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.

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.

In this thesis, a base for the modeling and analysis of laser guided weapons is constituted. In particular, the effects of several control schemes on the performance of a generic laser guided weapon system are investigated. In this generic model, it is assumed that the velocity pursuit guidance is employed via a velocity aligning seeker as the sole sensor. The laser seeker is modeled experimentally, based on data obtained by conducting a series of tests. The laser reflection is also modeled. Aerodynamic coefficients of the generic geometry are generated by the software Missile Datcom. A nonlinear, six degree of freedom simulation is constructed incorporating 10 Hz laser sensing, velocity pursuit guidance, seeker model, and multiple control schemes. The effects of bang-bang, bang-trail-bang, multiposition and continuous control techniques on weapon performance are investigated for stationary and moving targets under ideal and noisy conditions. Flight characteristics like miss distance, range envelope, impact speed, and time of flight are monitored. Weapon&amp / #8217 / s maneuverability is investigated and the effect of employing a theoretical down sensor on the performance is demonstrated. In the light of simulation results, comparisons between various schemes are carried out, improvements on them and their flight envelopes are emphasized. It is concluded that the multiposition scheme provides a significant performance increase in most delivery types and can be an alternative to the continuous scheme. It is shown that the continuous scheme can achieve longer ranges only if backed up by a down sensor.

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.

This thesis presents the development of wafer level vacuum packaging processes using Au-Si eutectic and glass frit bonding contributing to the improvement of packaging concepts for a variety of MEMS devices. In the first phase of this research, micromachined resonators and pirani vacuum gauges are designed for the evaluation of the vacuum package performance. These designs are verified using MATLAB and Coventorware finite element modeling tool. Designed resonators and pirani vacuum gauges and previously developed gyroscopes with lateral feedthroughs are fabricated with a newly developed Silicon-On-Glass (SOG) process. In addition to these, a process for the fabrication of similar devices with vertical feedthroughs is initiated for achieving simplified packaging process and lower parasitic capacitances. Cap wafers for both types of devices with lateral and vertical feedthroughs are designed and fabricated. The optimization of Au-Si eutectic bonding is carried out on both planar and non-planar surfaces. The bonding quality is evaluated using the deflection test, which is based on the deflection of a thinned diaphragm due to the pressure difference between inside and outside the package. A 100% yield bonding on planar surfaces is achieved at 390&ordm / C with a v holding time and bond force of 60 min and 1500 N, respectively. On the other hand, bonding on surfaces where 0.15&mu / m feedthrough lines exist can be done at 420&ordm / C with a 100% yield using same holding time and bond force. Furthermore, glass frit bonding on glass wafers with lateral feedthroughs is performed at temperatures between 435-450&ordm / C using different holding periods and bond forces. The yield is varied from %33 to %99.4 depending on the process parameters. The fabricated devices are wafer level vacuum packaged using the optimized glass frit and Au-Si eutectic bonding recipes. The performances of wafer level packages are evaluated using the integrated gyroscopes, resonators, and pirani vacuum gauges. Pressures ranging from 10 mTorr to 60 mTorr and 0.1 Torr to 0.7 Torr are observed in the glass frit packages, satisfying the requirements of various MEMS devices in the literature. It is also optically verified that Au-Si eutectic packages result in vacuum cavities, and further study is needed to quantify the vacuum level with vacuum sensors based on the resonating structures and pirani vacuum gauges.

The task of automatically recognizing targets in IR imagery has a history of approximately 25 years of research and development. ATR is an application of pattern recognition and scene analysis in the field of defense industry and it is still one of the challenging problems. This thesis may be viewed as an exploratory study of ATR problem with encouraging recognition algorithms implemented in the area. The examined algorithms are among the solutions to the ATR problem, which are reported to have good performance in the literature. Throughout the study, PCA, subspace LDA, ICA, nearest mean classifier, K nearest neighbors classifier, nearest neighbor classifier, LVQ classifier are implemented and their performances are compared in the aspect of recognition rate. According to the simulation results, the system, which uses the ICA as the feature extractor and LVQ as the classifier, has the best performing results. The good performance of this system is due to the higher order statistics of the data and the success of LVQ in modifying the decision boundaries.

Electrical Submersible Pumping (ESP) is a well known artificial lift technique in reservoirs having high-water cut and low gas-oil ratio. It is known as an effective and economical method of producing large volumes of fluid under different well conditions. ESP equipments are capable of producing in a range of 200 b/d to 60.000 b/d. A case study was done, by designing 10 deviated or horizontal wells selected from the Y-oilfield in Western Siberia. SubPUMP software developed by IHS Energy is used for designing the ESP systems of these wells. These 10 wells will be working with variable speed drives. After selecting the available equipment from the inventory, the best running frequencies are selected for these wells. Evaluations of the designs are made from the pump performance graphs of each well. The pumps should work within their optimum efficiency ranges. These ranges can be seen from the pump performance curves. If the designs made are not within these efficiency ranges, designs should be evaluated and selecting new equipment should be should be an option. Because working outside the optimum efficiency ranges will decrease the production, shorten the runlifes of the pumps and the production will not be stable.

This thesis reports both the optimizations of the vanadium oxide (VOx) thin film as an active infrared detector material by the magnetron sputtering deposition method and its use during fabrication of proper resistors for the microbolometers. Vanadium oxide is a preferred material for microbolometers, as it provides high TCR value, low noise, and reasonable resistance, and a number of high-tech companies have used this material to obtain state-of-the-art microbolometer arrays. This material is first used in microbolometers by Honeywell, who provides its recipe with license agreements, and there is not much information in the literature for its deposition recipe. This is the first study at METU for development of vanadium oxide thin film for microbolometers. The VOx material deposition studies started by identifying the deposition parameters of the magnetron sputtering system in order to obtain proper VOx resistors for the readout electronics. The obtained recipe includes high temperature deposition conditions of VOx, however, this causes a diffusion problem on the electrodes, preventing to obtain a good contact to VOx. Also, high oxygen level in the depositions makes a contamination on the electrodes. A number of studies were done to determine a proper electrode material which is proper with the deposition conditions of the VOx. Characterization of the vanadium oxide samples is done by XRD and XPS measurements to see the relation between the phases and resistivity of the vanadium oxide. It is known that V2O5 phase provides a high TCR and resistivity value, and the XRD results show that this phase is dominant in the highly-oxygen doped or annealed resistors. The TCR and noise measurements are done using resistors implemented with the developed VOx film, after the etching processes of the both VOx and the electrodes are optimized. The contamination on the electrodes is prevented by the help of a newly designed process. The TCR measurement results show that annealing of the resistors affect the TCR values, i.e., increasing the annealing duration increases the TCR values of the resistors. Two different resistors with different deposition conditions are annealed to see the effect of annealing, where TCR results of the resistors are -0.74%/K and -0.8 %/K before annealing. The TCR values of these resistors increase to -1.6 %/K and -4.35 %K, respectively, after annealing in same conditions, showing that both the deposition conditions and annealing change the TCR significantly. Although good TCR values are obtained, the noise values of the VOx resistors are much higher than the expected values, which suggest a further study to determine the cause of this noise.