This thesis documents the research which has led to the design of a number of techniques aiming to improve the performance of content-based image retrieval (CBIR) systems in wavelets domain using texture analysis. Attention was drawn on CBIR in transform domain and in particular wavelets because of the excellent characteristics for compression and texture extraction applications and the wide adoption in both the research community and the industry. The issue of performance is addressed in terms of accuracy and speed. The rationale for this research builds upon the conclusion that CBIR has not yet reached a good performance balance of accuracy, efficiency and speed for wide adoption in practical applications. The issue of bridging the sensory gap, which is defined as "[the difference] between the object in the real world and the information in a (computational) description derived from a recording of that scene." has yet to be resolved. Furthermore, speed improvement remains an uncharted territory as is feature extraction directly from the bitstream of compressed images. To address the above requirements the first part of this work introduces three techniques designed to jointly address the issue of accuracy and processing cost of texture characterization in wavelets domain. The second part introduces a new model for mapping the wavelet coefficients of an orthogonal wavelet transformation to a circular locus. The model is applied in order to design a novel rotation-invariant texture descriptor. All of the aforementioned techniques are also designed to bridge the gap between texture-based image retrieval and image compression by using appropriate compatible design parameters. The final part introduces three techniques for improving the speed of a CBIR query through more efficient calculation of the Li-distance, when it is used as an image similarity metric. The contributions conclude with a novel technique which, in conjunction with a widely adopted wavelet-based compression algorithm, extracts texture information directly from the compressed bit-stream for speed and storage requirements savings. The experimental findings indicate that the proposed techniques form a solid groundwork which can be extended to practical applications.

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Stream programming is a paradigm where a program is structured by a set of computational nodes connected by streams. Focusing on data moving between computational nodes via streams, this programming model fits well for applications that process long sequences of data. We call such applications reactive stream programs (RSPs) to distinguish them from stream programs with rather small and finite input data. In stream programming, concurrency is expressed implicitly via communication streams. This helps to reduce the complexity of parallel programming. For this reason, stream programming has gained popularity as a programming model for parallel platforms. However, it is also challenging to analyse and improve the performance without an understanding of the program's internal behaviour. This thesis targets an effi cient execution model for deploying RSPs on parallel platforms. This execution model includes a monitoring framework to understand the internal behaviour of RSPs, scheduling strategies for RSPs on uniform shared-memory platforms; and mapping techniques for deploying RSPs on heterogeneous distributed platforms. The foundation of the execution model is based on a study of the performance of RSPs in terms of throughput and latency. This study includes quantitative formulae for throughput and latency; and the identification of factors that influence these performance metrics. Based on the study of RSP performance, this thesis exploits characteristics of RSPs to derive effective scheduling strategies on uniform shared-memory platforms. Aiming to optimise both throughput and latency, these scheduling strategies are implemented in two heuristic-based schedulers. Both of them are designed to be centralised to provide load balancing for RSPs with dynamic behaviour as well as dynamic structures. The first one uses the notion of positive and negative data demands on each stream to determine the scheduling priorities. This scheduler is independent from the runtime system. The second one requires the runtime system to provide the position information for each computational node in the RSP; and uses that to decide the scheduling priorities. Our experiments show that both schedulers provides similar performance while being significantly better than a reference implementation without dynamic load balancing. Also based on the study of RSP performance, we present in this thesis two new heuristic partitioning algorithms which are used to map RSPs onto heterogeneous distributed platforms. These are Kernighan-Lin Adaptation (KLA) and Congestion Avoidance (CA), where the main objective is to optimise the throughput. This is a multi-parameter optimisation problem where existing graph partitioning algorithms are not applicable. Compared to the generic meta-heuristic Simulated Annealing algorithm, both proposed algorithms achieve equally good or better results. KLA is faster for small benchmarks while slower for large ones. In contrast, CA is always orders of magnitudes faster even for very large benchmarks.

We present an analysis framework for performance evaluation of large-scale resource-bound (LSRB) computer systems. LSRB systems are those whose resources are continually in demand to serve resource users, who appear in large populations and cause high contention. In these systems, the delivery of quality service is crucial, even in the event of resource failure. Therefore, various techniques have been developed for evaluating their performance. In this thesis, we focus on the technique of quantitative modelling, where in order to study a system, first its model is constructed and then the system’s behaviour is analysed via the model. A number of high level formalisms have been developed to aid the task of model construction. We focus on PEPA, a stochastic process algebra that supports compositionality and enables us to easily build complex LSRB models. In spite of this advantage, however, the task of analysing LSRB models still poses unresolved challenges. LSRB models give rise to very large state spaces. This issue, known as the state space explosion problem, renders the techniques based on discrete state representation, such as numerical Markovian analysis, computationally expensive. Moreover, simulation techniques, such as Gillespie’s stochastic simulation algorithm, are also computationally demanding, as numerous trajectories need to be collected. Furthermore, as we show in our first contribution, the techniques based on the mean-field theory or fluid flow approximation are not readily applicable to this case. In LSRB models, resources are not assumed to be present in large populations and models exhibit highly noisy and stochastic behaviour. Thus, the mean-field deterministic behaviour might not be faithful in capturing the system’s randomness and is potentially too crude to show important aspects of their behaviours. In this case, the modeller is unable to obtain important performance indicators, such as the reliability measures of the system. Considering these limitations, we contribute the following analytical methods particularly tailored to LSRB models. First, we present an aggregation method. The aggregated model captures the evolution of only the system’s resources and allows us to efficiently derive a probability distribution over the configurations they experience. This distribution provides full faithfulness for studying the stochastic behaviour of resources. The aggregation can be applied to all LSRB models that satisfy a syntactic aggregation condition, which can be quickly checked syntactically. We present an algorithm to generate the aggregated model from the original model when this condition is satisfied. Second, we present a procedure to efficiently detect time-scale near-complete decomposability (TSND). The method of TSND allows us to analyse LSRB models at a reduced cost, by dividing their state spaces into loosely coupled blocks. However, one important input is a partition of the transitions defined in the model, categorising them into slow or fast. Forming the necessary partition by the analysis of the model’s complete state space is costly. Our process derives this partition efficiently, by relying on a theorem stating that our aggregation preserves the original model’s partition and therefore, it can be derived by an efficient reachability analysis on the aggregated state space. We also propose a clustering algorithm to implement this reachability analysis. Third, we present the method of conditional moments (MCM) to be used on LSRB models. Using our aggregation, a probability distribution is formed over the configurations of a model’s resources. The MCM outputs the time evolution of the conditional moments of the marginal distribution over resource users given the configurations of resources. Essentially, for each such configuration, we derive measures such as conditional expectation, conditional variance, etc. related to the dynamics of users. This method has a high degree of faithfulness and allows us to capture the impact of the randomness of the behaviour of resources on the users. Finally, we present the advantage of the methods we proposed in the context of a case study, which concerns the performance evaluation of a two-tier wireless network constructed based on the femto-cell macro-cell architecture.

The use of interactive multimedia within live performance is now well established and a significant body of exciting and sophisticated work has been produced. However, almost all work in the field seems to start by creating at least some of the software and hardware systems that will provide the infrastructure for the project, an approach which might involve significant duplication of effort. The research described in this thesis sets out to discover if there are common features in the practice of artists from a range of performance backgrounds and, if so, whether the features of a system which might support these common aspects could be established. Based on evidence from a set of interviews, it is shown that there are indeed common factors in work in this field, especially the intensive linking of elements in performances and the use of triggering or cuing. A statement of requirements for a generic system to support work in digital performance is then established based on interview analysis and personal creative work. A general model of live performance, based on set theory, is described which provides a rationale for the integration of digital technology within live performance. A computational model outlining the formal requirements of a general system for use in live performance is then presented. The thesis then describes the creation of a domain specific language specifically for controlling live performance and the development of a prototype reference implementation of a generic system, the Live Interactive Multimedia Performance Toolkit (LIMPT). The system is then evaluated from a number of standpoints including a set of criteria established earlier in the study. It is concluded that, while there are many resources currently used by artists working in digital performance (a comprehensive survey of current resources is presented), none offer the combination of functionality, usability and scalability offered by the prototype LIMPT system. The thesis concludes with a discussion of possible future work and the potential for increased creative activity in multimedia and live performance.

Considerando-se o contexto de gerenciamento energético em grades móveis, neste trabalho foram propostos dois algoritmos de escalonamento (Maximum Regret e Greedy) que, além de minimizar o consumo de energia, visam assegurar o cumprimento dos requisitos de qualidade de serviço das aplicações submetidas pelos usuários. Tais algoritmos foram projetados a partir de soluções heurísticas para o problema de escalonamento ciente de consumo de energia em grades móveis, que foi modelado como um problema de otimização envolvendo variáveis binárias. Por meio de experimentos, que consideraram tanto cenários estáticos quanto dinâmicos, foi demonstrada a viabilidade dos algoritmos de escalonamento propostos em relação à redução do consumo de energia. Em seu pior caso, o algoritmo Maximum Regret foi 12,18% pior que o referencial determinado pela melhor solução do solver Gurobi; já no pior caso do algoritmo Greedy, tal diferença foi de apenas 8,14% / Considering the context of energy management in mobile grids, this work proposes two scheduling algorithms (Maximum Regret and Greedy) that aim not only to reduce the energy consumption of the mobile devices, but also to ensure the QoS (Quality of Service) requirements of the running applications. These algorithms were designed based on heuristics for the energy aware scheduling problem in mobile grids, which was modeled as an optimization problem with integer variables. The performances of the proposed scheduling algorithms were evaluated by an extensive set of experiments, which demonstrated the feasibility of the adopted approach regarding energy consumption minimization. In its worst case, the Maximum Regret algorithm was 12.18% worse than the best solution provided by the Gurobi solver. While in the Greedys worst case the performance difference was just 8.14%

Strategic decision makers typically use a wide range of communication and information media in complex, uncertain and often ambiguous or politically charged organizational contexts. However, little help is available in ensuring that their information behaviour is efficient and effective. This study evaluates the use of information systems (IS) as communication media in strategic decision-making processes (SDMPs), focusing on strategic information processing, and how context affects its performance. The analysed strategic decisions (n = 113) were taken in the time period between 2000 and 2008 in large Western organisations. The aim of this investigation was to look at the link between the use of IS during the decision-making process and the performance of the SDMP, taking into account internal and external contextual factors. Using existing information processing theory and research on the SDMP as a theoretical basis, hypotheses were developed and environmental contingencies and political information behaviour were selected as moderating effects on the relationship of IS use and the performance of strategic decisions. A survey and complementary semi-structured interviews were conducted, which studied particular strategic decisions through quantitative and qualitative methods. Results provide support for a number of the study's hypotheses; however, several interesting findings regarding contextual factors, such as information anarchy and environmental munificence/hostility, do not support the hypotheses. Implications for theory and practice concerning information behaviour and its context are discussed.

Decomposition methods based on the hierarchical partitioning of the state space of queueing network models offer powerful evaluation tools for the performance analysis of computer systems and communication networks. These methods being conventionally implemented capture the exact solution of separable queueing network models but their credibility differs when applied to general queueing networks. This thesis provides a universal information theoretic framework for the implementation of hierarchical decomposition schemes, based on the principle of minimum relative entropy given fully decomposable subset and aggregate utilization, mean queue length and flow-balance constraints. This principle is used, in conjuction with asymptotic connections to infinite capacity queues, to derive new closed form approximations for the conditional and marginal state probabilities of general queueing network models. The minimum relative entropy solutions are implemented iteratively at each decomposition level involving the generalized exponential (GE) distributional model in approximating the general service and asymptotic flow processes in the network. It is shown that the minimum relative entropy joint state probability, subject to mean queue length and flow-balance constraints, is identical to the exact product-form solution obtained as if the network was separable. An investigation into the effect of different couplings of the resource units on the relative accuracy of the approximation is carried out, based on an extensive experimentation. The credibility of the method is demonstrated with some illustrative examples involving first-come-first-served general queueing networks with single and multiple servers and favourable comparisons against exact solutions and other approximations are made.

p. 1-77 / Submitted by Santiago Fabio (fabio.ssantiago@hotmail.com) on 2013-05-07T19:36:53Z No. of bitstreams: 1 6666666666666.pdf: 313932 bytes, checksum: 01602d9f39df99bc370c99662eeaebd4 (MD5) / Approved for entry into archive by Maria Creuza Silva(mariakreuza@yahoo.com.br) on 2013-05-13T13:45:11Z (GMT) No. of bitstreams: 1 6666666666666.pdf: 313932 bytes, checksum: 01602d9f39df99bc370c99662eeaebd4 (MD5) / Made available in DSpace on 2013-05-13T13:45:11Z (GMT). No. of bitstreams: 1 6666666666666.pdf: 313932 bytes, checksum: 01602d9f39df99bc370c99662eeaebd4 (MD5) Previous issue date: 2008 / A avaliação de desempenho dos sistemas de saúde das nações vem ganhando importância crescente entre os gestores do setor saúde e o setor acadêmico, sendo considerada como etapa essencial para o planejamento das ações voltadas para garantia da qualidade da atenção e subsidiar decisões que atendam às necessidades da população, constituindo-se uma tecnologia de gestão. Este artigo objetiva conhecer as características atuais do debate sobre a avaliação de desempenho dos sistemas de saúde, por meio da procedência da produção científica, dos atributos relacionados à estrutura, processo e resultado dos sistemas, bem como das opções metodológicas utilizadas para desenvolver estes estudos. A coleta de dados foi realizada por meio das bases de dados indexadas: Medline, Lilacs e Scielo, no período de 1990 a 2007, sendo excluídos da pesquisa os estudos que trataram da avaliação de ações e programas de saúde. Foram analisados os atributos relativos à eficiência, qualidade e efetividade, estando os mesmos relacionados à estrutura, processo e resultado, respectivamente. Os resultados apontam uma tendência de maior preocupação da academia dos países desenvolvidos com o desempenho dos sistemas de saúde, especialmente com os aspectos de qualidade. Entretanto, os sistemas de saúde do Brasil têm dedicado mais espaço à produção científica deste tema. Foram os estudos de avaliação de intervenção, aqueles desenvolvidos em maior número, cuja relação se dá com a estrutura dos sistemas de saúde. Uma maior freqüência de estudos empíricos certamente contribuirá com a institucionalização da avaliação de desempenho, especialmente nos países em desenvolvimento. / Salvador

Considerando-se o contexto de gerenciamento energético em grades móveis, neste trabalho foram propostos dois algoritmos de escalonamento (Maximum Regret e Greedy) que, além de minimizar o consumo de energia, visam assegurar o cumprimento dos requisitos de qualidade de serviço das aplicações submetidas pelos usuários. Tais algoritmos foram projetados a partir de soluções heurísticas para o problema de escalonamento ciente de consumo de energia em grades móveis, que foi modelado como um problema de otimização envolvendo variáveis binárias. Por meio de experimentos, que consideraram tanto cenários estáticos quanto dinâmicos, foi demonstrada a viabilidade dos algoritmos de escalonamento propostos em relação à redução do consumo de energia. Em seu pior caso, o algoritmo Maximum Regret foi 12,18% pior que o referencial determinado pela melhor solução do solver Gurobi; já no pior caso do algoritmo Greedy, tal diferença foi de apenas 8,14% / Considering the context of energy management in mobile grids, this work proposes two scheduling algorithms (Maximum Regret and Greedy) that aim not only to reduce the energy consumption of the mobile devices, but also to ensure the QoS (Quality of Service) requirements of the running applications. These algorithms were designed based on heuristics for the energy aware scheduling problem in mobile grids, which was modeled as an optimization problem with integer variables. The performances of the proposed scheduling algorithms were evaluated by an extensive set of experiments, which demonstrated the feasibility of the adopted approach regarding energy consumption minimization. In its worst case, the Maximum Regret algorithm was 12.18% worse than the best solution provided by the Gurobi solver. While in the Greedys worst case the performance difference was just 8.14%