Yazılım Mühendisliği Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/2146

Browse

Now showing 1 - 18 of 18

Block Size Analysis for Discrete Wavelet Watermarking and Embedding a Vector Image as a Watermark
(Zarka Private Univ, 2019) Sever, Hayri; Şenol, Ahmet; Elbaşı, Ersin; 11916
As telecommunication and computer technologies proliferate, most data are stored and transferred in digital format. Content owners, therefore, are searching for new technologies to protect copyrighted products in digital form. Image watermarking emerged as a technique for protecting image copyrights. Early studies on image watermarking used the pixel domain whereas modern watermarking methods convert a pixel based image to another domain and embed a watermark in the transform domain. This study aims to use, Block Discrete Wavelet Transform (BDWT) as the transform domain for embedding and extracting watermarks. This study consists of 2 parts. The first part investigates the effect of dividing an image into non overlapping blocks and transforming each image block to a DWT domain, independently. Then, effect of block size on watermark success and, how it is related to block size, are analyzed. The second part investigates embedding a vector image logo as a watermark. Vector images consist of geometric objects such as lines, circles and splines. Unlike pixel-based images, vector images do not lose quality due to scaling. Vector watermarks deteriorate very easily if the watermarked image is processed, such as compression or filtering. Special care must be taken when the embedded watermark is a vector image, such as adjusting the watermark strength or distributing the watermark data into the image. The relative importance of watermark data must be taken into account. To the best of our knowledge this study is the first to use a vector image as a watermark embedded in a host image.
A Combined Spatial and Frequency Based Texture Model for Organ Segmentation in Computed Tomography Examinations
(Amer Scientific Publishers, 2014) Hassanpour, Reza; Shahbahrami, Asadollah; Wong, Stephan; Jafari, Aref
The organ segmentation in computed tomography (CT) examination is a tedious and error prone task. The local similarity of the pixels from different organs, and the differences between the pixels of the same organ observed in different examinations are two most challenging problems affecting the segmentation process. In this study, statistical and spectral texture properties are combined with the a-priori knowledge about the human body to develop a model for reliably segmenting organs in CT examinations. The main goal of the developed model is fusing local and global statistics to support spatial-frequency analysis and to maximize the simultaneous localization of energy in both spatial and frequency domains. The feature space dimension is reduced by means of a wrapper technique applied as a pre-processing filter. The proposed classifier utilizes a linear combination (ensemble) of two support vector machines (SVM) where the first SVM classifies the input samples according to their textural information and the second one correct the results of the first classifier by searching the spatial information of those samples in a statistical atlas.
Citation - Scopus: 1
Component-Based Project Estimation Issues for Recursive Development
(Springer, 2008) Altunel, Yusuf; Tolun, Mehmet R.; 1863
In this paper we investigated the component-based specific issues that might affect project cost estimation. Component-based software development changes the style of software production. With component-based approach the software is developed as the composition of reusable software components. Each component production process must be treated as a stand-alone software project, which needs individual task of management. A typical pure component-based development can be considered as decomposition/integration activities successively applied at different levels and therefore results in recursive style of development. We analyzed and presented our results of studies on the component-based software development estimation issues from recursive point of view.
Citation - WoS: 15
Citation - Scopus: 15
A Constraint Programming Approach To a Real-World Workforce Scheduling Problem for Multi-Manned Assembly Lines With Sequence-Dependent Setup Times
(Taylor & Francis Ltd, 2024) Kandiller, Levent; Drake, John H.; Guner, Funda; Gorur, Abdul K.; Satir, Benhur; 54700
For over five decades, researchers have presented various assembly line problems. Recently, assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles, where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study, however, concentrates on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods, one using mixed integer linear programming and the other using constraint programming, to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations.
Citation - WoS: 20
Citation - Scopus: 29
Creating Consensus Group Using Online Learning Based Reputation in Blockchain Networks
(Elsevier, 2019) Ozsoy, Adnan; Oztaner, Serdar Murat; Sever, Hayri; Bugday, Ahmet; 11916
One of the biggest challenges to blockchain technology is the scalability problem. The choice of consensus algorithm is critical to the practical solution of the scalability problem. To increase scalability, Byzantine Fault Tolerance (BFT) based methods have been most widely applied. This study proposes a new model instead of Proof of Work (PoW) for forming the consensus group that allows the use of BFT based methods in the public blockchain network. The proposed model uses the adaptive hedge method, which is a decision-theoretic online learning algorithm (Qi et al., 2016). The reputation value is calculated for the nodes that want to participate in the consensus committee, and nodes with high reputation values are selected for the consensus committee to reduce the chances of the nodes in the consensus committee being harmful. Since the study focuses on the formation of the consensus group, a simulated blockchain network is used to test the proposed model more effectively. Test results indicate that the proposed model, which is a new approach in the literature making use of machine learning for the construction of consensus committee, successfully selects the node with the higher reputation for the consensus group. (C) 2019 Elsevier B.V. All rights reserved.
Detection of Stylometric Writeprint From the Turkish Texts
(Ieee, 2020) Canbay, Pelin; Sever, Hayri; Sezer, Ebru Akcapinar; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Authorship attribution studies aim to extract information about the author by analyzing the data in the text form. With the increase of anonymous authors in digital environments, the need for these works is increasing day by day. Although there exists lots of studies focuse on stylometric writeprint detection in different languages using different attributes, there is no standard feature set and detection algorithm to be evaluated in these studies. Giving priority to Turkish texts, in this study, which features are more distinctive for determining stylistic writeprint of text, and which methods will contribute to increase the success to be achieved are shown with experimental studies.
Hand Gesture Recognition in Variable Length Sequences
(2005) Choupanı, Roya; Choupani, R.; Tolun, M.R.; Tolun, Mehmet Reşit; 1863; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Using hand gestures in human computer interaction has been a major challenge during the recent years. Many of the hand gesture recognition systems however, have been based on the recognition of hand postures and estimating the related gesture which is restricted to a few numbers of possible movements. However when dealing with applications such as understanding sign languages which include a large number of classes, an automatic learning method based on matching a sequence of postures with the characterizing feature sequence of each class is necessary. An important characteristic of this method is that each sample sequence of a class may have a variable length and different position of the key features. In this paper a syntactic method has been proposed for classifying the input sequences. An algorithm foe extracting the grammar of the method during training stage is also given.
Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error
(2015) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
In video coding, dependencies between frames are being exploited to achieve compression by only coding the differences. This dependency can potentially lead to decoding inaccuracies when there is a communication error, or a deliberate quality reduction due to reduced network or receiver capabilities. The dependency can start at the reference frame and progress through a chain of dependent frames within a group of pictures (GOP) resulting in the so-called drift error. Scalable video coding schemes should deal with such drift errors while maximizing the delivered video quality. In this paper, we present a multi-layer hierarchical structure for scalable video coding capable of reducing the drift error. Moreover, we propose an optimization to adaptively determine the quantization step size for the base and enhancement layers. In addition, we address the trade-off between the drift error and the coding efficiency. The improvements in terms of average PSNR values when one frame in a GOP is lost are 3.70(dB) when only the base layer is delivered, and 4.78(dB) when both the base and the enhancement layers are delivered. The improvements in presence of burst errors are 3.52(dB) when only the base layer is delivered, and 4.50(dB) when both base and enhancement layers are delivered.
Citation - WoS: 20
Citation - Scopus: 23
Investigation of Equatorial Plasma Bubble Irregularities Under Different Geomagnetic Conditions During the Equinoxes and the Occurrence of Plasma Bubble Suppression
(Pergamon-elsevier Science Ltd, 2020) Timocin, Erdinc; Inyurt, Samed; Temucin, Huseyin; Ansari, Kutubuddin; Jamjareegulgarn, Punyawi; 182651
In this study, we investigated the behavior of equatorial plasma bubble irregularities under different geomagnetic conditions during March 2015 and September 2017. It was used Total Electron Content (TEC) data obtained from SGOC (6,89 degrees N, 79,87 degrees E), IISC (12,94 degrees N, 77,57 degrees E) and HYDE (17,40 degrees N, 78,50 degrees E) receiver stations which located between the trough and the crest of the equatorial ionization anomaly (EIA). We used the Rate of TEC change (ROT) and Rate of TEC change index (ROTI) to represent plasma bubbles irregularities. These indices are a well proxy for the ionospheric fluctuations and can be used to describe features of plasma bubbles irregularities. The equatorial plasma bubble irregularities for all stations were observed between 13 UT and 20 UT (during postsunset period) during equinoxes. The intensity level of ROTI during postsunset periods was greater than 1 TECU min(-1). Also, the values of mean ROTI (ROTIave) between 13 UT and 20 UT have values greater than 0,4 TECU min(-1) while the values of ROTIave at the other hours have values less than 0,4 TECU min(-1). The geomagnetic activity has a significant effect on the occurrence of equatorial plasma bubbles irregularities. The occurrence rate of equatorial plasma bubble irregularities observed during postsunset hours increased as geomagnetic activity increases. It also was observed that the main phases of geomagnetic storms have the triggering effect of storms on equatorial plasma bubble irregularities observed at postsunset hours while the recovery phases of geomagnetic storms have the suppression effect of storms on equatorial plasma bubble irregularities. Asymmetry between two equinoxes was observed. The occurrence rate of equatorial plasma bubble irregularities in the March equinox was much larger than that of the September equinox. The occurrence probability of equatorial plasma bubbles for March Equinox was maximum with 45,1% at 17 UT while the occurrence probability of equatorial plasma bubbles for September Equinox was maximum with 11,5% at 16 UT. The enhancements and reductions in the latitudinal gradient of VTEC show similar behaviors with the occurrence of equatorial plasma irregularities. The EIA during postsunset hours contributes significantly to the occurrence of equatorial plasma bubbles irregularities.
Citation - WoS: 6
Citation - Scopus: 7
An Island Parallel Harris Hawks Optimization Algorithm
(Springer London Ltd, 2022) Dokeroglu, Tansel; Sevinc, Ender; 234173
The Harris hawk optimization (HHO) is an impressive optimization algorithm that makes use of unique mathematical approaches. This study proposes an island parallel HHO (IP-HHO) version of the algorithm for optimizing continuous multi-dimensional problems for the first time in the literature. To evaluate the performance of the IP-HHO, thirteen unimodal and multimodal benchmark problems with different dimensions (30, 100, 500, and 1000) are evaluated. The implementation of this novel algorithm took into account the investigation, exploitation, and avoidance of local optima issues effectively. Parallel computation provides a multi-swarm environment for thousands of hawks simultaneously. On all issue cases, we were able to enhance the performance of the sequential version of the HHO algorithm. As the number of processors increases, the suggested IP-HHO method enhances its performance while retaining scalability and improving its computation speed. The IP-HHO method outperforms the other state-of-the-art metaheuristic algorithms on average as the size of the dimensions grows.
Citation - WoS: 2
Multiple Description Scalable Coding for Video Transmission Over Unreliable Networks
(Springer-verlag Berlin, 2009) Choupanı, Roya; Choupani, Roya; Tolun, Mehmet Reşit; Wong, Stephan; Tolun, Mehmet R.; 1863; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Developing real time multimedia applications for best effort networks such as the Internet requires prohibitions against jitter delay and frame loss. This problem is further complicated in wireless networks as the rate of frame corruption or loss is higher in wireless networks while they generally have lower data rates compared to wired networks. On the other hand, variations of the bandwidth and the receiving device characteristics require data rate adaptation capability of the coding method. Multiple Description Coding (MDC) methods are used to solve the jitter delay and frame loss problems by making the transmitted data more error resilient, however, this results in reduced data rate because of the added overhead. MDC methods do not address the bandwidth variation and receiver characteristics differences. In this paper a new method based on integrating MDC and the scalable video coding extension of H.264 standard is proposed. Our method can handle both jitter delay and frame loss, and data rate adaptation problems. Our method utilizes motion compensating scheme and, therefore, is compatible with the current video coding standards such as MPEG-4 and H.264. Based on the simulated network conditions, our method shows promising results and we have achieved tip to 36dB for average Y-PSNR.
Citation - WoS: 3
Citation - Scopus: 5
A New Robust Harris Hawk Optimization Algorithm for Large Quadratic Assignment Problems
(Springer London Ltd, 2023) Dokeroglu, Tansel; Ozdemir, Yavuz Selim; 234173
Harris Hawk optimization (HHO) is a new robust metaheuristic algorithm proposed for the solution of large intractable combinatorial optimization problems. The hawks are cooperative birds and use many intelligent hunting techniques. This study proposes new HHO algorithms for solving the well-known quadratic assignment problem (QAP). Large instances of the QAP have not been solved exactly yet. We implement HHO algorithms with robust tabu search (HHO-RTS) and introduce new operators that simulate the actions of hawks. We also developed an island parallel version of the HHO-RTS algorithm using the message passing interface. We verify the performance of our proposed algorithms on the QAPLIB benchmark library. One hundred and twenty-five of 135 problems are solved optimally, and the average deviation of all the problems is observed to be 0.020%. The HHO-RTS algorithm is a robust algorithm compared to recent studies in the literature.
Optimized Multiple Description Coding for Temporal Video Scalability
(Springer Verlag, 2013) Choupani, R.; Wong, S.; Tolun, M.
The vast application of video streaming over the Internet requires video adaptation to the fluctuations of the available bandwidth, and the rendering capabilities of the receiver device. On the other hand, the available video coding standards are designed for optimum bit rate which makes them susceptible to packet losses. A combination of video adaptation methods and error resilient methods can make the video stream more robust against networking problems. In this paper, an optimization for combining scalable video coding with multiple description coding schemes have been proposed. Our proposed method is capable of creating balanced descriptions with optimum coding efficiency.
Recent Challenges in Video Coding and Streaming
(2006) Choupanı, Roya; Choupani, R.; Tolun, M.R.; Tolun, Mehmet Reşit; 1863; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Video streaming over the Internet has gained popularity during the recent years which is mainly the result of the introduction of video-conferencing and videotelephony. These in turn have made it possible to bring to life many applications such as transmitting video over the Internet and telephone lines, surveillance and monitoring, telemedicine (medical consultation and diagnosis at a distance), and computer based training and education. These applications need a large bandwidth which is not available in all cases. Many video encoding standards have been introduced to deal with video compression and transmission problems. In this study, we have discussed the main technical features of the most important video coding standards in a comparative approach. The appropriateness of these features is application and transmission environment dependant. Manipulating video stream features or video transcoding methods are discussed as well.
Citation - Scopus: 3
Scalable Video Transmission Over Unreliable Networks Using Multiple Description Wavelet Coding
(2011) Choupanı, Roya; Choupany, R.; Wong, S.; Tolun, M.; 1863; Bilgisayar Mühendisliği
Scalable video coding (SVC) and multiple description coding (MDC) are the two different adaptation schemes for video transmission over heterogenous and best-effort networks such as the Internet. We present a new approach to combine the advantages of SVC and MDC to provide reliable video communication over a wider range of communication networks and/or satisfy application requirements. Our proposed method utilizes 3D discrete wavelet transform and a modified embedded zero tree data structure to group the coefficients in different descriptions. The proposed method reduces the impact of the drift error by organizing the frames in a hierarchical structure. © 2011 AICIT.
Sınıflandırmada Küçük ve Dengesiz Veri Kümesi Problemi
(2019) Par, Öznur Esra; Akçapınar Sezer, Ebru; Sever, Hayri; 11916
Verilerinin sınıflandırılması, veri kümesinin küçük ve dengesiz olması durumunda zorlaşmakta ve sınıflama performansını direkt etkilemektedir. Veri setinin küçük olması ve/veya sınıflar arasında dengesizlik olması veri madenciliğinde büyük bir sorun haline gelmiştir. Sınıflama algoritmaları, veri setlerinin yeterli büyüklüğe sahip, dengeli olduğu varsayımı üzerine geliştirilmiştir. Bu algoritmaların çoğu, azınlık sınıfındaki örnekleri göz ardı ederken veya yanlış sınıflandırırken, çoğunluk sınıfa odaklanır. Medikal veri madenciliğinde bazı kısıtlardan dolayı küçük ve dengesiz veri seti problemi ile sıklıkla karşılaşılmaktadır. Çalışma kapsamında erişime açık hepatit veri seti, küçük veri setlerine bölünmüş, oluşturulan her bir veri seti uzaklık tabanlı yöntemlerle çoğaltılmıştır. Çoğaltılan veri setleri dört farklı makine öğrenmesi algoritması (Yapay Sinir Ağları, Destek Vektör Makineleri, Naive Bayes ve Karar Ağacı) kullanılarak sınıflandırılmış, elde edilen sınıflama sonuçları karşılaştırılmıştır.
Türk Beyin Cerrahlarının Teknolojiye Ulaşım İmkanları
(2018) Çağıltay, Nergiz; 113411
Yeşil BHT Bilgi ve Haberleşme Teknolojileri Akademisyen ve Uygulayıcılar Açısından Bir İnceleme
(2011) Akba, Fırat; Medeni, İhsan Tolga; Medeni, Tunç Durmuş; Tolun, Mehmet Reşit; Öztürk, Mehmet; 181215

Browse

Browsing Yazılım Mühendisliği Bölümü by Access Right "info:eu-repo/semantics/closedAccess"