Browsing by Author "Tuna, Hakan"

Now showing 1 - 6 of 6

Open Access
3D electron density estimation in the ionosphere
(IEEE, 2014) Tuna, Hakan; Arıkan, Orhan; Arıkan, F.
Ionosphere has ion distribution which is variable in space and time. There have been physical and empirical studies for modeling the ionosphere. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) is the most recent model developed for this purpose. However, IRI-Plas presents a model about the ionosphere and its compliance with the instantaneous state of the ionosphere does not provide the accuracy needed for engineering purposes. One of the important information sources about the instantaneous state of the ionosphere is GPS signals. In this study, constructing the ionosphere which is compatible with both the instantaneous ionosphere measurements and the physical structure of the ionosphere is presented as an optimization problem, and solved by using the particle swarm optimization technique. The ionosphere over Turkey is investigated by using the proposed optimization method and the importance of the instantaneous ionosphere measurements obtained from GPS signals is demonstrated.
Open Access
3D electron density estimation in the ionosphere by using IRI-Plas model and GPS measurements
(2016-05) Tuna, Hakan
Three dimensional imaging of the electron density distribution in the ionosphere is a crucial task for investigating the ionospheric effects. Dual-frequency Global Positioning System (GPS) satellite signals can be used to estimate the Slant Total Electron Content (STEC) along the propagation path between a GPS satellite and ground based receiver station. However, the estimated GPS-STEC are very sparse and highly non-uniformly distributed for obtaining reliable 3D electron density distributions derived from the measurements alone. Standard tomographic re- construction techniques are not accurate or reliable enough to represent the full complexity of variable ionosphere. On the other hand, model based electron density distributions are produced according to the general trends of the iono- sphere, and these distributions do not agree with measurements, especially for geomagnetically active hours. In this thesis, a novel regional 3D electron density distribution reconstruction technique, namely IONOLAB-CIT, is proposed to as- similate GPS-STEC into physical ionospheric models. The IONOLAB-CIT is based on an iterative optimization framework that tracks the deviations from the ionospheric model in terms of F2 layer critical frequency and maximum ionization height resulting from the comparison of International Reference Ionosphere ex- tended to Plasmasphere (IRI-Plas) model generated STEC and GPS-STEC. The IONOLAB-CIT is applied successfully for the reconstruction of electron den- sity distributions over Turkey, during calm and disturbed hours of ionosphere using Turkish National Permanent GPS Network (TNPGN-Active). Reconstruc- tions are also validated by predicting the STEC measurements that are left out in the reconstruction phase. The IONOLAB-CIT is compared with the real ionosonde measurements over Greece, and it is shown that the IONOLAB-CIT results are in good compliance with the ionosonde measurements. The results of the IONOLAB-CIT technique are also tracked and smoothed in time by using Kalman filtering methods for increasing the robustness of the results.
Open Access
Çarpıcıdan bağımsız ortak fark matrisi kullanarak video ve görüntü işleme
(IEEE, 2009-04) Çetin, A. Enis; Duman, Kaan; Tuna, Hakan; Eryıldırım, Abdulkadir
Bu bildiride gerçel sayılar üzerinde yarı grup kuran yeni bir iletmen tanımlayarak elde edilen bir bölge betimleyicisi ile hareketli obje takibi, yüz sezimi, plaka bulma, bölge betimleme için kullanılabilecek hızlı bir algoritma sunuyoruz. Bu yeni iletmen hiçbir çarpma gerektirmez. Bu iletmeni kullanarak, imge bölgelerini nitelendiren ve ortak fark adı verilen bir matris tanımlıyoruz. Plaka bulma uygulamasında ortak fark matrislerinı plaka bölgelerinden kestirip, bunları bir veritabanında saklıyoruz. Plaka bölgelerini gerçek zamanlı videoda tanımlamak için ilk önce videodaki hareketli bölgeleri taşıyan imgeleri belirliyoruz, sonra hareketli bölgelerin içinde ya da bütün resim içinde plaka büyüklüğündeki bölgelerin ortak ayrık matrislerini veritabanındaki plaka ortak ayrık matrisleriyle karşılaştırarak bölge içinde plaka olup olmadığını belirliyoruz.
Open Access
Detection and classification of objects and texture
(2009) Tuna, Hakan
Object and texture recognition are two important subjects in computer vision. An efficient and fast algorithm to compute a short and efficient feature vector for classification of images is crucial for smart video surveillance systems. In this thesis, feature extraction methods for object and texture classification are investigated, compared and developed. A method for object classification based on shape characteristics is developed. Object silhouettes are extracted from videos by using the background subtraction method. Contour of the objects are obtained from these silhouettes and this 2-D contour signals are transformed into 1-D signals by using a type of radial transformation. Discrete cosine transformation is used to acquire the frequency characteristics of these signals and a support vector machine (SVM) is employed for classification of objects according to this frequency information. This method is implemented and integrated into a real time system together with object tracking. For texture recognition problem, we defined a new computationally efficient operator forming a semigroup on real numbers. The new operator does not require any multiplications. The codifference matrix based on the new operator is defined and an image descriptor using the codifference matrix is developed. Texture recognition and license plate identification examples based on the new descriptor are presented. We compared our method with regular covariance matrix method. Our method has lower computational complexity and it is experimentally shown that it performs as well as the regular covariance method.
Open Access
Reconstruction of 3-D ionospheric electron density distribution by using GPS measurements and IRI-Plas model
(Asia Oceania Geosciences Society, 2012-08) Arıkan, Orhan; Tuna, Hakan; Arıkan, F.; Gulyaeva, T. L.
Open Access
Space weather studies of IONOLAB group
(IEEE, 2016) Arıkan, F.; Sezen, U.; Toker, C.; Artuner, H.; Bulu, G.; Demir, U.; Erdem, E.; Arıkan, Orhan; Tuna, Hakan; Gulyaeva, T. L.; Karatay, S.; Mosna, Z.
IONOLAB is an interdisciplinary research group dedicated for handling the challenges of near earth environment on communication, positioning and remote sensing systems. IONOLAB group contributes to the space weather studies by developing state-of-the-art analysis and imaging techniques. On the website of IONOLAB group, www.ionolab.org, four unique space weather services, namely, IONOLAB-TEC, IRI-PLAS-2015, IRI-PLAS-MAP and IRI-PLAS-STEC, are provided in a user friendly graphical interface unit. Newly developed algorithm for ionospheric tomography, IONOLAB-CIT, provides not only 3-D electron density but also tracking of ionospheric state with high reliability and fidelity. The algorithm for ray tracing through ionosphere, IONOLAB-RAY, provides a simulation environment in all communication bands. The background ionosphere is generated in voxels where IRI-Plas electron density is used to obtain refractive index. One unique feature is the possible update of ionospheric state by insertion of Total Electron Content (TEC) values into IRI-Plas. Both ordinary and extraordinary paths can be traced with high ray and low ray scenarios for any desired date, time and transmitter location. 2-D regional interpolation and mapping algorithm, IONOLAB-MAP, is another tool of IONOLAB group where automatic TEC maps with Kriging algorithm are generated from GPS network with high spatio-temporal resolution. IONOLAB group continues its studies in all aspects of ionospheric and plasmaspheric signal propagation, imaging and mapping.