[pt] A estimação de direção de chegada (DoA) é uma importante área de processamento de arranjos de sensores que é encontrada em uma ampla gama de aplicações de engenharia. Este fato, juntamente com o desenvolvimento da área de Compressed Sensing (CS) nos últimos anos, são a principal motivação desta dissertação. Nesta dissertação, é apresentada uma formulação do problema de estimação de direção de chegada como um problema de representação esparsa da sinal e vários algoritmos de recuperação esparsa são derivados e investigados para resolver o problema atual. Os algoritmos propostos são baseados na incorporação da informação prévia sobre o sinal esparso no processo de estimativa. Na primeira parte, nos concentramos no desenvolvimento de dois algoritmos Bayesianos , que se baseiam principalmente no algoritmo iterative hard thresholding (IHT). Devido ao desempenho inferior dos algoritmos convencionais de estimação de chegada em cenários com fontes correlacionadas, nós prestamos atenção especial ao desempenho dos algoritmos propostos nesta condição. Na segunda parte, o problema de otimização baseados na minimização da norma l1 é apresentado e um algoritmo bayesiano é proposto para resolver o problema chamado basis pursuit denoising (BPDN). Os resultados da simulação mostram que os estimadores Bayesianos superam os estimadores não Bayesianos e que a incorporação do conhecimento prévio da distribuição do sinal melhorou substancialmente o desempenho dos algoritmos. / [en] Direction of arrival (DoA) estimation is a key area of sensor array processing which is encountered in a broad range of important engineering applications. This fact together with the development of the Compressed Sensing (CS) area in the last years are the principal motivation of this thesis. In this dissertation, a formulation of the source localization problem as a sparse signal representation problem is presented and several sparse recovery algorithms are derived and investigated for solving the current problem. The proposed algorithms are based on the incorporation of the prior information about the sparse signal in the estimation process. In the first part, we focus on the development of two Bayesian greedy algorithms which are principally based on the iterative hard thresholding (IHT) algorithm. Due to the inferior performance of the conventional DoA estimation algorithm in scenarios with correlated sources, we pay special attention to the performance of the proposed algorithms under this condition. In the second part, the optimization problem using a l1 penalty is introduced and a Bayesian algorithm for solving the basis pursuit denoising problem is presented. Simulation results shows that Bayesian estimators which take into account the prior knowledge of the signal distribution outperform and improve substantially the performance of the non-Bayesian estimators.

[Context] Interdisciplinary healthcare teams (IHTs) include practitioners from different disciplines who collaborate for providing care to patients. IHTs often follow clinical workflows composed of tasks that must be executed by practitioners with specific capabilities. The membership in an IHT can however evolve over time for a given patient. [Problem] Existing Business Process Management (BPM) suites and their workflow execution engines are designed for supporting and monitoring general workflows, but they are insufficient in supporting the allocation of tasks to the most suitable practitioners during the execution of healthcare workflows in a dynamic context. [Methodology] Using Design Science Research, this thesis builds on top of an existing semantic layer, which includes an ontology defining IHT team concepts and relationships that are used to reason automatically about team dynamics, in order to add dynamic team management to BPM suites. It does so by proposing and designing middleware (including a generic interface) that enables the semantic layer to command the BPM suite to allocate suitable practitioners to tasks during the execution of clinical workflows. The design and implementation of this middleware are discussed, and the latter is tested on a commercial BPM suite for two realistic clinical processes. [Results] The proof-of-concept implementation demonstrates the feasibility of using middleware with a generic interface to add support for IHT executing BPM suite when managing a patient. In addition, the thesis also demonstrates that the ontology used in the semantic layer is minimal, that is, all of its concepts and relationships are necessary for the required team functionalities (usually absent from BPM tools) to work properly.

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Fire resistance is becoming increasingly important along with the development of new concrete types with high strength and dense structure with reduced porosity. Such concrete types are susceptible to fire spalling and extensive crack formation. At the moment, there are a limited number of methods for enhancement of fire resistance of existing structures, which could be applied in underground structures with restricted space and limited air exchange, such as tunnels, underground garages or nuclear powerplants. This work is focused on the development of two methods, and both are dealing with porous structure modification. The first method is intentional heat treatment (IHT) method, suitable for the enhancement of fire resistance of existing structures. The second method emphasized the design of air-entrained concrete (AeA-FiResCrete) with the use of “new generation” air-entraining agents suitable for enhancement of fire resistance of newly designed concrete. Testing of compressive strength, porous structure modification was completed by the analysis of “moisture clog,” which contributes to explosive spalling and extensive cracking. The efficiency of developing methods was verified during large-scale testing according to modified ISO834 (m-ISO) curve. No extensive crack formation or explosive spalling was observed during the exposure period during the large-scale testing of slabs with the applied IHT method. The total thickness of the IHT method with configuration IHT200/2, composed of IHT zone and IHT transition zone, penetrated to the depth of 25,5 to 43,0 mm depending upon various concrete types. Moisture clog in AeA-FiResCrete was more significant than in the case of slabs with applied IHT method, and it could be concluded that the IHT method enhances fire resistance of concrete exposed to elevated temperatures without influencing its compressive strength and durability. Results from AeA-FiResCrete testing showed only a slight improvement of its fire resistance.