya O.; Altintas, Ayhan
    The problem of high-frequency diffraction by a smooth lossy body with high conductivity is considered. In addition to the geometrical optics approximation, additional asymptotic terms are derived to take into account the curvature of the boundary and material properties. Since these higher-order terms are derived by taking into account exact boundary conditions, it is easy to learn about the limitations of impedance conditions and to determine more accurate approximate conditions. The obtained higher-order boundary conditions and their limitations are numerically validated by solving Muller's second-kind integral equations.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023-10) Altindis, Fatih; Banerjee, Antara; Phlypo, Ronald; Yilmaz, Bulent; Congedo, Marco
    This article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12 +/- 1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.
  • Editorial
    Citation - WoS: 19
    Citation - Scopus: 19
    Special Section on Industrial Wireless Sensor Networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2014-02) Hancke, Gerhard P., Jr.; Gungor, V. Cagri; Hancke, Gerhard P., Sr.
  • Article
    Citation - WoS: 74
    Citation - Scopus: 89
    QERP: Quality-Of (QoS) Aware Evolutionary Routing Protocol for Underwater Wireless Sensor Networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2018-09) Faheem, Muhammad; Tuna, Gurkan; Gungor, Vehbi Cagri
    Quality-of-service (QoS) aware reliable data delivery is a challenging issue in underwater wireless sensor networks (UWSNs). This is clue to impairments of the acoustic transmission caused by excessive noise, extremely long propagation delays, high bit error rate, low bandwidth capacity, multipath effects, and interference. To address these challenges, meet the commonly used UWSN performance indicators, and overcome the inefficiencies of the existing clustering-based routing schemes, a novel QoS aware evolutionary cluster based routing protocol (QERP) has been proposed for UWSN-based applications. The proposed protocol improves packet delivery ratio, and reduces average end-to-end delay and overall network energy consumption. Our comparative performance evaluations demonstrate that QERP is successful in achieving low network delay, high packet delivery ratio, and low energy consumption.
  • Article
    Citation - WoS: 145
    Citation - Scopus: 177
    Packet Size Optimization in Wireless Sensor Networks for Smart Grid Applications
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017-03) Kurt, Sinan; Yildiz, Huseyin Ugur; Yigit, Melike; Tavli, Bulent; Gungor, Vehbi Cagri
    Wireless sensor networks (WSNs) are envi-sioned to be an important enabling technology for smart grid (SG) due to the low cost, ease of deployment, and versatility of WSNs. Limited battery energy is the tightest resource constraint on WSNs. Transmission power control and data packet size optimization are powerful mechanisms for prolonging network lifetime and improving energy effi-ciency. Increasing transmission power will reduce the bit error rate (BER) on some links, however, utilizing the high-est power level will lead to inefficient use of battery energy because on links with low path loss achieving low BER is possible without the need to use the highest power level. Utilizing a large packet size is beneficial for increasing the payload-to-overhead ratio, yet, lower packet sizes have the advantage of lower packet error rate. Furthermore, trans-mission power level assignment and packet size selection are interrelated. Therefore, joint optimization of transmission power level and packet size is of utmost importance in WSN lifetime maximization. In this study, we construct a de-tailed link layer model by employing the characteristics of Tmote Sky WSN nodes and channel characteristics based on actual measurements of SG path loss for various envi-ronments. A novel mixed integer programming framework is created by using the aforementioned link layer model for WSN lifetime maximization by joint optimization of trans-mission power level and data packet size. We analyzed the WSN performance by systematic exploration of the parameter space for various SG environments through the numer-ical solutions of the optimization model.
  • Article
    Citation - WoS: 80
    Citation - Scopus: 95
    Packet Size Optimization for Lifetime Maximization in Underwater Acoustic Sensor Networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2019-02) Yildiz, Huseyin Ugur; Gungor, Vehbi Cagri; Tavli, Bulent
    Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors (i.e., packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.
  • Article
    Citation - WoS: 22
    Citation - Scopus: 24
    Online Condition Monitoring of Battery Systems With a Nonlinear Estimator
    (IEEE-Inst Electrical Electronics Engineers Inc, 2014-03) Ablay, Gunyaz
    The performance of batteries as uninterruptable power sources in any industry cannot be taken for granted. The failures in battery systems of safety-related electric systems can lead to performance deterioration, costly replacement, and, more importantly, serious hazards. The possible failures in battery systems are currently determined through periodic maintenance activities. However, it is desirable to be able to detect the underlying degradation and to predict the level of unsatisfactory performance by an online real-time monitoring system to prevent unexpected failures through early fault diagnosis. Such an online fault diagnosis method can also contribute to better maintenance and optimal battery replacement programs. A robust nonlinear estimator-based online condition monitoring method is proposed to determine the state of health of the battery systems online in industry. Real-world experimental data of a modern battery system are used to assess the efficiency of the proposed approach in the existence of parameter uncertainties.
  • Article
    Citation - WoS: 24
    Citation - Scopus: 31
    On the Lifetime of Compressive Sensing Based Energy Harvesting in Underwater Sensor Networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2019-06-15) Erdem, Huseyin Emre; Yildiz, Huseyin Ugur; Gungor, Vehbi Cagri
    Recently, there has been a growing interest in academia and industry on the development of underwater acoustic sensor networks (UASNs) for scientific, commercial, and military purposes. Severe underwater channel conditions and limited battery energy of underwater nodes pose great challenges to prolong UASNs lifetime. Compressive sensing (CS), energy harvesting (EH), and transmission power control (TPC) are three promising solutions to improve UASNs lifetime. This paper aims to quantitatively investigate the joint impact of CS, EH, and TPC methods on the lifetime of UASNs. A novel Mixed Integer Programming framework is developed to maximize the network lifetime by joint consideration of CS, EH, and TPC. The performance results show that the impact of CS on the network lifetime is higher than that of EH when both methods are combined with TPC. Moreover, when all three methods are combined, the network lifetime can be extended up to three times as compared to the case when all three methods are not utilized.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 12
    Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024-07) Kose, Abdulkadir; Lee, Haeyoung; Foh, Chuan Heng; Shojafar, Mohammad
    Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.