Cultivating the City: Infrastructures of abundance in urban Brazil

Urban agriculture, if it is to become integrated into the city, needs landscape architectural thinking in order to be woven into the larger urban fabric. Thinking at the scale of ecosystems running through a city creates a framework for spatial change; thinking in assemblages of stakeholders and actors creates a framework for social investment and development. These overlapping frameworks are informed and perhaps even defined by the emergent field of landscape democracy. Cultivating the City is a prospective design project seeking to embody landscape democratic principles. The intention is to reclaim the meaning of landscape as the relationship between people and place, both shaping each other. The design in question is a proposed network of urban agriculture typologies in Porto Alegre, Brazil. These hypothetical designs, emphasizing agroforestry with native species, serve as a basis for dialogue between potential stakeholders and as catalysts for future projects. This landscape architecture project sets out to be a mediator in processes of spatial evolution in order to envision just and sustainable urban landscapes.

Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.