Wos İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4807
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Item Effect Of Singular Value Decomposition Based Preconditioning On Compressive Classification(2022) Orman, Ozgur Devrim; Yilmaz, DeryaDue to the rapid increase in the amount of data being stored and processed in the world, innovative solutions in the fields of data storage and data processing are increasingly needed; Compressive Sampling (CS) and Compressive Classification (CC) are two approaches that provide solutions for both areas, respectively. The use of CC to obtain information from the data through classification reduces the processing load as it enables the classification to be performed directly in the measurement domain obtained by CS. CS makes possible a lossless reconstruction with a high probability of less samples than the amount required by the Shannon sampling theorem, and by applying Preconditioning (PC) to the measurement matrix used, the amount of data required for reconstruction can be further reduced due to the number of samples required for reconstruction. The contribution of the use of the matrix derived from the measurement matrix by Singular Value Decomposition (SVD) as the measurement matrix in the CS, on the reconstruction performance has been studied only experimentally in the literature. In this study, as a first, it has been shown analytically that this approach based on SVD is a PC (SVD-PC) and will reduce the number of samples required for reconstruction in CS, meanwhile two different Monte Carlo (MC) simulations were carried out regarding to this finding. The SVD-PC performance supported by simulations is evaluated experimentally with SS applications performed on two different data sets and using three different classifiers, moreover the effect of SVD-PC on CC performance is investigated for the first time in the literature in this study.Item Quantitative Data for Transcutaneous Electrical Nerve Stimulation and Acupuncture Effectiveness in Treatment of Fibromyalgia Syndrome(2019) Yueksel, Merve; Ayas, Sehri; Cabioglu, Mehmet Tugrul; Yilmaz, Derya; Cabioglu, Cagri; 0000-0002-1903-7132; 0000-0002-5078-6529; 30949223Aim. To evaluate the effects of acupuncture and transcutaneous electric nerve stimulation (TENS) applications on the quantitative electroencephalography (qEEG) changes and to evaluate their therapeutic effects in patients with fibromyalgia syndrome (FMS). The study included 42 patients with FMS and 21 healthy volunteers. The patients were randomly assigned to two groups (n=21 in each) to undergo either TENS or acupuncture application. In both acupuncture and TENS groups, baseline electroencephalography (EEG) recording was performed for 10min and, then, TENS or acupuncture was performed for 20min, followed by another 10min EEG recording. Baseline qEEG findings of FMS patients in the TENS and acupuncture groups were similar. Delta and theta powers over the frontal region of FMS patients were lower than controls. Theta powers of right posterior region were also lower than controls. In the TENS group, after the treatment, an increase was observed in the alpha power of the left anterior region as well as a decrease in pain scores. In the acupuncture group, an increase was determined in the alpha power of the right and left posterior regions as well as a decrease in pain score after the treatment. The power of low- and moderate-frequency waves on resting EEG was decreased in the patients with FMS. Decreased pain and increased inhibitor activity were found on qEEG after TENS and acupuncture applications. In conclusion, both TENS and acupuncture applications seem to be beneficial in FMS patients.Item A New Motion Model Selection Approach for Multi-Model Particle Filters(2019) Ucar, Barkan; Yilmaz, DeryaOne of the important factors in real-time tracking of the moving radar targets is the speed of the algorithm. In the multi-model particle filters (WPFs) which is frequently preferred tracking of such targets, the numbers of particles and motion models are important parameters determining the speed of the filter. Reducing the number of particles and/or the model transitions processes as much as possible will facilitate real-time tracking of moving targets by accelerating the algorithm. In this study, for reducing the time cost of the MMPF, a new approach called weighted statistical model selection (WSMS) which reduces the number of model estimation calculations is proposed. A new basic MMPF algorithm that allows the use of the WSMS approach is also constituted. In order to evaluate the success of the WSMS; the WPFs integrated with the WSMS, are simulated for different noise variances, particle numbers, and scenarios. The simulation results are compared based on processing time and prediction error criterions. The results demonstrate that the WSMS approach increases the speed of the algorithm by reducing the processing time at high rates without any change in the prediction error and, thus it can be used in real-time tracking of the moving targets.