Compressed sensing is an emerging approach for signal acquisition wherein theory has shown that a small number of linear, random projection of a signal contains enough information for reconstruction of the signal. Despite its potential to enable lightweight and inexpensive sensing hardware that simultaneously combines signal acquisition and dimensionality reduction, the compressed sensing of images and video still entails several challenges, in particular, a sensing-measurement operator which is difficult to apply in practice due to the heavy memory and computational burdens. Block-based random image sampling coupled with a projection-driven compressed-sensing recovery is proposed to address this challenge. For images, the block-based image acquisition is coupled with reconstruction driven by a directional transform that encourages spatial sparsity. Specifically, both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. Also considered is an extension of the basic reconstruction algorithm that incorporates block-based measurements in the domain of a wavelet transform. The pro-posed image recovery algorithm and its extension yield images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing. For video, motion estimation and compensation is utilized to promote temporal sparsity. Because video sequences have temporal redundancy in locations in which objects are moving while the background is still, a residual between the current frame and the previous frame compensated by object motion is shown to be more sparse than the orig-inal frame itself. By using residual reconstruction, information contained in the previous frame contributes to the reconstruction of the current frame. The proposed block-based compressed-sensing reconstruction for video outperforms a simple frame-byrame reconstruction as well as a 3D volumetric reconstruction in terms of visual quality. Finally, quantization of block-based compressed-sensing measurements is considered in order to generate a true bitstream from a compressed-sensing image acquisition. Specifically, a straightforward process of quantization via simple uniform scalar quantization applied in conjunction with differential pulse code modulation of the block-based compressed-sensing measurements is proposed. Experimental results demonstrate significant improvement in rate-distortion performance as compared scalar quantization used alone in several block-based compressed-sensing reconstruction algorithms. Additionally, rate-distortion performance superior to that of alternative quantized-compressed-sensing techniques relying on optimized quantization or reconstruction is observed.

No description available.

No / In this paper we present a constrained inverse kinematics algorithm for real-time motion capture in virtual environments, that has its origins in the simulation of multi-body systems. We apply this algorithm to an articulated human skeletal model using an electromagnetic motion tracking system with a small number of sensors to create avatar postures. The method offers efficient inverse kinematics computation and it is also generalised for the configurations of an articulated skeletal model. We investigate the possibility of capturing fast gestures by analysing the convergence patterns of the algorithm with the motion tracking sampling frequency for a range of actions.

Yes / Cybercrime is on the increase and attacks are becoming ever more sophisticated. Organisations are investing huge sums of money and vast resources in trying to establish effective and timely countermeasures. This is still a game of catch up, where hackers have the upper hand and potential victims are trying to produce secure systems hardened against what feels like are inevitable future attacks. The focus so far has been on technology and not people and the amount of resource allocated to countermeasures and research into cyber security attacks follows the same trend. This paper adds to the growing body of work looking at social engineering attacks and therefore seeks to redress this imbalance to some extent. The objective is to produce a model for social engineering that provides a better understanding of the attack process such that improved and timely countermeasures can be applied and early interventions implemented.

A scaled gradient descent method for competition of applications of conjugate gradient with priori estimations of the minimum value / This research proposes a novel method of improving the Gradient Descent method in an effort to be competitive with applications of the conjugate gradient method while reducing computation per iteration. Iterative methods for unconstrained optimization have found widespread application in digital signal processing applications for large inverse problems, such as the use of conjugate gradient for parallel image reconstruction in MR Imaging. In these problems, very good estimates of the minimum value at the objective function can be obtained by estimating the noise variance in the signal, or using additional measurements. The method proposed uses an estimation of the minimum to develop a scaling for Gradient Descent at each iteration, thus avoiding the necessity of a computationally extensive line search. A sufficient condition for convergence and proof are provided for the method, as well as an analysis of convergence rates for varying conditioned problems. The method is compared against the gradient descent and conjugate gradient methods. A method with a computationally inexpensive scaling factor is achieved that converges linearly for well-conditioned problems. The method is tested with tricky non-linear problems against gradient descent, but proves unsuccessful without augmenting with a line search. However with line search augmentation the method still outperforms gradient descent in iterations. The method is also benchmarked against conjugate gradient for linear problems, where it achieves similar convergence for well-conditioned problems even without augmenting with a line search. / Thesis / Master of Science (MSc) / This research proposes a novel method of improving the Gradient Descent method in an effort to be competitive with applications of the conjugate gradient method while reducing computation per iteration. Iterative methods for unconstrained optimization have found widespread application in digital signal processing applications for large inverse problems, such as the use of conjugate gradient for parallel image reconstruction in MR Imaging. In these problems, very good estimates of the minimum value at the objective function can be obtained by estimating the noise variance in the signal, or using additional measurements. The method proposed uses an estimation of the minimum to develop a scaling for Gradient Descent at each iteration, thus avoiding the necessity of a computationally extensive line search. A sufficient condition for convergence and proof are provided for the method, as well as an analysis of convergence rates for varying conditioned problems. The method is compared against the gradient descent and conjugate gradient methods. A method with a computationally inexpensive scaling factor is achieved that converges linearly for well-conditioned problems. The method is tested with tricky non-linear problems against gradient descent, but proves unsuccessful without augmenting with a line search. However with line search augmentation the method still outperforms gradient descent in iterations. The method is also benchmarked against conjugate gradient for linear problems, where it achieves similar convergence for well-conditioned problems even without augmenting with a line search.

No description available.

No description available.

No description available.

No description available.

Riemann problems are of prime importance in computational fluid dynamics simulations using finite elements or finite volumes discretizations. In some applications, billions of Riemann problems might need to be solved in a single simulation, therefore it is important to have reliable and computationally efficient algorithms to do so. Given the nonlinearity of the flux function in most systems considered in practice, to obtain an exact solution for the Riemann problem explicitly is often not possible, and iterative solvers are required. However, because of issues found with existing iterative solvers like lack of convergence and high computational cost, their use is avoided and approximate solvers are preferred. In this thesis work, motivated by the advances in computer hardware and algorithms in the last years, we revisit the possibility of using iterative solvers to compute the exact solution for Riemann problems. In particular, we focus on the development, implementation, and performance comparison of iterative Riemann solvers for the shallow water and Euler equations. In a one-dimensional homogeneous framework for these systems, we consider several initial guesses and iterative methods for the computation of the Riemann solution. We find that efficient and reliable iterative solvers can be obtained by using recent estimates on the Riemann solution to modify and combine well-known methods. Finally, we consider the application of these solvers in finite volume simulations using the wave propagation algorithms implemented in Clawpack.