Department of Electrical & Electronics Engineering

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http://hdl.handle.net/20.500.11905/1212

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    PERFORMANCE EVALUATION OF PREPROCESSING TO PCA COMBINED MACHINE LEARNING TECHNIQUES ON PHARMACEUTICAL AND MINERAL SAMPLES BY LASER-INDUCED BREAKDOWN SPECTROSCOPY
    (2023-01-27) YAZICI, Göktuğ; DORUK, Reşat Özgür
    For the purpose of identifying and analyzing materials, laser-induced breakdown spectroscopy (LIBS) is a quick optical nuclear discharge spectroscopy. It has the advantages of in-situ analysis, removal of rigorous sample processing, and micro-destructive properties for the substance being evaluated. LIBS uses brief bursts of laser beams to stimulate the material to a certain threshold, resulting in plasma formation. The plasma properties, which include wavelength value and intensity amplitude, are affected by the material and the surroundings of the experiment. The spectrum profiles of medication and mineral samples were obtained using LIBS in this study. The collection of pharmaceutical samples comprises two distinct concentrations of both paracetamol-based drugs, Aferin and Parafon. Aluminum (Al), Bizmut (Bi), Copper (Cu), Iron (Fe), Manganese (Mn), Nickel-Aluminum (NiAl), Tin (Sn), and Zinc (Zn) are among the mineral samples in the dataset. The samples' spectrum data were preprocessed by replacing missing values with shape-preserving piecewise cubic spline interpolation, filling outliers based on quartiles, smoothing spectra to remove noise, and normalizing both the wavelength and intensity axes. Statistical information was acquired, and both the preprocessed and raw datasets were subjected to principal component analysis (PCA). The machine learning models were built using two distinct train-test splits: 70% training - 30% test and 80% training - 20% test. Cross-validation was employed to keep the models from being overfit, hence the sample size is small. Both splits' machine learning outcomes from preprocessed and raw datasets were compared. This is the first time that all supervised machine learning classification algorithms, including Decision Trees, Discriminant, Nave Bayes, Support Vector Machines (SVM), k-NN (k-Nearest Neighbor), Ensemble Learning, and Neural Network algorithms, have been applied to LIBS datasets of both paracetamol-based pharmaceutical samples and 8 different mineral samples, as well as their preprocessed and raw datasets, to investigate the effect of preprocessing.
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    A STUDY ON MICROSTRIP ANTENNA DESIGN FOR 77 GHZ RADAR SYSTEMS
    (2023-01-20) YILMAZ, Selen; KARA, Ali; DALVEREN, Yaser
    This thesis presents a comprehensive investigation into the design and operational behavior of series-fed microstrip patch antenna array for the 77 GHz automotive radar. Initially, the theoretical background information on the theory of microstrip antenna, patch antenna array, frequency scanning array and Chebyshev array are provided. A full-wave finite element method-based simulation tool is used to design and slightly tune the dimensions of the antennas as a parametric study. At the first stage, a series fed linear Chebyshev patch array with resonance at 76.5 GHz is designed representing one transmit channel of the antenna. Shorting pins are loaded to transition structure of ground-signal-ground (GSG) padding to enhance the total gain. Comparative analysis between vialess and via loaded cases is conducted in terms of bandwidth and gain. At the last stage, 76.5 GHz linear patch antenna array is converted into a 79 GHz linear patch antenna array by optimizing the GSG padding dimensions, scaling the spacings between each two adjacent array elements and the length of array elements. Two designs are proposed to assess the effect of scaling method at this stage. Comparative analysis in terms of the beam steering angle, the impedance bandwidth, the overall gain and the sidelobe level suppression is conducted between these two designs. Keywords: Dolph-Chebyshev Distribution, Frequency Scanning Array, Linear Array, mmWave, Patch Array Antenna.
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    MEDICAL DATASET CLASSIFICATION BASED ON DIFFERENT DEEP LEARNING TECHNIQUES AND META-HEURISTIC ALGORITHMS
    (2023-01-30) KADHIM, Yezi Ali; MISHRA, Alok; DORUK, Reşat Özgür
    Medicine is one of the fields where computer science advancement is making significant progress. The usage of Computers in Medical improves precision and accelerates data processing and diagnosis. There are currently a variety of computer assisted diagnostic systems, with deep-learning algorithms playing an important role. Systems that are more precise and faster are required. Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This study focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. In this thesis, several combinations of different deep learning techniques with the meta-heuristic algorithm, each of the deep learning methods either the convolutional neural network or the auto-encoder were applied to extract the effective features from two different medical datasets COVID-19, and the brain tumor with the aid of the meta-heuristic method to select the optimal features in order to cover a several medical datasets detections. The first combination of several pre-trained convolutional neural networks (CNN) AlexNet, GoogleNet, ResNet 50, and DenseNet 201 was used with three types of Meta-Heuristic Algorithms Ant Colony Optimization algorithm (ACO), Particle Swarm Optimization algorithm (PSO), and Genetic Algorithm (GA). The second combination was Auto-encoder with three types Meta-Heuristic Algorithms ACO, PSO, and GA which was an innovative method, that seeks to reduce the size of the dataset while maintaining the original performance of the data. The employing of deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using ACO or PSO, or GA. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed combination is evaluated with classifiers like decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), ensemble, Naive Bayes, and discriminant using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors. Also, in this thesis, a combination of different deep learning techniques with the meta-heuristic algorithm, each of the deep learning methods either the convolutional neural network or the auto-encoder were applied to extract the effective features from two different medical datasets, with the aid of the meta heuristic method to select the optimal features obtained by the particle swarm optimization algorithm (PSO) this combination is considered as an innovative method, seeks to reduce the size of the dataset while maintaining the original performance of the data. The covid-19 dataset found that the highest accuracy by the combination of CNN-PSO-SVM was 99.76%, and for the common brain tumor dataset, the accuracy of 99.51% as the highest was obtained by the combination method autoencoder-PSO KNN. We notice that the combination model of the deep learning method with the PSO feature selection algorithm takes a consuming time much longer than the same method with the ACO algorithm at the same time the accuracy of PSO is near to ACO accuracy.
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    MEDICAL DATASET CLASSIFICATION BASED ON DIFFERENT DEEP LEARNING TECHNIQUES AND META-HEURISTIC ALGORITHMS
    (2023-01-30) KADHIM, Yezi Ali; MISHRA, Alok; DORUK, Reşat Özgür
    Medicine is one of the fields where computer science advancement is making significant progress. The usage of Computers in Medical improves precision and accelerates data processing and diagnosis. There are currently a variety of computer assisted diagnostic systems, with deep-learning algorithms playing an important role. Systems that are more precise and faster are required. Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This study focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. In this thesis, several combinations of different deep learning techniques with the meta-heuristic algorithm, each of the deep learning methods either the convolutional neural network or the auto-encoder were applied to extract the effective features from two different medical datasets COVID-19, and the brain tumor with the aid of the meta-heuristic method to select the optimal features in order to cover a several medical datasets detections. The first combination of several pre-trained convolutional neural networks (CNN) AlexNet, GoogleNet, ResNet 50, and DenseNet 201 was used with three types of Meta-Heuristic Algorithms Ant Colony Optimization algorithm (ACO), Particle Swarm Optimization algorithm (PSO), and Genetic Algorithm (GA). The second combination was Auto-encoder with three types Meta-Heuristic Algorithms ACO, PSO, and GA which was an innovative method, that seeks to reduce the size of the dataset while maintaining the original performance of the data. The employing of deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using ACO or PSO, or GA. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed combination is evaluated with classifiers like decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), ensemble, Naive Bayes, and discriminant using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors. Also, in this thesis, a combination of different deep learning techniques with the meta-heuristic algorithm, each of the deep learning methods either the convolutional neural network or the auto-encoder were applied to extract the effective features from two different medical datasets, with the aid of the meta heuristic method to select the optimal features obtained by the particle swarm optimization algorithm (PSO) this combination is considered as an innovative method, seeks to reduce the size of the dataset while maintaining the original performance of the data. The covid-19 dataset found that the highest accuracy by the combination of CNN-PSO-SVM was 99.76%, and for the common brain tumor dataset, the accuracy of 99.51% as the highest was obtained by the combination method autoencoder-PSO KNN. We notice that the combination model of the deep learning method with the PSO feature selection algorithm takes a consuming time much longer than the same method with the ACO algorithm at the same time the accuracy of PSO is near to ACO accuracy.
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    COMPARATIVE ANALYSIS OF MPPT TECHNIQUES FOR SOLAR AND WIND SYSTEMS UNDER DIFFERENT OPERATING CONDITIONS
    (2022-12-26) AHMAD, Muhammad Saeed; SÜNTER, Sedat
    Renewable energy technologies have gained a lot of traction in the last few decades as a means of reducing reliance on fossil fuels and mitigating the impact of climate change. Renewable sources such as sunlight, wind, and water are clean and sustainable. These technologies have gained significant attention in recent years. While renewable energy technologies have many advantages, one of the main challenges is their relatively low efficiency compared to fossil fuels. As a result, renewable energy systems typically require more land and resources to produce the same amount of energy as fossil fuel-based systems. Additionally, the efficiency of renewable energy systems can vary depending on the weather and other environmental conditions. For example, solar panels are less effective on cloudy days and wind turbines are less effective in calm weather. This can make it difficult to predict and control the amount of energy that renewable systems will produce, which can create challenges for integrating them into the grid. The problem with efficiency can be dealt with the use of maximum power point tracking (MPPT) techniques. These techniques are used to optimize the performance of renewable energy systems by ensuring that they operate at the maximum power point, or the point at which they can generate the most power. There are several types of maximum power point tracking (MPPT) techniques, but they can be broadly classified into three categories: simple, artificial intelligence (AI), and hybrid. Simple MPPT techniques such as PO and IC are the most basic and widely used type of MPPT. These techniques use relatively simple algorithms to continuously adjust the operating conditions of the system to maintain the maximum power point. AI-based MPPT techniques like PSO and ANN use advanced algorithms and machine learning techniques to optimize the performance of renewable energy systems. These techniques can adapt to changing environmental conditions and can continuously adjust the operating conditions of the system in real-time. Hybrid MPPT techniques like ANFIS and PSO&PO are a combination of simple and AI based techniques. These techniques use simple algorithms to quickly track the maximum power point, and then use AI-based techniques to fine-tune the operating conditions of the system in real-time. A comparative analysis of simple, AI, ML, and hybrid MPPT techniques for hybrid energy (Solar and Wind) systems is discussed in this thesis. The MPPT algorithms were ranked based on different metrics such as efficiency, settling time, oscillations at MPPT and algorithm complexity. For PV system, AI based techniques performed best as compared to Hybrid and conventional techniques. For Wind system, hybrid techniques yield the best results as they combine the benefits of conventional and AI techniques.
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    AGILE SOFTWARE MAINTENANCE AND DEVELOPMENT USING CLOUD COMPUTING FRAMEWORK
    (2023-01-26) ALMASHHADANI, Mohammed; YAZICI, Ali; MISHRA, Alok
    Agile methods have emerged to overcome the obstacles faced in traditional software methodologies, such as the Waterfall, Prototype, Spiral, etc. There have been many studies that show the numerous features of the Agile methodologies, making them useful for software development. However, many studies have also proposed a framework to adapt the Agile methods to Cloud Computing to leverage the benefits from this environment. The existing studies focus on the adaptive development life cycle for Agile with the Cloud, but have so far been unable to include the maintenance process in a detailed manner. Among these attempts and as further contribution, the present work intends to introduce Agile software maintenance and development using Cloud Computing framework (ASMDCC) as a reference for developing software with the Cloud in respect of maintenance activities. The case study findings reveal that the combination of Agile with Cloud Computing can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in globally/distributed software maintenance. Furthermore, it is shown that Cloud Computing services play a vital part in resolving software maintenance. Finally, the results indicate that using the ASMDCC framework improves the challenges faced by the maintenance team compared to the traditional environment regarding management, infrastructure, collaboration, and transparency.
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    HIGH-ORDER CUMULANTS BASED CLASSIFICATION FOR COGNITIVE RADIO APPLICATIONS
    (2023-01-24) AL-SUDANI, Haidder Jalil Sahib; DALVEREN, Yaser
    Modern communication systems have witnessed dramatic changes due to the huge development of wireless technologies applications. These developments caused the scarceness of the spectrum and its inefficiency. However, cognitive radio (CR) is proposed as one of the best solutions to maintain high spectral efficiency (SE), and to treat spectrum scarcity. CR delivers the service to the unauthorized user to utilize the spectrum channel when the channel is out of the needs of the authorized user. However, spectrum sharing needs to be completed without signal interference. Thus, CR has many detection techniques for proper management of the frequency spectrum and interference avoidance. The major detection techniques of CR are Energy Detection (ED), Matched-filter Detection (MFD), and Feature-based Detection (FBD). In practice, each one of them has its own advantages and limitations. In this thesis, the use of statistical features in Machine Learning (ML) is proposed for a FBD. A MATLAB simulation is conducted to evaluate the effectiveness of the proposed detector. To this end, firstly, various modulation schemes with various noisy channels are generated. Then, the high-order moments and cumulants are extracted from the corrupted signals in noisy channels. In fact, these features are selected according to their strength in distinguishing between the signal and noise. The detection outcomes are employed in the support vector machine (SVM) classifier and the probability of detection (Pd) is obtained. The highest Pd value is achieved with 3 high-order cumulants in statistical detection. The same Pd value is obtained using SVM classifier by 1 high-order cumulant which reduces the amount of the processed data and simplify the detector complexity.
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    RADAR CROSS SECTION MEASUREMENT OF VARIOUS OBJECTS WITH 77-81 GHZ AUTOMOTIVE RADAR AND ASSESSMENT OF RADAR CROSS SECTION SIMULATION TOOLS
    (2022-12-19) SEZGİN, Deniz; AYDIN, Elif; KARA, Ali
    In this thesis, two of the most widely used computational EM methods which are being used for RCS prediction have been explained and compared. The methods involved in this comparison are Electric Field Integral Equation (EFIE) with Method of Moments (MoM) solution and Shooting and Bouncing Rays (SBR), which is a hybrid method consists of the combination of PO and GO. First of all, background informa tion about classical theory of electromagnetism and Radar Cross Section (RCS) and also the connection between them were presented. Then theoretical concepts were given for IE and SBR methods since this comparison also includes theoretical knowledge. Three targets were used in the measurements so that these methods could be compared with each other experimentally. RCS measurement were performed with a 77-81 GHz Commercial Off-The-Shelf (COTS) Frequency Modulated Continuous Wave (FMCW) radar system. In accordance with measurements, simulations were also performed at 77 GHz with both SBR and EFIE methods using two different commercial tools. Good agreement was found between measurement and simulation results, also it was observed that depending on the task both methods have various advantages over themselves.
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    MODELING SENSORY NEURONS FROM POISSON SPIKING PROCESS DATA
    (2022-11-17) AL-AKAM, Mohammed; DORUK, Reşat Özgür
    In this thesis, we introduce computational and theoretical work based on the estimation of the firing rate of an excitatory and inhibitory neuron model. Those firing rates had been recorded from realistic stimulus-response data where a previous study provides those stimulus and response records where this study performed a measurement from a nature (H1 neurons of the order Diptera flies). Maximum-likelihood was the method used in this thesis to conduct the parameter estimation of the neural network dynamics. This record had been segmented to increase the statistical content of information. since we have a stimulus-response data recording of 20 minutes, this record was segmented, and each individual segment is composed of each other. Due to the true values of the parameters for the neuron model cannot being measured which made those true values unknown as the synthetic data will not be used by the Neuron dynamics in this research. Based on this fact, we used two samples Kolmogorov-Smirnov test. where this test was applied to make a comparison between two recorded inter-spike intervals in addition to model responses. The estimation and analysis of outcomes will be presented graphically and also will be listed in a tabular form. Also, a comparison with previous research is made where this research used a modified Fitzhugh-Nagumo model.
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    EXAMINATION OF INDEPENDENT COMPONENT ANALYSIS IN AUDIO SOURCE SEPARATION
    (2022-06-22) GÜLER, Elif Ezgi; USLU, İbrahim Baran
    In this thesis, we examine the Independent Component Analysis (ICA) method in audio source separation. This method is a type of blind source separation where the sources observed in the mixture signals are unknown. We try to solve a cocktail party problem, by extract the independent signals which are mixed by an unknown mixing matrix. There are some sub-types of the ICA algorithm such as Gradient Ascent (ICA-GA), fastICA and Kernel-ICA. In this work, we study on ICA-GA algorithm. For this purpose, different scenarios where two or three audio sources are mixed with each other, are examined. In some of the tests carried out, we separated voice and noise signals clearly from each other. In other tests, voice signals were separated. In the experiments, we focused on the (step size) and the maximum iteration number parameters, also examined the value of parameters on performance of ICA-GA algorithm. We obtained that, ICA method is quiet successful in blind source separation. It was concluded that increasing the value of the maximum iteration parameter alone is not a sufficient parameter for performance. Because as the maximum number of iterations increased, the running time of the algorithm also too increased, that is, the elapsed time is not at the optimum value. We can say that, increasing the value of the step-size parameter alone has more successful results on the performance of algorithm than increasing the value of the maximum iteration parameter alone. The study recommends a solution to the ICA's ambiguity about order of output signals by using the correlation values of each source signal and each output signal.
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    OPTIMAL EXCLUSION ZONE ESTIMATION FOR CO-EXISTENCE OF GEOSTATIONARY AND NON-GEOSTATIONARY SATELLITE NETWORKS
    (2022-06-20) ÖZTÜRK, Faik; AYDIN, Elif; KARA, Ali
    In this thesis, the co-existence downlink interference from a typical Low Earth Orbit (LEO) constellation to earth stations of Geostationary Earth Orbit (GEO) satellites is analysed by performing minimization of Exclusion Zone (EZ) on the equatorial region. Using the Genetic Algorithm (GA), a multi-objective optimization problem (MOP) is formulated for non-dominant solutions set based on Exclusive Angle (EA) minimization and bandwidth utilization of the LEO communication link. At transmission bit rates of 100 Mbps and 200 Mbps, it is shown that the EA can be reduced up to % 21.3 and % 19.6, respectively, when compared to the initial anchor point. For the LEO communication system, the proposed optimal operational setting minimizes interference risk to the GEO satellite system as well as ensuring Quality of Service (QoS). Additionally, analysis and comparative evaluation of interference mitigation methods for coexisting Non-Geostationary Earth (NGEO) and Geostationary Earth (GEO) systems are discussed. Afterwards, the quantitative performance assessment of Spatial Isolation (SI), Power Control (PC), and Spatial Isolation-Based Link Adaptation (SILA) methodologies is performed. When compared to SI and PC techniques, the SILA technique utilizes the EA methodology more effectively. In various operating settings, the EA can be reduced up to % 8 for 100 Mbps and % 8.5 for 200 Mbps transmission bit rates when the PC method and the SILA method are combined. The performance assessment presented in this study may assist the satellite operator or decision-maker in determining the suitable mitigation strategy to apply in the event of co-existence interference.
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    DESIGN OF NONLINEAR PID CONTROLLER FOR DRILLING RIG WITH ROBUST AND ADAPTIVE CONTROL FOR ALL OPERATING MODES
    (2022-09-16) NOUMAN, Muhammad; ÖZBEK, Mehmet Efe
    Drilling towers have different operating modes during a real operation like drilling, tripping etc. Each mode of operation has certain external disturbances and uncertainties. In this study the hardware and software structures of the system are presented. The drilling rig is modelled in MATLAB/SIMULINK and interfacing with TwinCAT-3 to observe the dynamics of the drill string hook, while hoisting and lowering by using real time external mode of MATLAB/SIMULINK. The optimum values of rise time and overshoot of velocity have been analyzed. Various tuning methods were applied but their results were not satisfactory due to distortions and vibrations. This comparison is based on manual tuning of linear and nonlinear PID parameters. By using the nonlinear model for modes of the operation, robust and or adaptive control systems are designed. The smoothness of the system is achieved by using nonlinear pid parameters. The study presents the design process of the controller and evaluates the performance of the proposed control system to track the reference signal and reject the uncertainties and external disturbances. The behavior of the controllers and their stabilities are studied during the kinetics of drill sting hook and tripping operation on prototype setup in the laboratory.
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    SIMULATION FOR CONTROL SYSTEM OF DRAWWORKS AND ITS IMPLEMENTATION USING PLC FOR OIL AND GAS DRILLING
    (2022-08-14) MEMON, Sheeraz Ali; ÖZBEK, Mehmet Efe
    Drawworks is one of main component of drilling rig. To control drawworks it requires skill and experience. Driller that controls the drawworks has very difficult position for work because it is near to drilling rig. So, chances are more for unsafety. Failure of drawworks system could risk the life of driller if any error occurred. I have simulated drawworks control system and implemented the simulation to prototype model of drawworks control system. In prototype model of drawworks system for safety of driller and drawworks deadman button, ground fault error detector, deadband region has been added. To avoid jerks joystick analog signal has been smooth. Weight is one of most important parameters in drilling. Weight equilibrium have been done for travelling block if there is any change in weight, travelling block would move upward or downward according to weight. So, by applying these safety features drawworks system can be prevent from errors and jerks, this way safety is increased for drawworks system as well as for driller because of its work position near to the drilling rig. MATLAB and Twincat3 software has been used for simulation and implementation.
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    TOWARDS REAL TIME IMPLEMENTATION OF SPECIFIC EMITTER IDENTIFICATION ON PULSE WAVEFORMS: OPTIMIZATIONS ON A LOW COST COMMERCIAL PLATFORM
    (2012-01-25) ERDEM, Cihangir; KARA, Ali
    Specific Emitter Identification (SEI) methods are used in intercept systems for differentiating of same type of emitters in the environment. Most of the SEI implementations are designed for post processing (off line) by operators. This thesis presents results of study towards the real time (on line) implementation of a SEI method on signals after the IF stage, i.e. video signals (envelope) after the detector section. Initially, only the envelopes of the pulses are considered in SEI processing. Pulse width, rise time, fall time, rise slope angle, fall slope angle and pulse point are main parameters used in SEI algorithm developed. On the other hand, one of the main problems in operational environments is multipath distortion on received pulses. Moreover, creating and running of a library is another important issue. This thesis presents various approaches in both processing of multipath distorted pulses and wavelet based library creation methods on real-time SEI implementation for an intercept receiver.
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    TOWARDS REAL TIME IMPLEMENTATION OF SPECIFIC EMITTER IDENTIFICATION ON PULSE WAVEFORMS: OPTIMIZATIONS ON A LOW COST COMMERCIAL PLATFORM
    (2012-10-29) ERDEM, Cihangir; KARA, Ali
    Specific Emitter Identification (SEI) methods are used in intercept systems for differentiating of same type of emitters in the environment. Most of the SEI implementations are designed for post processing (off line) by operators. This thesis presents results of study towards the real time (on line) implementation of a SEI method on signals after the IF stage, i.e. video signals (envelope) after the detector section. Initially, only the envelopes of the pulses are considered in SEI processing. Pulse width, rise time, fall time, rise slope angle, fall slope angle and pulse point are main parameters used in SEI algorithm developed. On the other hand, one of the main problems in operational environments is multipath distortion on received pulses. Moreover, creating and running of a library is another important issue. This thesis presents various approaches in both processing of multipath distorted pulses and wavelet based library creation methods on real-time SEI implementation for an intercept receiver.
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    PERFORMANCE BENCHMARKING OF CELLULAR NETWORK OPERATORS IN TURKEY
    (2013-04-22) KADIOĞLU, Rana; KARA, Ali
    This thesis is prepared for assessing and benchmarking network operators‘ service quality on the basis of QoS. In order to make comparisons between mobile operators‘ service quality, appropriate performance indicators are identified. Performance is evaluated by key performance indicators. Performance indicators are obtained by vehicle test method on a specified route for each operator to be compared. One of the most important indicators, voice quality benchmarking is performed by χ² and Fisher test. These statistical methods for benchmarking are explained and results are discussed.
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    DETERMINATION OF SCATTERING CENTER OF MULTIPATH SIGNALS USING GEOMETRIC OPTICS AND FRESNEL ZONE CONCEPTS
    (2013-07-14) KAPUSUZ, Kamil Yavuz; KARA, Ali
    In this thesis, a method for determining scattering center (or center of scattering points) of a multipath is proposed, provided that the direction of arrival of the multipath is known by the receiver. The method is based on classical electromagnetic wave principles in order to determine scattering center over irregular terrain. Geometrical optics (GO) along with Fresnel zone concept is employed, as the receiver, the transmitter positions and irregular terrain data are assumed to be provided. The proposed method could be used at UHF bands, especially, operations of radars and electronic warfare applications.
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    SIGNAL AND SYSTEM LEVEL SIMULATIONS ON WIDEBAND INTERCEPT RECEIVERS
    (2014-03-10) KARADEDE, İlter; KARA, Ali
    Electronic Warfare (EW) simulations are mostly designed for only receiver front end or emitter parameter measurements. This thesis presents signal and system level simulations and emitter parameter measurements on proposed structures. To that end, a proposed wideband intercept receiver is employed and emitter environment is designed using commercial simulation tool. Then, parameter measurement part is employed to measure emitter parameters in a different simulation tool. Finally, simulation results are discussed for system level simulations for wideband intercept receivers and emitter parameter measurements.
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    COMPARISONS OF COMPUTER-BASED PROPAGATION MODELS WITH EXPERIMENTAL DATA COLLECTED IN AN URBAN AREA AT 1800 MHz
    (2017-09-06) ACAR, Tarık; AYDIN, Elif
    Nowadays a lot of models are set for the efficient and economic usage of frequency band which is a limited source. In this thesis, propagation models, developed and accepted in literature for this purpose, were studied. These are Free Space Path Loss (Fspl) + RMD (Epstein-Peterson), COST-HATA and COST-WI models. The district chosen for the model application has an irregular structure style. In the application of Free Space Path Loss (Fspl) + RMD (Epstein-Peterson), RTV Plan software is used; the other models were applied by calculations. In order to compare the success of the models, electric field strength measurements were taken in the chosen district (Mustafa Kemal Mahallesi-ANKARA), firstly at the random coordinate and secondly coordinates tracking on a single line. As a conclusion, measured values were compared with the results which were taken from the mentioned models with graphical presentations and the most appropriate model is tried be found and as another aim of the thesis study, a correction factor was generated as a modification for COST – HATA propagation model.
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    OPTIMIZATION OF SATELLITE TRANSPONDER UTILIZATION BASED ON SIMULATION RESULTS
    (2015-01-30) ULUBEY, Orhan; KARA, Ali
    Communication satellite transponder performance is characterized through the manufacturing process with extensive tests. However, such performance tests are always carried out with unmodulated carriers and they have shortcomings when it is necessary to analyze the transponder behavior with modulated multicarrier scenarios faced in actual utilization of the satellite. To overcome this problem it is necessary to simulate the behavior of transponder and obtain results to aid in link budget calculations. This thesis reviews the communication impairment sources on a satellite transponder and introduces a transponder simulator based on TURKSAT-3A satellite measured data. Simulation results are used to characterize the degradation introduced by the transponder for various actual utilization scenarios and suggestions made for optimal use.