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DOĞAN, BUKET

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DOĞAN

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2018 - 20203
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Author: DEMİR, ÖNDER ×

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Sharing Location Information in Multi-UAV Systems by Common Channel Multi-Token Circulation Method in FANETs
    (KAUNAS UNIV TECHNOLOGY, 2019-02-12) DOĞAN, BUKET; Ulku, Eyup Emre; Dogan, Buket; Demir, Onder; Bekmezci, Ilker
    Unmanned Aerial Vehicle (UAV) technology is being used increasingly for military and civilian purposes. The primary reason for this increase is that UAVs eliminate the risk to human life in difficult and dangerous missions, are cost effective, and easily are deployed. Developments in UAV technology and decreasing costs have increased UAV usage. However, when multiple UAVs are deployed, inter UAV communication becomes complicated. For this reason, communication in multi-UAV systems is the most important problem that needs to be solved. To enable communication among UAVs without infrastructure support, a Flying Ad Hoc Network (FANET) is used. A FANET provides UAVs to fly in tandem without colliding. To ensure coordinated flight, UAVs require the location information of other UAVs. In this study, we developed a common channel multi-token circulation protocol to share location information in multi-UAV systems that communicate using a FANET. The proposed method ensures that UAVs in multi-UAV systems know each other's coordinate information with minimum error.
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    Applying social networks to engineering education
    (WILEY, 2018) DOĞAN, BUKET; Dogan, Buket; Demir, Onder; Ulku, Eyup E.
    Social networking sites (SNSs) are a popular Internet-based means for users to communicate and interact with each other. Although they have caught the attention of many researchers and are already being used as educational tools, very few studies have investigated the effects of using an SNS in engineering education. This study, therefore, aims to analyze the effects of using the Edmodo platform as a teaching and learning support tool on students' academic and practical performance in the Introduction to Information Technology and Algorithms course, as well as in the Computer Programming course they took in the following semester. It also considers the students' opinions about the Edmodo system. For this study, a total of 62 students studying in the Electrical and Electronics Engineering Department during the 2016-2017 fall semester were divided into two equally sized groups. The control group underwent a traditional face-to-face education, whereas the experimental group augmented this using the Edmodo system. A mixed-methods approach with a post-test-only control group design was used: quantitative data were obtained from student tests, together with qualitative data from follow-up interviews. The students' grades were analyzed using Student's t-test and correlation analysis, showing that the experimental group performed better in their academic and laboratory assessments and that there was a moderately positive relationship between the post-test results and performance in the subsequent Computer Programming course.
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    PublicationOpen Access
    Tactile paving surface detection with deep learning methods
    (GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2020-04-07) AKTAŞ, ABDULSAMET; Aktas, Abdulsamet; Dogan, Buket; Demir, Onder
    Image processing applications in real-time systems have become a popular topic in recent years. Deep learning methods, one of the sub-branches of artificial intelligence, and image processing algorithms used in the field of object detection from images can be used together. In this way, applications are developed in many areas such as autonomous cars, autonomous unmanned aerial vehicles, assist robot technologies, assistant technologies for disabled and elderly individuals. This study aims to detect the tactile paving surfaces with deep learning methods in order to design an assistive technology system that can be used by visually impaired individuals, autonomous vehicles and robots. Contrary to traditional image processing algorithms, deep learning methods and image processing algorithms are used together in this study. The YOLO-V3 model, which is one of the best methods of object detection, is combined with the DenseNet model to create the YOLOV3-Dense model. YOLO-V2, YOLO-V3 and YOLOV3-Dense models were trained on the Marmara Tactile Paving Surface (MDPY) dataset, which was created by the researchers and included 4580 images and their performances were compared with each other on the test dataset. It was observed that YOLOV3-Dense model is better than other models in detecting tactile paving surface with 89% F1-score, 92% mean average Precision(mAP) and 81% IoU values.