Emin Kugu

ACO algorithms with multi-core implementation

Ant colony optimization is a successful swarm intelligence method for solving various combinatorial optimization problems. It uses a population-based meta-heuristic that is based on the foraging behavior of real ant colonies, and these ants use pheromones to communicate indirectly with others. While the scale of problem increases, ACO necessitates much more time and resource to solve the optimization problem. Two main solutions to this bottleneck can be used: distributed implementations and parallel implementations. The rapid development of computer architecture enables the easily reachable parallel implementation platforms by multi-core processors. In this paper, it is aimed to present the performance increase of two main ACO algorithms on multi-core processors with parallel programming. Parallelization is done on a single ant colony by using Java thread programming approach with minimal communication and coordination between threads. The paper also draws future works that can be done on this topic.

Parameter Optimization for Image Denoising Based on Block Matching and 3D Collaborative Filtering

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation.

Optimal path planning for UAVs using Genetic Algorithm

Unmanned Systems has been taking place of manned systems in several fields like aviation. Unmanned Aerial Vehicle (UAV), one of the most popular and effective unmanned systems, is gradually becoming the vital element of aviation because of its high success rate in both military and civilian missions. Basic problem of UAV is finding the best path in tough environment. Coverage zones of radars and complex environment are the main obstacles in this problem. A UAV intends to travel all control points in an optimal way to be more productive while avoiding radars. In this paper, we used Genetic Algorithm (GA), which is Evolutionary algorithm, to find the optimal flyable path for the UAVs in a 3D environment. Each generation is anticipated to be better than its previous generation in GA. For the purpose of reaching an optimal path, solving the Travelling Salesman Problem (TSP) is one of the major phases in the proposed method. In order to show the visual of solution in better quality, we preferred MATLAB as the implementation environment. Additionally, there is a shared library and mathematical calculations are easier in MATLAB. The complexity of our problem can be increased by adding extra constraints caused by the dynamic environment as the future works. Experimental results show that GA can be opted for optimal path planning for the UAVs.

Satellite image denoising using Bilateral Filter with SPEA2 optimized parameters

Satellite imaging is being the most attractive source of information for the governmental agencies and the commercial companies in last decade. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Many of these algorithms accept several parameters from the user to reach the best results. In the process of denoising, there is always a competition between the noise reduction and the fine preservation. If there is a competition between the objectives then an evolutionary multi objective optimization (EMO) is needed. In this work, the parameters of the image denoising algorithms have been optimized to minimize the trade-off by using improved Strength Pareto Evolutionary Algorithm (SPEA2). SPEA2 differs from the other EMO algorithms with the fitness assignment, the density estimation and the archive truncation processes. There is no single optimal solution in a multi objective problems instead there is a set of solutions called as Pareto efficient. Four objective functions, namely Mean Square Error (MSE), Entropy, Structural SIMilarity (SSIM) and Second Derivative of the image, have been used in this work. MSE is calculated by taking the square of difference between the noise free image and the deniosed image. Entropy is a measure of randomness of the content of difference image. The lower entropy is the better. The second derivate of an image can be achieved by convolving the image with the Laplacian Mask. SSIM algorithm is based on the similarities of the structures on the noise free image and the structures of the denoised image. For the image enhancement algorithms, Insight Segmentation and Registration Toolkit (ITK) is selected. ITK is an open source project and it is being developed in C++ to provide developers with a rich set of applications for image analysis. It includes tens of image filters for the registration and segmentation purposes. In this work, Bilateral Image Filter is evaluated in the field of satellite imaging for the noise removal process. The evaluated filter receives two parameters from the user side within their predefined ranges. Here, SPEA2 algorithm takes the responsibility to optimize these parameters to reach the best noise free image results. SPEA2 algorithm was implemented in Matlab and executable files of image filter were called in Matlab environment. The results of the work were represented graphically to show the effectiveness of selected method.

Benefits of the virtualization technologies with intrusion detection and prevention systems

In recent years, virtualization technologies were settled faster than anyone imagined and got placed big areas in our lives. Virtualization is a technology that eliminates the “uniqueness” dependency by creating an abstraction layer between the hardware and the software that runs on it. The aim of virtualization is to centralize the information system for facilitating administrative task while improving scalability. Virtualization technology which allows more efficient use of existing physical hardware, as well as some advantages including cost reduction, labor loss and performance improvement, contains several innovations in security, especially in system security. Software applications such as antivirus, firewall, end point security installed on the servers to ensure system security causes performance loss on the network and servers. Hence, security applications must be implemented on virtualization layer (hypervisor, Virtual Machine Monitor) to cover all the virtual system. In this paper, we aim to explain usage of Intrusion Detection Systems and Intrusion Prevention Systems in virtualization technologies and prologue possible security threats in virtual area.

Using agent based modeling and simulation for data mining

In recent years, there is an exponential growth of information sources, especially with the increasing usage of Internet. Therefore, there is a growing need for automated tools for obtaining valuable information from these raw data in different data warehouses. Data Mining represents the process of extracting valuable and useful knowledge from large amounts of data. Generating appropriate abstractions from these distributed data warehouses is a challenging task for data mining tools. Data mining is a multidisciplinary research area and it includes database technology, neural networks, artificial intelligence and machine learning etc. It enables valuable information to the end users. However, if the system is newly set and it is in the cold start position with no or little processed data, this influences the system efficiency. There is an additional mechanism for producing realistic data. Agent Based Modeling and Simulation system is a powerful technology by using autonomous intelligent agents and usually can run in distributed environment. This paper emphasizes the approach of using Agent Based Modeling and Simulation for Distributed Data Mining technologies.

Safe landing site detection using SRTM data for the Unmanned Aerial Vehicles

In this work, it is aimed to determine the suitable landing zones for the Unmanned Aerial Vehicles (UAVs) in case of an emergency during their missions. SRTM (Shuttle Radar Topography Mission) maps were used to reach that aim. Nowadays, it is observed that the UAVs are being used densely for both military and civilian purposes. So, it is inevitable to make the UAVs smarter and make them more autonomous to minimize their dependence on a person. UAVs can have unexpected problems during their missions such as motor fault, communication cut etc. In this situation, a UAV should activate the emergency landing systems and realize the landing safely. Image segmentation and blob analysis are used to determine the possible landing zones on the SRTM data. In this proposed system, the landing of a UAV to unwanted zones is limited and the UAV is guidance to the predetermined safe zones.

Finding smoothness area on the topographic maps for the unmanned aerial vehicle's landing site estimation

In this study, determining of the suitable landing areas on the topographical maps were determined for emergency landing of the Unmanned Air Vehicles (UAVs) during flight. In order to reach desired goals of this work, Shuttle radar topography mission maps (SRTM) was used. Nowodays, UAV have been intensively used in civillian and military applications. There are urgent needs on increasing of autonomy of the UAVs, decreasing human expertise and making smarter of UAV systems has become an inevitable necessity. Unexpectable stiuations (i.e. motor or comminatication failure, etc...) can arise while missions of the UAV. Emergency landing system must be activated by autonomously and then landed on the ground in safely while occuring some failure mentioned above. Two different techniques were chosed for determining probable landing areas by using digital elevation maps (SRTM). Firstly, surface fitting approximation was applied by using Least Squares Error (LSE). The slope of the points were calculated to specify of the smoothness rate of the landing areas. Smooth areas were signed by using SRTM datas. Image processing techniques were utilized for marking of the smooth areas and determinig boundries of the landing areas. The smooth landing areas were groupped with Blob analysis. The noise of the ground specified as landing areas were reduced with morphological image processing (performs morphological openning). UAVs system can be made smarter with specifying of the landing areas and planning of the path according to emergency cases. With designed systems, the UAV could be guided to the suitable landing zones vice versa undesirable areas by limiting of the landing path in the emergency cases of the UAV.

Fuzzy logic approach and sensitivity analysis for agent-based crowd injury modeling

A crowd is a group of people attending a public gathering with some joint purpose, such as protesting against the government or celebrating an event. In some countries, these kinds of activities are the only way to express public displeasure with their government. The government’s reactions to such activities may or may not be tolerant. For this reason, such situations must be eliminated by recognizing when and how they are likely to occur, and then providing guidelines to mitigate them. In urban areas, police and military forces use non-lethal weapons (NLWs), such as rubber bullets or clubs, to control a violent and destructive crowd. In order to estimate the results of this engagement, ensuring minimum injuries and reaching an optimal end state, simulating such actions in a virtual environment is necessary. In this work, a fuzzy logic-based crowd injury model for determining the physical effects of NLWs is proposed. Fuzzy logic concepts can be applied to a problem by using linguistic rules, which are determined by problem domain experts. A group of police and military officers were consulted for a set of injury model rules, and those rules were then included in the simulation platform. Sensitivity analysis has been conducted to analyze parameters in the model. As a proof of the concept, a prototype system was implemented using the Repast Simphony agent-based simulation toolkit. Simulation results illustrated the effectiveness of the simulation framework.

Simulation based multiple regression analysis of fuzzy logic crowd injury model

Realistic predictions for the future supply the decision makers to take important precautions especially for the public events to save the public peace in advance. Agent based simulations supported by well defined models can produce large amount of data for the analyzers to analyze that data and gather valuable information for the decision makers. In this work, we conducted a multiple regression analysis with a full factorial design to check the reliability and accuracy of the fuzzy logic based crowd injury model which is implemented in our previous works. Repast Simphony toolkit and its parameter sweep feature are in bath run mode to produce data for the analysis. The data gathered from the simulation are analyzed by using Minitab statistics software, and results showed that the injury model is acceptable and reliable with a high confidence level.