WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Citation - WoS: 1Citation - Scopus: 1Oscillator Phase Noise Impact on Monostatic/Bistatic Space-Borne Sub-THz ISAR(IEEE, 2025-05-21) Bekari, Ali; Gashinova, Marina; Bekar, Muge; Martorellai, Marco; Antonioni, Michail; Bekar, Ali; Martorella, Marco; Antoniou, MichailThis study develops an oscillator phase noise model and analyzes its effects on the performance of spaceborne monostatic and bistatic Inverse Synthetic Aperture Radar (B-ISAR) systems operating at the sub-THz band. The B-ISAR study is of current importance as it can provide a basis for distributed space-based ISAR to enable persistent co-operative space domain awareness (Co-SDA).Conference Object Citation - WoS: 4Citation - Scopus: 4Examining Tongue Movement Intentions in EEG With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(IEEE, 2024-08-26) Aslan, Sevgi Gokce; Yilmaz, BulentDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, Kernel, and Neural Network were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN and SVM, exhibited lower accuracy rates compared to ensemble methods like AdaBoost and Random Forest, which are effective in handling complex datasets. Additionally, a deep learning approach achieved an accuracy rate of 83%. Overall, this study demonstrates the promising role of BCI technologies and machine learning techniques in dysphagia rehabilitation.Conference Object Citation - WoS: 7Citation - Scopus: 7A Runtime Heuristic to Selectively Replicate Tasks for Application-Specific Reliability Targets(IEEE, 2016-09) Subasi, Omer; Yalcin, Gulay; Zyulkyarov, Ferad; Unsal, Osman; Labarta, JesusIn this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App_FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App_FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App_FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.