Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/45

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Citation - WoS: 11
Citation - Scopus: 11
Derivation of the Optical Constants of Spin Coated Ceo2-Tio2 Thin Films Prepared by Sol-Gel Route
(Pergamon-Elsevier Science Ltd, 2011) Ghodsi, Farhad E.; Tepehan, Fatma Zehra; Tepehan, Galip Gültekin
Ternary thin films of cerium titanium zirconium mixed oxide were prepared by the sol-gel process and deposited by a spin coating technique at different spin speeds (1000-4000 rpm). Ceric ammonium nitrate ce(NO3)(6)(NH4)(2) titanium butoxide Ti[O(CH2)(3)CH3](4) and zirconium propoxide Zr(OCH2CH2CH3)(4) were used as starting materials. Differential calorimetric analysis (DSC) and thermogravimetric analysis (TGA) were carried out on the CeO2-TiO2-ZrO2 gel to study the decomposition and phase transition of the gel. For molecular structural elemental and morphological characterization of the films Fourier Transform Infrared (FTIR) spectral analysis X-ray diffraction (XRD) energy dispersive X-ray spectroscopy (EDS) cross-sectional scanning electron microscopy (SEM) and atomic force microscopy (AFM) were carried out. All the ternary oxide thin films were amorphous. The optical constants (refractive index extinction coefficient band gap) and thickness of the films were determined in the 350-1000 nm wavelength range by using an nkd spectrophotometer. The refractive index extinction coefficient and thickness of the films were changed by varying the spin speed. The oscillator and dispersion energies were obtained using the Wemple-DiDomenico dispersion relationship. The optical band gap is independent of the spin speed and has a value of about E-g approximate to 2.82 +/- 0.04 eV for indirect transition. (C) 2011 Published by Elsevier Ltd.
Citation - WoS: 2
Citation - Scopus: 2
Developing Adaptive Multi-Device Applications With the Heterogeneous Programming Library
(Springer, 2015) Vinas, Moises; Bozkuş, Zeki; Fraguela, Basilio B.; Andrade, Diego; Doallo, Ramon
The usage of heterogeneous devices presents two main problems. One is their complex programming a problem that grows when multiple devices are used. The second issue is that even if the codes for these devices can be portable on top of OpenCL they lack performance portability effectively requiring specialized implementations for each device to get good performance. In this paper we extend the Heterogeneous Programming Library (HPL) which improves the usability of heterogeneous systems on top of OpenCL to better handle both issues. First we provide HPL with mechanisms to support the implementation of any multi-device application that requires arbitrary patterns of communication between several devices and a host memory. In a second stage HPL is improved with an adaptive scheme to optimize communications between devices depending on the execution environment. An evaluation using benchmarks with very different nature shows that HPL reduces the SLOCs and programming effort of OpenCL applications by 27 and 43 % respectively while improving the performance of applications that exchange data between devices by 28 % on average.
Citation - WoS: 11
Citation - Scopus: 10
Early Steps in Automated Behavior Mapping via Indoor Sensors
(MDPI, 2017) Arsan, Taner; Kepez, Orçun
Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies we chose Wi-Fi BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m(2) test area (6m x 6 m) 0.86 m for the BLE beacon in a 37.44 m(2) test area (9.6 m x 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m(2) test area (6 m x 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m(2) test area (7.35 m x 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally we demonstrated the use of ultra-wide band sensor technology for BM.
Citation - WoS: 64
Citation - Scopus: 88
Energy Aware Multi-Hop Routing Protocol for Wsns
(IEEE, 2018) Cengiz, Korhan; Dağ, Tamer
In this paper we propose an energy-efficient multi-hop routing protocol for wireless sensor networks (WSNs). The nature of sensor nodes with limited batteries and inefficient protocols are the key limiting factors of the sensor network lifetime. We aim to provide for a green routing protocol that can be implemented in a wireless sensor network. Our proposed protocol's most significant achievement is the reduction of the excessive overhead typically seen in most of the routing protocols by employing fixed clustering and reducing the number of cluster head changes. The performance analysis indicates that overhead reduction significantly improves the lifetime as energy consumption in the sensor nodes can be reduced through an energy-efficient protocol. In addition the implementation of the relay nodes allows the transmission of collected cluster data through inter cluster transmissions. As a result the scalability of a wireless sensor network can be increased. The usage of relay nodes also has a positive impact on the energy dissipation in the network.
Citation - WoS: 3
Citation - Scopus: 5
In Silico Identification of Critical Proteins Associated With Learning Process and Immune System for Down Syndrome
(Public Library Science, 2019) Kulan, Handan; Dağ, Tamer
Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later deep neural network gradient boosting tree support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system.
Citation - WoS: 2
Citation - Scopus: 2
Linear Expansions for Frequency Selective Channels in Ofdm
(Elsevier GMBH Urban & Fischer Verlag, 2006) Şenol, Hande; Çırpan, Hakan Ali; Panayırcı, Erdal
Modeling the frequency selective fading channels as random processes we employ a linear expansion based on the Karhumen-Loeve (KL) series representation involving a complete set of orthogonal deterministic vectors with a corresponding uncorrelated random coefficients. Focusing on OFDM transmissions through frequency selective fading this paper pursues a computationally efficient pilot-aided linear minimum mean square error (MMSE) uncorrelated KL series expansion coefficients estimation algorithm. Based on such an expansion no matrix inversion is required in the proposed MMSE estimator. Moreover truncation in the linear expansion of channel is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also provide performance analysis results studying the influence of the effect of SNR and correlation mismatch on the estimator performance. Simulation results confirm our theoretical results and illustrate that the proposed algorithm is capable of tracking fast fading and improving performance. (c) 2005 Elsevier GmbH. All rights reserved.
Machine Learning Approaches for Predicting Protein Complex Similarity
(Mary Ann Liebert Inc Publ, 2017) Farhoodi, Roshanak; Akbal-Delibas, Bahar; Haspel, Nurit
Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals electrostatic and desolvation forces) and the similarity of a conformation to its native structure the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network a multilayer neural network and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small often less than 1.5 angstrom error margin when trained with structures that have RMSD values of up to 7 angstrom. In our most recent experiments with the protein samples having RMSD values up to 27 angstrom the average prediction error was still relatively small attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.
New 2-Edge Graphs From Bipartite Graphs
(Wiley-Blackwell, 2016) Çalışkan, Cafer
Let G be a graph of order n satisfying that there exists lambda epsilon Z(+) for which every graph of order n and size t is contained in exactly. distinct subgraphs of the complete graph K-n isomorphic to G. Then G is called t-edge-balanced and. the index of G. In this article new examples of 2-edge-balanced graphs are constructed from bipartite graphs and some further methods are introduced to obtain more from old. (C) 2015 Wiley Periodicals Inc.
Citation - WoS: 1
Citation - Scopus: 1
New Infinite Families of 2-Edge Graphs
(Wiley-Blackwell, 2014) Çalışkan, Cafer; Chee, Yeow Meng
A graph G of order n is called t-edge-balanced if G satisfies the property that there exists a positive for which every graph of order n and size t is contained in exactly distinct subgraphs of Kn isomorphic to G. We call the index of G. In this article we obtain new infinite families of 2-edge-balanced graphs.
Citation - WoS: 11
Citation - Scopus: 13
Novel Application Software for the Semi-Automated Analysis of Infrared Meibography Images
(2019) Shehzad, Danish; Gorcuyeva, Sona; Dağ, Tamer; Bozkurt, Banu
Purpose: To develop semi-automated application software that quickly analyzes infrared meibography images taken with the CSO Sirius Topographer (CSO, Italy) and to compare them to the manual analysis system on the device (Phoenix software platform). Methods: A total of 52 meibography images verified as high quality were used and analyzed through manual and semi-automated meibomian gland (MG) detector software in this study. For the manual method, an experienced researcher circumscribed the MGs by putting dots around grape-like clusters in a predetermined rectangular area, and Phoenix software measured the MG loss area by percentage, which took around 10 to 15 minutes. MG loss was graded from 1 (<25%) to 4 (severe >75%). For the semi-automated method, 2 blind physicians (I and II) determined the area to be masked by putting 5 to 6 dots on the raw images and measured the MG loss area using the newly developed semi-automated MG detector application software in less than 1 minute. Semi-automated measurements were repeated 3 times on different days, and the results were evaluated using paired-sample t test, Bland-Altman, and kappa κ analysis. Results: The mean MG loss area was 37.24% with the manual analysis and 40.09%, 37.89%, and 40.08% in the first, second, and third runs with the semi-automated analysis (P < 0.05). Manual analysis scores showed a remarkable correlation with the semi-automated analysis performed by 2 operators (r = 0.950 and r = 0.959, respectively) (P < 0.001). According to Bland-Altman analysis, the 95% limits of agreement between manual analysis and semi-automated analysis by operator I were between -10.69% and 5% [concordance correlation coefficient (CCC) = 0.912] and between -9.97% and 4.3% (CCC = 0.923) for operator II. The limit of interoperator agreement in semi-automated analysis was between -4.89% and 4.92% (CCC = 0.973). There was good to very good agreement in grading between manual and semi-automated analysis results (κ 0.76-0.84) and very good interoperator agreement with semi-automated software (κ 0.91) (P < 0.001). Conclusions: For the manual analysis of meibography images, around one hundred dots have to be put around grape-like clusters to determine the MGs, which makes the process too long and prone to errors. The newly developed semi-automated software is a highly reproducible, practical, and faster method to analyze infrared meibography images with excellent correlation with the manual analysis.
Citation - WoS: 1
Citation - Scopus: 1
Optimal Power Allocation Between Training and Data for Mimo Two-Way Relay Channels
(IEEE-INST Electrical Electronics Engineers Inc, 2015) Li, Xiaofeng; Tepedelenlioğlu, Cihan; Şenol, Habib
Power allocation between training and data in MIMO two-way relay systems is proposed which takes into consideration both the symmetric and asymmetric cases of the two sources. For the former we present a closed form for the optimal ratio of data energy to total energy which is suitable for the single antenna case as well and can be simplified when the number of antennas is large. We also show that the achievable rate is a monotonically increasing function of the data time. Concerning the asymmetric case we prove that the difference of the two SNRs is either a concave or convex function of the energy ratio depending on the imbalance between the two sources. Using this the minimum SNR between the two sources is maximized.
Citation - WoS: 1
Citation - Scopus: 1
Orthogonal Projection and Liftings of Hamilton-Decomposable Cayley Graphs on Abelian Groups
(Elsevier Science Bv, 2013) Alspach, Brian; Çalışkan, Cafer; Kreher, Donald L.
In this article we introduce the concept of (p alpha)-switching trees and use it to provide sufficient conditions on the abelian groups G and H for when CAY (G x H
Citation - WoS: 1
Citation - Scopus: 1
Partitioning 3-Arcs Into Steiner Triple Systems
(Wiley, 2017) Çalışkan, Cafer
In this article it is shown that there is a partitioning of the set of 3-arcs in a projective plane of order three into nine pairwise disjoint Steiner triple systems of order 13.
Citation - WoS: 6
Citation - Scopus: 9
Three-fast-searchable graphs
(Elsevier Science Bv, 2013) Dereniowski, Dariusz; Diner, Öznur Yaşar; Dyer, Danny
In the edge searching problem searchers move from vertex to vertex in a graph to capture an invisible fast intruder that may occupy either vertices or edges. Fast searching is a monotonic internal model in which at every move a new edge of the graph G must be guaranteed to be free of the intruder. That is once all searchers are placed the graph G is cleared in exactly vertical bar E(G)vertical bar moves. Such a restriction obviously necessitates a larger number of searchers. We examine this model and characterize graphs for which 2 or 3 searchers are sufficient. We prove that the corresponding decision problem is NP-complete. (C) 2013 Elsevier B.V. All rights reserved.
Citation - WoS: 26
Citation - Scopus: 44
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

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