Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Book Part Citation - Scopus: 12Realizing the Wireless Technology in Internet of Things (IoT)(Springer Singapore, 2018) Κogias, DImitrios G.; Michailidis, Emmanouel T.; Tuna, Gürkan; Güngör, Vehbi Çağrı; Kogias, Dimitrios G.The evolution of the Internet of Things (IoT) has been highly based on the advances on wireless communications and sensing capabilities of smart devices, along with a, still increasing, number of applications that are being developed which manage to cover various small and more important aspects of every people's life. This chapter aims at presenting the wireless technologies and protocols that are used for the IoT communications, along with the main architectures and middleware that have been proposed to serve and enhance the IoT capabilities and increase its efficiency. Finally, since the generated data that are spread in an IoT ecosystem might include sensitive information (e.g., personal medical data by sensors), we will also discuss the security and privacy hazards that are introduced from the advances in the development and application of an IoT environment. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1A Federated Learning Framework for Classifying the Images in Ultrasonic Nondestructive Testing(Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Gulsen, Abdulkadir; Hacilar, Hilal; Kolukisa, Burak; Bakir-Güngör, BurcuUltrasonic inspection is a critical technique in non-destructive testing that ensures the safety and integrity of the material by detecting internal defects. Defect classification within this context is vital for preventing failures and extending the lifespan of materials. However, the advancement of ultrasonic testing technology is hindered by a scarcity of publicly available, realistic datasets, which are essential for developing accurate models. To address these challenges, this paper introduces a Federated Learning (FL) framework employing a Convolutional Neural Network (CNN) model for defect classification using ultrasonic inspection images. This innovative approach allows for the decentralized training of models on private datasets without the need for data exchange, thus preserving data privacy. Our comparative analysis demonstrates that the FL achieves performance comparable to traditional methods while maintaining the confidentiality of sensitive information. The framework also proves to be robust and scalable with an increase in the number of participating clients. This pioneering study highlights the potential of FL in transforming ultrasonic defect classification and suggests possibilities for its application in other areas of non-destructive testing where publicly available datasets are scarce. These findings would encourage researchers to develop a federated platform for enhanced collaboration and explore advanced CNN architectures to improve training efficiency. © 2025 Elsevier B.V., All rights reserved.