Different solutions have been used for each security aspects (access control, application security) to secure enterprise web applications. However combining &quot / enterprise-level&quot / and &quot / application-level&quot / security aspects in one layer could give great benefits such as reusability, manageability, and scalability. In this thesis, adding a new layer to n-tier web application architectures to provide a common evaluation and enforcement environment for both enterprise-level and application level policies to bring together access controlling with application-level security. Removing discrimination between enterprise-level and application-level security policies improves manageability, reusability and scalability of whole system. Resource Access Decision (RAD) specification has been implemented and used as authentication mechanism for this layer. RAD service not only provides encapsulating domain specific factors to give access decisions but also can form a solid base to apply positive and negative security model to secure enterprise web applications. Proposed solution has been used in a real life system and test results have been presented.

In this thesis the software development process and in particular, the requirements management processes in a major software development company have been investigated. The current problems related to requirements quality and process performances have been identified. Process improvement measures have been proposed based on the suggestions found in the relevant literature. The current process and the improved version have been compared with respect to the process evaluation metrics proposed particularly for software process improvement.

Computer interfaces has changed to 3D graphics environments due to its high number of applications ranging from scientific importance to entertainment. To enhance the realism of the 3D models, an established rendering technique, texture mapping, is used. In computer vision, a way to generate this texture is to combine extracted parts of multiple images of real objects and it is the topic studied in this thesis. While the 3D face model is obtained by using 3D scanner, the texture to cover the model is constructed from multiple images. After marking control points on images and also on 3D face model, a texture image to cover the 3D face model is generated. Moreover, effects of the some features of OpenGL, a graphical library, on 3D texture covered face model are studied.

Network-on-Chip (NoC) is communication infrastructure for future multi-core Systems-on-Chip (SoCs). NoCs are expected to overcome scalability and performance limitations of Point-to-Point (P2P) and bus-based communication systems. The routing algorithm of a given NoC affects the performance of the system measured with respect to metrics such as latency, throughput and load distribution. In this thesis, the popular Orthogonal One Turn (O1TURN) and Dimension Order Routing algorithms (DOR) for 2D-meshes are implemented by computer simulation. Investigating the effect of parameters such as packet, buffer and topology sizes on the performance of the network, it is observed that the center of the network is loaded more than the edges. A new routing algorithm is proposed and evaluated to achieve a more balanced load distribution. The results show that this goal is achieved with a trade off in latency and throughput in DOR and O1TURN.

Performance of neural network based restoration of optical networks is evaluated and a few possible improvements are proposed. Neural network based restoration is simulated with optical link capacities assigned by a new method. Two new improvement methods are developed to reduce the neural network size and the restoration time of severed optical connections. Cycle based restoration is suggested, which reduces the neural network structure by restoring the severed connections for each optical node, iteratively. Additionally, to reduce the restoration time, the necessary waiting time before the neuron outputs fire for the propagation delay over the network is computed and embedded in the control structure of the neural network. The improvement methods are evaluated by simulations in terms of restorability, restoration time, network redundancy and average length of restoration paths for different failure cases and different security requirements.

Providing an interoperability infrastructure for Electronic Healthcare Records (EHRs) is on the agenda of many national and regional eHealth initiatives. Two important integration profiles have been specified for this purpose: the &quot / IHE Cross-enterprise Document Sharing (XDS)&quot / and the &quot / IHE Cross Community Access (XCA)&quot / . XDS describes how to share EHRs in a community of healthcare enterprises and XCA describes how EHRs are shared across communities. However, currently no solution addresses some of the important challenges of cross community exchange environments. The first challenge is scalability. If every community joining the network needs to connect to every other community, this solution will not scale. Furthermore, each community may use a different coding vocabulary for the same metadata attribute in which case the target community cannot interpret the query involving such an attribute. Another important challenge is that each community has a different patient identifier domain. Querying for the patient identifiers in another community using patient demographic data may create patient privacy concerns. Yet another challenge in cross community EHR access is the EHR interoperability since the communities may be using different EHR content standards.

Multi camera measurement systems are widely used to extract information about the 3D configuration or &ldquo / state&rdquo / of one or more real world objects. Camera calibration is the process of pre-determining all the remaining optical and geometric parameters of the measurement system which are either static or slowly varying. For a single camera, this consist of the internal parameters of the camera device optics and construction while for a multiple camera system, it also includes the geometric positioning of the individual cameras, namely &ldquo / external&rdquo / parameters. The calibration is a necessary step before any actual state measurements can be made from the system. In this thesis, such a multi-camera state measurement system and in particular the problem of procedurally effective and high performance calibration of such a system is considered. This thesis presents a novel calibration algorithm which uses the known dynamics of a ballistically thrown target object and employs the Extended Kalman Filter (EKF) to calibrate the multi-camera system. The state-space representation of the target state is augmented with the unknown calibration parameters which are assumed to be static or slowly varying with respect to the state. This results in a &ldquo / super-state&rdquo / vector. The EKF algorithm is used to recursively estimate this super-state hence resulting in the estimates of the static camera parameters. It is demonstrated by both simulation studies as well as actual experiments that when the ballistic path of the target is processed by the improved versions of the EKF algorithm, the camera calibration parameter estimates asymptotically converge to their actual values. Since the image frames of the target trajectory can be acquired first and then processed off-line, subsequent improvements of the EKF algorithm include repeated and bidirectional versions where the same calibration images are repeatedly used. Repeated EKF (R-EKF) provides convergence with a limited number of image frames when the initial target state is accurately provided while its bidirectional version (RB-EKF) improves calibration accuracy by also estimating the initial target state. The primary contribution of the approach is that it provides a fast calibration procedure where there is no need for any standard or custom made calibration target plates covering the majority of camera field-of-view. Also, human assistance is minimized since all frame data is processed automatically and assistance is limited to making the target throws. The speed of convergence and accuracy of the results promise a field-applicable calibration procedure.

This thesis aims comparative analysis of the state of the art shot boundary detection algorithms. The major methods that have been used for shot boundary detection such as pixel intensity based, histogram-based, edge-based, and motion vectors based, are implemented and analyzed. A recent method which utilizes &ldquo / graph partition model&rdquo / together with the support vector machine classifier as a shot boundary detection algorithm is also implemented and analyzed. Moreover, a novel graph theoretic concept, &ldquo / dominant sets&rdquo / , is also successfully applied to the shot boundary detection problem as a contribution to the solution domain.

In this thesis, the extraction of sparse 3D points from calibrated image sequences is studied. The presented method for sparse 3D reconstruction is examined in two steps, where the first part addresses the problem of two-view reconstruction, and the second part is the extension of the two-view reconstruction algorithm to include multiple views. The examined two-view reconstruction method consists of some basic building blocks, such as feature detection and matching, epipolar geometry estimation, and the reconstruction of cameras and scene structure. Feature detection and matching is achieved by Scale Invariant Feature Transform (SIFT) method. For the estimation of epipolar geometry, the 7-point and 8-point algorithms are examined for Fundamental matrix (F-matrix) computation, while RANSAC and PROSAC are utilized for the robustness and accuracy for model estimation. In the final stage of two-view reconstruction, the camera projection matrices are computed from the F-matrix, and the locations of 3D scene points are estimated by triangulation / hence, determining the scene structure and cameras up to a projective transformation. The extension of the two-view reconstruction to multiple views is achieved by estimating the camera projection matrix of each additional view from the already reconstructed matches, and then adding new points to the scene structure by triangulating the unreconstructed matches. Finally, the reconstruction is upgraded from projective to metric by a rectifying homography computed from the camera calibration information. In order to obtain a refined reconstruction, two different methods are suggested for the removal of erroneous points from the scene structure. In addition to the examination of the solution to the reconstruction problem, experiments have been conducted that compare the performances of competing algorithms used in various stages of reconstruction. In connection with sparse reconstruction, a rate-distortion efficient piecewise planar scene representation algorithm that generates mesh models of scenes from reconstructed point clouds is examined, and its performance is evaluated through experiments.

Growing complexity of Systems on Chip (SoC) introduces interconnection problems. As a solution for communication bottleneck the new paradigm, Networks on Chip (NoC), has been proposed. Along with high performance and reliability, NoC brings in area and energy constraints. In this thesis we mainly concentrate on keeping communication-centric design environment fault-tolerant while considering area overhead. The previous researches suggest the adoption solution for fault-tolerance from multiprocessor architectures. However, multiprocessor architectures have excessive reliance on buffering leading to costly solutions. We propose to reconsider general router model by introducing central buffers which reduces buffer size. Besides, we offer a new fault-tolerant routing algorithm which effectively utilizes buffers at hand without additional buffers out of detriment to performance.