COMMUNITY DETECTION IN COMPLEX NETWORKS AND APPLICATION TO DENSE WIRELESS SENSOR NETWORKS LOCALIZATION

Complex network analysis is applied in numerous researches. Features and characteristics of complex networks provide information associated with a network feature called community structure. Naturally, nodes with similar attributes will be more likely to form a community. Community detection is described as the process by which complex network data are analyzed to uncover organizational properties, and structure; and ultimately to enable extraction of useful information. Analysis of Wireless Sensor Networks (WSN) is considered as one of the most important categories of network analysis due to their enormous and emerging applications. Most WSN applications are location-aware, which entails precise localization of the deployed sensor nodes. However, localization of sensor nodes in very dense network is a challenging task. Among various challenges associated with localization of dense WSNs, anchor node selection is shown as a prominent open problem. Optimum anchor selection impacts overall sensor node localization in terms of accuracy and consumed energy. In this thesis, various approaches are developed to address both overlapping and non-overlapping community detection. The proposed approaches target small-size to very large-size networks in near linear time, which is important for very large, densely-connected networks. Performance of the proposed techniques are evaluated over real-world data-sets with up to 106 nodes and syntactic networks via Newman\u27s Modularity and Normalized Mutual Information (NMI). Moreover, the proposed community detection approaches are extended to develop a novel criterion for range-free anchor selection in WSNs. Our approach uses novel objective functions based on nodes\u27 community memberships to reveal a set of anchors among all available permutations of anchors-selection sets. The performance---the mean and variance of the localization error---of the proposed approach is evaluated for a variety of node deployment scenarios and compared with random anchor selection and the full-ranging approach. In order to study the effectiveness of our algorithm, the performance is evaluated over several simulations that randomly generate network configurations. By incorporating our proposed criteria, the accuracy of the position estimate is improved significantly relative to random anchor selection localization methods. Simulation results show that the proposed technique significantly improves both the accuracy and the precision of the location estimation

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Sparse Representation-Based Framework for Preprocessing Brain MRI

This thesis addresses the use of sparse representations, specifically Dictionary Learning and Sparse Coding, for pre-processing brain MRI, so that the processed image retains the fine details of the original image, to improve the segmentation of brain structures, to assess whether there is any relationship between alterations in brain structures and the behavior of young offenders. Denoising an MRI while keeping fine details is a difficult task; however, the proposed method, based on sparse representations, NLM, and SVD can filter noise while prevents blurring, artifacts, and residual noise. Segmenting an MRI is a non-trivial task; because normally the limits between regions in these images may be neither clear nor well defined, due to the problems which affect MRI. However, this method, from both the label matrix of the segmented MRI and the original image, yields a new improved label matrix in which improves the limits among regions.DoctoradoDoctor en Ingeniería de Sistemas y Computació

Fuzzy-pso Control Of Linear And Nonlinear Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2014Bu tezin amacı, yeni optimizasyon yöntemi olan parçacık sürü optimizasyon algoritmasını MATLAB’e uygulayarak bulanık PID kontrolörü katsayıları ve Takagi-Sugeno kural tabanındaki keskin değerleri cevrimdışı optimize ederek doğrusal ve doğrusal olmayan sistemlerin belirli çalışma koşulları altında kontrolünü sağlamaktır. Parçacık sürü optimizasyonunun diğer optimizasyon yöntemlerinden, örnek olarak verilmesi gerekirse genetik algoritmadan, en önemli avantajı optimizasyon sırasında az sayıda iterasyon içermesi, kolay anlaşılabilir olması ve bize kompleks olmayan az sayıda yazılmış bilgisayar kodları ile kolay ve ucuz bir şekilde uğraşmamızı sağlamasıdır. Genetik algoritma ile olan benzerlikleri ise her ikiside populasyon tabanlı olup, tek set değerden diğer set değerlere geçerken deterministik ve olası kuralları kullanmaları sayılabilir. Son yapılan çalışmalara istinaden parçacık sürü optimizasyon yöntemi en az genetik algoritma kadar büyük oranda doğrusal olmayan yapıların çözülmesinde, yakınsama oranı ve yakınsama hassasiyeti bazında aynı sonuçları vermektedir. Ayrıca basit kodlar içermesinden dolayı hem bilgisayar hafızasından hem de zamandan tasarruf ettirip sonuclara en hızlı ve verimli şekilde ulaşmamıza yardımcı olmaktadır. Parçacık sürü optimizasyon yöntemi doğrusal olmayan ve zamanla değişen karakteristiğe sahip olan ikili tank sisteminde belirli çalışma aralıkları içerisinde bulanık PID kontrolör tasarımında kolayca ve başarılı bir şekilde uygulanabilmiştir. Yukarıda bahsedildiği gibi ikili tank sisteminin doğrusal olmayan ve zamanla değişen yapısından dolayı, kontrolör tasarımında tek set parametrelerin bulunması ve kontrol sırasında her bölge için aynı parametrelerin kullanılması neredeyse imkansızdır. Bu yüzden daha önceden belirlenmiş çalışma aralıkları içerisinde, Takagi-Sugeno kural tabanındaki parçacık sürü optimizasyon yöntemi ile optimize edilmiş katsayılar her bölge için sabit tutularak, değişik bölgeler için değişik optimal kontol parametreleri bulunup kontrol sırasında çevrimiçi olarak PID katsayılar hesaplanmıştır. Bulanık PID kontrolör parametreleri aynı zamanda ikili tank sisteminin ikinci tankındaki sıvı seviyesini giriş set değeri alarak farklı çalışma aralıklarında doğrusal regresyon yöntemi ile bulunan değişik kontrolör parametre fonksiyonları ile esnek bir yapıya dönüştürülüp farklı giriş değerleri, sistem gürültülerini hatta sistem hatalarını kompanze edecek duruma getirilimiştir. Böylelikle belirlenen çalışma bölgelerinde istenilen kontrol şartlarını sağlayan, değişik senaryolara sahip sistem hataları ve sistem gürültülerini bastıran adaptif yapıya sahip doğrusal olmayan bir sistemin geliştirilmiş parçacık sürü optimizasyonu yöntemi ve bulanık PID kontrolörü ile kontrolü sağlanmıştır.The goal of the thesis is to introduce a new global optimization method called particle swarm optimization that is implemented via MATLAB to use to find the optimal parameters for PID coefficients and Takagi-Sugeno rule base’s crisp values in order to control linear and nonlinear systems within specified operating conditions. The most important advantages of particle swarm optimization algorithm is that it requires less number of iterations and it enables us to deal with a few lines of computer codes in a cheapest manner rather than other optimization methods such as genetic algorithm. It requires only primitive mathematical operators in terms of both necessity of more available memory and speed. Particle swarm optimization method has been successfully applied to the design of coupled tanks system control with meaningful time domain criteria. Since the coupled tank system to be controlled is nonlinear and time varying charecteristic, it is almost not possible to find one set of parameters that satisfy for all operating conditions. Therefore some predetermined operating points have been chosen and find out the optimal control parameters’ values for the operating points while keeping Takagi-Sugeno crisps values constant for all operating points within the different ranges. Different functions are calculated for each controller parameters within different operating points based on the referenced height of tank two as an input value to the coupled tank system by using the predetermined points and least curve-fitting algorithm. It has been observed that these functions, which derive fuzzy controller parameters, have achieved very satisfactorly systems responses.The water levels between different ranges are chosen respectively as a three typical operating regions of second tank and input space is divided into three fuzzy subspaces based on operating regions. Fuzzy PID parameters have been calculated online by proposed method despite of the fact that Takagi Sugeno crisp values have been calculated offline and stored before calculating PID parameters for the three operating regions. We can generalize that Takagi-Sugeno crisp values, which are structural parameters, are determined offline design while the tuning parameters are calculated during online adjustment of fuzzy PID controller to enhance the process performance, as well as to accommodate the adaptive capability to system uncertainty and process disturbances. The proposed architecture is also tested in case of process disturbance and systems faults. Simulation results showed that the couple tank system was successfully controlled with acceptable performance criterions in both cases.Yüksek LisansM.Sc

Quality of service analysis of internet links with minimal information

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, julio de 201