Search

One of the main challenges for developed cities is vehicle traffic and its management. Because of physical structures and cross roads of urban intersections, traffic flows may cause time delay, congestion, traffic accidents and more fuel/time consumption. For these reasons, there are many studies on traffic management systems which are a part of smart city applications in both literature and practice. In recent years, it seems that VANET (Vehicular Ad Hoc Networks) architecture has been preferred in these studies, which enables vehicles to easily communicate with each other or devices on the edge of the road and also to transfer the related traffic data to the center. On the other hand, there are many studies on a new network paradigm called SDN (Software Defined Networks) that solves problems such as performance, programmability, scalability, security and management difficulties in today's traditional networks. Depending on all this, in this study, we will present an architectural proposal about how to use SDN and VANET network paradigms together in traffic management systems in order to use both existing intersection management system modules more adaptively and future traffic based services to be added in desired service quality without changing the communication infrastructure. Also in this study, a 3-stage fuzzy-decision tree model was proposed for traffic signaling which is the most important problems of urban traffic by focusing environmental factors (accident situation, priority vehicle transition etc.) and activity information (road construction, meeting, train and bus schedule etc.) not only vehicle density unlike other signaling studies in literature. The proposed model also generates/sends a dynamic flow input to SDN agent vehicles and SDN agent RSU (Road Side Unit) in intersection for making VANET routing protocols (AODV-ad hoc on-demand distance vector, DSDV-destination-sequenced distance vector) selection automatically according to traffic incident and density. In order to measure the functionality of the SDN based VANET architecture and 3 stage fuzzy-decision tree model on it for the transmission infrastructure of the traffic management, performance tests were performed on different traffic and network scenarios on Adapazari City Centre Model. SDN based VANET architecture was compared with traditional systems according to transmission infrastructure and also proposed 3-stage fuzzy-decision model was compared with signaling techiques in literature such as fixed-time signaling, Webster equation, ant colony algorithm and particle swarm optimization under same traffic and network scenarios. The results obtained from performance tests shows that the proposed 3-stage fuzzy-decision model has %15-17 better performance than fixed-time signaling and webster equation and also %9-11 better performance that particle swarm and ant colony optimizations in low density traffic scenarios. In high density and dynamic traffic scenarios these rates reach %20-22 compared to fixed-time signaling and webster equation and %14-15 compared to particle swarm and ant colony optimizations. In addition in this study, proposed SDN based VANET architecture was compared to traditional VANET ITS architectures within the same traffic and network scenarios. According to obtained results, it is shown that the proposed SDN based VANET has %30-40 better performance than traditional systems in end-end delay, throughput and packet loss rate criteria. (C) 2019 Elsevier B.V. All rights reserved.

Estimations of the solar potential from the building design files may affect placement considerations in favor of more sunlight reception that reduces the energy costs and saves the environment. In this study, a GPU based system (GPU-DSRM) is proposed to estimate direct and diffuse solar radiation aggregated on 3D structures at urban or individual scale. In the proposed approach, finite element method, back-face detection and ray-tracing algorithms are customized to run in parallel to reduce the execution time. Thus, real-time shadow analysis with adjustable sampling rate and time scale can be performed without compromising precision and accuracy of the estimations. The most important novel aspect of the study is that it can be used anywhere in the world without the need for meteorological data. Some of the test results obtained from a site with 10 buildings are presented in this paper that shows a speedup value of 45 with the new GPU-based implementation compared to the CPU-based model. The GPU-DSRM tool has also been compared with geometric tools in the literature. Solar energy potential analysis of building designs or existing urban formations can be completed faster and more precisely with this new approach.

Wireless Sensor Networks (WSN) are the networks that can realize data processing and computation skills of sensor nodes over the wireless channel and they have several communication devices. Wireless Multimedia Sensor Networks (WMSN) are the networks composed of low-cost sensor nodes that transmit real-time multimedia data like voice, image, and video to each other and to sink. WMSN needs more energy and bandwidth than WSN since they transmit a larger amount of data. The size of the data transmitted by the sensor nodes to each other or the sink becomes an important factor in their energy consumption. Energy consumption is a fundamental issue for WMSN. Other issues that affect the progress of WMSN are limited bandwidth and memory constraints. In these networks, for which the node battery lives are important sources, the limited sources must be effectively used by decreasing the transmitted data amount by removing the redundant data after proper processing of the environmental data. A new algorithm is developed to minimize the energy consumption during image data transmission between sensor nodes on WMSN, and so, make the nodes use their most important source, battery life ef-fectively in this study. This algorithm is named as Energy-aware Application Layer Algorithm based on Image Compression (EALAIC). This algorithm makes use of the top three image compression algorithms for WMSN and decides instantly to which one is the most efficient based on three parameters: the distance between the nodes, total node number, and data transmission frequency. In this way, the sensor node battery lives are used efficiently. The performance analysis of the developed algorithm is also done via Network Simulator - 2 (NS - 2) and it is compared by the existing algorithms in terms of energy rate (consumed energy/total energy) and PSNR (Peak Signal to Noise Ratio). WOS:000552257400009 2-s2.0-85088506916

In this study, a hybrid and layered Intrusion Detection System (IDS) is proposed that uses a combination of different machine learning and feature selection techniques to provide high performance intrusion detection in different attack types. In the developed system, firstly data preprocessing is performed on the NSL-KDD dataset, then by using different feature selection algorithms, the size of the dataset is reduced. Two new approaches have been proposed for feature selection operation. The layered architecture is created by determining appropriate machine learning algorithms according to attack type. Performance tests such as accuracy, DR, TP Rate, FP Rate, F-Measure, MCC and time of the proposed system are performed on the NSL-KDD dataset. In order to demonstrate the performance of the proposed system, it is compared with the studies in the literature and performance evaluation is done. It has been shown that the proposed system has high accuracy and a low false positive rates in all attack types.

When a disaster occurs, a huge amount of inconsistent victim or damage information data is received by many different sources. Disaster management systems achieve the completion of a significantly vital task, which is to reduce the number of victims or amount of damage caused by a disaster, with real-time information monitoring infrastructure. A fundamental role of these systems that could help rescue teams is to make a quick and accurate decision about the region that will be affected by the disaster and the possible effects of the tragedy. Employing IoT solutions in these systems provides the possibility of rapidly and precisely orienting rescue teams to be dispatched to the disaster area and also quickly receive specific information about the effects of the disaster. To achieve this purpose, we present a post-disaster framework using the IoT communication technologies for disaster management based on the proposed crowd sensing clustering algorithm in this paper. The proposed framework provides information about the damage status of buildings with crowd density data along with efficient real-time data collection, data aggregation, and the process of monitoring dissemination stages. This framework realizes clustering of resident density by using the cellular networks and Wi-Fi connections and calculating the damage status of buildings through the designed and specifically implemented IoT unit data. Furthermore, it employs a fuzzy logic-based decision support system to manage the resources. The proposed framework, on real base stations and access points dataset, has shown significant results for identifying crowd densities with the highlighting status of buildings in the disaster area.

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine. Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211] The work of Sultan Zavrak was supported by the Scienti~c and Technological Research Council of Turkey (TUBITAK) through the 2211/C Ph.D. Scholarship Programme for Priority Areas. WOS:000544044400003 2-s2.0-85086985318

In the early days of the Internet architecture, the most important aim is to transmit data over packet switched networks. The traditional Internet architecture used in these networks lacks quality of service. However, today, as realtime applications increase, it is needed. There are approaches to improving the quality of service using the flow label field in the Internet Protocol version 6 header. In this study, a novel algorithm that uses destination network parameters to reduce queuing and end-to-end delay is created. A round-robin-based time-aware priority queue new model is used within this algorithm. Data packets using this proposed queue are prioritized with metric values of the destination network. In order to provide end-to-end service quality, the prioritization value is used by placing it in the flow label field. For this purpose, a new approach to the use of this field is proposed. Delay, one of the most important factors affecting quality of service, is reduced with the proposed algorithm and flow label usage approach. As a result, the reduction in delay times between 22 and 39 ms resulted in various improvement rates between 16.79% and 35.13%.

In marketing research, one of the most exciting, innovative, and promising trends is quantification of customer interest. This paper presents a deep learning-based system for monitoring customer behavior specifically for detection of interest. The proposed system first measures customer attention through head pose estimation. For those customers whose heads are oriented toward the advertisement or the product of interest, the system further analyzes the facial expressions and reports customers' interest. The proposed system starts by detecting frontal face poses; facial components important for facial expression recognition are then segmented and an iconized face image is generated; finally, facial expressions are analyzed using the confidence values of obtained iconized face image combined with the raw facial images. This approach fuses local part-based features with holistic facial information for robust facial expression recognition. With the proposed processing pipeline, using a basic imaging device, such as a webcam, head pose estimation, and facial expression recognition is possible. The proposed pipeline can be used to monitor emotional response of focus groups to various ideas, pictures, sounds, words, and other stimuli.

The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewater treatment process. An estimation of the obtained wastewater treatment characteristics provides the information needed to set up the current process steps, and it is important to have an optimum treatment. In this study, an artificial immune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO, DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset is provided from the University of California Irvine (UCI) Machine Learning Library. The current dataset is analyzed by principal component analysis (PCA) to obtain maximum system performance. As a result of the simulation, the output parameters are successfully predicted using the AIS model with real data. The classifier's performance ratios are studied separately using the coefficient of determination (R$^{2})$ and the mean squared error of prediction (MSEP), and their rates are given in this study.

In the chaos literature, there has been much attention paid to chaotic systems with uncountable equilibrium points such as systems with line equilibrium, curve equilibrium. This paper reports a 3-D chaotic system with a closed curve of equilibrium points, which has the shape of a boomerang. Dynamics of the chaotic system with the boomerang equilibrium has been studied by using phase portraits, bifurcation diagram, Lyapunov exponents and Lyapunov dimension. Also, we design an electronic circuit implementation of the theoretical system to check its feasibility. As an application of the new chaotic system, we have derived new results for sound encryption with the new chaotic system.

In this study, a novel chaos-based hybrid encryption algorithm design for secure and effective image encryption is presented. To design the algorithm, the Zhongtang chaotic system has been selected because of its rich dynamic features and its dynamical analysis is performed. On the base of this system, a new chaos-based random number generator (RNG) is developed and usefulness of the designed RNG in an encryption process is shown over NIST 800-22 randomness tests. S-Box generation algorithm is designed, and the performance tests of S-Box are realized. By using the designed RNG and S-Box generation algorithms, the new hybrid image encryption algorithm based on AES (CS-AES) is developed. Image encryption applications are performed for comparison with other encryption algorithms in the literature to show its security level and efficiency. Security and performance analyses are made, and results are evaluated. So, it is proved that the proposed CS-AES algorithm is more secure and effective.

Wireless Multimedia Sensor Networks (WMSNs) transmit heterogeneous data having different quality of service and urgency constraints. WMSNs focus on offering QoS for multimedia transmission while Wireless Sensor Networks focus on minimizing energy consumption. To overcome urgency challenges, a new traffic and QoS-aware cross-layer MAC protocol for WMSNs namely urgMAC is proposed in this paper. The urgMAC aims to provide continuous QoS support with video quality tradeoff at the application layer dynamically for applications such as habitat monitoring, military border surveillance and border monitoring containing specific urgency challenges. To this end, the urgMAC includes new mechanisms called Two Tiered Service Differentiation Mechanism, Adaptive Data Rate Adjustment Mechanism, Urgency-based Contention Window Size Adaptation, Traffic Type Adaptive Duty Cycle and Multimedia Message Passing. The urgMAC has been modeled and simulated by Riverbed Modeling and Simulation Software. In addition, the urgMAC is compared with the recent protocols in the literature, and it achieves better results in terms of end-to-end delay and channel utilization.

Some solvents in commercial products may have harmful effects on human health. It is important to determine the percentage of this certain solvent in a product to detect any possible health hazards. In this paper, three different solvents, acetone, methanol, and chloroform, are used to form binary gas mixtures in a laboratory environment. Nine quartz-crystal microbalance sensors are used, and gas data are obtained through the responses of these sensors. First, the data set divided 11 times randomly for validation sensitivity of the results. For each of the binary gas mixtures, insignificant sensors are removed, considering multivariate analysis of variance analysis, and sensor data sets are obtained. The statistical multivariate linear regression (MvLR) method is used to determine the ratio of individual gasses in each binary gas mixture. Flexible models are created by removing insignificant sensor data from the equations in the MvLR. Prediction performances of 11 data sets reveal and validate that statistical methods can be used to detect the ratio of a certain gas within a gas mixture, and reliable results can be achieved.

In this study, an encryption algorithm that uses chaos based S-BOX is developed for secure and speed image encryption. First of all, a new chaotic system is developed for creating S-Box and image encryption algorithm. Chaos based random number generator is designed with the help of the new chaotic system. Then, NIST tests are run on generated random numbers to verify randomness. A new S-Box design algorithm is developed to create the chaos based S-Box to be utilized in encryption algorithm and performance tests are made. As the next step, the new developed S-Box based image encryption algorithm is introduced in detail. Finally, image encryption application is carried out. To show the quality and strong of the encryption process, security analysis are performed. Proposed algorithm is compared with the AES and chaos algorithms. According to tests results, the proposed image encryption algorithm is secure and speed for image encryption application. (C) 2016 Elsevier Ltd. All rights reserved.

Postupci na bežičnim senzorskim mrežama - Wireless Sensor Networks (WSN) i njihovim područjima uporabe sve su češći pa problem sigurnosti mreže postaje sve važniji. Budući da su snaga procesora, memorija i izvori energije ograničeni na čvorove bežičnih senzorskih mreža, tradicionalno strukturirani kodovi više nisu učinkoviti. Uzevši to u obzir, očita je potreba za manjim procesnim opterećenjem i potrošnjom energije te učinkovitim kodom. U ovom se radu razvija kaotični kodni sustav za zadovoljenje sigurnosnih potreba na WSN. Uspoređuju se ovdje razvijeni kaotični sustav i uobičajeno korišteni Skipjack kod uz pomoć algoritma OPNET Modeller softvera i konstatira se da su postignuti bolji rezultati., Processes on Wireless Sensor Networks (WSN) and their areas of use have become more widespread, and the issue of net-work security has appeared as one of the primary necessities. As power of the processor, memory and energy sources are limited on wireless sensor network nodes, traditional encryption structures are not found effective. With these criteria taken into consideration, the need for less process load and energy consumption as well as a powerful encryption is obvious. In this study, a chaotic encryption system to meet the security need on WSN using chaotic systems was carried out. The chaotic system developed here and the commonly used Skipjack encryption were compared with the help of algorithm OPNET Modeller software and better results were achieved.

Nowadays, the IT service environment develops in a dynamic, rapid, and unpredictable way. Microservices and application containers in this process have a significant impact on new generation IT service models. The fact that they have important capabilities such as modelability, presentability as service, and restructurability, are reasons for preferring them in many areas. Moreover, microservices can meet various needs of IT personnel. As it is known, all server system components, such as CPU, network, hard-drive I/O, affect energy consumption. At this point, microservices also play an important mediator role in resource management. Energy consumption of microservice-based applications is lower than that of the traditional approaches. However, there are still cases of recoverable and unnecessary consumption at some points. Microservices can be monitored and controlled using many methods. Thus, this provides us with opportunities to recover the wasted energy resources considerably. In this article, the effects of container-based microservice architectures on the energy consumption of the system and how to reduce these effects are presented. For this purpose, a methodology, which has 3 approaches (disconnect, pause, stop), and a tracing mechanism are proposed. The results show that this methodology has a considerable effect on energy efficiency.

This paper presents a new S-Box design algorithm of hidden hyperchaos in a 5-D hyperchaotic Burke-Shaw system, which is obtained by adding feedback controllers to the classic Burke-Shaw system. Of particular interest is that the hidden hyperchaotic attractors can be generated with three positive Lyapunov exponents in the proposed system with only one stable equilibrium. In addition, a new Pseudo Random Number Generator (PRNG) is developed by using the new 5-D Burke-Shaw system. Then, the S-Box design algorithm is developed based on the new PRNG. Furthermore, generated bit series have passed all NIST tests, which are run on bit series from the new PRNG. According to performance analysis, generated S-Box has better value than the chaos-based S-Box on the literature. Finally, Fuzzy-PID controller design is discussed for 5-D Burke-Shaw system with hidden hyperchaos. These results show the effectiveness of controller.

The energy consumed by appliances is increasing year by year for the purpose of providing desired occupant comfort in buildings. In this paper, a new Heating, ventilation, and air conditioning (HVAC) system is implemented in order to control and monitor the energy consumption of buildings by using fuzzy logic, machine to machine (M2M) communication, and Internet Technologies. The embedded design's charge is to save energy during peak hours, without affecting the comfort of the occupant and with self-control managing energy with Internet of Things (IoT) technology. Our first prototype was validated and applied to a realistic scenario with two air conditioners. The results show that the system developed is capable of automatically reducing energy consumption and maintaining steady comfort level required by the occupants in the building.

Chaotic systems without equilibrium are of interest because they are the systems with hidden attractors. A nonequilibrium system with chaos is introduced in this work. Chaotic behavior of the system is verified by phase portraits, Lyapunov exponents, and entropy. We have implemented a real electronic circuit of the system and reported experimental results. By using this new chaotic system, we have constructed S-boxes which are applied to propose a novel image encryption algorithm. In the designed encryption algorithm, three S-boxes with strong cryptographic properties are used for the sub-byte operation. Particularly, the S-box for the sub-byte process is selected randomly. In addition, performance analyses of S-boxes and security analyses of the encryption processes have been presented. National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61601306]; Shenzhen Overseas High Level Talent Peacock Project Fund [20150215145C] The author Xiong Wang was supported by the National Natural Science Foundation of China (No. 61601306) and Shenzhen Overseas High Level Talent Peacock Project Fund (No. 20150215145C).

Long-term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power and quadratic forms to model Uganda’s net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of the forecasting models using a hybrid algorithm based on particle swarm optimization and artificial bee colony algorithms. Secondly we model the forecast residuals using simple linear regression to obtain 90% significance level lower and upper error bounds. The lower and upper error bounds were used to construct predication intervals for Uganda’s net electricity consumption up to year 2040. We accessed the accuracy and performance of the models using MAPE and $$R^{2}$$ . We obtained 1.4387 and 1.1741% for MAPE and 0.9920 and 0.9948 for $$R^{2}$$ , respectively, for power and quadratic models. According to our results, in year 2040 Uganda’s electricity consumption will be between [35,471.5, 36,317.6] GWh for the power model indicating an annual average increase of 10.1–11.3%. For the quadratic model consumption is expected to be between [47,443.1, 48,289.4] GWh which indicates an average annual increase of 11.2–12.4%.

In this article a simple chaotic flow with hidden attractor is proposed. Various dynamics of this new system such as periodic and chaotic oscillations can be achieved by setting bifurcation parameters in a proper value. Nowadays chaos based engineering applications like encryption and hiding data face some significant problems. Chaotic systems with hidden attractors, which our system belongs to them, may unfold some primary steps to solve these problems. As the first step, chaotic behavior of our proposed system has been realised in physical existence by using the Orcard-PSpise software. We have found a proper qualitative likeness between experimental and simulation result. Then, chaos-based random number generator design is carried out using the proposed chaotic system. The numbers obtained from the random number generator are applied to the NIST 800-22 tests, and all of the random numbers are passed through the all tests. A chaos-based encryption algorithm with a random number generator is developed and image encryption application is implemented. Encryption results are compared with the AES algorithm, and performance and security evaluation of the developed encryption algorithm is performed.

One of the main challenges for developed cities is vehicle traffic management. There are many studies about urban intersection managements both in literature and in practice. In recent years, VANET (Vehicular ad hoc networks) has been preferred in these studies, which enables vehicles to easily communicate with each other or with RSUs. On the other hand, there are many studies on a new network paradigm called SDN (software defined networks) that solves problems such as performance, programmability, scalability etc. difficulties in today's networks. Depending this, in this study, we will present an architectural proposal about how to use SDN and VANET paradigms together in intersection management systems in order to use both existing and future intersection management system modules more adaptively and in desired service quality. In addition, to show the functionality of the proposed architecture for signaling system, we propose an ant colony algorithm based SDN controller's modules. The results obtained from ant colony algorithm based SDN modules were compared with other signaling techniques in literature on a isolated intersection. It shows that the proposed algorithm and architecture provide an improvement about % 15-22 according to existing systems and % 7-12 according to computational techniques in average delay and queueing.

Modeling and analyzing of human movements has become easier with the development of sensor technologies. Human movements can be modeled using image processing software with depth and motion sensors in 3D. Measurement errors are also observed in motion detection sensors as in most systems. Special filters have to be developed for each system in order to minimize this error rate and obtain more realistic measurements. Kalman Filter is a well-known method that is commonly used to minimize this type of measurement errors. In this study, the actual body lengths (upper arm, forearm, lower leg, upper leg) are measured and obtained from the human motion sensor. Kalman Filter and Extended Kalman Filter are applied to the obtained data from human motion sensor. All measurements are compared with the actual body lengths and error rate is calculated as using Mean Absolute Percentage Error (MAPE). Kinect data are compared with actual lengths and error rates were calculated at 20%, when the Kalman Filter is applied, the error rate decreased to 14%, while when the Extended Kalman filter is applied, it dropped to 8%. Human motion sensor data have been improved with using Extended Kalman Filter. Thus, actual measurements of candidatescan be easily obtained with only one useful sensor without taking any actual measurements by saving time and budget.

Today, the use of Ethernet-based protocols in industrial control systems (ICS) communications has led to the emergence of attacks based on information technology (IT) on supervisory control and data acquisition systems. In addition, the familiarity of Ethernet and TCP/IP protocols and the diversity and success of attacks on them raises security risks and cyber threats for ICS. This issue is compounded by the absence of encryption, authorization, and authentication mechanisms due to the development of industrial communications protocols only for performance purposes. Recent zero-day attacks, such as Triton, Stuxnet, Havex, Dragonfly, and Blackenergy, as well as the Ukraine cyber-attack, are possible because of the vulnerabilities of the systems; these attacksare carried by the protocols used in communication between PLC and I/O units or HMI and engineering stations. It is evident that there is a need for robust solutions that detect and prevent protocol-based cyber threats. In this paper, machine learning methods are evaluated for anomaly detection, particularly for EtherCAT-based ICS. To the best of the author's knowledge, there has been no research focusing on machine learning algorithms for anomaly detection of EtherCAT. Before testing anomaly detection, an EtherCAT-based water level control system testbed was developed. Then, a total of 16 events were generated in four categories and applied on the testbed. The dataset created was used for anomaly detection. The results showed that the k-nearest neighbors (k-NN) and support vector machine with genetic algorithm (SVM GA) models perform best among the 18 techniques applied. In addition to detecting anomalies, the methods are able to flag the attack types better than other techniques and are applicable in EtherCAT networks. Also, the dataset and events can be used for further studies since it is difficult to obtain data for ICS due to its critical infrastructure and continuous real-time operation.

Virtual local area network (VLAN)’s are being created for improve performance, easy to manage security and ensure address on local area networks. This paper introduces a new approach for load balancing on virtual local area networks. The method which is developed for this approach, is dynamically changing the clients ports VLAN membership according to VLAN’s total traffic of the same security policy. The clients which have to register to security VLAN, can access their permission level source at all physically location of LAN, this is the flexibility of the method. The VLAN count which have to be on the LAN, can adjust parametrically or default constantly. In the algorithm which developed for this approach, the hosts belong to traffic on the network, ensures as much as possible equal or nearest distributes homogeneous on the VLAN’s. In this way the VLAN’s have same or nearest traffic value. A software has developed for testing functionality of this method which using SNMP protocol and reached to the aims by testing on the real network. © 2019 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved. 2-s2.0-85064463766

Natural gas is the most basic energy source used today in energy production, heating and cooking. With its widespread network, houses, industrial enterprises and power plants can access this energy at any time. The natural gas used in Turkey is an imported energy source and its agreements is provided by long-term contracts. Long-term contracts are submitted to the domestic market by decision-makers. In this process, natural gas supply companies and wholesale companies, provide gas supplies to city distribution companies and industrial establishments with annual contracts (mid-term). City distribution or other companies are required to report monthly and year based daily consumption demand forecasts in these contracts. This paper studies forecasting of daily and monthly demand for mid-term natural gas as contract estimations using time series decomposition, Holt-Winters and ARIMA / SARIMA models, which are statistical methods, include univariate seasonality. In the study, 365-day forecast is performed on a daily basis and 12-month forecast is performed on a monthly basis at once. As a result of daily natural gas estimation, the lowest error is realized by ARIMA(0,0,1)1(0,1,1)365 model with 23.68% MAPE in the year ahead prediction. In the monthly conversion, the lowest estimation model is realized by ARIMA(1,0,1)1(1,1,1)365 model with 11.84% MAPE. The results show that seasonal ARIMA models are the most suitable among the univariate techniques. The fact that many predictions can be made at a time and the results are acceptable allow these techniques to be used by decision-makers., Doğal gaz günümüzde enerji üretimi, ısınma ve pişirmede kullanılan en temel enerji kaynağıdır. Yaygın ağ yapısı ile birlikte evler, sanayi kuruluşları, santraller istedikleri anlarda bu enerjiye erişebilmektedir. Türkiye’de doğal gaz ithal bir enerji kaynağıdır ve uzun dönemli sözleşmeler ile anlaşmalar sağlanmaktadır. Uzun dönemli sözleşmeler karar vericiler tarafından yurtiçine arz edilir. Bu arz sürecinde doğal gaz tedarik şirketleri ve toptan satış şirketleri şehir dağıtım şirketleri ve sanayi kuruluşlarına yıllık sözleşmeler ile gaz arzı sağlarlar. Şirketler ve şehir dağıtım şirketleri bu sözleşmelerde aylık, yıl içinde de günlük tüketim talep tahminlerini bildirmekle yükümlüdür. Bu çalışma günlük ve aylık temelde orta vadeli doğal gaz talep tahminini tek değişkenli mevsimsellik içeren istatistiki yöntemler olan zaman serileri ayrıştırılması, Holt-Winters ve ARIMA/SARIMA modelleri ile gerçekleştirmiştir. Yapılan çalışmada günlük temelde 365 günlük, aylık temelde de 12 aylık tahmin bir anda gerçekleştirilmiştir. Doğal gaz tahmini sonucu günlük temelde en düşük hata yıl öncesi tahminde ARIMA(0,0,1)1(0,1,1)365 modeli ile 23,68% MAPE ile gerçekleşmiştir. Aylık dönüşümde ise en düşük tahmin modeli ARIMA(1,0,1)1(1,1,1)365 modeli ile 11,84% MAPE ile gerçekleşmiştir. Bu sonuçlar mevsimsel ARIMA modellerinin tek değişkenli teknikler arasında en uygun olduğunu göstermiştir. Bir anda çok sayıda tahmin yapılabilmesine imkan tanıması ve sonuçlarının kabul edilebilir olması bu tekniklerin karar vericiler tarafından kullanılabilmesine olanak tanımaktadır.

Substitution Box (S-Box) is one of the most significant structures used to create an encryption which is strong and resistant against attacks in block encryption algorithms. S-Box plays an important role in data encryption. This paper presents a novel S-Box generation algorithm design based on scaled Zhongtang chaotic system. In this study, a new random number generator which uses the new scaled Zhongtang chaotic system with very complicated and interesting dynamic features is designed; also, a new effective and strong S-Box design algorithm utilizing this RNG (random number generator) is developed. Bits generated by RNG are put to NIST tests and they passed all the NIST tests. Non-linearity, bit independence criteria, strict avalanche criteria, differential approximation probability performance tests are run on the proposed S-Box produced by new S-Box design algorithm. The proposed S-Box is compared with other studies available in the literature, and it is proved stronger and more effective.

Objective: To evaluate diagnosis accuracy of immunos algorithms for classification of migraine, tension-type and cluster-type of headaches by using our website based diagnosis survey expert system. Methods: The headache diagnoses of eight-hundred and fifty patients from three different cities in Turkey were evaluated by using immunos algorithms. Data were collected through our website based diagnosis survey expert system under the guidance of neurologists. Results: Immunos algorithms for diagnosis have the maximum accuracy of 95.65% which can be used for classification. Conclusion: It is possible to classify primary headaches with immunos algorithm and using our website based diagnosis expert system will be helpful for neurologist in order to obtain precise results as well as easy information sharing.

The infrastructure required for mobile ad hoc networks (MANETs) should be continuously improved because of the increasing number of wireless devices in the world. Therefore, modelling and simulation is important for MANETs in order to detect problems that may arise when developing infrastructure solutions. Studies in literature show that simulation tools developed for cable networks have been transformed into MANET tools instead of developing MANET-specific simulation tools. In this paper, a simulation framework for MANETs is designed using the DEVS-Suite simulator tool, which is based on discrete event system specification (DEVS) formalism, and is used for the simulation of cable networks. A specific wireless node architecture for the nodes that form MANETs is developed for a simulation framework. In addition, the new framework includes a topography model to check the velocity and movements of the nodes, packet models roaming in MANET, and visualisation area to observe simulation events. A coupled model is generated by combining these two models. In addition, an ant colony-based load-balancing scheme is developed to test the model. As a result, the simulation tool developed for MANETs aims to keep memory consumption steady even when traffic and node intensity increases in simulators. The study also aims to undertake load-balancing tests to obtain results compatible with the values in literature. (C) 2015 Elsevier B.V. All rights reserved.

Recently, wireless sensor networks (WSNs) have been widely employed in many different fields such as military, surveillance, health, agricultural, automation, and environmental monitoring. This paper presents a designed and implemented WSN-based man-overboard emergency discovery system, abbreviated as W-MEDS, that discovers the location of a person in emergency circumstances and runs an alarm system on a ship. The developed W-MEDS carries out a fast man-overboard (MOB) discovery and initiates the vital rescue procedure. It mainly consists of a WSN and a control and discovery system. When a MOB accident occurs, this situation is easily detected through the WSN nodes capable of real-time sensing (i.e. temperature, humidity, and acceleration) and the system enables location estimation. The control and discovery system analyzes and displays the received data from the WSN nodes. The most noteworthy advantage of the proposed W-MEDS is that it enables both real-time alarm monitoring and fast recovery. In addition to the implementation of the W-MEDS, considering hardness in performance evaluation of the real system, it has also been simulated in the OPNET Modeler to confirm the accuracy for different sizes and numbers of nodes.

System identification is an important process to investigate and understand the behavior of an unknown system. It aims to establish an interface between the real system and its mathematical representation. Conventional system identification methods generally need differentiable search spaces and they cannot be used for nondifferentiable multimodal search spaces. On the other hand, metaheuristic search algorithms are independent from the search space characteristics and they do not need much knowledge about the real system. The migrating birds optimization algorithm is a recently introduced nature-inspired metaheuristic neighborhood search approach. It simulates the V flight formation of migrating birds, which enables birds to save energy during migration. In this paper, first, a set of comparative performance tests by using benchmark functions are performed on the migrating birds optimization algorithm and some other well-known metaheuristics. The same metaheuristic algorithms are then employed to solve several system identification problems. The results show that the migrating birds optimization algorithm achieves promising optimizations both for benchmark tests and for system identification problems.

Analysis of solar radiation on buildings at the urban or individual scale has an important share in the formation of a sustainable environment. The accuracy of solar radiation analysis in buildings depends on the holistic analysis of the buildings and the efficiency of the model. Traditional solar radiation analysis approaches use some technologies such as LIDAR, ALS, aerial photographs, satellite images, and MLS. These technologies are expensive and not suitable for non-existing structures. In this paper, a novel approach is presented to evaluate the potential direct and diffuse solar radiation aggregated on 3D structures which can be either in planning stage or completed at the urban or individual scale. During the study, Angstrom-Prescot model is validated for the target region, and it is used for estimating the global solar radiation potential at specific points. Dynamical changes of shadow areas on building surfaces affect solar energy estimations, different algorithms are required for precise radiation analyzes on 3D buildings. In the proposed approach, finite element method, back-face detection and ray-tracing algorithms are utilized to obtain more precise results. Thus, real-time shadow analysis and analysis of the desired sensitivity and time scale can be obtained using the proposed approach. In the last phase of the study, a nearby site with various building layout scenarios is designated as test bed. Each scenario is analyzed separately using the proposed approach and the results are presented in the paper. The proposed work-model creates an ideal tool for urban planners, architects, civil engineers and energy investors. (C) 2017 Elsevier B.V. All rights reserved.

Software updates and maintenance costs can be reduced by a successful quality control process. Defect prediction is particularly important during software quality control, and a number of methods have been applied to identify defects in a software system. Quality control studies are based on quality metrics and static code metrics, and each research uses different set of metrics during the process. However, it is uncertain which metric is more significant in a particular study. In this study, NASA software quality dataset is used, and the most significant metric in the dataset is determined using MANOVA. A data mining based fuzzy logic model is developed using the reduced dataset. Gini decision tree is used as the data mining algorithm. Results of ROC analysis showed that the hybrid data mining-fuzzy model produces successful results during defect detection in software quality.

Deep neural networks provide outstanding classification and detection accuracy in biomedical imaging applications. We present a study for mitochondria segmentation in electron microscopy (EM) images. Mitochondria play a significant role in cell cycle by generating the needed energy, and show quantifiable morphological differences with diseases such as cancer, metabolic disorders, and neurodegeneration. EM imaging allows researchers to observe the morphological changes in cells as part of disease process at a high resolution. Manual segmentation of mitochondria in large sequences of EM images is time consuming and prone to subjective delineation. Thus, manual segmentation may not provide the high accuracy needed for accurate quantification of morphological changes. We show that a convolutional neural network provides accurate mitochondria segmentation in CA1 hippocampus area of brain that is imaged by a focused ion beam scanning electron microscope (FIBSEM). We compare our results with other studies which report results on the same data set and with other deep neural network approaches, and provide quantitative comparison.

In the past several years, attacks over industrial control systems (ICS) have become increasingly frequent and sophisticated. The most common objectives of these types of attacks are controlling/monitoring the physical process, manipulating programmable controllers, or affecting the integrity of software and networking equipment. As one of the widely applied protocols in the ICS world, EtherCAT is an Ethernet-based protocol; thus, it is exposed to both TCP/IP and ICS-specific attacks. In this paper, we analyze EtherCAT field-level communication principles from the security viewpoint focusing on the protocol vulnerabilities, which have been rarely analyzed previously. Our research showed that it lacks the most common security parameters, such as authentication, encryption, and authorization, and is open to Media Access Control (MAC) spoofing, data injection, and other advanced attacks, which require superior skills. To prevent, detect, and reduce attacks over the EtherCAT-based critical systems, first, we improved the open-source Snort intrusion detection/prevention system (IDS/IPS) to support packets that are not processed over transport and network layers. Second, by incorporating a vulnerability analysis, we proposed the EtherCAT (ECAT) preprocessor. Third, we introduced a novel approach called trust-node identification and applied the approach as three rules into the preprocessor. In this sense, the ECAT preprocessor differs from other supported ICS preprocessors in the literature, such as DNP3 and Modbus/TCP. Besides supporting traditional rule expansion, it is also able to handle layer 2 packets and to apply deep packet inspection on EtherCAT packets using the trust-node approach. This method first identifies engineering-station approved nodes based on EtherCAT network information (ENI) configuration files and then deeply inspects incoming packets, considering protocol specifications. The improvements and approach have been tested on the physically developed testbed environment and we have proved that proposals can detect related attacks and provide a basic level of security over the EtherCAT-implemented systems.

In the chaos literature, significant attention has been paid to chaotic systems with uncountable equilibrium points such as chaotic systems with line equilibrium, curve equilibrium, etc. This paper reports a novel chaotic system with a cloud-shaped curve of equilibrium points with symmetry properties. A new control law for completely synchronising the new chaotic system with cloud-shaped equilibrium curve has been established via adaptive integral sliding mode control. Also, an electronic circuit implementation of the theoretical system is designed to check its feasibility. As an engineering application, new results are derived for sound encryption with the new chaotic system.

Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye-eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by similar to 2.5% compared to the best whole face facial recognition system while using only similar to 1/3 of the facial region. (c) 2018 SPIE and IS&T

This manuscript focuses on the results of a project study on the field programmable gate array (FPGA) and modular based microcomputer architecture design for improving the teaching and learning effectiveness in computer organization course and similar courses. The design mentioned in this manuscript is a unique system allowing to students to directly write their program and/or include their own designs. The main objective of this study is to improve the teaching effectiveness in the computer organization course at graduate level via hands-on learning. The goal of the project is to encourage students to create their unique microcomputer designs since this design has modular structure and the fundamental elements which a simple computer has. Also it is to make appropriate modifications using students' concerns and the results of project study for computer architecture and organization course for next semesters. To evaluate the improvement of the teaching and learning effectiveness, the students enrolled in the computer organization course in computer engineering department are divided into two groups. A student survey is conducted to provide students with an opportunity to have their views about this methodology at computer organization course. The results of the survey acquired with the probability density distribution function reveal the effectiveness of this methodology with FPGA development environment.

Pathogen-host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen-host interactions. Developing a computational method to predict pathogen-host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen-host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen-host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.

Presents a novel defect clustering method.Shed new light to defect prediction methods.Depicts the prominence of k-means++ for software testing.Unveils the density of error rates of web elements. With the increase of the web software complexity, defect detection and prevention have become crucial processes in the software industry. Over the past decades, defect prediction research has reported encouraging results for reducing software product costs. Despite promising results, these researches have hardly been applied to web based systems using clustering algorithms. An appropriate implementation of the clustering in defect prediction may facilitate to estimate defects in a web page source code. One of the widely used clustering algorithms is k-means whose derived versions such as k-means++ show good performance on large-data sets. Here, we present a new defect clustering method using k-means++ for web page source codes. According to the experimental results, almost half of the defects are detected in the middle of web pages. k-means++ is significantly better than the other four clustering algorithms in three criteria on four data set. We also tested our method on four classifiers and the results have shown that after the clustering, Linear Discriminant Analysis is, in general, better than the other three classifiers.

The photovoltaic power generation and active filter (PV-AF) system can realize photovoltaic grid-connected power regulation, and meanwhile it can also improve the power quality of power grid effectively. The four-leg grid-connected inverter has successfully been used in the three-phase four-wire system. In this paper, a three-dimensional space vector pulse width modulation (3D-SVPWM) method based on the gh$\gamma $ coordinate system for a four-leg inverter has been proposed. As the switching vectors of the four-leg inverter are in the three-dimensional space, it is difficult to realize the complex modulation process. In order to simplify the modulation process, the reference vector is placed into gh$\gamma $ coordinates in the proposed modulation strategy, and then the vectors are selected and the duration times are acquired directly by a group of intermediate variables, which could be obtained by linear transformation of the coordinates. As the intermediate variables are used, the modulation can be easily implemented by the proposed modulation strategy without excessive mathematical calculations. The simulation and experimental results all confirmed the validity and effectiveness of this strategy.

The rapid increase in network traffic with new bandwidth-hungry applications such as cloud computing and telemedicine makes disaster survivability a crucial concern as the data (and revenue) loss caused by large-scale correlated cascading failures can be very high. To alleviate their impact, new measures should be taken since the nature of the network changes dramatically as available resources decrease during disasters. We develop a metric, called degraded-service tolerance, which can reduce protection cost and network disruption, and support maximal carried traffic in case of disasters. Degraded-service-tolerant connections can be admitted and recovered with reduced bandwidth under resource crunch. Our scheme re-assigns resources among connections by leveraging their degraded-service tolerance. A case study shows how our proposal can be applied to boost network performance during the resource crunch following a disaster.

This paper reports about a novel three-dimensional chaotic system with three nonlinearities. The system has one stable equilibrium, two stable equilibria and one saddle node, two saddle foci and one saddle node for different parameters. One salient feature of this novel system is its multiple attractors caused by different initial values. With the change of parameters, the system experiences mono-stability, bi-stability, mono-periodicity, bi-periodicity, one strange attractor, and two coexisting strange attractors. The complex dynamic behaviors of the system are revealed by analyzing the corresponding equilibria and using the numerical simulation method. In addition, an electronic circuit is given for implementing the chaotic attractors of the system. Using the new chaotic system, an S-Box is developed for cryptographic operations. Moreover, we test the performance of this produced S-Box and compare it to the existing S-Box studies.

The purpose of this research was to evaluate the classification accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified patients with 96.9412% overall accuracy. Diagnosis accuracies of migraine, tension-type, and cluster headaches were 98.2%, 92.4%, and 98.2% respectively. The ant colony optimization-based algorithm has a successful classification potential on headache diagnosis. On the other hand, headache diagnosis using a website-based algorithm will be useful for neurologists in order to gather quick and precise results as well as tracking patients for their headache symptoms and medication usage by using electronic records from the Internet.

Digital technology and the widespread use of the Internet has increased the speeds at which digital data can be obtained and shared in daily life. In parallel to this, there are important concerns regarding the confidentiality of private data during data transmissions and the possibility that data might fall into the hands of third parties. Issues relating to data safety can also affect patients' medical images and other information relating to these images. In this study, we propose a new method based on block matching that can be used to hide the patient information in medical images. In this method, 8 scanning orders (6 of which are newly designed) are developed to provide high image quality. By diversifying the number of scanning orders, we aim to achieve the lowest number of bit changes. The performance of the developed method is measured using the number of bits subject to change, the peak signal-to-noise ratio and the mean structural similarity index measure image quality assessment metrics, and steganalysis attacks. The method we developed was found to be more effective in hiding data compared to the classical least significant bit method. https://doi.org/10.3906/elk-1506-189

SundarapandianPehlivan chaotic system (SPCS) has been modeled and simulated in three distinct platforms (Numerical, Pspice and FPGA-based).SPCS has been modeled in VHDL language by using RK4 algorithm and has been synthesized for Xilinx Virtex-6 FPGA chip in development environment.The maximum operation frequency of FPGA-based chaotic system is 293.815MHz and the system can calculate 1,000,000 data in 0.201s.The high speed TRNG model has been implemented on an FPGA using SPCS and the maximum operating frequency has been achieved as 293MHz with a speed of 58.76Mbit/s.Proposed new TRNG can be run as fast as been verified by two statistical based standards, FIPS-140-1 and NIST-800-22. Chaotic systems and chaos-based applications have been commonly used in the fields of engineering recently. The most essential part of them is the chaotic oscillator that has very critical role in some applications such as chaotic communications and cryptography. In this study, SundarapandianPehlivan chaotic system has been modeled and simulated in three distinct platforms to show the advantages of FPGA-based chaotic oscillator with respect to alternative solutions. In the first stage, the chaotic system has been modeled numerically by the help of fourth order of RungeKutta (RK4) method. Additionally, phase portraits of the system have been obtained and Lyapunov exponents have been examined. Secondly, the system has been modeled by using PSpice for the implementation of the chaotic system with analog circuit elements. Then, Pspice simulation results have been compared with the numerical outcome to justify the designed model. Furthermore, the chaotic system has been physically confirmed with real analog circuit elements. Signals obtained from the physical system have been verified with both numerical and PSpice results. It has been also modeled by the help of method of RK4 in a hardware description language (VHDL) and the model further has been synthesized and tested for Xilinx Virtex-6 FPGA chip. Finally, the chaotic oscillator designed has been tested for True Random Number Generators (TRNG) and the maximum operating frequency has been achieved as 293MHz with a speed of 58.76Mbit/s. Besides, the random bit sets produced by TRNG have been further verified by FIPS-140-1 and NIST-800-22 statistical standards and it has been proved that the proposed design can be used in embedded cryptologic applications.

Effective post-disaster cloud-network recovery can significantly improve users' access to important cloud services. We propose a joint progressive recovery strategy for optical network and datacenters to maximize content reachability to users at each repair stage.

WOS: 000396569100002, This paper presents a new approach to optimize reactive power ow of multiterminal high voltage direct current (HVDC) systems. Successful application of two-terminal DC systems worldwide makes the use of multiterminal direct current (MTDC) systems more attractive. Due to the economic and technical advantages of HVDC technology, MTDC systems have been used extensively in recent years. In this study, the artificial bee colony (ABC) algorithm is used for solution of the optimal reactive power ow problem of MTDC systems. In opposition to the current-balancing method used in the literature, this study represents a new approach for DC system power ow calculations. The proposed approach is tested on a sample IEEE MTDC test system. The results by the proposed approach are compared with those reported in the literature. Thus, the applicability and the efficiency of this approach used together with the ABC algorithm are shown.

The number of attempts to break modern encryption methods increases day by day and therefore the need for the design of systems to ensure higher security has occurred. This study aims, an encryption algorithm that combines the strong of asymmetric encryption algorithm and the rich dynamic behaviors of chaotic systems is developed. In this study, firstly a new chaotic system design with high dynamic features is performed and then circuit realization and analyses are made. A chaos based RNG (random number generator) is designed with the help of the new developed chaotic system, NIST and FIPS tests are run. Chaos based hybrid RSA (CRSA) encryption algorithm design in which RNG and RSA algorithms are used together is performed. Text and image encryption is carried out with the algorithm and security analyses of these applications are made. Security analyses results are compared with classical RSA algorithm. It is observed that the developed CRSA algorithm provided better results than RSA algorithm in security tests. (C) 2017 Published by Elsevier Ltd.