Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Start date: 2020Language: search.filters.language.eng

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Now showing 1 - 6 of 6
  • Doctoral Thesis
    FUNCTIONALIZED LOW LUMO [1]BENZOTHIENO[3,2-B][1]BENZOTHIOPHENE (BTBT)-BASED MOLECULAR SEMICONDUCTORS FOR ORGANIC FIELD EFFECT TRANSISTORS
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Özdemir, Resul; Usta, Hakan
    DAcTTs have provided an excellent π-framework for the development of high mobility p-type molecular semiconductors in the past decade. However, n-type DAcTTs are rare and their electron transporting characteristics remain largely unexplored. In the second chapter of this thesis, the first example of an n-type BTBT-based semiconductor, D(PhFCO)-BTBT, has been realized via a two-step transition metal-free process without using chromatographic purification. The corresponding TC/BG-OFET devices demonstrated μe (max) = ~0.6 cm2/Vs and Ion/Ioff ratio = 107-108. The large band-gap BTBT π-core is a promising candidate for high mobility n-type organic semiconductors and, combination of very large intrinsic charge transport capabilities and optical transparency, may open a new perspective for next-generation (opto)electronics. In the third chapter of this thesis, a series of BTBT-based small molecules, D(C7CO)-BTBT, C7CO-BTBT-CC(CN)2C7, and D(C7CC(CN)2)-BTBT, have been developed in “S-F-BTBT-F-S (F/S: functional group/substituent)” molecular architecture. Combining with D(PhFCO)-BTBT, a molecular library with systematically varied chemical structures has been studied herein for the first time for low LUMO DAcTTs, and key relationships have been elucidated. The molecular engineering perspectives presented in this thesis may give unique insights into the design of novel electron transporting thienoacenes for unconventional optoelectronics.
  • Doctoral Thesis
    A reliable and secure communication design for underwater sensor networks concerning energy efficiency
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) UYAN, Osman Gökhan; Güngör, Vehbi Çağrı
    Underwater Acoustic Sensor Networks (UASNs) recently attract scientists because of its wide range of applications and emerging technology. A design challenge in UASN's is the limited network lifetime and poor reliability caused by limited battery supply of sensors and harsh channel conditions in underwater environment. Moreover, sensors might transmit sensitive data that must be disguised against eavesdropping attacks. To maintain a reliability level, packet-duplication and multi-path routing method are suggested, which renders eavesdropping attacks easier. For data security, cryptographic encryption is the most acclaimed method. However, encryption needs extra computations, which consume extra energy and cause a decrease in the network lifetime. As a countermeasure along with encryption against silent listening, fragmenting data and transmitting in pieces over different paths has been proposed. To address these challenges, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption, and data fragmentation on network lifetime. However, the solution time of the proposed optimization model is quite high, and sometimes it cannot come up with feasible solutions. To this end, in this study, different regression and neural network methods have been proposed to predict the energy consumptions of underwater nodes as supplementary methods to optimization models. Performance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.
  • Book Part
    Citation - Scopus: 3
    People’s Republic of China
    (SPRINGER, 2022) Alsancak, İbrahim; Aydın, Güldenur; Islam, Md. Nazmul
    The People’s Republic of China (PRC) was established on October 1, 1949, and it is governed by a single-party system, The Communist Party of China (CCP). The president is also the Chinese Communist Party’s leader. The People’s Republic of China’s (PRC) new Constitution was approved in 1982. The Presidency of the People’s Republic of China, the State Council, the Central Military Commission, the National People’s Congress, the Political Consultative Conference of the People of China, the Supreme Court, and the Attorney General are the central organs of the state. Although PRC does not define itself as a federal state, it has five autonomous regions, territories, counties, rural towns, and it has created some constitutional governance understanding for these local governments. NGOs are recently developing in China. China is also leading country about artificial intelligence and high techs.
  • Book Part
    Citation - Scopus: 1
    Migration
    (Emerald Group Publishing Ltd., 2022) Sirkeci, Ibrahim; Teke-Lloyd, Fatma Armagan; Lloyd, Armagan Teke
    Migration
  • Article
    Citation - WoS: 2
    Citation - Scopus: 9
    Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm
    (SPRINGER, 2023-10-24) Alshakree, Firas; Akbas, Ayhan; Rahebi, Javad
    Palm print identification is a biometric technique that relies on the distinctive characteristics of a person’s palm print to distinguish and authenticate their identity. The unique pattern of ridges, lines, and other features present on the palm allows for the identification of an individual. The ridges and lines on the palm are formed during embryonic development and remain relatively unchanged throughout a person’s lifetime, making palm prints an ideal candidate for biometric identification. Using deep learning networks, such as GoogLeNet, SqueezeNet, and AlexNet combined with gray wolf optimization, we achieved to extract and analyze the unique features of a person’s palm print to create a digital representation that can be used for identification purposes with a high degree of accuracy. To this end, two well-known datasets, the Hong Kong Polytechnic University dataset and the Tongji Contactless dataset, were used for testing and evaluation. The recognition rate of the proposed method was compared with other existing methods such as principal component analysis, including local binary pattern and Laplacian of Gaussian-Gabor transform. The results demonstrate that the proposed method outperforms other methods with a recognition rate of 96.72%. These findings show that the combination of deep learning and gray wolf optimization can effectively improve the accuracy of human identification using palm print images.
  • Other
    Structure Health Monitoring Using Wireless Sensor Networks on Structural Elements (vol 82, pg 68, 2019)
    (ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020) Ayyildiz, Cem; Erdem, H. Emre; Dirikgil, Tamer; Dugenci, Oguz; Kocak, Taskin; Altun, Fatih; Gungor, V. Cagri
    This paper presents a system that monitors the health of structural elements in Reinforced Concrete (RC), concrete elements and/or masonry buildings and warn the authorities in case of physical damage formation. Such rapid and reliable detection of impairments enables the development of better risk management strategies to prevent casualties in case of earthquake and floods. Piezoelectric (PZT) sensors with lead zirconate titanate material are the preferred sensor type for fracture detection. The developed sensor mote hardware triggers the PZT sensors and collects the responses they gather from the structural elements. It also sends the collected data to a data center for further processing and analysis in an energy-efficient manner utilizing low-power wireless communication technologies. The access and the analysis of the collected data can be remotely performed via a web interface. Performance results show that the fractures serious enough to cause structural problems can be successfully detected with the developed system.