Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı Tez Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/418

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QOS-AWARE DOWNLINK SCHEDULING ALGORITHM FOR LTE NETWORKS: A CASE STUDY ON EDGE USERS
(Abdullah Gül Üniversitesi, 2016) UYAN, OSMAN GÖKHAN; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü; UYAN, OSMAN GÖKHAN
4G/LTE (Long Term Evolution) is the state of the art wireless mobile broadband technology. It allows users to take advantage of high internet speeds. It makes use of the OFDM technology to offer high speed, which supplies the system resources both in time and frequency domain. The allocation of these resources is operated by a scheduling algorithm running on the base station. In this thesis, we investigate the performance of existing downlink scheduling algorithms in two ways. First we look at the performance of the algorithms in terms of throughput and fairness metrics. Second, we suggest a new fairness criterion, QoS-aware fairness which accepts that the system is fair if it can supply the users with the packet delays that they demand, and we evaluate the performance of the algorithms according to this metric. We also propose a new algorithm according to these two metrics, which especially increase the throughput gained by the edge users, the QoS-fairness, and classical fairness of the system without causing a big degradation in cell throughput when compared to other schedulers.
Sliding mode and PID based tracking control of magnetic levitation plant and hil tests
(Abdullah Gül Üniversitesi, 2016) EROĞLU, YAKUP; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; EROĞLU, YAKUP
Magnetic levitation systems are convenient to provide frictionless, reliable, fast and economical operations in wide-range applications. The effectiveness and applicability of these systems require precise feedback control designs. The position control problem of the magnetic levitation plant can be solved with a cascade control method. In this thesis, sliding mode and PID based cascade controllers are designed to render high position control performance and robustness to the magnetic levitation. Sliding mode control (SMC) based cascade controller is proposed for controlling magnetic levitation. The SMC based controllers for the inner current loop are designed to eliminate the effects of the inductance related uncertainties of the electromagnetic coil of the plant. For the outer position loop, the integral SMC is designed to eliminate disturbances around operating point resulting from the linearization of the mechanical part. Finally, numerical simulation and experimental results for various cascaded controllers are provided and compared in order to validate the efficacy of the approaches.
PERFORMANCE EVALUATIONS OF SINGLE MODE OPTICAL RECEIVER FOR DEGRADED VISUAL FIELD AND PHOTONIC LANTERN BASED COHERENT DETECTION
(Abdullah Gül Üniversitesi, 2016) ORAN, ABDULLAH; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; ORAN, ABDULLAH
Imaging at degraded visual environments is one of the biggest challenges in today’s imaging technologies. Especially military and commercial rotary wing aviation is suffering from impaired visual field in sandy, dusty, marine and snowy environments. For example, during landing the rotor churns up the particles and creates dense clouds of highly scattering medium, which limits the vision of the pilot and may result in an uncontrolled landing. The vision in such environments is limited because of the high ratio of scattered photons over the ballistic photons that have the image information. In this thesis, we propose to use optical spatial filtering (OSF) method in order to eliminate the scattered photons and mainly collect the ballistic photons at the receiver. OSF is widely used in microscopy; to the best of our knowledge this thesis will be the first application of OSF for macroscopic imaging. Our experimental results show that most of the scattered photons are eliminated using the spatial filtering in a highly scattering degraded visual field. The results are compared with a standard broad area photo detector which shows the effectiveness of spatial filtering. Free space optical systems have applications in different areas such as laser ranging, three-dimensional imaging, weather predictions and optical wireless communication. Some applications require very high performance free space optical systems that are not available today. The need of systems with higher performance and lower size, weight and power (SWaP) is the biggest research motivation of free space optical systems. Between various detection techniques, vi coherent optical detection comes forward for applications that require high sensitivity and bandwidth. Coherent detection based LIDAR systems have the potential to provide quantum noise limited performance. However coherent systems suffer from poor free space to fiber collection efficiency due to the single mode detection characteristics and small size of the optical fiber. In order to overcome this problem, photonic lantern is introduced to effectively collect the multimode beam coming from free space and convert it to a number of single mode fibers. The photonic lantern consists of a multimode fiber to a number of single-mode fibers. The collection efficiency enhancement of photonic lanterns have been investigated, however there is no study on the signal to noise ratio –performance- improvement on the photonic lantern based free space coherent systems. In this thesis; the effect of random distribution of the optical power in the 19-port photonic lantern will be investigated mathematically. The photonic lantern based coherent detection system performance will also be simulated by using the MATLAB software. The output of this thesis may open the path to experimental demonstration and maybe even to a prototype.
Photometric modelling for efficient lighting and display technologies
(Abdullah Gül Üniversitesi, 2016) GENÇ, SİNAN; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; GENÇ, SİNAN
Using light emitting diodes (LEDs) as lighting devices has come to the sight as a compulsory step in terms of the energy efficiency. Almost a quarter of total consumed energy that generated whole around the world is used for lighting. Using incandescent bulbs as lighting devices is forbidden in most of Europe and light emitting devices are one of the most important choices in order to compensate that need. Their high performance in terms of both luminance levels and energy efficiency has opened a new research area to increase their performance. White light has different requirements based on an application area. Providing that necessities by engineering on light parameters is one of the main aims of this thesis. In display technology, the development from cathode ray tubes to organic light emitting devices has increased the performance of both display quality and energy efficiency. Enhancement of the color scale that can be perceived by the human eye is the main purpose so that the reference color area increases systematically. The last announced reference, Rec.2020, has two thirds of the colors perceived by human eye. In this thesis, considering the current references such as National Television System Committee (NTSC) color gamut, the broadening of Rec.2020 is also presented as a new important figure of merit. In this thesis, we have studied on the investigation of the parameters of the emitters, i.e., peak emission wavelength, full width at half maximum and peak intensity to achieve the desired quality white light. Although it is possible to get white light in each step, the high quality requirements have been implemented by four colors within the simulation vi range of thesis which possess color rendering index value >90, correlated color temperature <4000K and luminous efficacy of optical radiation 380 lm/Wopt. In addition, in terms of display technology, we have shown that using ultra narrow emitters is an optimal choice for achieving Rec.2020 color triangle. Using ultra-narrow emitters, it is possible to obtain 99,89% of the Rec.2020 that also almost covers the NTSC. As expected, using a fourth color component cyan has increased the reached area to 169,55% of NTSC on color space dramatically
Improving sensitivity biosensors by using micro/nano magnetic particles
(Abdullah Gül Üniversitesi, 2016) OMARY MUSTAFA, MZAVA; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; OMARY MUSTAFA, MZAVA
Currently micro/nanoparticles such as magnetic beads are not only used as labels to acquire signals from biosensors but they are also used to enhance the signals obtained from various biosensors. Magnetic beads or target are linked to other molecular labels such as fluorescence and chemiluminescence labels by biomolecules such as antibody to reach higher sensitivity and provide signal amplification for the measurement. This dependency on biomolecular binding has several disadvantages such as molecular binding is sensitive to environmental conditions such as pH and temperature, labels are costly and molecular binding may require extra time. In this thesis a time and cost efficient signal amplification method that does not need any biomolecular coating but based on magnetic interaction of magnetic micro/nanoparticles is developed. Magnetic particles subjected to external magnetic field are magnetized and form a local field around them, attract each other and accumulate along the magnetic field lines. These controlled accumulations can be used to amplify the pixel area or the contrast of magnetic particles. Accumulation dynamics of magnetic particles under magnetic field are studied and the application of this method to the Escherichia coli 0157:H7 sample is demonstrated. Lastly the integration of this signal amplification method to a flow chamber and a complete biosensing procedure is pursued. Magnetic micro/ nano particles that are immobilized on gold-coated surface under external magnetic field inside a flow chamber attract the iron nanoparticles in a running fluid to form chains of accumulations around them. The accumulations formed under magnetic field are used to improve the Contrast to Noise Ratio (CNR) of the images thus the sensitivity.
Usage of laser induced bubbles for measuring intraocular eye pressure
(Abdullah Gül Üniversitesi, 2017) ALTINDİŞ, FATİH; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; ALTINDİŞ, FATİH
There are different methods of measuring intraocular pressure in clinics, but those methods fail to measure intraocular pressure under certain conditions. Most common problem of these methods are that they are inapplicable to patients who had eye surgery. In this study laser induced bubble characteristics are investigated in order to develop a new method to measure intraocular eye pressure with lasers. For this purpose, first, intraocular environment is imitated to perform laser experiments. Then imaging system is developed to digitally visualize laser induced bubbles that are created in intraocularlike environment. Digital image processing algorithms were developed to detect and measure bubble features. Different fluid pressure levels were configured to investigate the pressure effect on laser induced bubbles. Results showed that volume of laser induced bubbles are higher in lower fluid pressure and bubble volume decrease with the increased fluid pressure. In light of these findings, it can be concluded that the change in volume can be used to estimate fluid pressure. Thus, this study proposes a new technique for measuring intraocular pressure by using volume feature of laser induced bubbles that are created in the anterior chamber of the eye.
DIMENSIONALITY REDUCTION FOR PROTEIN SECONDARY STRUCTURE PREDICTION
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) GÖRMEZ, Yasin; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Proteins are important for our lives and they execute essential metabolic processes. The functions of the proteins can be understood by looking at the three-dimensional structures of the proteins. Because the experimental detection of tertiary structure is costly computational systems that estimate the structure provides a convenient alternative. One of the important steps of protein structure estimation is the identification of secondary structure tags. As new feature extraction methods are developed, the data sets used for this estimation can have high dimensions and some of the attributes can contain noisy data. For this reason, choosing the right number of features and the right attributes is one of the important steps to achieve a good success rate. In this study, size reduction process is applied on two different datasets using a deep autoencoder and various dimension reduction and feature selection techniques such as basic component analysis, chi-square, information gain, gain ratio, correlation-based feature selection (CFS) and the minimum redundancy maximum relevance algorithm as well as search strategies such as best first, genetic search, greedy algorithm. To evaluate the prediction accuracy, a support vector machine classifier is employed.
DESIGNING RELIABLE MICROARCHITCTRURES ACCORDING TO APPLICATION REQUIREMENTS
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kahira, Albert; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
One of the most important factors to consider when designing a new computer architecture besides cost, energy consumption and performance is reliability. Reliability looks into how often the computer produces the correct results and when it’s expected to fail (Mean time to failure). Reliability heavily affects all the other factors such as cost, area and performance and therefore a careful tradeoff has to be made between reliability and the other factors. One factor that has come into play recently is application requirement. The need for more computing power by applications has been increasing. Because of this, designers have designed much more powerful and sophisticated architectures putting millions of transistors into a single chip and more recently increasing the number of chips. However, this has increased the likelihood of failures occurring. A study of these failures and the reliability of this microarchitectures is therefore required. In this study, we investigate the reliability of current micro architectures for different applications and further propose reliable microarchitectures for those applications or mechanisms to adjust reliability parameters based on the application. We mostly focus on fault tolerance as a reliability parameter.
OPTIMIZING CLASSIFIERS FOR PROTEIN SECONDARY STRUCTURE PREDICTION
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) UZUT, Ömmu Gülsüm; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Protein secondary structure prediction is important for understanding protein structure and function. PSSP can be seen as a bridge between amino acid sequence and 3D structure of a protein. Many methods have been performed to improve prediction accuracy rate and get good achievement. There are multiple situations that will affect the performance of a method. One of these situations is selection of correct parameter. Hyperparameters are parameters that cannot be directly learned from the regular training process. Although the methods have default hyperparameter values, it is possible to improve performance of methods by using those hyperparameters with different values which can be more convenient. Parameter optimization plays an important role at this stage. It applies to methods to find best hyperparameter values to apply methods. In our thesis, computational methods such as Random forest, Support vector machines and deep convolutional neural fields have been used and optimized on CB513 dataset. We have aimed to optimize methods with different values to improve the results and show the importance of parameter optimization in protein structure prediction. We also tried to use some ensemble methods to compare our results with individual classifiers to see the improvement of results.
PERFORMANCE ANALYSIS OF UNDERWATER COMMUNICATION WITH DIFFERENT MODULATION TECHNIQUES
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) BAHÇEBAŞI, Akif; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
There is an increasing interest in using Underwater Acoustic Sensor Networks (UASNs) for various oceanographic applications, such as pollution monitoring, seismic monitoring, environmental data collection, offshore exploration, and tactical surveillance. As well as underwater sensor nodes, unmanned underwater vehicles are used in some application scenarios of UASNs such as exploration of underwater resources and data gathering in collaboration-requiring missions. UASNs rely on acoustic communications; however, the underwater acoustic channel is highly variable and its link quality depends on environmental factors and the locations of the communicating nodes. Therefore, ensuring reliable communication in UASNs is quite difficult. Moreover, path losses and retransmissions lead to the wastage of energy resources and reduce the network lifetime. In this study, we used well-known underwater modulation schemes to analyse and simulate various underwater scenarios with different depth, distance and BER values in order to make a fair comparison between the modulation schemes and find the optimal transmission power. As proven in our simulation study 32-PSK and 16-QAM techniques achieved the minimum energy consumption rates. Therefore network designers can prolong the underwater network lifetime using 32-PSK and 16-QAM modulation techniques.
Early prognosis of breast cancer using image processing and machine learning
(Abdullah Gül Üniversitesi, 2018) TAŞDEMİR, SENA BÜŞRA YENGEÇ; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; TAŞDEMİR, SENA BÜŞRA YENGEÇ
Among females, leading cause of cancer death and the most common cancer type is breast cancer. Early detection is vital because it reduces the mortality rate. Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of the breast cancer. Automatic detection of tumorous area from the digital mammography image helps to locate the abnormal tissues, which may be analyzed further by a radiologist. It has two main stages: feature extraction and classification. In this work, numerous feature extraction methods have been tested such as 2D-DWT, HOG, Haralick’s textural features, TAS, LBP, Zernike and GLCM. In order to select the most suitable classifier, the following classifiers also have been tested: random forest, logistic regression, k-nearest neighbors, naïve Bayes, decision tree, support vector machines, Adaboost, radial basis function network, multilayer perceptron, convolutional neural network. Based on comprehensive experiments, the optimum combination of feature extraction, feature selection and classification methods are identified. The proposed method, which employs CLAHE as image pre-processing tool, 2D-DWT, HOG, Haralick as feature extraction methods, wrapper as the feature selection method and random forest as the classifier, attained an accuracy of 87.5%
Designing a system to manipulate micro magnetic beads and cells
(Abdullah Gül Üniversitesi, 2018) BÖYÜK, MUSTAFA; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; BÖYÜK, MUSTAFA
Magnetic tweezers are able to manipulate cells or biomolecules for various applications and measurements. In this study, an electromagnetic micromanipulator is designed, modeled and controlled for single magnetic bead manipulations. Electromagnetic tweezers are capable of controlling micron sized superparamagnetic particles with the help of appropriate control mechanism. Magnetic particles can be functionalized with receptors in order to capture the target biomolecules, and conjugated particles can be moved to a certain place by using an external magnetic field. Magnetic monopole and magnetic circuit approaches are used to model the dynamic equation of the magnetic system. An offset current based feedback linearizing is devised to ensure wide range of movement conditions with zero steady-state error. Image based algorithm is developed in order to find the position of the single particle. Numerical simulations are carried out in order to validate the derived model and the control system. The designed magnetic system is able to apply magnetic forces in the range of 1-100 pN to control a magnetic particle of 1 to 10 micrometer of diameter with a current less than 1 A. The magnetic micromanipulator system can be used for single cell separation, and biosensor applications.
Protein fragment selection using machine learning
(Abdullah Gül Üniversitesi, 2018) EMRE ULUTAŞ, ALPEREN; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; EMRE ULUTAŞ, ALPEREN
Protein fragment selection is an important step in predicting the three-dimensional (3D) structure of proteins. Selecting the right fragments contributes significantly to accurate prediction of 3D structure. In this thesis, a machine learning approach is employed to predict whether a pair of protein fragments have similar 3D structures or not, which can be used to select fragment structures for a target protein with unknown structure. To design input features, a concepy hierarchy is implemented, which considers sequence profile matrices, predicted secondary structure, solvent accessibility and torsion angle classes as features in various combinations and projections. Several machine learning classifiers and regressors are trained and optimized for predicting the structural similarity of 3-mer and 9-mer fragments including logistic regression, AdaBoost, decision tree, k-nearest neighbor, naive Bayes, random forest, SVM and multi-layer perceptron. The results demonstrate that combining different feature sets through concept hierarcy and model optimization improves the prediction accuracy substantially. Furthermore it is possible to predict the structural similarity of fragment pairs with high accuracy, which is assessed by perforing cross-validation experiments on fragment datasets. When the structural similarity of fragments is defined as a classification problem, the accuracy of different classifiers are obtained as similar to each other. Among the regression methods, random forest provided the best accuracy metrics.
Low dose CT imaging for cancer diagnosis and therapy
(Abdullah Gül Üniversitesi, 2018) SÜMER, ESRA; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; SÜMER, ESRA
Cancer is a common disease among human population and second leading cause of death. It is well known that diagnosing cancer at early stages is very critical for increasing success of therapy. There have been different imaging modalities used in diagnosing and staging of cancer. One of them is computed tomography (CT) that provides two-dimensional (2D) slices of three-dimensional (3D) object using the series of projections taken around the object. The main limitations of CT are radiation dose and low sensitivity to soft tissue. Firstly, fewer projections can be used to lower dose in CT which causes the reconstruction problem heavily underdetermined. Former studies proposed iterative reconstruction techniques to overcome this problem. The significant weakness of these methods is their computational expensiveness. In the present thesis, this problem is addressed by developing a computationally efficient filtered back projection (FBP) based method using total variation (TV) minimization. 2D modified Shepp-Logan phantom is used for performance evaluations. The superiority of the proposed method is shown both qualitatively and quantitatively. The second aim of the thesis is to enhance contrast capability of CT imaging by using novel magnetic nanoparticles (MNPs) as contrast agents which were fabricated at Mechanical Engineering Department of Istanbul Technical University. The pixel density enhancements of CT images induced by five different core types of MNPs in the agarose gel are analyzed. The results confirm the effectiveness of the MNPs as contrast media for CT imaging.
Improving short –term memory performance using alpha band neurofeedback
(Abdullah Gül Üniversitesi, 2018) GÖKŞİN, BARIŞ; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; GÖKŞİN, BARIŞ
Age-related memory degradation is a serious problem for individuals and there is no known satisfying medical treatment of memory disorders such as Alzheimer. Recent advances in BCI technology enable us to measure brain wave activity of individuals, and neurofeedback is one of the methods that uses BCI technology. Although there are many researches about applications of neurofeedback on psychological disorders, there exist limited research on the application of neurofeedback’s effect on short-term memory performance. This thesis explored the possibility of short-term memory improvement through alphaband neurofeedback training. EEG signals were collected from 11 healthy male participants using a wireless EEG device. The neurofeedback paradigm was used to enhance alpha-band power in real-time. Before and after 5 neurofeedback training sessions, a memorization test using 10 words was applied to all participants in order to evaluate the short-term memory performance improvement due to neurofeedback. The results indicated that 6 out of 11 participants were able to enhance their alpha-band power with respect to other bands in the frequency spectrum during neurofeedback sessions. However, there was no obvious improvement in their short-term memory performance. We may conclude that neurofeedback training was beneficial for the participants to focus their minds consciously. However, it is not easy to mention that neurofeedback training certainly improves or is irrelevant with short-term memory performance.
Developing machine learning methods for business intelligence
(Abdullah Gül Üniversitesi, 2018) KABORE, KADER MONHAMADY; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; KABORE, KADER MONHAMADY
Detection of key attributes in text is an area of research, which attracts attention due to the increase of data and the availability of massive documents. Key attributes serve as metadata for documents and the discovery of accurate characteristics allows to capture significant pieces of information from a lengthy text. They allow faster and efficient information retrieval on the web domain with an ever increasing number of websites. In this thesis, a novel two-stage machine learning method is developed to identify the company name from web page text. The problem is reduced to a classification task at the token (i.e. word) level followed by a post-processing phase for predicting the company name. Features are extracted using natural language processing techniques and by observing patterns present in textual data to reflect the properties and significance of the words in context. Derived features are sent as input to classification algorithms such as naive Bayes, decision tree, and random forest. In addition to the token-based classifier, a rule-based method is designed that also considers tokens from domain as well as page title and ranks tokens by computing similarity metrics. The results demonstrate high precision from the machine learning model along with high undefined cases whereas the rule-based approach obtained high accuracy with precision inferior to the token-based model. When the two classification strategies are combined into a two-stage classifier, high accuracy and precision scores are obtained.
CAMERA BASED SHEET MEASUREMENT SYSTEM FOR LASER CNC MACHINES
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2018) UMAR, Aamish; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Laser CNC machines are widely utilized for cutting metal sheets of varying thickness and materials. The sheets to be cut can be of varying dimensions and be placed at any desired area of the cutting table. The operator needs to assign the starting point of the laser manually along with the dimensions of the metal sheet in order to start the cutting process. The process of assigning the starting point and dimension of sheet are time consuming and can take few minutes before every cutting process and it sums up to hours by the end of daily cutting jobs, an automated process of sheet measurement can save considerable amount of time and speed up the process. In this thesis, a camera based system for automatic sheet measurement which includes measurement of starting point assignment, orientation, length and breadth has been developed. The algorithms have been implemented keeping in mind the importance of speed since the processing has to be done in real time and needs to be as fast as possible. The implemented algorithms can find all required parameters in about two seconds. The techniques utilized for its implementation have been discussed. The robustness of the system has been compared with other traditional methods of sheet measurement and orientation detection. The implemented system was tested on a real laser CNC machine over a period of six months and the test results have been discussed. Also, a camera based intrusion detection system for laser CNC machine has been developed in order to make it safe for human during operation. Patent application made for the implemented system.
Control algorithms for feedback tracking in the small populations of Hodgkin-Huxley neurons
(Abdullah Gül Üniversitesi, 2018) ŞENEL, ZEYNEP; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü; ŞENEL, ZEYNEP
The purpose of the thesis is to design powerful mathematical control algorithms for the tracking and modeling spiking and bursting behaviors of real biological neurons in 4-dimensional dynamical systems. For this aim, 4-dimensional Hodgkin-Huxley’s (HH) nonlinear dynamical system including differential equations preferred. Because HH model represents a realistic mathematical model for the real neurons and it analytically accepted. Applied external current as a control signal initiate stimulating of the neuron cells in the neuronal networks serve while the membrane action potentials are outputs. We applied two different control methods; speed gradient (SG) of Fradkov’s and target attractor (TA) of Kolesnikov’s feedbacks for the modeling and controlling spiking and bursting regime that axon membrane potential created by the control signal in HH neuron clusters. These algorithms show high effectiveness and robustness in the managed HH dynamical neuron system. This study provides generating arbitrary forms of single spikes, train of spikes and bursts for chosen cells in the various configurations of HH neuron clusters (linear chain and ring-type chain) with the control over a selected element of the network. In this study, developed algorithms applied to epileptiform collective bursting in a small cluster of HH neurons for make suppression. The scope of this thesis is to develop new control methods for mathematical modeling to control of real neurons and effectively can use in computational neuroscience and diagnosis or treatment of neural dysfunctions such as epileptiform or abnormal behavior in the HH neuron networks.
Cloud induced PV impact on voltage profiles for smart microgrids
(Abdullah Gül Üniversitesi, 2018) ÇAĞATAY KOÇER, MUSTAFA; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü; ÇAĞATAY KOÇER, MUSTAFA
In the history of humanity, no other invention has positively influenced everyone's life as much as the invention of electrical energy. With the electricity, the rise of civilization gained momentum, industrial technologies advanced, and scientific developments found more suitable habitat for themselves. However, in order to meet the growing demand for electricity, production costs had to be reduced. In this direction, the energy sector used fossil fuel-based solutions for cheap electricity production. However, nowadays, a tendency to use cleaner and more sustainable methods for electricity production has occurred since fossil fuel sources are limited and they increase the greenhouse gas emissions in the atmosphere. This trend brings renewable energy resources (RER) to the table as a new solution, especially in the modern electricity networks. However, since behaviors of the RERs are challenging to forecast and highly dependent on environmental factors, these resources have some severe problems in the integration into the grid, particularly in the low voltage networks, such as microgrids. In this thesis, the impact of the fluctuations in photovoltaic power (PV) generation, which happens because of frequently interrupted solar radiance by the chaotic movements of the clouds, on the load voltage levels of a real field microgrid system belonging to the Malta College of Arts Science and Technology (MCAST) campus is investigated. Also, the impact of the auxiliary sources (battery storage system and diesel generator) that are responsible for ensuring that the microgrid healthily continues its operation on the load voltage profiles is presented. The author used the MATLAB/Simulink platform for the necessary simulations and system designs
Phase noise filtering effects of mode-locked lasers
(Abdullah Gül Üniversitesi, 2018) MBONDE, HAMIDU; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; MBONDE, HAMIDU
The subject of Mode-Locked Lasers has experienced a massive growth over the last two decades. Previously meant as the source of ultra-short optical pulses, its concepts have recently expanded to be applicable in areas beyond Optics such as Biomedical[1], Micro-machining[2], Sensing[3] and RF/Microwaves communication[4]. In particular this thesis focuses on application of Mode-Locked Lasers in RF/Microwave communications. One of the common problems with RF communication systems is signal integrity. Due to the nature of oscillation systems that are used to produce RF signals there is always an inevitable amount of undesirable signal associated with main signal being generated. These spurious (noise) signals have significant effect on the efficient performance of particular RF system. Low noisy RF signals are highly desirable and have many applications in high speed communication, RADAR and electronic warfare. Therefore it is critical to have an efficient means of producing low noise RF signals. Generating RF signals by Optical means has emerged as a major solution to this problem. Various methods for optically generating lower noise RF signals of high frequency have been developed such as frequency stabilized mode-locked lasers[5], phase locked loop based oscillators[6] and optoelectronic oscillators[7]. In this thesis a novel approach to this problem is presented, instead of generating lower noise signals a unique method of efficiently filtering the noise of RF signal using Mode-Locked Laser is explained. The first two chapters give brief introduction to mode-locked lasers and phase noise in oscillator, the concepts which will be used throughout this thesis. Then the experimental setups of the proposed system with the results obtained are presented in Chapter 3. Furthermore, theoretical study and analysis of limitations of this method is presented in ii Chapter 4. This includes analysis of these limitations as well as supporting simulations results. Phase noise is frequency domain term which in time domain is referred to as jitters. For various applications it is necessary to determine total jitters value of the system in order to estimate its bit error rates and other performance features. Chapter 5 of this thesis is dedicated to introducing jitter concept and a numerical method of converting a phase noise spectrum into jitter Probability Density Function (PDF).Together with the MATLAB code for aforementioned simulation a special GUI (Graphical User Interface) has been developed for the purpose of converting any given phase noise spectrum into its corresponding jitter PDF. The last chapter gives some concluding remarks and look at the possible futures of this work.

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