<p>Metric indexing is a branch of search technology that is designed for search non-textual data. Examples of this includes image search (where the search query is an image), document search (finding documents that are roughly equal) to search in high-dimensional Euclidean spaces. Metric indexing is based on the theory of metric spaces, where the only thing known about a set of objects is the distance between them (defined by a metric distance function). A large number of methods have been proposed to solve the metric indexing problem. In this thesis, we have concentrated on new approaches to solving these problems, as well as combining existing methods to create better ones. The methods studied in this thesis include D-Index, GNAT, EMVP-Forest, HC, SA-Tree, SSS-Tree, M-Tree, PM-Tree, M*-Tree and PM*-Tree. These have all been implemented and tested against each other to find strengths and weaknesses. This thesis also studies a group of indexing methods called hybrid methods which combines tree-based methods (like SA-Tree, SSS-tree and M-Tree), with pivoting methods (like AESA and LAESA). The thesis also proposes a method to create hybrid trees from existing trees by using features in the programming language. Hybrid methods have been shown in this thesis to be very promising. While they may have a considerable overhead in construction time,CPU usage and/or memory usage, they show large benefits in reduced number of distance computations. We also propose a new way of calculating the Minimal Spanning Tree of a graph operating on metric objects, and show that it reduces the number of distance computations needed.</p>

<p>In this project, a complex, serial application that models networks of oil wells is analyzed for today's parallel architectures. By heavy use of the profiling tool Valgrind, several serial optimizations are achieved, causing up to a 30-50x speedup on previously dominant sections of the code, on different architectures. Our initial main goal is to parallelize our application for GPGPUs (General Purpose Graphics Processing Units) such as the NVIDIA GeForce 8800GTX. However, our optimized application is shown not to have a high enough computational intensity to be suitable for the GPU platforms, with the data transfer over the PCI-express port showing to be a serious bottleneck. We then target our applications for another, more common, parallel architecture -- the multi-core CPU. Instead of focusing on the low-level hotspots found by the profiler, a new approach is taken. By analyzing the functionality of the application and the problem it is to solve, the high-level structure of the application is identified. A thread pool in combination with a task queue is implemented using PThreads in Linux, which fit the structure of the application. It also supports nested parallel queues, while maintaining all serial dependencies. However, the sheer size and complexity of the serial application, introduces a lot of problems when trying to go multithreaded. A tight coupling of all parts of the code, introduces several race conditions, creating erroneous results for complex cases. Our focus is hence shifted to developing models to help analyze how suitable applications with traversal of dependence-tree structures, such as our oil well network application is, given benchmarks of the node times. First, we benchmark the serial execution of each child in the network and predict the overall parallel performance by computing dummy tasks reflecting these times on the same tree structure on two given well networks, a large and a small case. Based on these benchmarks, we then predict the speedup of these two cases, with the assumption of balanced loads on each level in the network. Finally, the minimum amount of time needed to calculate a given network is predicted. Our predictions of low scalability, due to the nature of the oil networks in the test cases, are then shown. This project thus concludes that the amount of work needed to successfully introduce multithreading in this application might not be worth it, due to all the serial dependencies in the problem the application tries to solve. However, if there are multiple individual networks to be calculated, we suggest using Grid technology to manage multiple individual instances of the application simultaneously. This can be done either by using script files or by adding DRMAA API calls in the application. This, in combination with further serial optimizations, is the way to go for good speedup for these types of applications.</p>

<p>Go is an ancient two player board game that has been played for several thousand years. Despite its simple rules, the game requires players to form long-term strategic plans and also possess strong tactical skills to handle the complex fights that often occur during a game. From an artificial intelligence point of view, Go is notable as a game that has been highly resistant to all traditional game playing approaches. In contrast to other board games such as chess and checkers, top human Go players are still significantly better than any computer Go playing programs. It is believed that the strategic depth of Go will require the use of new and more powerful artificial intelligence methods than the ones successfully used to create computer players for such other games. There have been some promising new developments using new Monte Carlo-based techniques to play computer Go in recent years, and programs based on this approach are currently the strongest computer Go players in the world. However, even these programs still play at an amateur level, and they cannot compete with professional or strong amateur human players. In this thesis we explore the idea of reusing experience from previous games to identify strategically important moves for a Go board position. This is based on finding a previous game position that is highly similar to the one in the current game. The moves that were played in this previous game are then adapted to generate new moves for the current game situation. A new computer Go playing system using Monte Carlo-based Go methods was designed as a part of this thesis work, and a prototype implementation of this system was also developed. We extended this initial prototype using case based reasoning (CBR) methods to quickly identify the most strategically valuable areas of the board at the early stages of the game, based on finding similar positions in a collection of professionally played games. The last part of the thesis is an evaluation of the developed system and the results observed using our implementation. These results show that our CBR-based approach is a significant improvement over the initial prototype, and in the opening game it allows the program to quickly locate the most strategically interesting areas of the board. However, by itself our approach does not find strong tactical moves within these identified areas, and thus it is most valuable when used to provide strategic guidelines for other methods that can find tactical plays.</p>

<p>The tasks : discriminating music, speech and other sounds and language identification have a broad range of applications in todays multilingual multimedia community. Both tasks gave a lot of possibilities regarding methods and development tools which also brings some risk. The Language Identification(LID) problem ended up with two different approaches. One approach was discarded due to poor results in the pre-study while the other approach had some promising potential but did not deliver as hoped in the first place. On the other hand, the music, speech discrimination was solved with great accuracy using 3 simple time domain features and Support Vector Machines(SVM). Adding 'other sounds' to this discrimination problem did complicate the problem but the final solution delivered great results using the enormous BBC Sound Effects library as examples of non speech and music. Both tasks were tried being solved using Gaussian Mixture Models(GMM) because of it's known great ability to model arbitrary feature space segmentations. The tools used were Matlab together with a number of different toolboxes explained further in the text.</p>

<p>The use of Linux in embedded systems is steadily growing in popularity. The UC3A is a series of high performance, low power 32-bit microcontrollers aimed at several industrial and commercial applications including PLC, instrumentation, phones, vending machines and more. The main goal of this project was to complete the adaptation of the Linux kernel, compiler and loader software, in order to enable the Linux kernel to load and run applications on this device. In addition, a set of useful applications should be picked, compiled and tested on the target platform to indicate a complete software solution. This master's thesis is a continuation, by the same three students, of the work of a student project during the fall of 2008. In this report we present in detail the findings, challenges, choices and and solutions involved in the working process. During the course of this project, we have successfully adapted the Linux kernel, and a toolchain for generating binaries loadable by Linux. A set of test applications have been compiled and tested on the resulting platform. This project has resulted in the submission of a revised patch series for the U-Boot boot loader, one patch series for Linux, and one for the toolchain. Requirements have been created, and tests for the requirements have been carried out.</p>

<p>In this project we seek to explore whether it is possible for an artificial agent to learn how to play curling. To achieve this goal we developed a simulator that works as an environment where different agents can be tested against each other. Our most successful agent use a Linear Target Function as a basis for selecting good moves in the game. This agent has become very adept at placing stones, but we discovered that it lacks the ability to employ advanced strategies that reach over more than just one stone. In an effort to give the agent this ability we expanded it using Q-learning with UCT, however this was not successful. For the agent to work we need a good representation of the information in curling, and our representation was quite broad. This caused the training of the agent to take an unreasonably large amount of time.</p>

<p>This masters thesis proposes that multimodal behaviour generation frameworks are an appropriate way to increase the believability of animated characters in virtual heritage applications. To investigate this proposal, an existing virtual museum guide application developed by the author is extended by integrating the Behavioural Markup Language (BML), and the open-source BML realiser SmartBody. The architectural and implementation decisions involved in this process are catalogued and discussed. The integration of BML and SmartBody results in a dramatic improvement in the quality of character animation in the application, as well as greater flexibility and extensibility, including the ability to create scripted sequences of behaviour for multiple characters in the virtual museum. The successful integration confirms that multimodal behaviour generation frameworks have a place in virtual heritage applications.</p>

<p>The gameplay of real-time strategy games can be divided into macromanagement and micromanagement. Several researchers have studied automated learning for macromanagement, using a case-based reasoning/reinforcement learning architecture to defeat both static and dynamic opponents. Unlike the previous research, we present the Unit Priority Artificial Intelligence (UPAI). UPAI is a case-based reasoning/reinforcement learning system for learning the micromanagement task of prioritizing which enemy units to attack in different game situations, through unsupervised learning from experience. We discuss different case representations, as well as the exploration vs exploitation aspect of reinforcement learning in UPAI. Our research demonstrates that UPAI can learn to improve its micromanagement decisions, by defeating both static and dynamic opponents in a micromanagement setting.</p>

<p>Coupling commodity CPUs and modern GPUs give you heterogeneous systems that are cheap, high-performance with incredible FLOPS counts. Recent evolution of GPGPU models and technologies make these systems even more appealing as compute devices for a range of HPC applications including image processing, seismic processing and other physical modeling, as well as linear programming applications. In fact, graphics vendor such as NVIDIA and AMD are now targeting HPC with some of their products. Due to the power and frequency walls, the trend is now to use multiple GPUs on a given system, much like you will find multiple cores on CPU-based systems. However, increasing the hierarchy of resource wides the spectrum of factors that may impact on the performance of the system. The lack of good models for GPU-based, heterogeneous systems also makes it harder to understand which factors impact performance the most. The goal of this thesis is to analyze such factors by investigating and benchmarking NVIDIA's multi-GPU solution, their recent NVIDIA Tesla S1070 Computing System. This system combines four T10 GPUs making available up to 4 TFLOPS of computational power. Based on a comparative study of fundamental parallel computing models and on the specific heterogeneous features exposed by the system, we define a test space for performance analysis. As a case study, we develop a red-black, SOR PDE solver for Laplace equations with Dirichlet boundaries, well known for requiring constant communication in order to exchange neighboring data. To aid both design and analysis, we propose a model for multi-GPU systems targeting communication between the several GPUs. The main variables exposed by the benchmark application are: domain size and shape, kind of data partitioning, number of GPUs, width of the borders to exchange, kernels to use, and kind of synchronization between the GPU contexts. Among other results, the framework is able to point out the most critical bounds of the S1070 system when dealing with applications like the one in our case study. We show that the multi-GPU system greatly benefits from using all its four GPUs on very large data volumes. Our results show the four GPUs almost four times faster than a single GPU, and twice as fast as two. Our analysis outcomes also allow us to refine our static communication model, enriching it with regression-based predictions.</p>

<p>This report will cover the physical realization of a public key algorithm based on multivariate quadratic quasigroups. The intension is that this implementation will use real keys and data. Efforts are also taken in order to reduce area cost as much as possible. The solution will be described and analyzed. This will show wether the measures were successfull or not.</p>