WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object A Novel Signaling Pathway That Governs Tumor Metastasis: Ceramide Regulates Direct Crosstalk Between TGF-Β and Sonic Hedgehog Signaling(Federation of American Society for Experimental Biology, 2016) Gencer, Salih; Ogretmen, BesimConference Object Ceramide Is a Key Factor That Regulates the Crosstalk Between TGF-Β and Sonic Hedgehog Signaling at the Basal Cilia to Control Cell Migration and Tumor Metastasis(Federation Amer Soc Exp Biol, 2016-07) Gencer, Salih; Oleinik, Natalia; Dany, Mohammed; Ogretmen, BesimConference Object Citation - WoS: 2Citation - Scopus: 194.8 Km-Range Direct Detection Fiber Optic Distributed Acoustic Sensor(Optica Publishing Group (Formerly OSA), 2019) Uyar, F.; Onat, T.; Unal, C.; Kartaloǧlu, T.; Ozdur, I.; Özbay, E.This work demonstrates an ultra-long range direct detection fiber optic distributed acoustic sensor which can detect vibrations at a distance of 94.8 km with 10 m resolution along the sensing fiber. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 2Citation - Scopus: 2Cascade Control of Magnetic Levitation with Sliding Modes(EDP Sciences edps@edpsciences.com, 2016) Eroǧlu, Y.; Ablay, G.The effectiveness and applicability of magnetic levitation systems need precise feedback control designs. A cascade control approach consisting of sliding mode control plus sliding mode control (SMC plus SMC) is designed to solve position control problem and to provide a high control performance and robustness to the magnetic levitation plant. It is shown that the SMC plus SMC cascade controller is able to eliminate the effects of the inductance related uncertainties of the electromagnetic coil of the plant and achieve a robust and precise position control. Experimental and numerical results are provided to validate the effectiveness and feasibility of the method. © 2016 Elsevier B.V., All rights reserved.Article TURKEY(ROUTLEDGE2 PARK SQ, MILTON PARK, ABINGDON OX14 4RN, OXFORD, ENGLAND, 2017) Salman, Yildiz; Polat, Ebru Omay; Yoney, Nilufer BaturayogluTURKEYConference Object Real-Time Robotic Car Control Using Brainwaves and Head Movement(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Ozturk, Nedime; Yilmaz, Bulent; Onver, Ahmet YasinEmotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications.Conference Object In-silico Identification of Papillary Thyroid Carcinoma Molecular Mechanisms(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019-04) Ersoz, Nur Sebnem; Guzel, Yasin; Bakir-Gungor, BurcuRepresenting approximately 70% to 80% of thyroid cancers, papillary thyroid cancer (PTC) is the most common type of thyroid cancers. PTC is seen in all age groups, but it is seen more frequently in women than in men. Detection of biomarker proteins of papillary thyroid cancinoma plays an important role in the diagnosis of the disease. In this study, we aim to find target genes and pathways that are associated with papillar thyroid carcinoma, by integrating different bioinformatics methods. For this purpose, usingin-silico methodologies, candidate genes and pathways that could explain disease development mechanisms are identified. Throughout this study, firstly we identified differentially expressed genes as the amount of their protein product differ between patient and healthy groups. Secondly, by using active subnetworks search algorithms, topologic analyses and functional enrichment tests, candidate proteins,which could be thought as PTC biomarkers, and affected pathways are identified.Conference Object Effect of Bilinear Interpolation on the Texture Analysis of Colonoscopy Images(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Kacmaz, Rukiye Nur; Yilmaz, BulentInterpolation is a method that is used to obtain unknown intensities with the help of known intensities on an image. This method is frequently used in the literature to eliminate light reflection on colonoscopy images. Texture features are the most important characteristics used to describe the region or objects of interest in the image. They are the measures of intensity variation of a surface that determine properties such as smoothness, roughness, and regularity. The aim of this study is to find out the how bilinear interpolation applied on colonoscopy images with reflection impact texture features obtained from the same images. A research carried out to make reasonable comparison between a texture feature from an image with no reflection and the same feature obtained from the same image with synthetically added reflections with various percentages. Using the approaches like gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM) 126 features were extracted from each 32x32 sub-images coming from 610 colonoscopy images. Several of the features extracted from sub-images with no reflection and reflection were not statistically significantly different, while majority of them were affected from the reflections.Conference Object Detection of Variation Instances on Colonoscopy Videos using Structural Similarity Index(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Kacmaz, Rukiye Nur; Yilmaz, BulentThe aim of this study is to reduce the number of images extracted from the videos recorded by the specialists during the colonoscopy process for further examination, thereby enabling the specialist to deal with fewer images. Since the images obtained from the videos are very similar, the main assumption of this study is that the whole video can be represented by fewer images. The approach used in this study is the structural similarity index. Totally, images were obtained from 4 different videos coming from healthy, ulcerative colitis, Crohn's, and polyp patients. The noisy images in these videos were eliminated manually. When the structural similarity index between two consecutive clear images was less than 0.83, the second image was selected and shown to the specialist for his/her examination. By this way, the frames carrying significantly new information from the videos were defined as the variation instances. The tests on healthy or diseased colon videos showed that only 5-10% of the clear images provide significantly new information.Conference Object Citation - WoS: 8Citation - Scopus: 9Computer-Aided Classification of Breast Cancer Histopathological Images(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017-10) Aksebzeci, Bekir Hakan; Kayaalti, OmerNowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-assisted diagnosis of breast cancer is a promising field.