WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
(Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
Akciğer Tümörlü Hastaların PET ve BT Görüntülerinin Çakıştırılıp Birleştirilmesi
(IEEE, 2015) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, Eser
Image fusion attracts attention in medical field due to complementary behavior and application such as diagnosis and treatment planning. In this study, first positron emission tomography (PET) and computed tomography (CT) images coming from 8 nonsmall cell lung cancer were registered then wavelet and principal component analysis methods were applied to fuse images. According to mutual information metric and nuclear medicine expert wavelet method gave better results when compared to PCA.
Citation - WoS: 1
Analysis of Battery-Powered Sensor Node Lifetime for Smart Grid Applications
(IEEE, 2016) Eris, Cigdem; Gungor, V. Cagri; Boluk, Pinar Sarisaray
Wireless Sensor Networks (WSNs) enable smart grids where sensor nodes monitor and control the important parameters of power grid components. However, energy-aware communication protocols should be developed to extend network lifetime of WSNs in smart grid environments. In this study, the lifetime of wireless sensor nodes has been analyzed for various smart grid environments, such as 500 kV substation, main power control room, and underground network transformer vaults. In addition, the effects of different operation modes of sensor nodes on node lifetime have been reviewed.
Citation - WoS: 3
Association Rules on Traffic Accident: Case of Ankara
(Ege Univ, Fac Economics & Admin Sciences, 2016) Soylemez, Ismet; Dogan, Ahmet; Ozcan, Ugur
In this study, association rules analysis of the data mining techniques are used for data of traffic accidents in 2010 and some rules are obtained. With this rules, what is the possibility of accident which resulted anybody injured for "different weather conditions (snowy, rainy etc.)", "where the accidents occurred (street, road etc.)" and "way situations (separated road or not)". Different algorithms are used to analyze the association rules. Apriori algorithm is selected for this study and SPSS Clementine 12.0 is used for this algorithm. Firstly, frequency of items are found. Then, items are grouped. In this study, data preprocessing is done and missing values are filled or rejected. In the second phase, outliers are rejected and data type is converted type of 1-0 (binary). In the third phase, Apriori algorithm is applied and results are evaluated.
Citation - WoS: 6
Citation - Scopus: 12
Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO
(IEEE, 2022) Kabas, Bilal
In this paper, a computer vision-based navigation system is proposed for autonomous unmanned aerial vehicles (UAV). The proposed navigation system is based on a deep reinforcement learning-based high-level controller. In this paper, proximal policy optimization (PPO), which is a deep reinforcement learning method, is used to train the artificial neural network in an end-to-end way using a continuous reward function. The proposed method has been tested on images obtained from different modalities (RGB and depth) in simulation environments that are created using Unreal Engine and Microsoft AirSim. For the navigation problem that this work is concerned with, a success rate of 96% has been obtained by using RGB cameras. Since RGB cameras are lighter than depth cameras and the trained artificial neural network has a parameter number less than 170.000, the proposed method is suitable to be deployed in micro aerial vehicles. Code is publicly available*.
Ball Lens Based Mobile Microscope
(Gazi Univ, 2016) Icoz, Kutay
In this paper we report a low cost, simple and mobile microscope based on attachment of a ball lens to a cell phone. The system's noise and parameters affecting the image quality is investigated. The ball lens provides approximately 100X magnification and together with the cell phone's integrated lens and image sensor, 3,4-micron resolution is reached. The field-of-view of the system is 1500x1500 mu m where the price of the ball lens and the holder is less than 10 cents. By using this system as an optical light microscope, we are able to acquire images of micro particles and micro sensors. When combined with image processing methods, this optical system is capable of doing complex analysis as an alternative to commercial optical light microscopes.
Citation - Scopus: 2
Beyin Bilgisayar Arayüzü Uygulamalari için Dinlenme, Harekete Niyet ve Hareket Ayırma
(Institute of Electrical and Electronics Engineers Inc., 2018) Oztürk, Nedime; Yilmaz, Bulent
Brain-computer interface (BCI) is a system that provides a means to control prosthesis, wheelchair, or similar devices using brain waves without direct motor nervous system involvement. For this purpose, brain waves obtained from multiple electrodes placed on the scalp (EEG, Electroencephalogram) are used. Emotiv Epoc used to obtain EEG signals is a low-cost device and has real-time applications. The aim of this study is the detection of rest, imagination and real movement using EEG signals obtained by Emotiv Epoc headset. As a result, As a result, the data obtained from 39 trials from a female subject were classified resting, motion imagination and movement, according to 97.4% accuracy by using the statistical features of distortion, logarithm energy entropy, energy, Shannon entropy and kurtosis. In this study, it has been shown that this system can be remarkably successful for BCI applications. © 2019 Elsevier B.V., All rights reserved.
Citation - Scopus: 2
Beyin Dalgalari ve Baş Hareketiyle Gerçek Zamanli Robotik Araba Kontrolü
(Institute of Electrical and Electronics Engineers Inc., 2018) Oztürk, Nedime; Yilmaz, Bulent; Onver, Ahmet Yasin
Emotiv 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. © 2019 Elsevier B.V., All rights reserved.
Blockchain Based User Management System
(IEEE, 2020) Temiz, Mustafa; Soran, Ahmet; Arslan, Halil; Erel, Hilal
Blockchain is a reliable and transparent structure formed by distributing the data in blocks connected to each other using various cryptography techniques to other points on the network. The difference from the existing database operations is that the authorities and responsibilities do not exist in a single central authority, and that these powers and responsibilities are distributed to the other nodes in the network and the assignment is shared. To provide this, peer to peer network infrastructure is used. However, at this stage, authentication in terms of security is one of the basic security mechanisms. In this study, a user management system which can be integrated with more reliable and current technologies, which is thought to be the solution to speed problems in blockchain, is proposed.
Citation - WoS: 4
Blockchain-Based Fog Computing Applications in Healthcare
(IEEE, 2020) Adanur, Beyhan; Bakir-Gungor, Burcu; Soran, Ahmet
Recently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed.
Citation - Scopus: 1
Comparison of Lung Tumor Segmentation Methods on PET Images
(IEEE, 2015) Eset, Kubra; Icer, Semra; Karacavus, Seyhan; Yilmaz, Bulent; Kayaalti, Omer; Ayyildiz, Oguzhan; Kaya, Eser
Lung cancer is the most common cause of cancer-related deaths that occur all over the world. Recently, various image processing approaches have been used on PET images in order to characterize the uniformity, density, coarseness, roughness, and regularity (i.e., texture properties) of the intratumoral F-18-fluorodeoxyglucose (FDG) uptake. The first and important step of this kind of analysis is to differentiate tumor region from other structures and background, which is called segmentation. In this study, k-means, active contour (snake), and Otsu's tresholding methods were applied on PET images obtained from 36 patients and the performances were compared by the nuclear medicine expert in our team. The results show that Otsu tresholding approach is more selective.
Citation - WoS: 2
Credit Card Fraud Detection With Machine Learning Methods
(IEEE, 2019) Goy, Gokhan; Gezer, Cengiz; Gungor, Vehbi Cagri
With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric.
Citation - WoS: 1
Citation - Scopus: 5
Credit Risk Analysis Based on Hybrid Classification: Case Studies on German and Turkish Credit Datasets
(IEEE, 2018) Cetiner, Erkan; Kocak, Taskin; Gungor, V. Cagri
In finance sector, credit risk analysis plays a major role in decision process. Banks and finance institutions gather large amounts of raw data from their customers. Data mining techniques can be employed to obtain useful information from this raw data. Several data mining techniques, such as support-vector machines (SVM), neural networks, naive-bayes, have already been used to classify customers. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. Furthermore, we compare these approaches' performance with respect to their classification accuracy. We work with two diverse datasets; namely, German credit dataset and Turkish bank dataset. The goal of using such diverse dataset is to show generalization capabality of our approaches. Experimental results provide three important consequences. First, feature selection stage has a major role both on result accuracy and calculation complexity. Second, hybrid approaches have better generalability over single classifiers. Third, using SVM-Radial Basis Function (RBF) as the base classifier and a hybrid model member gives the best accuracy and type-1 accuracy results among others.
Dayım: Bir İnsanoğlunun Portresi için İstatistikler
(Turkish Librarians Assoc, 2019) Donmez, Rasim Ozgur
This is a memoir written by his nephew about our colleague Ali Can, who passed away in last July.
Elastic Modulus Prediction for Fiber-Reinforced Concretes
(Pamukkale Univ, 2020) Yagmur, Eren
In this study, the effects of different discrete fiber types on the elastic modulus of concrete are investigated. For this purpose, 260 cylindrical pressure test specimens are compiled. The fiber types considered are steel, PVA, polypropylene, polyolefin, basalt and olefin. The results of the study are showed that if the ratio of coarse aggregate to fine aggregate exceeds 1.5 for all fiber types, the compressive strength of concrete decreases. It has been observed that the elastic modulus increases in cases where the fiber aspect ratio of the steel fibers is less than and equal to 60, while the elastic modulus decreases for values greater than 60. An elastic modulus equation, which applies to all fiber types considered, is proposed. The proposed equation is compared with the experimental results and the other formulas in the literature and the validity of the equations for different cases are questioned.
Emotion Elicitation Analysis in Multi-Channel EEG Signals Using Multivariate Empirical Mode Decomposition and Discrete Wavelet Transform
(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent
In recent years, wavelet-based, Fourier-based and Hilbert-based time-frequency methods attracted attention in emotion state classification studies in human machine interaction. In particular, the Hilbert-based Empirical Mode Decomposition and Wavelet-based Discrete Wavelet Transform have found applications in emotional state analysis. In this study, a model of emotional elicitation is proposed in which the classification is made by using the features of the wavelet coefficients obtained after applying the Discrete Wavelet Transform to IMFs achieved by using Multivariate Empirical Mode Decomposition. Accordingly, EEG data available in the DEAP database were classified as low / high for valence, activation, and dominance dimensions, and 4 different classifiers were used in the classification phase. The best ratios of valence, activation and dominance were obtained ideally 70.1%, 58.8%, 60.3% respectively.
Citation - Scopus: 2
Emotion Recognition Classification in EEG Signals Using Multivariate Synchrosqueezing Transform
(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent
Electrophysiological data processing can take place both in time and in frequency domains as well as in the joint time-frequency domain. Short Time Fourier Transform and Wavelet Transform are commonly used time-frequency analysis methods. The limitations of these methods initiated the use of methods such as synchrosqueezing and multivariate synchrosqueezing methods. In our proposed method 88.9%, 77.8%, 80.6% accuracy rates were obtained respectively for the valence, activation and dominance parameters using and multivariate synchrosqueezing methods and support vector machines(SVM) which yields better results than most of the other methods mentioned in the literature.
Citation - Scopus: 2
Emotional State Sensing by Using Hybrid Multivariate Empirical Mode Decomposition and Synchrosqueezing Transform
(IEEE, 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent
In recent years, utilizing Hilbert-based time frequency methods in emotional state sensing research attracted attention in the brain computer interfaces. Primarily, Hilbert Transform-based empirical mode decomposition (EMD) was found to be suitable for emotional state modeling studies. In more recent studies, models of emotional state recognition were proposed in which the classification was implemented by using the features obtained after applying the time, frequency, and time frequency domain methods to intrinsic mode functions achieved by operating EMD. In this study, an analysis of emotional state recognition is proposed by using the features of the synchrosqueezing coefficients obtained in the classification process after applying the Synchrosqueezing Transform to intrinsic mode functions achieved by using Multivariate EMD. As a result, EEG data available in the DEAP database were categorized as low and high for valence, activation, and dominance dimensions, and 4 different classifiers were utilized in the classification process. The most satisfying ratios of valence, activation and dominance were attained 76%, 68%, and 68% respectively.
Citation - WoS: 1
Citation - Scopus: 3
Endüstriyel Kablosuz Algılayıcı Ağlarda Hata Kontrol Sistemlerinin Ağ Yaşam Süresine Etkileri
(IEEE, 2019) Tekin, Nazli; Gungor, V. Cagri
Due to the harsh channel conditions of the industrial environment, the data transmission over wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of vital importance for battery-powered Wireless Sensor Networks (WSNs). In this paper, the performance evaluation of error control schemes namely, Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ) in industrial environment in terms of energy efficiency is presented. The impact of the existing error control schemes on the industrial wireless sensor network lifetime is analyzed. A novel Mixed Integer Programming (MIP) framework is developed to maximize network lifetime. Performance results show that utilizing BCH (31,21,5) for Telos at the link layer maximizes the network lifetime while attaining the desired application reliability rate.
Citation - WoS: 5
Enerji Hasadı Yapan Sualtı Kablosuz Duyarga Düğümlerinin Yaşam Ömrü Analizi
(IEEE, 2017) Erdem, H. Emre; Gungor, V. Cagri
The application of Wireless Sensor Networks (WSNs) in underwater environments poses various challenges. One of the most important problems is the limited lifetime of underwater sensor nodes. Considering how challenging and costly it is to change the batteries of sensor nodes in underwater environments, energy harvesting methods arc rendered as a promising solution. In this study, the contributions of energy harvesting via turbine and hydrophone harvesters as well as schedule and trigger driven energy management methods on node lifetime have been analyzed. Performance evaluations have been conducted considering real-life conditions, e.g. flow rates, of Istanbul Bosphorus Strait.

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