Today&rsquo / s reconfigurable devices successfully combine &lsquo / reconfigurable computing machine&rsquo / paradigm and &lsquo / high degree of parallelism&rsquo / and hence reconfigurable computing emerged as a promising alternative for computing-intensive applications. Despite its superior performance and lower power consumption compared to general purpose computing using microprocessors, reconfigurable computing comes with a cost of design complexity. This thesis aims to reduce this complexity by providing a flexible and user friendly development environment to application programmers in the form of a complete reconfigurable computing platform. The proposed computing platform is specially designed for real time embedded applications and supports true multitasking by using available run time partially reconfigurable architectures. For this computing platform, we propose a novel hardware task model aiming to minimize logic resource requirement and the overhead due to the reconfiguration of the device. Based on this task model an optimal 2D surface partitioning strategy for managing the hardware resource is presented. A mesh network-on-chip is designed to be used as the communication environment for the hardware tasks and a runtime mapping technique is employed to lower the communication overhead. As the requirements of embedded systems are known prior to field operation, an oine design flow is proposed for generating the associated bit-stream for the hardware tasks. Finally, an online real time operating system scheduler is given to complete the necessary building blocks of a reconfigurable computing platform suitable for real time computing-intensive embedded applications. In addition to providing a flexible development environment, the proposed computing platform is shown to have better device utilization and reconfiguration time overhead compared to existing studies.

The estimation of effort is at the heart of project tasks, since it is used for many purposes such as cost estimation, budgeting, monitoring, project planning, control and software investments. Researchers analyze problems of the estimation, propose new models and use new techniques to improve accuracy. However up to now, there is no comprehensive estimation methodology to guide companies in their effort estimation tasks. Effort estimation problem is not only a computational but also a managerial problem. It requires estimation goals, execution steps, applied measurement methods and updating mechanisms to be properly defined. Besides project teams should have motivation and responsibilities to build a reliable database. If such methodology is not defined, common interpretation will not be constituted among software teams of the company, and variances in measurements and divergences in collected information prevents to collect sufficient historical information for building accurate models. This thesis proposes a methodology for organizations to manage and execute effort estimation processes. The approach is based on the reported best practices, v empirical results of previous studies and solutions to problems &amp / conflicts described in literature. Five integrated processes: Data Collection, Size Measurement, Data Analysis, Calibration, Effort Estimation processes are developed with their artifacts, procedures, checklists and templates. The validation and applicability of the methodology is checked in a middle-size software company. During the validation of methodology we also evaluated some concepts such as Functional Similarity (FS) and usage of Base Functional Components (BFC) in effort model on a reliable dataset. By this way we evaluated whether these subjects should be a part of methodology or not. Besides in this study it is the first time that the COSMIC has been used for Artificial Neural Network models.

Moving object detection techniques can be divided into two categories based on the type of the camera which is either static or active. Methods of static cameras can detect moving objects according to the variable regions on the video frame. However, the same method is not suitable for active cameras. The task of moving object detection for active cameras generally needs more complex algorithms and unique solutions. The aim of this thesis work is real time detection and tracking of moving objects with an active camera. For this purpose, feature based algorithms are implemented due to the computational efficiency of these kinds of algorithms and SURF (Speeded Up Robust Features) is mainly used for these algorithms. An algorithm is developed in C++ environment and OpenCV library is frequently used. The developed algorithm is capable of detecting and tracking moving objects by using a PTZ (Pan-Tilt-Zoom) camera at a frame rate of approximately 5 fps and with a resolution of 640x480.

The availability of digital media has grown tremendously with the fast-paced ever-growing storage and communication technologies. As a result, today, we are facing a problem in indexing and browsing the huge amounts of multimedia data. This amount of data is impossible to be indexed or browsed by hand so automatic indexing and browsing systems are proposed. Audio Event Detection is a research area which tries to analyse the audio data in a semantic and perceptual manner, to bring a conceptual solution to this problem. In this thesis, a method for detecting several audio events in TV broadcast is proposed. The proposed method includes an audio segmentation stage to detect event boundaries. Broadcast audio is classified into 17 classes. The feature set for each event is obtained by using a feature selection algorithm to select suitable features among a large set of popular descriptors. Support Vector Machines and Gaussian Mixture Models are used as classifiers and the proposed system achieved an average recall rate of 88% for 17 different audio events. Comparing with the results in the literature, the proposed method is promising.

We propose a method to detect events and event boundaries in soccer videos by using web-casting texts and audio-visual features. The events and their inaccurate time information given in web-casting texts need to be aligned with the visual content of the video. Most match reports presented by popular organizations such as uefa.com (the official site of Union of European Football Associations) provide the time information in minutes rather than seconds. We propose a robust method which is able to handle uncertainties in the time points of the events. As a result of our experiments, we claim that our method detects event boundaries satisfactorily for uncertain web-casting texts, and that the use of audio-visual features improves the performance of event boundary detection.

As the number of the published web services increase, discovery of the web services with the desired functionality and quality is becoming a challenging process. Selecting the appropriate web services among the ones that oer the same functionality is also a challenging task. The web service repositories like UDDI (Universal Description Discovery and Integration) support only the syntactic searchs. Quality of service parameters for the published web services can not be queried over these repositories. We have proposed a query language that aims to overcome these problems. It enables its users to query the web services both syntactically and semantically. We also allow the users to specify the quality of service criteria which the desired web services should satisfy. We have developed a graphical query interface to assist the users in query sentence formulation process. The proposed work is developed as a submodule of the Domain Specific Web Service Discovery with Semantics (DSWSD-S) System. Aforementioned query language and the query interface are explained in detail in this thesis.

Novel methods of object recognition that form a bridge between today&rsquo / s local feature frameworks and previous decade&rsquo / s strong but deserted geometric invariance field are presented in this dissertation. The rationale behind this effort is to complement the lowered discriminative capacity of local features, by the invariant geometric descriptions. Similar to our predecessors, we first start with constrained cases and then extend the applicability of our methods to more general scenarios. Local features approach, on which our methods are established, is reviewed in three parts / namely, detectors, descriptors and the methods of object recognition that employ them. Next, a novel planar object recognition framework that lifts the requirement for exact appearance-based local feature matching is presented. This method enables matching of groups of features by utilizing both appearance information and group geometric descriptions. An under investigated area, scene logo recognition, is selected for real life application of this method. Finally, we present a novel method for three-dimensional (3D) object recognition, which utilizes well-known local features in a more efficient way without any reliance on partial or global planarity. Geometrically consistent local features, which form the crucial basis for object recognition, are identified using affine 3D geometric invariants. The utilization of 3D geometric invariants replaces the classical 2D affine transform estimation /verification step, and provides the ability to directly verify 3D geometric consistency. The accuracy and robustness of the proposed method in highly cluttered scenes with no prior segmentation or post 3D reconstruction requirements, are presented during the experiments.

An assembled algorithm for pricing American options with absolute, discrete dividends using adaptive lattice methods is described. Considerations for hardware-conscious programming on both CPU and GPU platforms are discussed, to provide a foundation for the investigation of several approaches for deploying the program onto GPU architectures. The performance results of the approaches are compared to that of a central processing unit reference implementation, and to each other. In particular, an approach of designating subtrees to be calculated in parallel by allowing multiple calculation of overlapping elements is described. Among the examined methods, this attains the best performance results in a "realistic" region of calculation parameters. A fifteen- to thirty-fold improvement in performance over the CPU reference implementation is observed as the problem size grows sufficiently large.

With recent technological advances in wireless communication networks, the need for an efficient architecture for IP mobility is becoming more apparent. Enabling IP mobility architecture is a significant issue for making use of various portable devices appearing on the Internet. Mobile IP, the current standard for IP based mobility management, is capable of providing wireless Internet access to mobile users. The most important feature of Mobile IP is its ability to support the changing point of attachment of the mobile user by an algorithm known as handover. A handover algorithm is needed to maintain connectivity to the Internet whenever the mobile users move from one subnet to another, while simultaneously providing minimum disruption to ongoing sessions. This thesis gives an overview of Mobile IP, its open issues, some of the subsequent enhancements and extensions related to the handover management problem of the mobile user. Description and evaluation of various handover algorithms for Mobile IP which have been proposed to reduce packet loss and delay during handover constitute the core of the thesis. In this thesis, a comparative performance evaluation of the proposed protocols and the combination of them is also presented through simulations.

A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages / preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this work, a separate network was build for each person. The input face is projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person&amp / #8217 / s network, trained earlier. The one with maximum output is selected and reported as the host if it passes predefined recognition threshold. The algorithms that have been developed are tested on ORL, Yale and Feret Face Databases.