Bilgisayar Mühendisliği Bölümü Koleksiyonuhttps://hdl.handle.net/20.500.12900/692024-03-29T14:22:17Z2024-03-29T14:22:17ZThe number of codes over rings of order containing a hull of given typeDougherty, Steven T.Saltürk, Esengülhttps://hdl.handle.net/20.500.12900/2722023-12-27T10:56:43Z2023-01-01T00:00:00ZThe number of codes over rings of order containing a hull of given type
Dougherty, Steven T.; Saltürk, Esengül
We study the hull, that is the intersection of a code with its orthogonal, of both linear and additive codes over the rings with unity of order 4, where the orthogonal is the Euclidean orthogonal for linear codes and for additive codes it is determined using characters. We relate the hull of the code with the hull of its image under the corresponding Gray map and use this to count the number of codes with a given hull for additive codes for the rings with characteristic 2. We investigate the codes over these rings which have a hull of given type which gives the cardinality of the hull.
2023-01-01T00:00:00ZNew geodetic constraints to reveal seismic potential of central Marmara region, TurkeyYavasoglu, H. H.Tiryakioglu, IKarabulut, M. F.Eyubagil, E. E.Ozkan, A.Masson, F.Klein, E.Gülal, Vahap EnginAlkan, R. M.Isiler, M.Arslan, A. E.Severi, Paolohttps://hdl.handle.net/20.500.12900/2172023-12-05T12:19:31Z2021-01-01T00:00:00ZNew geodetic constraints to reveal seismic potential of central Marmara region, Turkey
Yavasoglu, H. H.; Tiryakioglu, I; Karabulut, M. F.; Eyubagil, E. E.; Ozkan, A.; Masson, F.; Klein, E.; Gülal, Vahap Engin; Alkan, R. M.; Isiler, M.; Arslan, A. E.; Severi, Paolo
The North Anatolian Fault (NAF) is a fault zone that produced destructive earthquakes (Erzincan 1939 and 1992, Ladik 1943, Gerede 1944, Duzce 1999, Izmit 1999) in the last century. After this destructive earthquake migration, it is forecasted that the next seismic event on the NAF could be in the western part of the fault, which passes through the Marmara region. Due to the possibility of an earthquake in Istanbul, the most crowded and historical city in Turkey, researchers have increasingly paid attention to the western segment of the NAF within the Marmara Sea since the 1999 earthquakes. Many scientists from different disciplines such as geodesy, geology, geophysics, etc. have been trying to understand this phenomenon. However, it is understood from the literature that a comprehensive geodetic study is crucial to constrain the NAF segment between Istanbul and Tekirdag provinces. Therefore, we created a new network consisting of continuous GPS stations with 10-km interdistances along the shoreline, which was integrated with existing GNSS networks in the Marmara region. Data acquisition was carried out between August 2017 and February 2020. In this study, preliminary results obtained from the integration of the newly established network with the other GNSS networks are presented.
2021-01-01T00:00:00ZA deep learning approach to permanent tooth germ detection on pediatric panoramic radiographsKaya, EmineGüneç, Hüseyin GürkanAydın, Kader CesurÜrkmez, Elif SeydaDuranay, RecepAteş, Hasan Fehmihttps://hdl.handle.net/20.500.12900/1142022-12-06T16:41:41Z2022-01-01T00:00:00ZA deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
Kaya, Emine; Güneç, Hüseyin Gürkan; Aydın, Kader Cesur; Ürkmez, Elif Seyda; Duranay, Recep; Ateş, Hasan Fehmi
Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ
detection on pediatric panoramic radiographs.
Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of
age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to
automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and
tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion
matrix was used to evaluate the performance of the model.
Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided
an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average
YOLOv4 inference time was 90 ms.
Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based
approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners
find more accurate treatment options while saving time and effort.(Imaging Sci Dent 2022; 52: 275-81)
2022-01-01T00:00:00Z