WoS İndeksli Yayınlar Koleksiyonu

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

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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: 31
    Citation - Scopus: 35
    Transmitter Localization in Vessel-Like Diffusive Channels Using Ring-Shaped Molecular Receivers
    (IEEE-Inst Electrical Electronics Engineers Inc, 2018-12) Turan, Meric; Akdeniz, Bayram Cevdet; Kuran, Mehmet Comma Sukru; Yilmaz, H. Birkan; Demirkol, Ilker; Pusane, Ali E.; Tugcu, Tuna; Birkan Yilmaz, H.
    Molecular communication via diffusion in vessellike environment targets critical applications such as the detection of abnormal and unhealthy cells. In this letter, we derive the analytical formulation of the channel model for diffusion dominated movement, considering ring-shaped (i. e., patch) observing receivers, and Poiseuille flow with the aim of localization of the transmitter cell. Then, we derive formulations using this channel model for two different application scenarios. We assume that the emission start time is known in the first scenario and unknown in the second one. We successfully localize the transmitter cell using a single receiver for the first scenario, whereas two receivers are used to localize the transmitter cell in the second scenario. At last, the devised analytical framework is validated with simulations.
  • 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.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 10
    Traffic-Adaptive Inter Wavelength Load Balancing for TWDM PON
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020-02) Memon, Kamran Ali; Zhang, Qi; Butt, Rizwan Aslam; Mohammadani, Khalid Hussain; Faheem, Muhammad; ul Ain, Noor; Xin, Xiangjun
    This study presents a dynamic inter wavelength migration scheme for the optical network units (ONUs) employing linear regression machine learning method to equalize the traffic volume on all the wavelengths in time and wavelength division multiplexed passive optical network (TWDM PON). The proposed traffic-adaptive wavelength and bandwidth assignment (TA-WBA) scheme not only decreases upstream traffic delays but also offers 2.3% and 30% less delay on the wavelengths balancing the excessive load and 7% less upstream bandwidth waste, when evaluated against other load-balancing scheme.
  • 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: 16
    Citation - Scopus: 23
    Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Al-Abri, Tariq; Onen, Ahmet; Al-Abri, Rashid; Hossen, Abdulnasir; Al-Hinai, Amer; Jung, Jaesung; Ustun, Taha Selim
    With the rapid transformation of the energy sector towards modern power systems represented by smart grids (SGs), microgrids (MG), and distributed generation, blockchain (BC) technology has shown the capability for solving security, privacy, and reliability challenges that hinder progress. Currently, the energy structure is forming a decentralized system that prioritizes customer satisfaction. BC technology undertakes power network stockholders in a secure energy market, transparent transactions, and fair competition and offers promising energy solutions. This paper is a comprehensive review of energy applications using BC integration. Firstly, we introduce the drivers of BC leverage that make it a potentially important component of the power network. Following that, we provide background information on BC and its application in areas other than the energy sector. Subsequently, we discuss studies and sort potential energy applications from various recent papers and surveys that have already adopted BC technology in the energy sector. Then, we summarize the pricing infrastructure for applying BC in the energy sector and identify the requirements to build it. Finally, energy security and privacy challenges based on BC are highlighted, along with potential drawbacks and concerns related to the pricing infrastructure.
  • Article
    Citation - WoS: 115
    Citation - Scopus: 173
    Peer-to-Peer Energy Trading in Virtual Power Plant Based on Blockchain Smart Contracts
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Seven, Serkan; Yao, Gang; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.
    A novel Peer-to-peer (P2P) energy trading scheme for a Virtual Power Plant (VPP) is proposed by using Smart Contracts on Ethereum Blockchain Platform. The P2P energy trading is the recent trend the power society is keen to adopt carrying out several trial projects as it eases to generate and share the renewable energy sources in a distributed manner inside local community. Blockchain and smart contracts are the up-and-coming phenomena in the scene of the information technology used to be considered as the cutting-edge research topics in power systems. Earlier works on P2P energy trading including and excluding blockchain technology were focused mainly on the optimization algorithm, Information and Communication Technology, and Internet of Things. Therefore, the financial aspects of P2P trading in a VPP framework is focused and in that regard a P2P energy trading mechanism and bidding platform are developed. The proposed scheme is based on public blockchain network and auction is operated by smart contract addressing both cost and security concerns. The smart contract implementation and execution in a VPP framework including bidding, withdrawal, and control modules developments are the salient feature of this work. The proposed architecture is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Ropsten Test Network.
  • Article
    Citation - WoS: 23
    Citation - Scopus: 26
    Optimal Location and Sizing of Electric Bus Battery Swapping Station in Microgrid Systems by Considering Revenue Maximization
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Kocer, Mustafa Cagatay; Onen, Ahmet; Jung, Jaesung; Gultekin, Hakan; Albayrak, Sahin
    The radical increase in the popularity of electric vehicles (EVs) has in turn increased the number of associated problems. Long waiting times at charging stations are a major barrier to the widespread adoption of EVs. Therefore, battery swapping stations (BSSs) are an efficient solution that considers short waiting times and healthy recharging cycles for battery systems. Moreover, swapping stations have emerged as a great opportunity not only for EVs, but also for power systems, with regulation services that can be provided to the grid particularly for small networks, such as microgrid (MG) systems. In this study, the optimum location and size that maximize the revenue of a swap station in an MG system are investigated. To the best of our knowledge, this study is first to solve the placing and sizing problem in the MG from the perspective of a BSS. The results indicate that bus 23 is the BSS's optimal location and is crucial for maximizing revenue and addressing issues like the provision of ancillary services in microgrid system. Finally, the swap demand profile of the station serving electric bus public transportation system was obtained using an analytical model based on public transportation data collected in Berlin, Germany.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Karakullukcu, Nedime; Altindis, Fatih; Yilmaz, Bulent
    This study addresses the challenge faced by individuals with upper-limb prostheses in regulating grip force and adapting movements to different object weights. Despite limited exploration, this research pioneers the use of EEG to estimate object weight perception in the context of upper-limb prostheses. Investigating neural correlates in this population provides valuable insights and aids the development of neurofeedback-based strategies for weight perception. Our objective is to identify EEG features predicting the weight perception of held objects. Employing Fourier-based synchrosqueezing transform (FSST) and regularized Common Spatial Patterns (CSP) features, we classify motor imagery waves representing three weight categories (light, medium, heavy). Subjects perform actual motor tasks before imagery sessions, and our approach integrates EEG features of both movements to train subject-specific machine learning models. Results reveal that FSST- singular value decomposition (SVD) features for medium and heavy objects are most distinctive. Achieving up to 90% accuracy, spatial features demonstrate effective classification of motor imagery for different weights. Unlike weight prediction studies, our focus is on visual perception and imagination of object weights, enhancing prosthetic hand system preconditioning. Binary classification surpasses 70% accuracy in predicting object weights, uniquely utilizing actual movement data for CSP algorithm regularization coefficient estimation.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 22
    Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Altinisik, Enes; Tasdemir, Kasim; Sencar, Husrev Taha
    The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos. Tests on a public dataset also verify that the proposed method improves the attribution performance by increasing the accuracy and allowing the use of smaller length videos to perform attribution.