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 - WoS: 1Citation - Scopus: 2Integrated Space Domain Awareness and Communication System(IEEE, 2023) Geçgel Çetin, Selen; Özbek, Berna; Karabulut Kurt, GüneşSpace has been reforming and this evolution brings new threats that, together with technological developments and malicious intent, can pose a major challenge. Space domain awareness (SDA), a new conceptual idea, has come to the forefront. It aims sensing, detection, identification and countermeasures by providing autonomy, intelligence and flexibility against potential threats in space. In this study, we first present an insightful and clear view of the new space. Secondly, we propose an integrated SDA and communication (ISDAC) system for attacker detection. We assume that the attacker has advanced communication capabilities to vary attack scenarios, such as random attacks on some receiver antennas. To track random patterns and meet SDA requirements, a lightweight convolutional neural network architecture is developed. The proposed ISDAC system shows superior and robust performance under 12 different super-attacker configurations with a detection accuracy of over 97.8%. © 2023 IEEE.Article Citation - WoS: 8Citation - Scopus: 9Dementia diagnosis by ensemble deep neural networks using FDG-PET scans(Springer, 2022) Yiğit, Altuğ; Baştanlar, Yalın; Işık, ZerrinDementia is a type of brain disease that affects the mental abilities. Various studies utilize PET features or some two-dimensional brain perspectives to diagnose dementia. In this study, we have proposed an ensemble approach, which employs volumetric and axial perspective features for the diagnosis of Alzheimer’s disease and the patients with mild cognitive impairment. We have employed deep learning models and constructed two disparate networks. The first network evaluates volumetric features, and the second network assesses grid-based brain scan features. Decisions of these networks were combined by an adaptive majority voting algorithm to create an ensemble learner. In the evaluations, we compared ensemble networks with single ones as well as feature fusion networks to identify possible improvement; as a result, the ensemble method turned out to be promising for making a diagnostic decision. The proposed ensemble network achieved an average accuracy of 91.83% for the diagnosis of Alzheimer’s disease; to the best of our knowledge, it is the highest diagnosis performance in the literature.Article Citation - WoS: 3Citation - Scopus: 6Improved Cell Segmentation Using Deep Learning in Label-Free Optical Microscopy Images(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2021) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, DevrimThe recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the decoder. This alteration makes the model superconvergent yielding improved performance results on two challenging optical microscopy image series: a phase-contrast dataset of our own (MDA-MB-231) and a brightfield dataset from a well-known challenge (DSB2018). We utilized the U-Net with pretrained ResNet-18 as the encoder for the segmentation task. Hence, following the modifications, we redesign a novel skip-connection to reduce the semantic gap between the encoder and the decoder. The proposed skip-connection increases the accuracy of the model on both datasets. The proposed segmentation approach results in Jaccard Index values of 85.0% and 89.2% on the DSB2018 and MDA-MB-231 datasets, respectively. The results reveal that our method achieves competitive results compared to the state-of-the-art approaches and surpasses the performance of baseline approaches.Conference Object Ampute Elektromiyografi Sinyallerinin Evrişimli Sinir Ağları Kullanılarak Sınıflandırılması(IEEE, 2020) Onay, Fatih; Mert, AhmetThe classification of EMG signals for the amputees is important to develop a powered-prosthetic that is capable of replacing with lost limbs. The EMG signals collected from residual limbs reduce the classification accuracy due to muscle movements that cannot be realized properly. In this study, classification performance is aimed to be increased by combining CNN with root mean square (RMS) and waveform length (WL) that are used in analysis of EMG signals successfully. The features such as RMS and WL extracted from EMG signals for the classification of six hand movements at the low, medium, and high force levels were applied to CNN input, and classification results were compared with nearest neighbour and linear discriminant analysis.Article Citation - WoS: 43Citation - Scopus: 47Semantic Segmentation of Outdoor Panoramic Images(Springer, 2021) Orhan, Semih; Baştanlar, YalınOmnidirectional cameras are capable of providing 360. field-of-view in a single shot. This comprehensive view makes them preferable for many computer vision applications. An omnidirectional view is generally represented as a panoramic image with equirectangular projection, which suffers from distortions. Thus, standard camera approaches should be mathematically modified to be used effectively with panoramic images. In this work, we built a semantic segmentation CNN model that handles distortions in panoramic images using equirectangular convolutions. The proposed model, we call it UNet-equiconv, outperforms an equivalent CNN model with standard convolutions. To the best of our knowledge, ours is the first work on the semantic segmentation of real outdoor panoramic images. Experiment results reveal that using a distortion-aware CNN with equirectangular convolution increases the semantic segmentation performance (4% increase in mIoU). We also released a pixel-level annotated outdoor panoramic image dataset which can be used for various computer vision applications such as autonomous driving and visual localization. Source code of the project and the dataset were made available at the project page (https://github.com/semihorhan/semseg-outdoor-pano). © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Article Citation - WoS: 3Citation - Scopus: 3Elimination of Useless Images From Raw Camera-Trap Data(Türkiye Klinikleri Journal of Medical Sciences, 2019) Tekeli, Ulaş; Baştanlar, YalınCamera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast Fourier transform. To eliminate the images without animals, we propose a system combining convolutional neural networks and background subtraction. We experimentally show that the proposed approach keeps 99% of photos with animals while eliminating more than 50% of photos without animals. We also present a software prototype that employs developed algorithms to eliminate useless images.