Industrial production of hydrogen from the syngas, generated from steam reformation of natural gas or coal gasification, sets conditions for hydrogen separation membranes in terms of operating conditions. An alternative source for hydrogen is a syngas generated by gasification of municipal solid wastes which are likely to set more stringent conditions for the separation membranes. There is therefore, a growing demand for separation membranes with improved permeability and particularly of low cost. Among various alternatives, metallic membranes are particularly attractive due to their high selectivity and permeability for hydrogen, exemplified by palladium (Pd). However, due to high cost of Pd there is still a demand to develop alternative metallic membranes that are of low cost and have improved durability. Efforts have therefore concentrated on either alloying Pd so as to reduce its cost or on alternative membrane compositions of particularly b.c.c. structure. The current study deals with hydrogen separation membranes and aims to develop infrastructure for rapid identification of membrane compositions with improved permeability. The study is made up of three parts / i) development of sputter deposition system that would allow deposition of multiple compositions in a single experiment, ii) development of substrate material that would support the thin film membranes and would allow permeability measurement and iii) development of a set-up to measure the permeability of the thin film membranes. In the present thesis, a sputter deposition system incorporating three targets was successfully constructed. The system as tested with palladium-niobium-titanium (Pd-Nb-Ti) ternary system after necessary adjustment would yield thin films of homogenous thickness (&le / 7%) over a sample area of &asymp / 150 mm diameter. A total of 21 substrates each in 19 mm diameter arranged in triangular form in the substrate holder could successfully be deposited where composition distributions covered a greater portion of Pd-Nb-Ti ternary phase diagram. The structure of the deposited thin films can successfully be controlled by substrate temperature as well as by the pressure of plasma gas (argon). With the help of these parameters, structural diversity can also be produced beside the compositional variation. As for substrates, two materials were investigated. These were titanium dioxide (TiO2) modified porous stainless steel (PSS) and anodic porous alumina (AAO). TiO2 modified PSS due to its associated surface roughness leads to the deposition of films with defected structure which as a result is not gas tight. AAO produced via anodization of aluminum foil had a regular (40-60 nm) pore structure that provides a suitable surface for thin film depositions which could be defect free. However, AAO is very delicate and fragile which makes it difficult to adapt it as a support material for permeability measurement/hydrogen separation purposes. Finally, a set-up was developed for measurement of hydrogen permeability which is capable of measurement over a wide pressure and temperature conditions, i.e. hydrogen pressures up to 10 bar and temperature as high as 450 &deg / C. It is recommended that so as to identify compositions with improved permeability, Nb or a similar metal which has extremely high permeability could be used as a support material. This would tolerate the evaluation of the films which are not totally defect free.

Multiview video is one of the emerging research areas especially among the video coding community. Transmission of multiview video over an error prone network is possible with efficient compression of these videos. But along with the studies for efficiently compressing the multiview video, new error concealment and error protection methods are also necessary to overcome the problems due to erroneous channel conditions in practical applications. In packet switching networks, packet losses may lead to block losses in a frame or the loss of an entire frame in an encoded video sequence. In recent years several algorithms are proposed to handle the loss of an entire frame efficiently. However methods for full frame losses in stereoscopic or multiview videos are limited in the literature. In this thesis a stereoscopic approach for full frame loss concealment of multiview video is proposed. In the proposed methods, the redundancy and disparity between the views and motion information between the previously decoded frames are used to estimate the lost frame. Even though multiview video can be composed of more than two views, at most three view are utilized for concealment. The performance of the proposed algorithms are tested against monoscopic methods and the conditions under which the proposed methods are superior are investigated. The proposed algorithms are applied to both stereoscopic and multiview video.

The aim of this thesis is to develop a methodology for detection of naturally occurring offshore oil slicks originating from hydrocarbon seeps using satellite remote sensing techniques. In this scope, Synthetic Aperture Radar (SAR) imagery has been utilized. Case study area was Andrusov High in the Central Black Sea. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface. They form thin oil films on the sea surface called oil slicks. Presence of seeps and surface oil slicks for the offshore basins is a trace of depleted oil traps. Spatial distribution of oil slicks is closely related to sea waves, dominant wind patterns and weathering factors. Even though, there are oil slick detection techniques available with optical remote sensing, laser fluorosensors, and hyperspectral remote sensing, the most efficient results can be obtained from active microwave sensors like synthetic aperture radar (SAR). SAR sensors simply measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. In this context an adapted methodology has been proposed, including three levels namely / visual inspection, image filtering and object based fuzzy classification. With visual inspection, targets have been identified and subset scenes have been created. Subset scenes have been categorized into 3 cases based on contrast difference of dark spots to the surroundings. Then object based classification has been utilized with the fuzzy membership functions defined by extracted features of layer values, shape and texture from segmented and filtered SAR subsets. As a result, oil slicks have been discriminated from look-alikes which are the phenomena resembling oil slicks. The overall classification accuracy obtained by averaging three different cases is 83 % for oil slicks and 77 % for look-alikes. The results of this study can considered to be a preliminary work and supplementary information for determining the best operational procedure of offshore hydrocarbon exploration.

In this study, the aim is to investigate the stiffness, longitudinal tensile strength and circumferential tensile strength of short fiber reinforced polyester composite pipes produced by centrifugal casting production method. To achieve this aim, theoretical calculation of modulus of elasticity of pipes was done and then test program was carried out on pipe samples produced with three different resin types which were orthophthalic, isophthalic and vinyl ester resin and three different fiber types which were E glass fiber, ECR glass fiber and basalt fiber. The tests were performed according to ISO (International Organization for Standardization) standards. When resin type and fiber type effect on the fiber reinforced polyester pipe samples were evaluated, calculated elastic modulus values were in accordance with the test results. According to the experimental test data, which were used to evaluate the effect of resin type on fiber reinforced polyester pipe properties, there is not a significant difference was observed in the stiffness, longitudinal and circumferential tensile strength test results of pipes having different resin types. In other words, there was not a significant effect of resin type on the stiffness, longitudinal tensile strength and circumferential tensile strength of short fiber reinforced pipes produced by centrifugal casting method. According to the experimental test data, which were used to evaluate the effect of fiber type on the properties of fiber reinforced polyester pipe, basalt fiber reinforced pipe samples showed higher mechanical performance over E glass fiber and ECR glass fiber reinforced pipes. However, the test results of basalt reinforced polyester pipe were not as good as the individual properties of basalt fiber. Finally, by comparing the basalt fiber reinforced pipe samples having almost the same stiffness and tensile test results as E glass fiber reinforced pipe samples, the gain in fiber and resin amount were investigated. Basalt fiber reinforced pipes were slightly lighter and thinner than E glass fiber reinforced pipes. However, the decrease in the amount of the fiber and resin in basalt reinforced pipe did not result in an overall cost reduction.

Today, infectious animal diseases and the propagation speeds of these diseaseshave been threatening the human health. Threats from animal disease outbreakssuch as Avian Influenza have increased in both number and complexity. So, it is extremely important to determine the animal diseases at first appearances and totake precautions according to propagation speeds of the diseases. Geographic Information Systems (GIS) have become an important tool inveterinary epidemiology, surveillance and monitoring of animal diseases. Such approaches can be used for public health planning and predicting disease risks. This study aims to build a GIS web-based animal health surveillance system in Turkey in order to monitor and analyse disease outbreaks. Different sources of data / geographical data, animal holding locations, disease outbreak recordings, reporting information and special GIS functions have been incorporated in the application. It enables to determine the first, second and third degree risk zones of a disease, query the animals, holdings and disease events, create thematic maps and show the results of explored landscape features associated with Avian Influenza outbreak of 2006 and present graphically illustrated reports. This study will make the management of the disease outbreak situation easier, enhance the response mechanism of the decision makers, help to make better decisions, control the disease as quickly as possible, protect both the animals and humans against diseases, also provide a tool to evaluate different strategies to prevent the spread of infectious diseases. So, in an infectious disease case, emergency precautions can be taken and control strategies can be planned.

Apatite was formed on the surface of titanium dioxide (TiO2) powders by a biomimetic process. The deposition was accomplished by immersing TiO2 powders in simulated body fluid (SBF) for 1, 3, 6, 12, and 24 h. SBF used throughout this study had calcium and phosphate ion concentrations 10 times greater than those of human blood plasma. Photocatalytic activity of the apatite-deposited TiO2 powders was investigated in terms of the decomposition of methylene blue solution under ultraviolet (UV) irradiation. It has been shown that apatite deposition enhanced the photocatalytic activity of TiO2. The best photocatalytic performance was acquired on the powders that are immersed in SBF for 3 h. The time required for the complete degradation of methylene blue decreased from 3.5 h to 2 h upon immersion of powders in SBF for 3 h. Photochemical durability of poly(methyl methacrylate) increased when it was mixed with apatite-deposited TiO2 powders.

This thesis describes the theoretical bases, development and testing of an integrated moving object detection framework in 2D and 3D scenes. The detection problem is analyzed in stationary and non-stationary camera sequences and different algorithms are developed for each case. Two methods are proposed in stationary camera sequences: background extraction followed by differencing and thresholding, and motion detection using optical flow field calculated by &ldquo / Kanade-Lucas Feature Tracker&rdquo / . For non-stationary camera sequences, different algorithms are developed based on the scene structure and camera motion characteristics. In planar scenes where the scene is flat or distant from the camera and/or when camera makes rotations only, a method is proposed that uses 2D parametric registration based on affine parameters of the dominant plane for independently moving object detection. A modified version of the 2D parametric registration approach is used when the scene is not planar but consists of a few number of planes at different depths, and camera makes translational motion. Optical flow field segmentation and sequential registration are the key points for this case. For 3D scenes, where the depth variation within the scene is high, a parallax rigidity based approach is developed for moving object detection. All these algorithms are integrated to form a unified independently moving object detector that works in stationary and non-stationary camera sequences and with different scene and camera motion structures. Optical flow field estimation and segmentation is used for this purpose.

Hydrogen and oxygen or air polymer electrolyte membrane fuel cell is one of the most promising electrical energy conversion devices for a sustainable future due to its high efficiency and zero emission. Membrane electrode assembly (MEA), in which electrochemical reactions occur, is stated to be the heart of the fuel cell. The aim of this study was to develop methods for preparation of MEA with alternative polymer electrolyte membranes and compare their performances with the conventional Nafion&reg / membrane. The alternative membranes were sulphonated polyether-etherketone (SPEEK), composite, blend with sulphonated polyethersulphone (SPES), and polybenzimidazole (PBI). Several powder type MEA preparation techniques were employed by using Nafion&reg / membrane. These were GDL Spraying, Membrane Spraying, and Decal methods. GDL Spraying and Decal were determined as the most efficient and proper MEA preparation methods. These methods were tried to improve further by changing catalyst loading, introducing pore forming agents, and treating membrane and GDL. The highest performance, which was 0.53 W/cm2, for Nafion&reg / membrane was obtained at 70 0C cell temperature. In comparison, it was about 0.68 W/cm2 for a commercial MEA at the same temperature. MEA prepared with SPEEK membrane resulted in lower performance. Moreover, it was found that SPEEK membrane was not suitable for high temperature operation. It was stable up to 80 0C under the cell operating conditions. However, with the blend of 10 wt% SPES to SPEEK, the operating temperature was raised up to 90 0C without any membrane deformation. The highest power outputs were 0.29 W/cm2 (at 70 0C) and 0.27 W/cm2 (at 80 0C) for SPEEK and SPEEK-PES blend membrane based MEAs. The highest temperature, which was 150 0C, was attained with PBI based MEA during fuel cell tests.

Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.

In this study, a P300 based Brain-Computer Interface (BCI) system design is realized by the implementation of the Spelling Paradigm. The main challenge in these systems is to improve the speed of the prediction mechanisms by the application of different signal processing and pattern classification techniques in BCI problems. The thesis study includes the design and implementation of a 10 channel Electroencephalographic (EEG) data acquisition system to be practically used in BCI applications. The electrical measurements are realized with active electrodes for continuous EEG recording. The data is transferred via USB so that the device can be operated by any computer. v Wiener filtering is applied to P300 Speller as a signal enhancement tool for the first time in the literature. With this method, the optimum temporal frequency bands for user specific P300 responses are determined. The classification of the responses is performed by using Support Vector Machines (SVM&rsquo / s) and Bayesian decision. These methods are independently applied to the row-column intensification groups of P300 speller to observe the differences in human perception to these two visual stimulation types. It is observed from the investigated datasets that the prediction accuracies in these two groups are different for each subject even for optimum classification parameters. Furthermore, in these datasets, the classification accuracy was improved when the signals are preprocessed with Wiener filtering. With this method, the test characters are predicted with 100% accuracy in 4 trial repetitions in P300 Speller dataset of BCI Competition II. Besides, only 8 trials are needed to predict the target character with the designed BCI system.