Linear hashtable motion estimation algorithm for distributed video processing

This paper presents a parallel Linear Hashtable Motion Estimation Algorithm (LHMEA). Most parallel video compression algorithms focus on Group of Picture (GOP). Based on LHMEA we proposed earlier [1][2], we developed a parallel motion estimation algorithm focus inside of frame. We divide each reference frames into equally sized regions. These regions are going to be processed in parallel to increase the encoding speed significantly. The theory and practice speed up of parallel LHMEA according to the number of PCs in the cluster are compared and discussed. Motion Vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass Hexagonal Search (HEXBS) motion estimation, which only searches a small number of Macroblocks (MBs). We evaluated distributed parallel implementation of LHMEA of TPA for real time video compression.

A policy model for secure information flow

When a computer program requires legitimate access to confidential data, the question arises whether such a program may illegally reveal sensitive information. This paper proposes a policy model to specify what information flow is permitted in a computational system. The security definition, which is based on a general notion of information lattices, allows various representations of information to be used in the enforcement of secure information flow in deterministic or nondeterministic systems. A flexible semantics-based analysis technique is presented, which uses the input-output relational model induced by an attacker's observational power, to compute the information released by the computational system. An illustrative attacker model demonstrates the use of the technique to develop a termination-sensitive analysis. The technique allows the development of various information flow analyses, parametrised by the attacker's observational power, which can be used to enforce what declassification policies.

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

Immersive open surgery simulation

Realistic medical simulation has great potential for augmenting or complimenting traditional medical training or surgery planning, and Virtual Reality (VR) is a key enabling technology for delivering this goal. Although, medical simulators are now widely used in medical institutions, the majority of them are still reliant on desktop monitor displays, and many are restricted in their modelling capability to minimally invasive or endoscopic surgery scenarios. Whilst useful, such models lack the realism and interaction of the operating theatre. In this paper, we describe how we are advancing the technology by simulating open surgery procedures in an Immersive Projection Display CAVE environment thereby enabling medical practitioners to interact with their virtual patients in a more realistic manner.

MPJ express meets gadget: towards a java code for cosmological simulations

Gadget-2 is a massively parallel structure formation code for cosmological simulations. In this paper, we present a Java version of Gadget-2. We evaluated the performance of the Java version by running colliding galaxies simulation and found that it can achieve around 70% of C Gadget-2's performance.

Developing collaborative social software

The Internet has an increasing role in facilitating communication between people and groups of people. As access to the Internet and World Wide Web is widely available, collaborative services enabled over the Internet are also burgeoning. In this paper, we present the current issues and our techniques for developing collaborative social software. We discuss online communities in the context of social collaborative systems. We then describe our approach to the development of supporting software for online communities and collaboration.

A sparse parallel hybrid Monte Carlo algorithm for matrix computations

In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.

Applying semiotic analysis to the design and modeling of distributed multimedia systems

Semiotics is the study of signs. Application of semiotics in information systems design is based on the notion that information systems are organizations within which agents deploy signs in the form of actions according to a set of norms. An analysis of the relationships among the agents, their actions and the norms would give a better specification of the system. Distributed multimedia systems (DMMS) could be viewed as a system consisted of many dynamic, self-controlled normative agents engaging in complex interaction and processing of multimedia information. This paper reports the work of applying the semiotic approach to the design and modeling of DMMS, with emphasis on using semantic analysis under the semiotic framework. A semantic model of DMMS describing various components and their ontological dependencies is presented, which then serves as a design model and implemented in a semantic database. Benefits of using the semantic database are discussed with reference to various design scenarios.

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Orthogonal forward selection for constructing the radial basis function network with tunable nodes

An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.

Landscape state machines: Tools for evolutionary algorithm performance analyses and landscape/algorithm mapping

Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the 'real' landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutation-only algorithms, but there are various ways to address this and other limitations.

Error analysis of a Monte Carlo algorithm for computing bilinear forms of matrix powers

In this paper we present error analysis for a Monte Carlo algorithm for evaluating bilinear forms of matrix powers. An almost Optimal Monte Carlo (MAO) algorithm for solving this problem is formulated. Results for the structure of the probability error are presented and the construction of robust and interpolation Monte Carlo algorithms are discussed. Results are presented comparing the performance of the Monte Carlo algorithm with that of a corresponding deterministic algorithm. The two algorithms are tested on a well balanced matrix and then the effects of perturbing this matrix, by small and large amounts, is studied.

Monte Carlo numerical treatment of large linear algebra problems

In this paper we deal with performance analysis of Monte Carlo algorithm for large linear algebra problems. We consider applicability and efficiency of the Markov chain Monte Carlo for large problems, i.e., problems involving matrices with a number of non-zero elements ranging between one million and one billion. We are concentrating on analysis of the almost Optimal Monte Carlo (MAO) algorithm for evaluating bilinear forms of matrix powers since they form the so-called Krylov subspaces. Results are presented comparing the performance of the Robust and Non-robust Monte Carlo algorithms. The algorithms are tested on large dense matrices as well as on large unstructured sparse matrices.

Complexity of Monte Carlo algorithms for a class of integral equations

In this work we study the computational complexity of a class of grid Monte Carlo algorithms for integral equations. The idea of the algorithms consists in an approximation of the integral equation by a system of algebraic equations. Then the Markov chain iterative Monte Carlo is used to solve the system. The assumption here is that the corresponding Neumann series for the iterative matrix does not necessarily converge or converges slowly. We use a special technique to accelerate the convergence. An estimate of the computational complexity of Monte Carlo algorithm using the considered approach is obtained. The estimate of the complexity is compared with the corresponding quantity for the complexity of the grid-free Monte Carlo algorithm. The conditions under which the class of grid Monte Carlo algorithms is more efficient are given.

A Monte Carlo Approach for the Cook -Torrance Model

In this work we consider the rendering equation derived from the illumination model called Cook-Torrance model. A Monte Carlo (MC) estimator for numerical treatment of the this equation, which is the Fredholm integral equation of second kind, is constructed and studied.