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Bitcoin, merkezi otoriteye ihtiyaç duymayan, dağıtık ve dijital bir para birimi olarak, son yıllarda finans dünyasındaki popüler konuların başında gelmektedir. İlk dönemde Bitcoin’in finansal özelliklerine duyulan ilgi, zaman içerisinde Bitcoin’e hayat veren blok zinciri teknolojisine doğru kaymaya başlamıştır. Hemen her gün yeni bir uygulama alanını duymaya başladığımız blok zinciri teknolojisi alanındaki akademik araştırmalar sınırlı sayıdadır. Ülkemizde de gerek uygulama gerekse akademik anlamda yeterli sayıda çalışma olmadığını söyleyebiliriz. Bu inceleme, blok zinciri teknolojisini sistematik olarak gözden geçirerek, kullanım alanlarını, açık noktalarını ve gelecek beklentilerini tartışmaktadır., In recent years, as being a distributed and digital currency, and not being in need of a centralized authority, Bitcoin has become the foremost among the most popular subjects of the financial world. The initial interest in Bitcoin’s financial features change course into blockchain technology, which essentially vitalizes the Bitcoin. While we have started to hear about the new implementations of blockchain technology almost every day, worldwide academic research is still limited. Unfortunately, implementation efforts and academic studies on blockchain in our country is also scarce. This paper systematically investigates the blockchain technology to discuss its usage areas, open issues, and future expectations.

Date of Conference: 2-5 May 2018 In this paper, we study online nonlinear data regression and propose a highly efficient long short term memory (LSTM) network based architecture. Here, we also introduce on-line training algorithms to learn the parameters of the introduced architecture. We first propose an LSTM based architecture for data regression. To diminish the complexity of this architecture, we use an energy efficient operator (ef-operator) instead of the multiplication operation. We then factorize the matrices of the LSTM network to reduce the total number of parameters to be learned. In order to train the parameters of this structure, we introduce online learning methods based on the exponentiated gradient (EG) and stochastic gradient descent (SGD) algorithms. Experimental results demonstrate considerable performance and efficiency improvements provided by the introduced architecture.

Date of Conference: 2-5 May 2018 In this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations of this structure in a state space form for each node in our network. Using this form, we then learn the parameters via our Distributed Particle Filtering based (DPF) training method. Our training method asymptotically converges to the optimal parameter set provided that we satisfy certain trivial requirements. While achieving this performance, our training method only causes a computational load that is similar to the efficient first order gradient based training methods. Through real life experiments, we show substantial performance gains compared to the conventional methods.

In traditional networking architecture, for each problem a specific protocol is dedicated to network devices and these devices are configured manually. Since they are hardware-based, the implementation of new ideas and technologies to these devices is very difficult. However, today's increasing use of smartphones and other mobile data devices has increased the data traffic, and with the addition of service diversity and the needs of users, the existing network structure has become much more complex. Software Defined Networks (SDN) is a new network paradigm that allows a programmable central controller mechanism which separates the control plane from the data plane. With the advantage of a centralized control mechanism, SDN ensures that network structures can respond more quickly to current demands, be more flexible and reprogrammable. In addition, all these processes are implemented at a lower cost compared to traditional networking architecture. To evaluate the performance of SDN architectures, testbeds are used by researchers. In this paper, a small-scale and cost-effective SDN testbed is designed and implemented. The testing environment is realized using Raspberry Pi 3, Floodlight and Open vSwitch. Controller performance has been observed with the MACtracker application.

24th Signal Processing and Communication Application Conference (SIU) (2016 : Zonguldak, TURKEY), Critical Infrastructure Security had always been an issue. With the technological advancements, there has been a continuous inflow of novel methods for critical infrastructure security. In this paper, design, implementation, and field tests of a novel critical infrastructure security system based on wireless sensor networks is presented.