With the advent of telecommunication technology, the need to transport multimedia content is increasing day by day. Successful video transmission over the wireless network faces a lot of challenges because of the limited resource and error prone nature of the wireless environment. To deal with these two challenges, not only the video needs to be compressed very efficiently but also the compression scheme needs to provide some error resilient features to deal with the high packet loss probability. In this thesis, we have worked with the H.264/ Advanced Video Coding (AVC) video compression standard since this is the most recent and most efficient video compression scheme. Also H.264 provides novel error resilient features e.g. slicing of the frame, Flexible Macroblock Ordering (FMO), data partitioning etc. In this thesis, we investigate how to utilize the error resilient schemes of H.264 to ensure a good quality picture at the receiving end. In the first part of the thesis, we find the optimum slice size that will enhance the quality of video transmission in a 3G environment. In the second part, we jointly optimize the data partitioning property and partial reliability extension property of the new transport layer protocol, Stream Control Transmission Protocol (SCTP). In the third and last part, we focus more on the network layer issues. We obtain the optimum point of application layer Forward Error Correction (FEC) and Medium Access Control (MAC) layer retransmission in a capacity constrained network. We assume that the bit rate assigned for the video application is more than the video bit rate so that the extra capacity available can be used for error correction.

The purpose of this research was to synthesize the constrained geometry catalyst Ti[(C5Me4)SiMe2(tBuN)]Cl2 (1) with MAO as a cocatalyst for ethylene homopolymerization and copolymerizations with 1-TMSO-alkenes to produce a copolymer with polar functionality. Three 1-alkenols of varying length were purchased and derivatized and used for the copolymerization experiments: 2-propen-1-ol, 3-buten-1-ol and 9-decen-1-ol. Several variables were tested to determine their effects on comonomer incorporation such as temperature, equivalents of comonomer, equivalents of MAO and two different solvents. Higher catalytic activities were correlated with fewer equivalents of polar comonomer, lower temperatures, and no fewer than 1000 equivalents of MAO. Toluene was found to be a far more effective reaction solvent than dichloromethane, as polymer yields were on average thirteen times higher. All polymer samples were analyzed by high temperature 1H NMR spectroscopy and selected samples were analyzed by DSC and IR spectroscopy. DSC determined that the polyethylene produced by 1 was substantially linear HDPE with long chain branching and that comonomer incorporation reduced the Tc and Tm, probably due to increased short chain branching. 1-TMSO-9-Decene was the most effective comonomer, as it had the highest incorporation rates (8.0 mol%) of all three of the polar comonomers. The two shorter comonomers exhibited no incorporation at all. This confirmed the hypothesis that polar comonomers with longer chains would be less prone to poisoning the electrophilic catalyst. / Thesis (Master, Chemistry) -- Queen's University, 2009-04-27 10:16:46.356

The primary aim of this study is to assess the applicability and performance of the finite element method (FEM) in solving structural optimization problems with stability constraints. In order to reach this goal, several optimization problems are solved using FEM which are briefly described as follows: The strongest column problem is one of the oldest optimization problems for which analytical solutions exist only for some special cases. Here, both unimodal and bimodal optimization of columns under concentrated and/or distributed compressive loads with several different boundary conditions and constraints are performed using an iterative method based on finite elements. The analytical solutions available in the literature for columns under concentrated loads and an analytical solution derived for simply supported columns under distributed loads are used for verification purposes. Optimization results are presented for fibre-reinforced composite rectangular plates under inplane loads. The non-uniformity of the in-plane stresses due to stress diffusion and/or in-plane boundary conditions is taken into account, and its influence on optimal buckling load is investigated. It is shown that the exclusion of the in-plane restraints may lead to errors in stability calculations and consequently in optimal design. The influences of the panel aspect ratio, stacking sequence, panel thickness, and the rotational edge restraints on the optimal axially compressed cylindrical and non-cylindrical curved panels are investigated, where the optimal panel is the one with the highest failure load. The prebuckling and the first-ply failure loads of the panels are calculated and minimum of these two is selected as the failure load. The results show that there are distinct differences between the behaviour of cylindrical and non-cylindrical panels. The formulations of the finite elements which are used throughout the study are given and several verification problems are solved to verify the accuracy of the methodology. The computer codes written in Matlab are also given in the appendix sections accompanied with the selected codes used for optimization purposes. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2006.

Many domains, such as emergency assistance, agriculture, construction, and planetary exploration, will increasingly require effective coordination of teams of robots and humans to accomplish a collection of spatially distributed heterogeneous tasks. Such coordination problems range from those that require loosely coordinated teams in which agents independently perform their assigned tasks, to those that require tightly coordinated teams where all actions of the team members need to be tightly synchronized. The scenarios of interest to this thesis lie between these two extremes, where some tasks are independent and others are related by constraints such as precedence, simultaneity, or proximity. These constraints may be a result of different factors including the complementary capabilities of different types of agents which require them to cooperate to achieve certain goals. The manner in which the constraints are satisfied influences the overall utility of the team. This thesis explores the problem of task allocation, scheduling, and routing for heterogeneous teams with such cross-schedule dependencies. We first describe and position this coordination problem in the larger space of multi-robot task allocation problems and propose an enhanced taxonomy for this space of problems. Recognizing that solution quality is important in many domains, we then present a mathematical programming approach to computing a bounded-optimal solution to the task allocation, scheduling and routing problem with cross-schedule dependencies. Specifically, we present a branch-and-price algorithm operating on a set-partitioning formulation of the problem, with side constraints. This bounded optimal “anytime” algorithm computes progressively better solutions and bounds, until it eventually terminates with the optimal solution. By examining the behavior of this algorithm, we gain insight into the impact on problem difficulty of various problem features, particularly different types of cross-schedule dependencies. Lastly, the thesis presents a flexible execution strategy for the resulting team plans with cross-schedule dependencies, and results demonstrating the approach on a team of indoor robots

In this thesis we present a method for detecting planar targets in real-time on power-constrained, or low-powered, hand-held devices such as mobile phones. We adopt the feature recognition (also referred to as feature matching) approach and employ fast-to-compute local feature descriptors to establish point correspondences. To obtain a satisfactory localization accuracy, most local feature descriptors seek a transformation of the input intensity patch that is invariant to various geometric and photometric deformations. Generally, such transformations are computationally intensive, hence are not ideal for real-time applications on limited hardware platforms. On the other hand, descriptors which are fast to compute are typically limited in their ability to provide invariance to a vast range of deformations. To address these shortcomings, we have developed a learning-based approach which can be applied to any local feature descriptor to increase the system’s robustness to both affine and perspective deformations. The motivation behind applying a learning-based approach is to transfer as much of the computational burden (as possible) onto an offline training phase, allowing a reduction in cost during online matching. The approach comprises of identifying keypoints which remain stable under artificially induced perspective transformations, extracting the corresponding feature vectors, and finally aggregating the feature vectors of coincident keypoints to obtain the final descriptors. We strictly focus on objects which are planar, thus allowing us to synthesize images of the object in order to capture the appearance of keypoint patches under several perspectives.

With the advent of telecommunication technology, the need to transport multimedia content is increasing day by day. Successful video transmission over the wireless network faces a lot of challenges because of the limited resource and error prone nature of the wireless environment. To deal with these two challenges, not only the video needs to be compressed very efficiently but also the compression scheme needs to provide some error resilient features to deal with the high packet loss probability. In this thesis, we have worked with the H.264/ Advanced Video Coding (AVC) video compression standard since this is the most recent and most efficient video compression scheme. Also H.264 provides novel error resilient features e.g. slicing of the frame, Flexible Macroblock Ordering (FMO), data partitioning etc. In this thesis, we investigate how to utilize the error resilient schemes of H.264 to ensure a good quality picture at the receiving end. In the first part of the thesis, we find the optimum slice size that will enhance the quality of video transmission in a 3G environment. In the second part, we jointly optimize the data partitioning property and partial reliability extension property of the new transport layer protocol, Stream Control Transmission Protocol (SCTP). In the third and last part, we focus more on the network layer issues. We obtain the optimum point of application layer Forward Error Correction (FEC) and Medium Access Control (MAC) layer retransmission in a capacity constrained network. We assume that the bit rate assigned for the video application is more than the video bit rate so that the extra capacity available can be used for error correction.

Research Doctorate - Doctor of Philosophy (PhD) / This thesis addresses the problem of robustness in model predictive control (MPC) of discrete-time systems. In contrast with most previous work on robust MPC, our main focus is on robustness in the face of both imperfect state information and dynamic model uncertainty. For linear discrete-time systems with model uncertainty described by sum quadratic constraints, we propose output-feedback MPC policies that: (i) treat soft constraints using quadratic penalty functions; (ii) respect hard constraints using 'tighter' constraints; and (iii) achieve robust closed-loop stability and non-zero setpoint tracking. Our two main tools are: (1) a new linear matrix inequality condition which parameterizes a class of quadratic MPC cost functions that all lead to robust closed-loop stability; and (2) a new parameterization of soft constraints which has the advantage of leading to optimization problems of prescribable size. The stability test we use for MPC design builds on well-known results from dissipativity theory which we tailor to the case of constrained discrete-time systems. The proposed robust MPC designs are shown to converge to well-known nominal MPC designs as the model uncertainty (description) goes to zero. Furthermore, the present approach to cost function selection is independently motivated by a novel result linking MPC and minimax optimal control theory. Specifically, we show that the considered class of MPC policies are the closed-loop optimal solutions of a particular class of minimax optimal control problems. In addition, for a class of nonlinear discrete-time systems with constraints and bounded disturbance inputs, we propose state-feedback MPC policies that input-to-state stabilize the system. Our two main tools in this last part of the thesis are: (1) a class of N-step affine state-feedback policies; and (2) a result that establishes equivalence between the latter class and an associated class of N-step affine disturbance-feedback policies. Our equivalence result generalizes a recent result in the literature for linear systems to the case when N is chosen to be less than the nonlinear system's 'input-state linear horizon'.

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 62-64). Also available on the World Wide Web.

A new progressive lossy-to-lossless coding method for arbitrarily-connected triangle mesh models of bivariate functions is proposed. The algorithm employs a novel representation of a mesh dataset called a bivariate-function description (BFD) tree, and codes the tree in an efficient manner. The proposed coder yields a particularly compact description of the mesh connectivity by only coding the constrained edges that are not locally preferred Delaunay (locally PD). Experimental results show our method to be vastly superior to previously-proposed coding frameworks for both lossless and progressive coding performance. For lossless coding performance, the proposed method produces the coded bitstreams that are 27.3% and 68.1% smaller than those generated by the Edgebreaker and Wavemesh methods, respectively. The progressive coding performance is measured in terms of the PSNR of function reconstructions generated from the meshes decoded at intermediate stages. The experimental results show that the function approximations obtained with the proposed approach are vastly superior to those yielded with the image tree (IT) method, the scattered data coding (SDC) method, the average-difference image tree (ADIT) method, and the Wavemesh method with an average improvement of 4.70 dB, 10.06 dB, 2.92 dB, and 10.19 dB in PSNR, respectively. The proposed coding approach can also be combined with a mesh generator to form a highly effective mesh-based image coding system, which is evaluated by comparing to the popular JPEG2000 codec for images that are nearly piecewise smooth. The images are compressed with the mesh-based image coder and the JPEG2000 codec at the fixed compression rates and the quality of the resulting reconstructions are measured in terms of PSNR. The images obtained with our method are shown to have a better quality than those produced by the JPEG2000 codec, with an average improvement of 3.46 dB. / Graduate

abstract: In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined. / Dissertation/Thesis / M.S. Computer Science 2013