Doktora Tezleri

Permanent URI for this collectionhttp://standard-demo.gcris.com/handle/123456789/3642

Browse

Now showing 1 - 20 of 23

Analysis of the electromagnetic scattering from flat plates by using different sinc-type basis fuctions in method of moments
(Izmir Institute of Technology, 2012) Özbakış, Başak; Kuştepeli, Alp
Electromagnetic scattering from three dimensional arbitrary flat surfaces illuminated by a plane wave is investigated by using sinc-type basis functions in the method of moments (MoM) in this study. Sinc-sinc based Galerkin MoM formulation is obtained and its fortran program is developed firstly. The number of integrals to be computed is decreased by using analytical properties of the sinc function in the formulation. Therefore, the CPU time for obtaining the coefficient matrix is decreased appreciably. The relative error during the generation of the main matrix elements is obtained between 0.058% and 0.095% for considerably large matrices. Rooftop based MoM formulation is developed and it is coded with a similar approach used in sinc based program. The accuracy and CPU time of the sinc based bistatic scattering results are compared with those of rooftop. The MoM formulation of the sinc-pulse (sinctop) basis functions with non-Galerkin case is developed to reduce the overflow problem at the edges. Induced surface currents and far-field results of the sinc-sinc and sinc-pulse based formulations are compared with those of the rooftop basis functions and with the ones obtained from SuperNEC. Both sinc-sinc and sinc-pulse based simulation results are in good agreement with the results of rooftop based and SuperNEC simulation results. The main purpose in this study is to obtain accurate bistatic scattering results by using different sinc-type basis functions in MoM procedure while filling the main matrix in less CPU time when compared with the well-known methods.
Automatic identification of abnormal regiones in digitized histology cross-sections of colonic tissues and adenocarcinomas using quasi-supervised learning
(Izmir Institute of Technology, 2012) Önder, Devrim; Karaçalı, Bilge
In this thesis, a framework for quasi-supervised histopathology image texture identi- cation is presented. The process begins with extraction of texture features followed by a quasi-supervised analysis. Throughout this study, light microscopic images of the hematoxylin and eosin stained colorectal histopathology sections containing adenocarcinoma were quantitatively analysed. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly and in bulk, and the other containing an unlabelled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues commonly used for conventional supervised learning and signicantly reduces the expert intervention. Several texture feature vector datasets corresponding to various feature calculation parameters were tested within the proposed framework. The resulting labelling and recognition performances were compared to that of a conventional powerful supervised classier using manually labelled ground-truth data that was withheld from the quasi-supervised learning algorithm. That supervised classier represented an idealized but undesired method due to extensive expert labelling. Several vector dimensionality reduction techniques were evaluated an improvement in the performance. Among the alternatives, the Independent Component Analysis procedure increased the performance of the proposed framework. Experimental results on colorectal histopathology slides showed that the regions containing cancer tissue can be identied accurately without using manually labelled ground-truth datasets in a quasi-supervised strategy.
Automatic transcription of traditional Turkish art music recordings: A computational ethnomusicology appraoach
(Izmir Institute of Technology, 2012) Gedik, Ali Cenk; Savacı, Ferit Acar
Music Information Retrieval (MIR) is a recent research field, as an outcome of the revolutionary change in the distribution of, and access to the music recordings. Although MIR research already covers a wide range of applications, MIR methods are primarily developed for western music. Since the most important dimensions of music are fundamentally different in western and non-western musics, developing MIR methods for non-western musics is a challenging task. On the other hand, the discipline of ethnomusicology supplies some useful insights for the computational studies on nonwestern musics. Therefore, this thesis overcomes this challenging task within the framework of computational ethnomusicology, a new emerging interdisciplinary research domain. As a result, the main contribution of this study is the development of an automatic transcription system for traditional Turkish art music (Turkish music) for the first time in the literature. In order to develop such system for Turkish music, several subjects are also studied for the first time in the literature which constitute other contributions of the thesis: Automatic music transcription problem is considered from the perspective of ethnomusicology, an automatic makam recognition system is developed and the scale theory of Turkish music is evaluated computationally for nine makamlar in order to understand whether it can be used for makam detection. Furthermore, there is a wide geographical region such as Middle-East, North Africa and Asia sharing similarities with Turkish music. Therefore our study would also provide more relevant techniques and methods than the MIR literature for the study of these non-western musics.
A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function
(Izmir Institute of Technology, 2013) Göçeri, Evgin; Akan, Aydın
Developing a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term.
Control of redundant robot manipulators with telerobotic applications
(Izmir Institute of Technology, 2016-11) Çetin, Kamil; Tatlıcıoğlu, Enver
This thesis focuses on task-space control of kinematically redundant robot manipulators with telerobotic applications. The first aim is to design asymptotically stable sub-task controllers for kinematically redundant robot manipulators subject to parametric uncertainties in their dynamics. Initially, a novel combined analysis of the task-space tracking and sub-task controllers is performed for redundant robots having only one extra degree of freedom. Next, an extended task-space controller is designed by integrating manipulator Jacobian with the sub-task Jacobian. Both controllers ensure task-space tracking and sub-task objectives at the amount of redundant degree of freedom. As the second aim, two robust control methods are proposed for task-space tracking of robot manipulators. First, a novel continuous robust controller is designed despite dynamic model and Jacobian uncertainties to ensure asymptotic task-space tracking while requiring measurements of joint positions and velocities. Then, a robust output feedback controller is proposed to ensure ultimately bounded task-space tracking requiring neither measurements of joint positions or velocities nor accurate knowledge of kinematic and dynamic models. The third aim is to develop a passive decomposition method for task-space control of bilateral teleoperation systems. The proposed method ensures coordination of master and slave robots while achieving a desired overall motion for the bilateral teleoperation system. The proposed method is firstly considered for teleoperation systems consisting of kinematically similar master and slave robots, then extended to be applicable to kinematically redundant teleoperation systems. Simulation and experimental studies are performed to present the viability of the proposed methods.
Design and fabrication of a fiber-integrated mode-selective photopolymer grating coupler
(Izmir Institute of Technology, 2014-07) Sümer, Can; Dinleyici, Mehmet Salih
The aim of this study is to develop a node for an optical add/drop multiplexer, which is capable of packet switching at very high speeds and is controlled by light. The D-Fiber is employed as the transmission medium and the grating formation and polymerization is implemented on the fiber. The optical multiplexer is constructed by cascading two different photonic structures formed on the planar side of the D-Fiber. Multiplexing is performed by forming a temporary (transient) grating; which is the subject of another project in parallel at our laboratory. Right after this grating, will be the structure that is the subject of this thesis, which is the permanent structure fabricated via photopolymerization. The temporary grating breaks the resonance using nonlinear optical effects, coupling the fiber mode into the higher-order mode and the permanent structure extracts the mode out selectively to another fiber. Due to the very fast response of the nonlinear effects, an optical switch capable of transferring optical packets to other waveguides is formed. Photopolymerization is a method that can be used to fabricate photonic structures such as photonic crystals or permanent gratings, holographically or by direct-writing. The project involves the fabrication of the aforementioned photonic structure via photopolymerization. Even though the main aim of this project is not materials research, extensive effort has been put into optimizing the polymer recipe and characterization of the materials and processing properties.
Development of a unified analysis framework for multicolor flow cytometry data based on quasi-supervised learning
(Izmir Institute of Technology, 2017-07) Köktürk Güzel, Başak Esin; Karaçalı, Bilge
In this dissertation, automatic compensation and gating strategies are investigated for multi-color flow cytometry data analysis. We propose two clustering algorithms that combine the quasi-supervised learning algorithm with an expectation-maximization routine for automatic gating. The quasi-supervised learning algorithm estimates the posterior probabilities of the different cell populations at each sample in a dataset in a manner that does not involve fitting a parametric model to the data. We have developed two different binary divisive clustering algorithms based on expectation maximization with responsibility values calculated using the quasi-supervised learning algorithm instead of the probabilistic models used in conventional expectation maximization applications. Our clustering algorithms determine the number of clusters in run-time by measuring the overlap between the estimated clusters in each provisional division and comparing it with the previous one to determine whether the division is warranted or not. Since this type of clustering is indifferent to the underlying distribution of dataset, it is well suited to automatic flow cytometry gating. The second clustering algorithm improves upon the first one using a simulated annealing approach. Its iterative structure allows finding the global minimum of a cost functional that achieves the best separation point by gradually smoothing the decision regions in each iteration. Finally, we have developed a joint diagonalization and clustering method for automatic compensation of flow data based on the methods above. The proposed method identifies cell sub groups using the annealing-based model-free expectation-maximization algorithm and finds a data transformation matrix that achieves orthogonality of the covariance structure of each identified cell cluster using fast Frobenius diagonalization. We have tested all proposed algortihms on both synthetically created datasets and real multi-color flow cytometry datasets. The results show that our automated gating algorithms are very successful in identifying the distinct cell groups so long as there is enough statistical evidence for their presence. In addition, the automated compensation procedure was also successfully applied on the synthetically created dataset and real multi-color flow cytometry data of lymphocytes that are a low autofluorescence cell group. However, the automated compensation algorithm needs further study to be generalized to high autofluorescence cell types where proper compensation does not necessarily coincide with an orthogonal covariance structure.
Development of nonlinear robust control techniques for unmanned aerial vehicles
(Izmir Institute of Technology, 2015-07) Tanyer, İlker; Tatlıcıoğlu, Enver
In this thesis, model reference output tracking control of unmanned aircraft vehicles are aimed. The control problem is complicated due to the lack of accurate knowledge of nonlinear system dynamics and additive state-dependent nonlinear disturbancelike terms. Only the output of the vehicle is considered to be available for control design purposes. A novel robust controller is designed that ensured a global asymptotic stability result. In the design of the controller, proportional integral controller is fused with the integral of the signum of the tracking error to compensate uncertainties. Lyapunov type stability analysis are utilized to prove asymptotic convergence of the output tracking error. Extensions to optimal, adaptive and neural network controllers are also designed. Simulation and experiment results are presented to illustrate the performance of the robust controllers.
Effects of electromagnetic fields on the performance of molecular communications
(01. Izmir Institute of Technology, 2022-12) Taşçı, Aslı; Atakan, Barış
This thesis analyzes molecular communication (MC) systems’ performance under electromagnetic fields. The aim of the thesis is to model and study molecular behavior under electromagnetic fields (EMF). The thesis starts with the theoretical explanation of classic electromagnetism. The directional and thermal changes are the main effects of EMF on particles. The directional effects of EMF are studied with regard to electromagnetic forces. The applied electromagnetic forces are presented for different types of particles. The effect of EMF on magnetically susceptible particles is analyzed in particular. Furthermore, molecular movement is analyzed by considering four fundamental forces on diffusing molecules under EMF. The energy transfer between EMF and particles is studied to understand the thermal effects of EMF. AnMCscheme that transmits information with magnetically susceptible molecules is studied in the second part of the thesis. The molecular type and the configuration of EMF are studied to understand the effect of EMF on the diffusion rate. The effects of magnetic field gradient (MFG) and concentration gradient magnetic force (CGMF) are analyzed to model the change in the diffusion rate and concentration of magnetically susceptible molecules. The last part of the thesis focuses on molecular dynamics under EMF. The effect of thermal changes on the molecular reaction rate and binding kinetics is modeled with reaction-diffusion systems. The specific reaction rate constant is analyzed to determine the effect of temperature change caused by the EMF. The movement of molecules is modeled by Langevin’s diffusion model. The probability distribution functions of the molecule’s velocity and displacement are studied to understand and model the molecular behavior under EMF. Moreover, the mean-squared displacement is employed to analyze the diffusion type under EMF.
Generalized Bayesian model selection using reversible jump Markov chain Monte Carlo
(Izmir Institute of Technology, 2017-11) Karakuş, Oktay; Altınkaya, Mustafa Aziz; Kuruoğlu, Ercan Engin
The main objective of this thesis is to suggest a general Bayesian framework for model selection based on reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. In particular, we aim to reveal the undiscovered potentials of RJMCMC in model selection applications by exploiting the original formulation to explore spaces of di erent classes or structures and thus, to show that RJMCMC o ers a wider interpretation than just being a trans-dimensional model selection algorithm. The general practice is to use RJMCMC in a trans-dimensional framework e.g. in model estimation studies of linear time series, such as AR and ARMA and mixture processes, etc. In this thesis, we propose a new interpretation on RJMCMC which reveals the undiscovered potentials of the algorithm. This new interpretation, firstly, extends the classical trans-dimensional approach to a much wider meaning by exploring the spaces of linear and nonlinear models in terms of the nonlinear (polynomial) time series models. Polynomial process modelling is followed by the definition of a new type of RJMCMC move that performs transitions between various generic model spaces irrespective of model sizes. Then, we apply this new framework to the identification of Volterra systems with an application of nonlinear channel estimation of an OFDM communication system. The proposed RJMCMC move has been adjusted to explore the spaces of di erent distribution families by matching the common properties of the model spaces such as norm, and this leads us to perform a distribution estimation study of the observed real-life data sets including, impulsive noise in power-line communications, seismic acceleration time series, remote sensing images, etc. Simulation results demonstrate the remarkable performance of the proposed method in nonlinearity degree estimation and in transitions between di erent classes of models. The proposed method uses RJMCMC in an unorthodox way and reveals its potential to be a general estimation method by performing the reversible jump mechanism between spaces of di erent model classes.
Interference alignment techniques for heterogeneous wireless networks
(Izmir Institute of Technology, 2016-09) Aycan Beyazıt, Esra; Özbek, Berna
In this thesis, we study the stream selection based interference alignment (IA) algorithms, which can provide large multiplexing gain, to deal with the interference in the heterogeneous networks. Firstly, different deployment scenarios for the pico cells are investigated assuming perfect channel state information (CSI) at the transmitters. Two different stream selection IA algorithms are proposed for fully and partially connected interference networks and selecting at least one stream is guaranteed for each user. A stream sequence is selected among a predetermined set of sequences that mostly contribute to the sum-rate while performing an exhaustive search. In the proposed algorithms, the complexity of the exhaustive search is significantly decreased while keeping the performance relatively close. After selecting a stream, the interference generated between the selected and the unselected streams is aligned by orthogonal projections. Then, the influence of the imperfect CSI on the proposed algorithms is analyzed and it is observed that the intra-stream interference causes a significant degradation in the performance due to the quantization error. Therefore, we propose an algorithm for the limited feedback scheme. Finally, adaptive bit allocation schemes are presented to maximize the overall capacity for all the proposed algorithms. The performance evaluations are carried out considering different scenarios with different number and placements of pico cells. It is shown that the proposed algorithm for the limited feedback is more robust to channel imperfections compared to the existing IA algorithms. The presented bit allocation schemes improve the performances of the algorithms compared to the equal bit allocation.
Inverse problems and regularization in signal processing with applications to wireless channel estimation
(Izmir Institute of Technology, 2011) Şahin, Ahmet; Altınkaya, Mustafa Aziz
The research presented in this thesis is on inverse problems encountered in the field of signal processing. Theory, classification and solution techniques of linear discrete inverse problems (LDIP) are investigated. LDIP are classified as underdetermined LDIP (ULDIP) and overdetermined LDIP (OLDIP). The solution methods developed for LDIP are applied to the particular problems of signal processing mainly channel estimation, equalization and compressive sampling. A new solution technique named constraint removal (CR) is presented for ULDIP type problems with sparse inputs. CR is applied to terrestrial digital TV (DTV) channel estimation. CR is also compared with subspace pursuit (SP) and linear programming. Regularization and optimum regularization parameter selection for ill-posed OLDIP type problems are discussed. Sparse channel estimation for wireless digital communications is investigated. A new channel estimation method, permuted deconvolution (PDEC), for long delay spread channels with short training sequences is proposed and compared with other methods. A review on equalization is presented. Different equalization techniques are discussed and compared. DFE is explained from an inverse problem perspective. A new non-feedback equalization technique called frequency compensated linear equalization (FC-LE) for sparse channels is presented and compared with DFE.
Joint reconstruction of surface geometry and reflection properties by using image based methods
(Izmir Institute of Technology, 2015-07) Ozan, Şükrü; Gümüştekin, Şevket
In this thesis, we aim to capture realistic geometrical descriptions of real world scenes and objects with a special effort to characterize reflection properties. After a brief review of the stereo imaging literature, we show our contributions to enhance stereo matching performance by identifying and eliminating specular surface reflections. The identification of specular reflection can be done both passively and actively. We use dichromatic-based methods to identify and eliminate specular reflections passively. We utilize polarization imaging methods to do the same job actively. In this work we also study structured light based methods that can give better reconstruction results compared to stereo imaging methods. We propose three laser scanners equipped with a pair of line lasers and a method to calibrate these systems. Another convenient way to obtain good surface reconstruction results using structured light is to use projectors that can be used as a light source that project complicated patterns. We show our results from a digital camera-projector-based scanning system as well. This system can robustly generate a very dense reconstruction of surfaces. We also use the projector based scanning system to determine the surface reflection properties. Using high dynamic range imaging (HDRI) techniques makes it possible for us to estimate scene radiance values. Since we can determine the incoming and outgoing light directions, we are able to measure bidirectional reflectance distribution function (BRDF) values from reconstructed surface points for corresponding directions. If the sample surface have not only diffuse reflection components but also a sufficient amount of specular highlights, it is possible to approximate BRDF corresponding to a surface by fitting an analytical BRDF model to the measured data. In our work we preferred to use Phong BRDF model. Finally, we present results with rendered synthetic images where the parameter values of the Phong model were estimated using scans of real objects.
Medium-aware inference for wireless sensor networks
(Izmir Institute of Technology, 2020-12) Wahdan, Muath Abed Alrauf; Altınkaya, Mustafa Aziz; Izmir Institute of Technology
In a wireless sensor network, multilevel quantization is necessary in order to find a compromise between the smallest possible power consumption of the sensors and the detection performance at the fusion center (FC). The general methodology is using distance measures such as J-divergence (JD) and Bhattacharyya distance in this quantization. This thesis proposes a different approach which is based on maximizing the average output entropy of the sensors under both hypotheses of a binary hypothesis test and utilizes it in a Neyman-Pearson (NP) criterion based distributed detection scheme in order to detect a point source. Firstly, a deterministic signal and isotropic propagation model is considered. The receiver operating characteristics of the proposed maximum average entropy (MAE) meth\-od in quantizing sensor outputs was obtained for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent $M$-ary frequency shift keying communication is used for transmitting MAE based multilevel quantized sensor outputs over a Rayleigh fading channel. Secondly, the sequential testing version of the first problem is considered for both unquantized and quantized data transmissions. MAE and maximum JD (MJD) quantization methods for $M$-levels were applied in the sequential probability ratio test of Wald. The average sample number (ASN) required for the target probabilities of detection and a false alarm was the performance criterion: the smaller, the better. The performance of this test improves monotonically with the number of local sensors. Lastly, spatial correlation of the sensors is taken into the account. For this case, a Gaussian isotropic event source was applied. The computational requirements in evaluating multidimensional cumulative densities necessitated proposing a rectangular grid model of sensor deployment and block-diagonal approximations of covariance matrix related to the event signal at the sensors without losing generality. The simulation studies show the success of the MAE both in the cases of fusing error-free sensor outputs and in the case where the effect of the wireless channel is incorporated. As expected the performance gets better as the level of quantization increases and with six-level quantization, it approaches the performance of non-quantized data transmission. In the sequential tests again MAE was more successful compared to MJD resulting in smaller ASNs. It was observed that spatial correlation degrades system performance.
Modeling and analysis of molecular signals in multiscale molecular communication
(Izmir Institute of Technology, 2021-07) Güleç, Fatih; Atakan, Barış; Izmir Institute of Technology
This thesis focuses on modeling, analysis, and novel experimental techniques in molecular communication (MC). The objective of this thesis is to develop novel engineering solutions and modeling approaches to enable MC applications. The first part of the thesis is about microscale MC studies. In this part, a model of how a receiver nanomachine measures and reconstructs a molecular signal is proposed with a probabilistic approach. In the second part, macroscale MC studies with active transmitters are given. An experimental setup which includes a sprayer emitting alcohol molecules as a transmitter and an alcohol sensor as the receiver is employed. Using the data collected by this setup, five statistical methods, a feature extraction algorithm and the fluid dynamics-based distance estimation algorithm are proposed for distance estimation. Furthermore, a novel droplet-based signal reconstruction approach to channel modeling is proposed. Moreover, MC is utilized to propose an end-to-end system model which considers pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output. In addition, the concept of mobile human ad hoc network which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses MC as the enabling paradigm is introduced. Finally, macroscale MC studies with passive transmitters are detailed in the third part. A novel experimental platform which consists of an evaporating alcohol source and a sensor network is proposed. A sensor network based clustered localization algorithm is proposed to estimate the location of the passive transmitter.
On the characterization of motor imagery functions based on systematic timing organization of the human brain
(Izmir Institute of Technology, 2021-07) Olcay, Bilal Orkan; Karaçalı, Bilge; Izmir Institute of Technology
The main objective of this thesis is to analyze the timing organization of the brain. The human brain is known to adjust its localized and also the reciprocal operations for each different cognitive task adaptively. This flexibility of the brain has attracted considerable interest in neuroscience. Elucidation of timing adaptation property of brain, however, remains as unresolved due to dynamically changing and nonlinear nature of the brain. In this thesis, we characterize the timing organization of the brain during motor imagery activity using electroencephalography signals. First, we propose a novel motor imagery activity recognition method that relies on the activity-specific time-lag between electroencephalography signals obtained from different brain regions. Next, we generalize this approach into three-parameter formulation to determine the timing profiles of activity-specific short-lived synchronization. The identification of activity-specific timing parameters was carried out using a heuristic approach that maximizes the average pairwise channel synchronizations during associated activity periods. Thereafter, we propose a novel BCI framework that find and use the timings of electroencephalography signals of localized brain regions that elicit localized activity-specific features. We identify the timings for each different brain regions by adopting a heuristic-probabilistic method. Finally, we propose a novel autoregressive modeling framework that finds a representative model for each different cognitive activity. We demonstrated the efficacy of the proposed methods on publicly available brain-computer interfacing datasets on motor imagery. The performance results indicate that considering the timing organization of the brain is crucial for accurate characterization of cognitive activity. In addition, it may also account for the inconsistency of brain computer interfacing performance obtained from different subjects.
Online time delay identification and adaptive control for general classes of nonlinear systems
(Izmir Institute of Technology, 2013) Bayrak, Alper; Tatlıcıoğlu, Enver
In this dissertation, online identification of time delays is discussed. Specifically, a novel online time delay identification algorithm for nonlinear systems is presented. As a novel departure from the existing literature, in the design of the time delay identification algorithm, time delays are considered as nonlinear parameters effecting the system and nonlinear parameter estimation techniques are adopted. The presented time delay iden~ tification technique is based on a min-max optimization algorithm. The stability of the proposed time delay identification algorithm is investigated via Lyapunov-based stability analysis techniques. It is shown that the developed estimator identifies unknown time delays, upon satisfaction of a nonlinear persistent excitation condition, within a desireq precision that may be adjusted to be very small. The proposed time delay identification method is then modified to be applicable for sig~ nal processing applications. Afterwards, the control of nonlinear systems subject to state delays is considered. The control objective is to ensure output tracking of a time-varying reference trajectory while identifying unknown state delays. Two cases are considered~ First, only the state delays are assumed to be unknown in the nonlinear system dynamics, Second, linear parameters in the system dynamics are assumed to be unknown along witll unknown time delays. To meet the control objectives, the proposed time delay identifica~ tion technique is fused with a control algorithm, and in both cases, both identification anq control objectives are ensured.
Random communication systems based on alpha-stable processes
(Izmir Institute of Technology, 2018-11-08) Ahmed, Areeb; Savacı, Ferit Acar
This thesis presents alpha-stable carrier based random communication systems (RCSs) as an alternate way to perform covert transmission. The first objective is to develop an optimized model of RCS which consists of a receiver that requires less computational complexity and outperforms the previously proposed receivers. Next, in order to solve the existing synchronization issue in RCSs, the general behavior of fractional lower-order covariance method in α-stable noise environments has been evaluated to establish synchronization in RCSs. An optimized range of values for the associated parameters of α-stable carrier has also been presented to optimize the proposed synchronization method. The second objective is to establish criteria for evaluating and quantifying the security and covertness of RCSs. Therefore, the first security performance tradeoff characteristics (SPTC) have been proposed to compare the security of different RCSs. Moreover, the proposed optimized model of RCS has also been analyzed with respect to the developed security scale, i.e. SPTC. Secondly, the criterion to quantify the covertness of RCSs has also been developed to analyze the proposed RCS. Thirdly, an attack for RCS has also been proposed which highlights the potential vulnerabilities of RCSs. However, the counter-measure guidelines have been prescribed to further enhance the security of RCSs. An inverse system approach has been adopted to propose α-stable noise driven linear time invariant system based transmitter and its corresponding inverse system based receiver as a third objective. It can be considered as the most secure model for αstable noise carrier based RCS till now.
Realization of all optical switch and routing devices exploiting third order nonlinear optical properties
(Izmir Institute of Technology, 2012) Akın, Osman; Dinleyici, Mehmet Salih
In this thesis, we developed an in-fiber all optical switching device exploiting a transient grating as a control mechanism that was formed by interference of laser light beams via the Kerr effect. The switching device is designed by partial removal of the fiber cladding and replacing the polished cladding with a nonlinear polymer film (slab waveguide) that exhibits higher third order (Kerr) optical nonlinearity. The proposed device structure is analyzed considering Four Wave Mixing (FWM) of Gaussian beams of the grating and propagating modes of the optical fiber in the evanescent region where the nonlinear material is placed. The fields of modes and the grating forming beams interact in the nonlinear medium according to the matching conditions and evoke power transfer among the fiber modes. Thus, the coupling between the modes is directed by means of the transient grating. In the experimental part, polymeric thin films are prepared and linear refractive indices are measured using Fresnel diffraction method (developed in the laboratory) by matching the model with the experimental output. Then, z-scan method is employed to characterize the third order nonlinear optical properties of the thin films and pump-probe experiments are exploited to ensure existence of the transient grating and its diffraction capability. Finally, the side polished fiber is coated with the Methyl Red doped PVA composite polymer and by generating transient gratings on the polymer film, the switching capability of the device is introduced. The switching can be achieved either by a bulk refractive index change (no grating) or a transient grating between the modes of the optical fiber.
Resource management for multiuser systms with multiple antennas in wireless networks
(Izmir Institute of Technology, 2014-03) Baştürk, İlhan; Özbek, Berna
In this thesis, we explore radio resource management algorithms for OFDMA based cellular networks. Firstly, we combine the OFDMA technology with multiple antennas technology and handle the resource allocation problem for the MISO-OFDMA systems. We take care of the fairness issue among users to prevent the users, which have good channel conditions, to obtain most of the system resources. Thus, we propose a fairness aware resource allocation algorithm and compare it with the existing schemes. Next, we enhance the conventional cellular network structure with mobile relays and examine relay selection and resource allocation algorithms for the OFDMA-based mobile relay enhanced cellular networks. We propose a novel relaying frame structure with efficient resource management algorithms in order to reveal the opportunities of the mobile relays. Then, we consider the queue-lengths of the users and propose a queue and channel aware joint relay selection and resource allocation algorithm to use the system resources efficiently. Finally, we combine mobile relaying and offloading technologies in order to overcome the capacity and coverage problems of the conventional cellular networks. We focus on the radio resource management problem for the OFDMA-based mobile relay-enhanced heterogenous cellular networks that contains multiple radio access technologies. We propose a network interface selection algorithm that consider the bandwidth availability information of each network in order to prevent sending the users to overloaded networks.

Browse

Browsing Doktora Tezleri by Department "Electrical and Electronics Engineering"