Resolution is the most important criterion for the clarity of details on an image. Therefore, high resolution images are required in numerous areas. However, obtaining high resolution images has an evident technological cost and the value of these costs change with the quality of used optical systems. Image processing methods are used to obtain high resolution images with low costs. This kind of image improvement is named as superresolution image reconstruction. This thesis focuses on two main titles, one of which is the identification methods of blur parameters, one of the degradation operators, and the stochastic SR image reconstruction methods. The performances of different stochastic SR image reconstruction methods and blur identification methods are shown and compared. Then the identified blur parameters are used in superresolution algorithms and the results are shown.

Image generator host is the interface of the host computer systemof a flight simulator to its image generator. Image generator host, updates positions of the entities by sending operational codes to the image generator. Positional data of the entities is pipelined by tactic interface of the simulator at host update rate. A network jitter, latency, packet loss or inadequate bandwidth may disturb the smoothness of this pipelined entity information packets. This study presents an algorithm for the host system of a flight simulator, intending to minimize model flickering in the image generator display output.

In this thesis, a feedback-based contention avoidance technique based on weighted Dijkstra algorithm is proposed to address the contention avoidance problem for Optical Burst Switching networks. Optical Burst Switching (OBS) has been proposed as a promising technique to support high-bandwidth, bursty data traffic in the next-generation optical Internet. Nevertheless, there are still some challenging issues that need to be solved to achieve an effective implementation of OBS. Contention problem occurs when two or more bursts are destined for the same wavelength. To solve this problem, various reactive contention resolution methods have been proposed in the literature. However, many of them are very vulnerable to network load and may suffer severe loss in case of heavy traffic. By proactively controlling the overall traffic, network is able to update itself in case of high congestion and by means of this method / contention avoidance can be achieved efficiently. The performance analysis of the proposed algorithm is presented through network simulation results provided by OMNET++ simulation environment. The simulation results show that the proposed contention avoidance technique significantly reduces the burst loss probability as compared to networks without any contention avoidance techniques.

General video stabilization techniques which are digital, mechanical and optical are discussed. Under the concept of video stabilization, various digital motion estimation and motion correction algorithms are implemented. For motion estimation, in addition to digital approach, a mechanical approach is implemented also. Then all implemented motion estimation and motion correction algorithms are compared with respect to their computational times and accuracies over various videos. For small amount of jitter, digital motion estimation performs well in real time. But for big amount of motion, digital motion estimation takes very long time so for these cases mechanical motion estimation is preferred due to its speed in estimation although digital motion estimation performs better. Thus, when mechanical motion estimation is used first and then this estimate is used as the initial estimate for digital motion estimation, the same accuracy as digital estimation is obtained in approximately the same time as mechanical estimation. For motion correction Kalman and Fuzzy filtering perform better than lowpass and moving average filtering.

3D object recognition is performed by using geometric hashing where transformation and scale invariant 3D surface features are utilized. 3D features are extracted from object surfaces after a scale space search where size of each feature is also estimated. Scale space is constructed based on orientation invariant surface curvature values which classify each surface point&#039 / s shape. Extracted features are grouped into triplets and orientation invariant descriptors are defined for each triplet. Each pose of each object is indexed in a hash table using these triplets. For scale invariance matching, cosine similarity is applied for scale variant triple variables. Tests were performed on Stuttgart database where 66 poses of 42 objects are stored in the hash table during training and 258 poses of 42 objects are used during testing. %90.97 recognition rate is achieved.

This thesis focuses on three of the main components of an e-learning system: Infrastructure model, data integration and personalization. For the infrastructure model, our aim is to get best use of heterogeneously structured, geographically distributed data resources. Therefore, a detailed analysis of the available infrastructure models is carried out and an open source reference implementation based on grid technology is implemented. Furthermore, a simple data integration mechanism is proposed for the suggested reference implementation. For personalization, a statistical algorithm is proposed based on extracting and utilizing the learner model. The learner model based on Felder-Silverman learning style is extracted automatically using NBTree classification algorithm in conjunction with Binary relevance classifier for basic science learners. Experimental results show that the performance of the proposed automated learner modelling approach is consistent with the results, obtained by the questionnaires traditionally used for learning style assessment. In the thesis, the classification results are further utilized for providing the user with personalized queries. Keywords: Interactive learning environments / personalization in e-learning

The aim of this thesis is to develop a network, whose nodes communicate with the ZigBee wireless network protocol, and control this network with a PC through the Internet. One of the nodes of this network is designed to be master node. The other nodes are slave nodes. The master node can be connected to an Ethernet connected to the Internet. A PC can communicate with the master node via a specific web application over the Internet. The communication between a web server, in which the specific web application is loaded, and the master node is performed using a specific application protocol working over TCP/IP and defined in this thesis. The master node controls the slave nodes of the wireless network formed according to the commands given by the user of a PC over the Internet. The master node contains an implementation of the ZigBee stack along with a suitable application software to communicate with the slave nodes. The master node also contains an implementation of the TCP/IP stack along with a suitable application software to communicate with a web server in which the specific web application is loaded. The slave nodes contain an implementation of the ZigBee stack along with a suitable application software to communicate with the master node. For each type of node, appropriate hardware which is compliant with the software contained by that type of node is used. Each type of node uses microcontroller-based hardware.

A generic missile warner decision algorithm for airborne platforms with an emphasis on multiple classifier systems is proposed within the scope of this thesis. For developing the algorithm, simulation data are utilized. The simulation data are created in order to cover a wide range of real-life scenarios and for this purpose a scenario creation methodology is proposed. The scenarios are simulated by a generic missile warner simulator and tracked object data for each scenario are produced. Various feature extraction techniques are applied to the output data of the scenarios and feature sets are generated. Feature sets are examined by using various statistical methods. The performance of selected multiple classifier systems are evaluated for all feature sets and experimental results are presented.

In multi-level decision making (DM) approaches, the final decision is reached by going through a finite number of DM levels. Usually, in each level, a raw decision is produced first and then a suitable decision fusion technique is employed to merge the lower level decisions with the raw decision in the construction of the final decision of the present level. The basic difficulty in these approaches is the determination of how the consecutive levels should interact with each other. In this thesis, two different multi-level DM models have been proposed. The main idea in the first model, &ldquo / hierarchical DM&rdquo / (HDM), is to transfer the decisions of previous hierarchical levels to an upper hierarchy with some reliability values. These decisions are then fused using a suitable decision fusion technique to attain more consistent decisions at an upper level. The second model &ldquo / local DM in multiplelevels&rdquo / (LDM-ML) depends on what may be called as local DM process. Instead of designing an agent to perform globally, designing relatively simple agents which are supposed to work in local regions is the essence of the second idea. Final decision is partially constructed by contribution of a sufficient number of local DM agents. A successful local agent is retained in the agent pool whereas a local agent not successful enough is eliminated and removed from the agent pool. These models have been applied on two case studies associated with fault detection in a four-tank system and prediction of lotto sales.

The increasing use of electronic components in today&rsquo / s automobiles demands more powerful in-vehicle network communication protocols. FlexRay protocol, which is expected be the de-facto standard in the near future, is a deterministic, fault tolerant and fast protocol designed for in vehicle communication. In the near future, safety critical X-by-Wire applications will be available in the automobiles and FlexRay networks can be used to provide communication for the Electronic Control Units (ECUs) that perform related functions of X-by-Wire applications. In this thesis the performance of the FlexRay networks with various communication scenarios is evaluated in a real time environment and the results are presented. Communication scenarios investigate both static and dynamic segment of the FlexRay and allow evaluating the capabilities of the protocol. Several performance metrics such as utilization, static slot allocation, jitter are defined for the evaluation of the results.