Wireless sensor networks applications and design of a sensor node

Yüksek Lisans Teziİletişim teknolojilerindeki gelişmeler ve ihtiyaçların kablosuz ortama doğru kayması sonucunda günümüzde iletişim yöntemleri, kablosuz iletişim teknolojilerinin üzerine yoğunlaşılmasına neden olmuştur. Son yıllarda iletişim teknolojilerinde en çok gelişme ise Kablosuz Algılayıcı Ağlar üzerinde olduğu görülmektedir. Kablosuz Algılayıcı Ağları diğer ağlara göre farklılıklar göstermektedir. En belirgin özellikleri arasında; diğer kablosuz ağlarda tek taraflı iletişim var iken, veri gönderme veya alma, kablosuz algılayıcı Ağlarda ise çift taraflı iletişim söz konusudur. Ayrıca Kablosuz Algılayıcı Ağlar diğer ağlara göre Akıllı Ağ (Smart Network) sınıfına girmektedir. Bu ağlar; veriyi alma, gönderme ve yorumlama özelliklerine de sahiptirler. Kablosuz Algılayıcı Ağlar ilk zamanlarda özellikle askeri alanda yoğun olarak kullanılmaya başlanmıştır. Buna ek alarak teknolojik gelişmeler ve algılayıcı fiyatlarındaki düşüş nedeniyle sağlık, çevre ve habitat izleme alanında yoğunlaşmıştır. Daha sonra ise Tarım, Endüstri, Trafik, Eğitim gibi alanlarda yaygın olarak kullanılmaya başlamış ve neredeyse bütün sektörlere yayılmıştır. Bu tezde Kablosuz Algılayıcı Ağları'nın geçmişten günümüze kullanım alanları detayları olarak incelenmiştir. Bu inceleme sonuçları sektörel bazda sınıflandırılarak detaylı olarak anlatılmıştır. Bu tezde PIC tabanlı bir Kablosuz Algılayıcı Düğümü tasarlanmıştır. Bu düğüm bir Algılayıcı Düğüm ve bir Ana Düğümden oluşmaktadır. Düğümlerde PIC'ler C dili ile kodlanmıştır. Algılayıcı düğümde sıcaklık algılayıcı kullanılmaktadır. Alınan sıcaklık bilgisi RF aracılığıyla kablosuz olarak Ana Düğüme iletilmektedir. Yapılan düğüm tasarımının veri iletişimi bir insan üzerinde incelenmiştir. Alınan verilerin analizi için bir Seriport Arayüz Programı (Kablosuz Algılayıcı Uygulaması) C# programlama dilinde yazılmış ve verilerin bilgisayar ortamında grafik analizi ve veritabanına kaydedilmesi gerçekleştirilmiştir.AbstractToday communication methods, as a result of the developments and necessities moving towards wireless environment in communication technologies, resulted in focusing on wireless communication technologies. In recent years, a great progress has been seen on wireless sensor networks in communication technologies. Wireless sensor networks show differences compared to other networks. The most distinct features present; while there is a one-sided communication in other networks,whether sending or receiving data, in wireless sensor networks there is a mutual communication. Besides wireless sensor networks are classified in Smart Network class when compared to other networks. These networks have features such as receiving, sending and interpreting the data. In the beginning, wireless sensor networks were intensively used particularly for military use. In addition to this, as a result of the technological developments and decline in the prices of sensors, wireless sensor networks started to be used densely in health, environment and monitoring habitat areas. Later on, they have been widely used in Agriculture, Industry, Traffic and Education and penetrated to nearly all fields. In this thesis, areas of use of Wireless Sensor Networks was examined from past to present in a detailed way. These examination results was put forward via classifying in sectoral basis elaboratively. In this thesis, a PIC based Wireless Sensor Node was designed. This node is composed of a Sensor Node and a Main Node. In the nodes, PIC?s were coded via C. Temperature Detector is used in sensor node. The temperature data is transmitted into Main Node wirelessly via RF. The data communication of node design was examined on a human. A Serial Port Interface Programme (Wireless Sensor Application) was written in C# for the analysis of the data taken and graphical analysis and saving of the data to data base in computer environment was executed

INVESTIGATION OF EMOTIONAL INTELLIGENCE LEVEL OF PROFESSIONAL FOOTBALL PLAYERS IN TURKISH 3TH LEAGUE

The research was conducted to examine the emotional intelligence levels of the professional footballers who played in Turkey 2016-2017 Spor Toto 3rd League. For this purpose 112 male professional players who played in Ankara Adliye Sports, Ankara Demir Sports, Kocaeli Sports, Adana Kozan sports and Erzin sports teams in 2016-2017 Turkey Sports Toto 3rd League participated in this research voluntarily. In the study, descriptive scanning method with quantitative pattern was used. As a data collection tools, the personal information form prepared by the researcher and the revised Schutte Emotional Intelligence Scale, developed by Schutte et al. (1998), revised as 41 items by Austin et al. (2004), and adapted to Turkish by Tatar, Tok and Saltukoğlu (2011), were used. SPSS 21 statistical program was used in the calculation and evaluation of the obtained data. When the results of the study were examined, it was seen that there was no statistically significant difference between licensed sports and emotional intelligence. It was found that there was no statistically significant difference between the age of being a professional and optimism / mood analysis and the use of emotions. There was a significant difference in the evaluation of emotions sub-dimension and overall mean. It was determined that there was no statistically significant difference between position variable and optimism / mood and sub-dimensions of emotions. A significant difference was found in the evaluation of emotions sub-dimension and overall mean

Designing a new fpga-based real-time traffic classification engine using machine learning techniques

İnternetin kullanımının artması birçok konuda olduğu gibi internet trafik sınıflandırmaya olan ihtiyacı giderek arttırmaktadır. İnternet trafik sınıflandırma internet servis sağlayıcılar (ISS), kamu kurumları veya özel şirketler için oldukça önemli bir kullanım alanı oluşturmaktadır. Bunun nedeni, her bir kuruluşun kendi internet trafiğini izlemek istemesidir. Buna ilaveten internet trafik sınıflandırma trafik önceliklendirme, trafik şekillendirme ve bant genişliği paylaşımı sağlama gibi ağ yönetim görevleri için kullanılmaktadır. Ayrıca, ağ güvenliği, dinamik erişim kontrolü ve saldırı tespiti gibi internet ağ güvenliği sağlama ve yeni nesil internet ağ mimarilerinin tasarımın da internet trafik sınıflandırmaya ihtiyaç vardır. İnternet trafik sınıflandırma mimarisi tasarımında önemli kriterlerden biri yüksek hızlarda yüksek sınıflandırma doğruluğunu destekleyebiliyor olmasıdır. Özellikle gerçek zamanlı trafik sınıflandırma yapabilmek için 100+ Gpbs hızlara ulaşabilen trafik sınıflandırma mimarisine ihtiyaç vardır. Önerilen mevcut sınıflandırma yöntemlerinin çoğu yazılım tabanlı çözümler olmakla birlikte bu çözümlerin bu hızlara ulaşabilmesi oldukça güçtür. Dolayısıyla yüksek hızlarda internet trafik sınıflandırma yapabilmek için yazılım tabanlı çözümler yerine yüksek hızlarda trafik sınıflandırma yapabilen donanım tabanlı mimariler tercih edilmektedir. Donanım tabanlı mimariler, yüksek hızlara ulaşmalarının yanı sıra bellek verimliliği, çıkan yüksek iş oranı (throughput), dinamik güncelleme ve düşük gecikme gibi trafik sınıflandırma kriterlerinde de yüksek başarım sağlamaktadırlar. Önerilen mevcut internet trafik sınıflandırma çözümlerinden port tabanlı, DPI tabanlı ve sezgisel tabanlı yöntemler şifreli trafik altında ve dinamik port atamalarında düşük performans göstermektedir. Son yıllarda, özellikle şifreli trafik ve dinamik port atamaları altında yüksek doğruluk elde eden makine öğrenmesi (machine learning - ML) tabanlı yöntemler tercih edilmektedir. ML tabanlı yöntemler, trafik akışının (traffic flow) sadece istatistiksel özelliklerine bakarak trafik sınıflandırma yapmaktadır. Yüksek hızlı ve gerçek zamanlı ML tabanlı trafik sınıflandırma için donanım mimarileri tasarımına ihtiyaç vardır. Bu tezde gerçek zamanlı, yüksek hızlarda ve doğrulukta trafik sınıflandırma yapabilmek için makine öğrenmesi tabanlı ve donanım üzerinde gerçeklenen trafik sınıflandırma yöntemleri incelendi. Bu tezin ana katkısı olarak paralel boru hatlı (pipeline) mimariler üzerinde uygulanan yüksek hızlarda ve doğrulukta sınıflandırma yapabilen makine öğrenmesi tabanlı Genişletilmiş Simple CART (E-SC) mimarisi önerilmiştir. Aynı zamanda, benzer katkıları sağlamak amacıyla her bir uygulama sınıfından bir ağaç elde edilen ve ağaçları bitmaplerle zenginleştirilmiş iki aşamalı hibrit bir yapı olan Simple CART Ormanları (SCF) mimarisi önerilmiştir. Son olarak yüksek doğrulukta ve oldukça düşük sınıflandırma gecikmesi sağlayan tek adımlı Bitmap Kodlu Simple CART (BC-SC) veri yapısı önerilmiştir. Alanda Programlanabilir Kapı Dizilimleri (FPGA) tabanlı paralel ve boru hattı üzerinde tasarlanmıştır.Increasing use of the internet is growing the need for internet traffic classification as for many subjects. Internet traffic classification covers a very important usage area for private companies, public institutions, governments and Internet Service Providers (ISPs). The reason for this is that each institution wants to monitor its own internet traffic. In addition, internet traffic classification is used for network management tasks such as traffic prioritization, traffic shaping and bandwidth sharing provisioning. Additionally, internet networking security such as dynamic access control and intrusion detection, and the design of next-generation internet network architectures also require internet traffic classification. One of the important criteria in designing internet traffic classification architecture is that it can support high classification accuracy at high speeds. Especially, in order to make real time traffic classification, reaching up to 100+ Gbps speed internet traffic classification architecture is needed. While most of the proposed classification methods are software based solutions, it is very difficult for these solutions to reach such speeds. Thus, in order to make internet traffic classification faster, hardware-based architectures that can make traffic classification in high speeds are preferred on behalf of softwarebased solutions. Hardware-based architectures are highly successful on traffic classification criterion such as low latency, dynamic update, high throughput, memory efficiency besides reaching higher speeds. Among the proposed Internet traffic classification solutions, port based, DPI based and intuitive based methods show poor performance under encrypted traffic and dynamic port assignments. In recent years, machine learning (ML) based methods, which obtain high accuracy especially under encrypted traffic and dynamic port assignments, have been preferred. ML based methods perform traffic classification by only examining the statistical features of traffic flow. There is a need for designing hardware architectures for high-speed and real-time ML based traffic classification. In this thesis, machine-learning based hardware traffic classification methods were examined in order to achieve real time, high speed and accuracy traffic classification. As the main contribution on this dissertation, Extended-Simple CART (ESC) architecture is proposed that can achieve high accuracy and speed on parallel pipelined architectures. In the meantime, in order to make the similar contributions, Simple CART Forests (SCF) architecture is proposed, which is a two-staged hybrid structure and whose trees are enriched with bitmaps. Finally, a single-step Bitmap Coded Simple CART (BC-SC) data structure is recommended that provides high accuracy and fairly low classification latency. The BC-SC data structure is designed on Field Programmable Gate Arrays (FPGAs) based parallel and pipeline

Simple CART Based Real-Time Traffic Classification Engine on FPGAs

International Conference on Reconfigurable Computing and FPGAs (ReConFig) -- DEC 04-06, 2017 -- Cancun, MEXICO -- Natl Inst Astrophy Opt & Elect Mexico, Virginia Tech, Univ N Carolina Charlotte, IEEE, IEEE Circuits & Syst Soc, XILINXTraffic classification is a process which assorts computer network traffic into predefined traffic classes by utilizing packet header information or network packet statistics. Real-time traffic classification is mainly used in network management tasks comprising traffic shaping and flow prioritization as well as in network security applications for intrusion detection. Machine Learning (ML) based traffic classification that exploits statistical characteristics of traffic, has come into prominence recently, due to its ability to cope with encrypted traffic and newly emerging network applications utilizing non-standard ports to circumvent firewalls. To meet high data rates and achieve online classification with ML-based techniques, Field Programmable Gate Arrays (FPGAs) providing abundant parallelism and high operating frequency is the most appropriate platform. In this paper, we propose to use Simple Classification and Regression Trees (Simple CART) machine learning algorithm for traffic classification. However, the variations in node sizes of Simple CART decision tree caused by discretization pre-process incur memory and resource inefficiency problems when the tree is directly mapped onto the hardware. To resolve these problems, we propose to represent Simple CART decision tree by two stage hybrid data structure (Extended-Simple CART) that comprises multiple range trees in Stage 1 and a Simple CART decision tree enriched with bitmaps at its nodes in Stage 2. Our design is implemented on parallel and pipelined architectures using Field Programmable Gate Arrays (FPGAs) to acquire high throughput. Extended-Simple CART architecture can sustain 557 Gbps or 1741 million classification per second (MCPS) (for the minimum packet size of 40 Bytes) on a state-of-the-art FPGA and achieve an accuracy of 96.8% while classifying an internet traffic trace including eight application classes.WOS:0004265297000422-s2.0-8504695262

Real-Time Traffic Classification using Simple CART Forest on FPGAs

IEEE 19th International Conference on High Performance Switching and Routing (HPSR) -- JUN 17-20, 2018 -- Bucharest, ROMANIA -- IEEE, IEEE Commun SocTraffic classification process categorizes internet traffic into application classes by exploiting packet header data or collected packet statistics. Real-time internet traffic classification is mostly required for network management and security applications. Machine Learning (ML) based traffic classification approaches which utilize statistical properties of traffic flows, have recently attracted great deal of attention from the researches due to its operability under encrypted traffic conditions. In this paper, we propose to use Simple Classification and Regression Trees Forest (SCF) for internet traffic classification. Our proposed scheme comprising multiple parallel trees demonstrates a substantial improvement in search delay and throughput as well as in the memory footprint when compared to a traditional single Simple CART structure. To reach high data rates for real-time classification, we also proposed a parallel and pipelined architecture on Field Programmable Gate Arrays (FPGAs) that support SCF data structure. The post place-and-route FPGA results demonstrate that our design can sustain 854 Gbps or 2669 million classification per second (MCPS) for the minimum packet size of 40 Bytes. Furthermore, our architecture shows an accuracy of 96.6719% for real internet traffic with eight application classes.WOS:00051661800001

Weight loss methods and effects on the different combat sports athletes

*Çolak, Adem ( Aksaray, Yazar )Study Objectives: This study was carried out to determine the weight reduction methods of athletes engaged in weight sports. The sample of the study consists of 99 judo players, 89 taekwondo players, 74 wrestlers and 262 athletes in Ankara. Methods: In this study, Athlete weight reduction methods and effects scale was developed by Yarar et al. (2016) and personal information form developed by the researcher were used as data collection tools. Independent sample test and one-way ANOVA were used for percentage, frequency, arithmetic distributions, and Tukey HSD and Spearman Correlation test, which are the second level tests, were used for significant differences between ANOVA analyzes. Results: As a result, judo, taekwondo and the wrestling athletes were slightly affected by the diet size. There was a difference in weight reduction behaviors according to gender. Again, male athletes were more affected by fluid loss than girls. Significant differences were found in case of physiological, Psychological and fluid loss sub-dimensions. It was determined that wrestlers used Psychological methods. According to the age variable of the athletes, the sub-dimensions are mostly used by athletes aged 19 and over. Conclusion: It is a point that regularly athletes’ bodyweight must be controlled to prevent their loss of weight in pre-competitive. Such an approach is beneficial for both athletes’ health and performance. In cases where adult athletes must lose weight, it would yield better results to determine the percentage of body fat and to lose weight by reducing the rate of fat