Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Conference Object
    Citation - Scopus: 4
    Real Time Computer Vision Based Robotic Arm Controller With Ros and Gazebo Simulation Environment
    (Institute of Electrical and Electronics Engineers Inc., 2023) Aksoy,E.; Çakir,A.D.; Erol,B.A.; Gumus,A.
    Robotic arms are widely prevalent and find utility in a variety of applications. However, a significant and widespread challenge faced by these arms is their inability to replicate the intricate functionalities of a human hand, primarily due to the distinct structure of the hand. The primary objective of this work is to create a simulation of a robotic arm that can replicate the movements and functions of a human hand in real-time. Data obtained from hand and arm gestures created with Mediapipe will be transferred in real-time to the robotic arm that is visualized and simulated on ROS and Gazebo. Thus, the hand and arm movements of the user in front of the camera will be effective in real-time on the manipulable joints. This advancement holds the potential to facilitate the construction of a robot capable of emulating both the hand and the arm of humans with high fidelity, thereby enabling comprehensive control over the robotic arm's actions in real-time. © 2023 IEEE.
  • Conference Object
    Citation - Scopus: 3
    Ethereum Blockchain Smart Contract Vulnerability Detection Using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Demir,H.O.; Parlat,S.Z.; Gumus,A.
    Blockchain technology, employing advanced cryptography, stands as an optimal means to establish trust among unfamiliar online counterparts. It facilitates secure transactions and consensus among participants. Ethereum, a prominent blockchain network, extends this utility by introducing smart contracts. These are predefined programs containing data and methods for execution. Once deployed, these contracts remain unalterable due to blockchain's immutable nature. However, unlike conventional software that can be readily patched, they may harbor vulnerabilities. Smart contracts operate with the Ethereum cryptocurrency Ether, rendering fixes intricate and economically impactful. Static analyzers exist to spot vulnerabilities in smart contacts during development, but they are time-intensive. We propose a machine learning-based approach for detecting reentrancy vulnerabilities in smart contracts. Our system comprises three components: data preparation, Op2Vec, and an LSTM model. We collected 30,000 smart contracts, dividing them into two sets of 15,000 each for Op2Vec generation and LSTM training, respectively. We mapped opcode keywords to vector representations using a Skip-Gram algorithm, resulting in a 100-dimensional dictionary with 72 unique opcodes. Labeling was done using the Slither static analyzer, with 116 contracts identified as vulnerable and an additional 132 clean contracts for dataset balance. A Bidirectional LSTM (Bi-LSTM) model was devised by employing assembly data to detect flaws. The developed Bi-LSTM model demonstrated promise in reentrancy vulnerability detection, achieving a 96% accuracy rate in testing and reducing the analysis time to less than a fifth of that required by static analyzers. The codes and data are shared on GitHub as an open-source software package in a way that benefits everyone interested: https://github.com/miralabai/blockchain-vulnerability-detection. © 2023 IEEE.
  • Conference Object
    Citation - Scopus: 1
    Open-Source Visual Target-Tracking System Both on Simulation Environment and Real Unmanned Aerial Vehicles
    (Springer Science and Business Media Deutschland GmbH, 2024) Yılmaz,C.; Ozgun,A.; Erol,B.A.; Gumus,A.
    This work presents an investigation into the domain of dynamic target tracking through object detection, particularly emphasizing the context of open-source applications like PX4, ROS, and YOLO. Over the years, achieving real-time object tracking on UAVs in dynamic environments has been a formidable challenge, necessitating offline computations or substantial onboard processing resources. However, contemporary UAVs are now equipped with advanced edge embedded devices, sensors, and cameras, enabling the integration of deep learning-based vision applications. This advancement offers the prospect of directly deploying cutting-edge applications onto UAVs, thereby expanding their utility in areas such as surveillance, search and rescue, and videography. To fully harness the potential of these vision applications, a communication infrastructure interfacing with the UAV’s underneath closed controllers becomes imperative. We’ve developed an integrated visual target-tracking system that connects a flight controller unit with a graphical unit by leveraging ROS tools and open-source deep learning packages. The overall integrated system based on ROS, deep learning applications, and custom PID controllers is shared on GitHub as open-source software package in a way that benefits everyone interested: https://github.com/miralab-ai/vision-ROS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.