Yazar "Oniz, Y." seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • Küçük Resim Yok
    Öğe
    An EMG Controlled Bionic Arm with Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Uygun, M.I.; Recber, B.; Celikli, M.A.; Oniz, Y.
    This study uses electromyography (EMG) signals and machine learning techniques to create a bionic arm specifically made for amputees. The user's intended movements can be decoded and converted into orders for the bionic arm by observing and analyzing these signals. An EMG sensor was initially placed on the surface of particular muscles in the lower arm as part of the project's data-collection phase. As the muscles performed various movements, the electrodes captured the bioelectric signals they produced. These recorded bioelectrical signals are classified according to each movement by going through a series of processes, and it was aimed to increase the functionality of the bionic arm by gaining many movements control. The results showed promising advancements in the field and highlighted the potential for improving the quality of living for people with limb loss. © 2023 IEEE.
  • Küçük Resim Yok
    Öğe
    Automated Student Attendance System Using Face Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2020) Akay, E.O.; Canbek, K.O.; Oniz, Y.
    In this study, an automated attendance taking system is developed and implemented. Two different face detection algorithms, namely Histogram of Oriented Gradients and Haar-Cascade algorithms, are applied and their performances are compared. Deep learning based on convolutional neural networks (CNNs) is employed for the identification of the students in the classroom. Furthermore, a mask checking feature is also included as a measure against the Covid-19 pandemic. A graphical user interface (GUI) system is designed using Python. © 2020 IEEE.
  • Küçük Resim Yok
    Öğe
    Handwritten Digit Recognition using Spiking Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ozcan, O.; Oniz, Y.; Ayyildiz, M.
    In this work, both simulated and experimental studies have been carried out using Spiking Neural Networks (SNNs) on the handwritten digit recognition problem. The design of SNN is performed using Spike Response Model (SRM). A gradient based algorithm is applied for the learning of SNN. For the simulations, the proposed algorithms have been applied on the MNIST data set. To provide a basis for comparison, the same studies have been also performed for an equivalent non-spiking artificial neural network (ANN) structure. Following the simulated studies, experiments utilizing a multi touch IR frame have been carried out for both network structures. Two different approaches have been adopted to evaluate the performance of the proposed network models: In the first one, the training of the network structures has been accomplished on the MNIST data set, whereas the data acquired from the experimental setup have been used in the testing phase. Next, the data obtained from the experimental setup have been employed in both training and testing of the neural networks. The results of the simulations and experimental studies reveal that the SNN outperforms the conventional ANN. © 2022 IEEE.
  • Küçük Resim Yok
    Öğe
    Real-Time Implementation of an Interval Type-2 Fuzzy Logic Controller for the Trajectory Tracking of an UAV
    (Institute of Electrical and Electronics Engineers Inc., 2021) Oguz Canbek, K.; Oniz, Y.
    In this study, a Type-2 Fuzzy Logic Controller that directly generates the necessary control signals is used to achieve the trajectory tracking control of an unmanned aerial vehicle (UAV). The proposed control scheme takes the quadcopter's divergence from the given trajectory and its derivative as inputs. A type-1 fuzzy logic controller is also designed and implemented on the quadcopter to assess the performance of the type-2 controller. Real-time flight experiments are conducted for a skewed circular trajectory. The results of these experiments show that the type-2 fuzzy controller provides better transient and steady-state response compared to a conventional fuzzy controller. © 2021 IEEE.
  • Küçük Resim Yok
    Öğe
    Target Search and Tracking with an Autonomous Quadcopter using Type-2 Fuzzy Logic Controller
    (Institute of Electrical and Electronics Engineers Inc., 2022) Abdelmoaty, O.K.H.; Canbek, K.O.; Oniz, Y.
    In this study, an interval type-2 fuzzy logic controller has been proposed for the target search and tracking mission of an unmanned aerial vehicle. The visual data coming from the onboard camera sensor of the quadcopter have been processed to provide visual feedback from the environment and to detect the target. In the given mission, the quadcopter first performed a systematic search over the area until the target has been detected, and then proceeded with the tracking of the target. The ROS/Gazebo platform has been used to test the performance of the proposed control scheme. A fuzzy type-1 controller has been also developed to assess the performance of the type-2 controller. The results of the simulations indicate that type-2 fuzzy logic controller can provide better steady state and transient characteristics compared to its type-1 counterpart. © 2022 IEEE.
  • Küçük Resim Yok
    Öğe
    Trajectory Tracking of a Quadcopter Using Adaptive Neuro-Fuzzy Controller with Sliding Mode Learning Algorithm
    (Springer, 2021) Kemik, H.; Dal, M.B.; Oniz, Y.
    In this work, the trajectory tracking control of an unmanned aerial vehicle (UAV) has been accomplished using adaptive neuro-fuzzy controllers. The update rules of the proposed controller have been derived based on the sliding mode control theory, where a sliding surface has been generated utilizing the parameters of the neuro-fuzzy controller to direct the error towards zero in a stable manner. To assess the effectiveness of the proposed control scheme, Parrot AR.Drone 2.0 has been utilized as the test platform, on which conventional PID and fuzzy logic controllers have been also implemented to provide means for comparing the performance of the proposed controller. Different reference trajectories have been generated for the real-time experimental studies, in which the discrepancies from these trajectories are used to determine the input signals to be applied to the proposed controllers. The analytical claims have been justified by the obtained results from the real-time experiments in the presence of large nonzero initial errors. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • Küçük Resim Yok
    Öğe
    Trajectory Tracking of a Quadcopter Using Fuzzy-PD Controller
    (Institute of Electrical and Electronics Engineers Inc., 2021) Canbek, K.O.; Oniz, Y.
    In this work, the trajectory tracking control of an unmanned aerial vehicle (UAV) has been accomplished using an adaptive proportional-derivative (PD) controller, in which fuzzy logic controllers have been utilized to determine the controller gain values resulting in better transient and steady-state characteristics. The real-time flight tests have been carried out on AR.Drone 2.0 for two different reference trajectories. The drone's discrepancies from these trajectories along with their derivatives have been employed as the input signals to the proposed control scheme. To provide means for comparison, a conventional PD controller has been also implemented. The obtained results from the real-time experiments indicate that the adaptation of the PD gains can give rise to smaller overshoot, shorter settling time, and less deviation from the target trajectory. © 2021 Chamber of Turkish Electrical Engineers.