In this thesis, a mixed type video segmentation algorithm is implemented to find the scene cuts in MPEG compressed video data. The main aim is to have a computationally efficient algorithm for real time applications. Due to this reason partial decoding of the bitstream is used in segmentation. As a result of partial decoding, features such as bitrate, motion vector type, and DC images are implemented to find both continuous and discontinuous scene cuts on a MPEG-2 coded general TV broadcast data. The results are also compared with techniques found in literature.

This dissertation addresses the confidentiality aspect of the information security problem from the viewpoint of the software architecture. It presents a new approach to secure system design in which the desired security properties, in particular, confidentiality, of the system are proven to hold at the architectural level. The architecture description language Wright is extended so that confidentiality authorizations can be specified. An architectural description in Wright/c, the extended language, assigns clearance to the ports of the components and treats security labels as a part of data type information. The security labels are declared along with clearance assignments in an access control lattice model, also expressed in Wright/c. This enables the static analysis of data flow over the architecture subject to confidentiality requirements as per Bell-LaPadula principles. An algorithm takes the Wright/c description and the lattice model as inputs, and checks if there is a potential violation of the Bell-LaPadula principles. The algorithm also detects excess privileges. A software tool, which features an XML-based front-end to the algorithm is constructed. Finally, the algorithm is analyzed for its soundness, completeness and computational complexity.

Technology is integrated into daily life more than before as it evolves through communication area. In the past, it started with audio devices that help us to communicate while far between two ends of communication line. Nowadays visual communication comes in front considering the communication technology. This became possible with the improvement in the compression techniques of visual data and increasing speed, optimized architecture of the new family processors. These type processors are named as Digital Signal Processors (DSP&rsquo / s). Texas Instruments TMS320C6000 Digital Signal Processor family offers one of the fastest DSP core in the market. TMS320C64x sub-family processors are newly developed under the TMS320C6000 family to overcome disadvantages of its predecessor family TMS320C62x. TMS320C64x family has optimized architecture for packed data processing, improved data paths and functional units,improved memory architecture and increased speed. These capabilities make this family of processors good candidate for real-time video processing applications. Advantages of this core are used for implementing newly established H.264 Recommendation. Highly optimizing C Compiler of TMS320C64x enabled fast running implementation of encoder blocks that bring heavy computational load to encoder. Such as fast implementation of Motion Estimation, Transform, Entropy Coding became possible. Simplified Densely Centered Uniform-P Search algorithm is used for fast estimation of motion vectors. Time taking parts enhanced to improve the performance of the encoder.

Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) hybrid methods, and 4) bayesian methods. In this thesis, major image segmentation methods belonging to first three categories are examined and tested on typical images. Moreover, improvements are also proposed to well-known Recursive Shortest-Spanning Tree (RSST) algorithm. The improvements aim to better model each region during merging stage. Namely, grayscale histogram, joint histogram and homogeneous texture are used for better region modeling.

This study discusses the routing optimization techniques and algorithms for communication networks. Preventing data loss on overloaded communication links and utilizing link bandwidths efficiently are the main problems of traffic engineering. Load balancing and routing problems are solved using both by heuristics such as genetic algorithms, and simulation techniques. These algorithms work on destination-based or flow-based routing techniques and mainly change the link weight system or try to select the best routes set upon K-routes routing table respectively to optimize network utilization. In this study, first a definition of the network routing optimization problem will be made. Then the heuristics to solve the problem will be discussed and finally an analysis of these heuristics will be made on sample network models. This thesis includes a discussion about the performance of different optimization heuristics working as a part of the centralized network load balancing systems.

The High Level Architecture (HLA), IEEE Std. 1516-2000, provides a general framework for distributed modeling and simulation applications, called federations. HLA focuses on interconnection of interacting simulations, called federates, with special emphasis on reusability and interoperability. An HLA object model, be it a simulation object model (SOM), a federation object model (FOM) or the management object model (MOM), describes the data exchanged during federation execution. This thesis introduces a metamodel for the HLA Object Model, fully accounting for IEEE Std. 1516.2. The metamodel is constructed with GME (Generic Modeling Environment), a meta-programmable tool for domain-specific modeling, developed at Vanderbilt University. GME generates a design environment for HLA object models having the HLA OM metamodel as input. This work can be regarded as a step for bringing model-integrated computing to bear on HLA-based distributed simulation.

Minimum Concave Cost Multicommodity Network Design Problem arises in many application areas, such as transportation planning, distributed energy system and especially both circuit and packet switching backbone network design. Exact concave optimization algorithms have been developed, but these methods are applicable if the network size is small. Therefore, these problems are usually solved by non-exact iterative methods. In this thesis work, methods proposed for circuit switching and packet switching network design are evaluated in detail. After a comprehensive literate survey, Yaged&rsquo / s Linearization, Minoux greedy and Minoux accelerated greedy methods are found to be applicable to circuit switching network design when both solution quality and computational time is considered. Previously, it has been found that Minoux greedy methods may create routings with cycles and in order to eliminate these cycles a modification has been proposed. In this work, this modification is extended and evaluated in detail. Similarly, Gerla and Kleinrock&rsquo / s Concave Branch Elimination, Gersht&rsquo / s greedy and Stacey&rsquo / s Concave Link Elimination methods are investigated within the context of packet switching network design. All of these methods consider aggregate flows on each link simultaneously re-routing more than one commodity in one step. This thesis work also considers an alternative disaggregate approach, where only one commodity is handled at a time. Finally, algorithms proposed for circuit switching network design problem are adapted to the packet switching case and an extensive comparative computational study is performed to point out the best method with respect to time and solution quality for a number of networks and cost structure. Computational results have shown that modification on Minoux greedy to eliminate cycles leads to considerable improvements and the disaggregate approach gives the best result in some networks and cost structure.

Textured motion - generally known as dynamic or temporal texture - is a popular research area for synthesis, segmentation and recognition. Dynamic texture is a spatially repetitive, time-varying visual pattern that forms an image sequence with certain temporal stationarity. In dynamic texture, the notion of self-similarity central to conventional image texture is extended to the spatiotemporal domain. Dynamic textures are typically videos of processes, such as waves, smoke, fire, a flag blowing in the wind, a moving escalator, or a walking crowd. Creation of synthetic frames is a key issue especially for movie screen industry to enrich their scenes from a white screen into a shining reality. In robotics world, for example an autonomous vehicle must decide what is traversable terrain (e.g. grass) and what is not (e.g. water). This problem can be addressed by classifying portions of the image into a number of categories, for instance grass, dirt, bushes or water. If these parts are identifiable, then segmentation and recognition of these textures results with an efficient path planning for the autonomous vehicle. In this thesis, we aimed to characterize these textured motions like mentioned above. We tried to implement several known techniques and compared the results.

In this thesis, a modified 2D Discrete Cosine Transform based electro-optic and IR image fusion algorithm is proposed and implemented on an FPGA platform. The platform is a custom FPGA board which uses ALTERA Stratix III family FPGA. The algorithm is also compared with state of the art image fusion algorithms by means of an image fusion software application GUI developed in Matlab&reg / . The proposed algorithm principally takes corresponding 4x4 pixel blocks of two images to be fused and transforms them by means of 2D Discrete Cosine Transform. Then, the L2 norm of each block is calculated and used as the weighting factor for the AC values of the fused image block. The DC value of the fused block is the arithmetic mean of the DC coefficients of both input blocks. Based on this mechanism, the whole two images are processed in such a way that the output image is a composition of the processed 4x4 blocks. The proposed algorithm performs well compared to the other state of the art image fusion algorithms both in subjective and objective quality evaluations. In hardware, v the implemented algorithm can accept input videos as fast as 65 MHz pixel clock with a resolution of 1024x768 @60 Hz.

Up to now various recommendation systems have been proposed for web based applications such as e-commerce and information retrieval where a large amount of product or information is available. Basically, the task of the recommendation systems in those applications, for example the e-commerce, is to find and recommend the most relevant items to users/customers. In this domain, the most prominent approaches are collaborative filtering and content-based filtering. Sometimes these approaches are called as user profiling as well. In this work, a context-aware recommendation system is proposed for mobile environment, which also can be considered as an extension of those recommendation systems proposed for web-based information retrieval and e-commerce applications. In the web-based information retrieval and e-commerce applications, for example in an online book store (e-commerce), the users&amp / #8217 / actions are independent of their instant context (location, time&amp / #8230 / etc). But as for mobile environment, the users&amp / #8217 / actions are strictly dependent on their instant context. These dependencies give raise to need of filtering items/actions with respect to the users&amp / #8217 / instant context. In this thesis, an approach coupling approaches from two different domains, one is the mobile environment and other is the web, is proposed. Hence, it will be possible to separate whole approach into two phases: context-aware prediction and user profiling. In the first phase, combination of two methods called fuzzy c-means clustering and learning automata will be used to predict the mobile user&amp / #8217 / s motions in context space beforehand. This provides elimination of a large amount of items placed in the context space. In the second phase, hierarchical fuzzy clustering for users profiling will be used to determine the best recommendation among the remaining items.