Grid computing provides a powerful, virtual parallel system known as a computational Grid on which users can run parallel applications to solve problems quickly. However, users must be careful to allocate tasks to nodes properly because improper allocation of only one task could result in lengthy executions of applications, or even worse, applications could crash. This allocation problem is called the mapping problem, and an entity that tackles this problem is called a mapper. In this thesis, we aim to develop an efficient, practical, portable mapper. To study the mapping problem, researchers often make unrealistic assumptions such as that nodes of Grids are always reliable, that execution times of tasks assigned to nodes are known a priori, or that detailed information of parallel applications is always known. As a result, the practicality and portability of mappers developed in such conditions are uncertain. Our review of related work suggested that a more efficient tool is required to study this problem; therefore, we developed GMap, a simulator researchers/developers can use to develop practical, portable mappers. The fact that nodes are not always reliable leads to the development of an algorithm for predicting the reliability of nodes and a predictor for identifying reliable nodes of Grids. Experimental results showed that the predictor reduced the chance of failures in executions of applications by half. The facts that execution times of tasks assigned to nodes are not known a priori and that detailed information of parallel applications is not alw ays known, lead to the evaluation of five nearest-neighbour (nn) execution time estimators: k-nn smoothing, k-nn, adaptive k-nn, one-nn, and adaptive one-nn. Experimental results showed that adaptive k-nn was the most efficient one. We also implemented the predictor and the estimator in GMap. Using GMap, we could reliably compare the efficiency of six mapping algorithms: Min-min, Max-min, Genetic Algorithms, Simulated Annealing, Tabu Search, and Quick-quality Map, with none of the preceding unrealistic assumptions. Experimental results showed that Quick-quality Map was the most efficient one. As a result of these findings, we achieved our goal in developing an efficient, practical, portable mapper.

Feedforward neural networks have been widely studied and used in many applications in science and engineering. The training of this type of networks is mainly undertaken using the well-known backpropagation based learning algorithms. One major problem with this type of algorithms is the slow training convergence speed, which hinders their applications. In order to improve the training convergence speed of this type of algorithms, many researchers have developed different improvements and enhancements. However, the slow convergence problem has not been fully addressed. This thesis makes several contributions by proposing new backpropagation learning algorithms based on the terminal attractor concept to improve the existing backpropagation learning algorithms such as the gradient descent and Levenberg-Marquardt algorithms. These new algorithms enable fast convergence both at a distance from and in a close range of the ideal weights. In particular, a new fast convergence mechanism is proposed which is based on the fast terminal attractor concept. Comprehensive simulation studies are undertaken to demonstrate the effectiveness of the proposed backpropagataion algorithms with terminal attractors. Finally, three practical application cases of time series forecasting, character recognition and image interpolation are chosen to show the practicality and usefulness of the proposed learning algorithms with comprehensive comparative studies with existing algorithms.

<p>In todays GSM network more and more interfaces are run over IP instead of classic synchronized networks. This rises new issues to be solved, for example handling of jitter that use of IP networks introduces. The jitter can be handled by a jitter buffer which ensures that the packets are forwarded in evenly spaced intervals.</p><p>In GSM, data is requested a certain time in advance before delivery to a cellphone. This "time in advance" needs to be adjusted according to the delay of the channel. For an IP network this delay varies (jitter), which means that it would be beneficial to have an algorithm which continuously adjusts how long in advance the packets should be requested. The adjustment is made according to current channel delay and jitter size.</p><p>In this thesis work a model of a general IP network has been developed and isthen used for development of two algorithms for jitter buffer handling. Once the algorithms have been developed they are evaluated and compared to each other and previous solutions to the problem. One of the algorithms is new and the other is an already existing algorithm that has been extended.</p><p>The simplified conclusion is that the behaviors of both algorithms are very similar. They mainly have small packet loss but sometimes the packets are requested earlier than needed and therefore are kept in the buffer a bit longer than necessary. When comparing the two developed algorithms with previous solutions it is visible that they improve the buffer handling a great deal.</p>

<p>X3 C is a Swedish company which develops a world wide good tracking system by using ARFID </p><p>tags placed on every item which has to be delivered and base stations as gateway in a wireless </p><p>sensor network. The requirement of a long lifespan of their ARFID tags made it difficult to </p><p>implement security. Firstly an evaluation of possible security mechanisms and their power </p><p>consumption was done by measuring the avalanche effect and character frequency of the sym- </p><p>metric algorithms Blowfish, RC2 and XTEA. Secondly, the required CPU time which is needed </p><p>by each algorithm for encrypting a demo plaintext, was measured and analyzed. Summariz- </p><p>ing both analysis, the XTEA algorithm, run in CBC mode, is the recommendation for the XC </p><p>ARFID tags. The testing processes and the results are presented in detail in this thesis.</p>

We present an immersed interface algorithm for the incompressible Navier Stokes equations. The interface is represented by cubic splines which are interpolated through a set of Lagrangian control points. The position of the control points is implicitly updated using the fluid velocity. The forces that the interface exerts on the fluid are computed from the constitutive relation of the interface and are applied to the fluid through jumps in the pressure and jumps in the derivatives of pressure and velocity. A projection method is used to time advance the Navier-Stokes equations on a uniform cartesian mesh. The Poisson-like equations required for the implicit solution of the diffusive and pressure terms are solved using a fast Fourier transform algorithm. The position of the interface is updated implicitly using a quasi-Newton method (BFGS) within each timestep. Several examples are presented to illustrate the flexibility of the presented approach. / Singapore-MIT Alliance (SMA)

Given an undirected network with L possible facility types for each edge, and a partition of the nodes into L levels, the Multi-level Network Design (MLND) problem seeks a fixed cost minimizing design that spans all the nodes and connects the nodes at each level by facilities of the corresponding or higher type. This problem generalizes the well-known Steiner network problem and the hierarchical network design problem, and has applications in telecommunication, transportation, and electric power distribution network design. In a companion paper we introduced the problem, studied alternative model formulations, and analyzed the worst-case performance of heuristics based on Steiner network and spanning tree solutions. This paper develops and tests a dual-based algorithm for the Multi-level Network Design (MLND) problem. The method first performs problem preprocessing to fix certain design variables, and then applies a dual ascent procedure to generate upper and lower bounds on the optimal value. We report extensive computational results on large, random networks (containing up to 500 nodes, and 5000 edges) with varying cost structures. The integer programming formulation of the largest of these problems has 20,000 integer variables and over 5 million constraints. Our tests indicate that the dualbased algorithm is very effective, producing solutions guaranteed to be within 0 to 0.9% of optimality.

We present a faster implementation of the polynomial time primal simplex algorithm due to Orlin [23]. His algorithm requires O(nm min{log(nC), m log n}) pivots and O(n2 m ??n{log nC, m log n}) time. The bottleneck operations in his algorithm are performing the relabeling operations on nodes, selecting entering arcs for pivots, and performing the pivots. We show how to speed up these operations so as to yield an algorithm whose running time is O(nm. log n) per scaling phase. We show how to extend the dynamic-tree data-structure in order to implement these algorithms. The extension may possibly have other applications as well.

The efficiency of interior-point algorithms for linear programming is related to the effort required to factorize the matrix used to solve for the search direction at each iteration. When the linear program is in symmetric form (i.e., the constraints are Ax b, x > 0 ), then there are two mathematically equivalent forms of the search direction, involving different matrices. One form necessitates factoring a matrix whose sparsity pattern has the same form as that of (A AT). The other form necessitates factoring a matrix whose sparsity pattern has the same form as that of (ATA). Depending on the structure of the matrix A, one of these two forms may produce significantly less fill-in than the other. Furthermore, by analyzing the fill-in of both forms prior to starting the iterative phase of the algorithm, the form with the least fill-in can be computed and used throughout the algorithm. Finally, this methodology can be applied to linear programs that are not in symmetric form, that contain both equality and inequality constraints.

Two different types of switching mechanism are considered in this thesis; one is featured with abrupt/sudden switching while the other one shows gradual changing behavior in its dynamics. It is shown that, through the comparison of the identification results from the proposed method and a benchmark method, the proposed robust identification method can achieve better performance when dealing with the data set mixed with outliers. To model the switched systems exhibiting gradual or smooth transition among different local models, in addition to estimating the local sub-systems parameters, a smooth validity (an exponential function) function is introduced to combine all the local models so that throughout the working range of the gradual switched system, the dynamics of the nonlinear process can be appropriately approximated. Verification results on a simulated numerical example and CSTR process confirm the effectiveness of the proposed Linear Parameter Varying (LPV) identification algorithm. / Process Control

Many industrial problems in various fields, such as logistics, process management, orproduct design, can be formalized and expressed as optimization problems in order tomake them solvable by optimization algorithms. However, solvers that guarantee thefinding of optimal solutions (complete) can in practice be unacceptably slow. Thisis one of the reasons why approximative (incomplete) algorithms, producing near-optimal solutions under restrictions (most dominant time), are of vital importance. Those approximative algorithms go under the umbrella term metaheuristics, each of which is more or less suitable for particular optimization problems. These algorithmsare flexible solvers that only require a representation for solutions and an evaluation function when searching the solution space for optimality.What all metaheuristics have in common is that their search is guided by certain control parameters. These parameters have to be manually set by the user andare generally problem and interdependent: A setting producing near-optimal resultsfor one problem is likely to perform worse for another. Automating the parameter setting process in a sophisticated, computationally cheap, and statistically reliable way is challenging and a significant amount of attention in the artificial intelligence and operational research communities. This activity has not yet produced any major breakthroughs concerning the utilization of problem instance knowledge or the employment of dynamic algorithm configuration. The thesis promotes automated parameter optimization with reference to the inverse impact of problem instance diversity on the quality of parameter settings with respect to instance-algorithm pairs. It further emphasizes the similarities between static and dynamic algorithm configuration and related problems in order to show how they relate to each other. It further proposes two frameworks for instance-based algorithm configuration and evaluates the experimental results. The first is a recommender system for static configurations, combining experimental design and machine learning. The second framework can be used for static or dynamic configuration,taking advantage of the iterative nature of population-based algorithms, which is a very important sub-class of metaheuristics. A straightforward implementation of framework one did not result in the expected improvements, supposedly because of pre-stabilization issues. The second approach shows competitive results in the scenario when compared to a state-of-the-art model-free configurator, reducing the training time by in excess of two orders of magnitude.