Yazar "Balim, Mustafa Alper" seçeneğine göre listele

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    Benchmarking domain adaptation for LiDAR-based 3D object detection in autonomous driving
    (Springer London Ltd, 2025) Balim, Mustafa Alper; Hanilci, Cemal; Acir, Nurettin
    The generalization capability of 3D object detection models is crucial for ensuring robust perception in autonomous driving systems. While state-of-the-art models such as Voxel R-CNN, PV-RCNN, and CenterPoint have demonstrated strong performance on publicly available datasets (e.g., KITTI, Waymo, and nuScenes). In this study, we conduct a comprehensive benchmark evaluation. We introduce two custom datasets: (i) a real-world dataset collected using KARSAN's autonomous minibus equipped with a 128-channel LiDAR sensor under diverse traffic conditions, and (ii) a simulated dataset generated using the AWSIM simulation platform, capturing over five hours of synthetic driving data with virtual LiDAR sensors. Our results indicate that 3D object detection performance is highly dataset-dependent, as no single model achieves superior results across all datasets and metrics. Cross-dataset evaluation highlights the challenges of domain mismatch, which causes significant performance degradation when models are tested on our custom datasets, particularly in the synthetic domain. To mitigate these effects, we explore six domain adaptation techniques and demonstrate that their application substantially improves model performance. Bi3D, SESS, and Uni3D outperform UDA, CLUE, and ST3D, yielding more robust generalization across both real-world and simulated environments. These findings shed light on the potential of domain adaptation to improve model performance across domain shifts, despite the ongoing challenges in achieving consistent outcomes across all environments.
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    Classification of motor imagery signals by convolutional neural network for BCI applications
    (Institute of Electrical and Electronics Engineers Inc., 2019) Balim, Mustafa Alper; Acir, Nurettin
    Electroencephalography (EEG) signals have been using for clinical purposes for many years. However, studies on the use of EEG signals in brain computer interface (BBA) applications are increasing. It is possible to control machines using only mental activities, especially for patients with limited mobility. Motor imagery signals (MIS) which are formed as a result of the imagination of moving a limb are one of the most common signal used for this purpose. In this study, it is aimed to classify MIS signals with Convolutional Neural Network by using BCI-IV 2b dataset. As a result, higher (%75,7) performance was obtained with lower number of parameters compared to similar previous studies. © 2019 IEEE.
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    Motor Imagery Signal Classification Using Constant-Q Transform for BCI Applications
    (European Signal Processing Conference, EUSIPCO, 2021) Balim, Mustafa Alper; Hanilçi, Cemal; Acir, Nurettin
    Electroencephalography (EEG) signals have been using for brain-computer interface applications for the last two decades. Motor imagery (MI) signals are one of the EEG signal types formed by imagining a limb's movement. Recently with the help of deep neural networks (DNN) for classifying MI signals using time-frequency (TF) features, considerable performance improvement has been reported. This paper proposes using a well-known TF representation technique called Constant-Q Transform (CQT) for the MI signal classification. Experiments conducted on BCI IV 2b dataset with DNN classifier using CQT spectrogram show that CQT outperforms traditional short-time Fourier transform (STFT) representation.