Dağ, Tamer

Dağ, Tamer

Name Variants

Dağ, Tamer
T.,Dağ
T. Dağ
Tamer, Dağ
Dag, Tamer
T.,Dag
T. Dag
Tamer, Dag
Tamer Dağ
Da?, Tamer

Job Title

Doç. Dr.

Email Address

Tamer.dag@khas.edu.tr

Full item page

Scholarly Output

41

Articles

10

Citation Count

0

Supervised Theses

11 Scholarly Output Search Results

Now showing 1 - 10 of 41

Citation Count: 29
Received Signal Strength Based Least Squares Lateration Algorithm for Indoor Localization
(Pergamon-Elsevier Science Ltd, 2018) Dağ, Tamer; Arsan, Taner
Following the success of accurate location estimation for outdoor environments locating targets in indoor environments has become an important research area. Accurate location estimation of targets for indoor environments has the potential for the development of many different applications such as public safety social networking information and mapping services. However the GPS (Global Positioning System) technology used for outdoor environments is not applicable to indoor environments making accurate location estimation a challenging issue for indoor environments. In this paper we propose a received signal strength based least squares lateration algorithm which uses the existing infrastructure. By employing redundancy in the number of access points and applying least squares approximations to the received signal strength values the lateration algorithm increases the accuracy of location estimations. The usage of the existing infrastructure makes the proposed algorithm low cost when compared to other positioning algorithms which need very precise high cost components. (C) 2017 Elsevier Ltd. All rights reserved.
Citation Count: 0
Implementation of a Sdn (software Defined Network)
(International Institute of Informatics and Systemics IIIS, 2014) Ergen, Mithat Sinan; Kutlan, Sarp; Dağ, Tamer; Dağ, Hasan
Improving resource efficiency enhancing network security and achieving simpler network management have become the main goals for networking in the previous years. To accomplish these ideas an efficient routing traffic monitoring access control and server load balancing systems need to be designed. However these objectives make the optimization and management of networks rather difficult. In this paper Software Defined Networks (SDN) an alternative way for creating an optimized network by taking into account the difficulties met today is introduced. Software Defined Networks provide the separation of control and data planes for the switches which allows programming for a customized control plane. With simplified network management through SDN it can be possible to dynamically adjust the behavior of the network equipment independently from equipment manufacturers. New mechanisms with new potential benefits can easily be explored and used. Quality of Service and network security problems can be solved rapidly. In this paper it is described how to build a virtualized SDN in order to show the benefits of SDN over a distributed network. Virtualization can provide deployment and delivery flexibility cost savings and improved user experience.
Indoor Location Estimation Through Redundant Lateration for Indoor Positioning System
(Kadir Has Üniversitesi, 2017) Khudhuragha, Mohammed; Da?, Tamer
The simplest way to describe indoor Positioning Systems (iPS) is that it’s like a Global Positioning System (GPS) for indoor environments. iPS can be used to locate objects or people inside buildings typically via a mobile device such as a smart watch orsmart phone or tablet. Although the technology is newer than GPS services that leverage iPS are quickly gaining attention in places like art galleries museums shopping malls hospitals airports and other indoor venues where navigation and other location-based services (LBS) can prove to be necessary. in this thesis we are suggesting a new method of location estimation by inhancing the lateration method by using the redundant method which uses normal lateration to calculate the location of the point by calculating the location of the same point 4 times but with different groups of access points (123 124 134 and 345). Then we started collecting the RSSi for these groups and convert it in to distances and estimate the location from these distances and the results for these tests will be compared with the final results. Our results are statistical results for comparison in real life. Our algorithm will chose the best result out of the 4 groups which is average error 2.470399 minimum error 0.254138 maximum error 9.822816 standard deviation 1.371947 and the number points with error above 3 meters 48.
Dynamic Multi Threshold Priority Packet Scheduling Algorithms for Wireless Sensor Networks
(Kadir Has Üniversitesi, 2015) Uzungenç, Sezer; Dağ, Tamer
Kablosuz sensör ağlarında farklı türlerde özellikle gerçek zamanlı ve gerçek olmayan zamanlı paket zamanlama gereklidir. Sensörlerin enerji kullanımlarını ve iletim gecikmelerini azaltmak önemlidir. Tezimde yeni paket zamanlama algoritmalarını geliştirerek bunu kablosuz sensör ağlarına entegre etmeye çalışarak enerji kullanımını ve iletim gecikmelerini geliştirerek daha verimli yapıyorum. Tasarladığım dinamik çoklu eşik ve öncelikli paket zamanlama algoritmaları, düşük öncelikli veriler için gecikme zamanını ve veri kaybını azaltarak bunu yüksek öncelikli verilere adil bir şekilde davranarak yapıyor. Eşik algoritmaları günümüzde en çok kullanılan paket zamanlama algoritmalarıyla kıyaslanıyor. Bunlar ilk gelen ilk servis edilir algoritması ile öncelikli paket zamanlama algoritmasıdır. Simülasyon sonuçları gösteriyor ki dinamik çoklu eşik ve öncelikli paket zamanlama algoritmaları düşük öncelikli verilerin servis kalitesini arttırıyor ve bunu yüksek öncelikli verilerin servis kalitesini koruyarak yapıyor.
Design and Implementation of a Mobile Prescription System With Patient-Healthcare Professional Interaction
(Kadir Has Üniversitesi, 2015) Öztürk, Çağdaş Egemen; Dağ, Tamer
In this thesis, a mobile prescription reminder and scheduling system application with patient-healthcare professional interaction is designed and implemented. By using the application, different types of users are able to create and manage prescriptions and are reminded to take the medication based on the prescription data. Besides, users are able to reach the prospectuses of the drugs. One of the most important functionality of the application is to provide patient-healthcare professional interaction. By using the application, healthcare professionals can assign prescriptions to their patients and they can also monitor their medicine compliance. Since the medicine compliance is very important to have an effective treatment, the main goal of this application is to help people to take their medicines on time and find the prospectus information of the drugs easily by using their mobile phones.
Comparison and Analysis of Various Indoor Positioning Systems Techniques
(Kadir Has Üniversitesi, 2016) Demirkol, Derya; Da?, Tamer
indoor Positioning has been a research subject in order to facilitate people life easier. Different type of methods has been implemented and tested by the years. The Global Positioning System (GPS) is a satellite based navigation system. This technique is using outdoor environment to navigate people or buildings. in indoor positioning GPS signals are usually too weak to provide accurate positioning estimate. Other technique need to investigate to get better result of accuracy. Ultrasonic Positioning Systems RFiD Computer Vision System RF Wireless indoor Positioning System has been used. Recent year Wireless indoor Positioning Technique is most popular technique. it is easier to set up every indoor environment by using Access Points (AP) and costs are very low comparing to other techniques. Also Wireless technique does not need any extra component or effort. in this thesis indoor Positioning techniques were investigated. Different algorithms were implemented to estimate location in indoor areas and accuracy comparison has been made by using Wireless technology.
Developing Novel Techniques for Spatial Domain Lsb İmage Steganography
(Kadir Has Üniversitesi, 2019) Shehzad, Danish; Dağ, Tamer
Steganography is one of the most noteworthy information hiding mechanism, which is used as an alternative to cryptography in order to provide adequate data security. Image steganography is one of the key types of steganography where a message to be transmitted is hidden inside a cover image. The most commonly used techniques for image steganography rely on LSB Steganography. In this thesis, new techniques are developed for LSB image steganography to achieve maximum security, optimal data capacity along with provisioning of efficient steganography mechanism. In the first part of this work, a novel technique based on pairs matching is developed for LSB image steganography. In this technique MSBs along with LSBs are used in a delicate method for data hiding for the first time. The message bits from the secret information are compared with all defined pixel pairs and replace the least two significant bits with respective matched pair number. This technique shows good quality of stego image along with adequate peak signal to noise ratio and provides high payload of secret message. In the second part, threshold-based LSB image steganography technique is developed. This technique also works in spatial domain and categorizes the pixels based on threshold defined categories. Maximum four bits and minimum one bit is embedded in pixel based on its category. The prominence in THBS is on security and payload as it uses bits proficiently for data embedding. ETHBS allows efficient execution of the algorithm along with provisioning of optimal security. In the last part 1LSB Image steganography technique based on blocks matrix determinant is developed. It is a technique in which data is embedded by making minimal changes in image pixels. This technique is 1LSB substitution technique that works on matrix determinant of 2 by 2 blocks of image pixels. This technique ensures high PSNR and ensures good quality of stego image.
Location-Allocation Through Machine Learning for E-Commerce Logistic Services
(Kadir Has Üniversitesi, 2022) TOPUZ, TAYYİP; Tamer Dağ
Companies desire to expand their businesses in such a way that there will not be any loss in their revenues. An e-commerce logistics company functions as the distribution and delivery of goods to buyers. To expand the business, opening new branches is a critical decision since determining the location of a branch correctly will not only help an e commerce logistics company to increase its revenue but also improve customer satisfaction. The logistic network, which is based on locations, is the most vital input for their business. For such decisions, data science is becoming an essential tool in recent years. Research shows that demographic information has a considerable impact on consumer behavior in e-commerce. In this thesis, the demand potential is studied by using demographic data and current demand for an e-commerce logistics company. The outcome of this work can be used to determine the location of new branches. Machine learning techniques are being used to decide the location of a new branch with the help of delivery demand potential prediction.
Low Energy Fixed Clustering Algorithm for Wireless Sensor Networks
(Kadir Has Üniversitesi, 2016) Cengiz, Korhan; Dağ, Tamer
Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence the life span and the energy consumption of a WSN has become a challenging research area. According to the existing studies instead of using direct transmission or multi-hop routing clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. in this thesis low energy fixed clustering algorithm (LEFCA) and multihop low energy fixed clustering algorithm (M-LEFCA) are proposed for WSNs. With LEFCA the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation significantly. in addition proposed M-LEFCA uses multi-hop intra cluster communication approach. it selects optimum forward neighbor cluster heads (CHs) as relay nodes (RNs). M-LEFCA aims to reduce energy dissipation and prolong network lifetime of LEFCA by combining clustering and multi-hop routing approaches.
Identification of Critical Proteins Associated With Learning Process for Down Syndrome
(Kadir Has Üniversitesi, 2020) Kulan, Handan; Dağ, Tamer
DS protein profilleri laboratuvarda biyokimyasal teknikler uygulayarak gözlemlenmektedir. Fakat, elde edilen protein listesi uzundur ve listedeki her protein DS ile alakalı değildir. Bu yüzden, DS analizi ve tedavisinde, protein ifade miktarları istatiksel metodlar ve makine öğrenmesi teknikleri uygulayarak analiz edilmektedir. Bu tezde, önceki çalışmalara kıyasla, farklı öndeğerlendirme adımları, özellik seçimi ve sınıﬂandırma teknikleri, farklı veri setleri için protein altkümeleri belirlenmesi için uygulanmıştır. Bu protein altkümeleri fareleri daha doğru şekilde ayrıştırır. Spesifik DS özelliklerinin kritik yolaklara etki eden bu altkümelerdeki proteinler tek tek analiz edildiğinde, seçilmiş proteinlerin öğrenme ve hafıza, sinyal yolakları, Alzheimer hastalığı, bağışıklık sistemi ve hücre ölümü gibi önemli süreçlerde rol aldığı gözlemlenmiştir. Bu tezde seçilen protein alt kümelerinden DS un farklı semptomlarını anlamak için yararlanılabilinir ve DS tedavisinde etkili ilaçlar geliştirmek için kullanılabilinir. The protein proﬁles of people with DS are observed by applying biochemical tech niques in laboratory. However, the list of analyzed proteins is long and not all proteins in list are not related to DS. Thus, for the analysis and the treatment of DS, protein expression levels have been analyzed by applying statistical procedures and machine learning techniques. In this thesis, compared to previous works, different preprocessing steps, feature selection and classiﬁcation techniques are applied to deﬁne the subsets of proteins for datasets. These subsets diﬀerentiate mice more accurately. When these subsets which aﬀect the critical pathways of speciﬁc DS aspects are analyzed, it is monitored that selected proteins have vital roles in the processes, such as apoptosis, learning and memory, signaling pathways, immune sys tem and Alzheimers disease (AD). The subsets of proteins selected in this thesis can be applied to interpret the causes of diﬀerent symptoms in DS and can be utilized to foster eﬀective drugs for the cure of DS.