In this thesis, we consider a vector Gaussian multiple access channel (MAC) with users demanding reliable communication at specific (Shannon-theoretic) rates. The objective is to assign vectors and powers to these users such that their rate requirements are met and the sum of powers received is minimum. We identify this power minimization problem as an instance of a separable convex optimization problem with linear ascending constraints. Under an ordering condition on the slopes of the functions at the origin, an algorithm that determines the optimum point in a finite number of steps is described. This provides a complete characterization of the minimum sum power for the vector Gaussian multiple access channel. Furthermore, we prove a strong duality between the above sum power minimization problem and the problem of sum rate maximization under power constraints. We then propose finite step algorithms to explicitly identify an assignment of vectors and powers that solve the above power minimization and sum rate maximization problems. The distinguishing feature of the proposed algorithms is the size of the output vector sets. In particular, we prove an upper bound on the size of the vector sets that is independent of the number of users. Finally, we restrict vectors to an orthonormal set. The goal is to identify an assignment of vectors (from an orthonormal set) to users such that the user rate requirements is met with minimum sum power. This is a combinatorial optimization problem. We study the complexity of the decision version of this problem. Our results indicate that when the dimensionality of the vector set is part of the input, the decision version is NP-complete.

Data Distribution Management (DDM) deals with two basic problems: how to distribute data generated at the application layer among underlying nodes in a distributed system and how to retrieve data back whenever it is necessary. This thesis explores DDM in two different network environments: peer-to-peer (P2P) overlay networks and cluster-based network environments. DDM in P2P overlay networks is considered a more complete concept of building and maintaining a P2P overlay architecture than a simple data fetching scheme, and is closely related to the more commonly known associative searching or queries. DDM in the cluster-based network environment is one of the important services provided by the simulation middle-ware to support real-time distributed interactive simulations. The only common feature shared by DDM in both environments is that they are all built to provide data indexing service. Because of these fundamental differences, we have designed and developed a novel distributed data structure, Hierarchically Distributed Tree (HD Tree), to support range queries in P2P overlay networks. All the relevant problems of a distributed data structure, including the scalability, self-organizing, fault-tolerance, and load balancing have been studied. Both theoretical analysis and experimental results show that the HD Tree is able to give a complete view of system states when processing multi-dimensional range queries at different levels of selectivity and in various error-prone routing environments. On the other hand, a novel DDM scheme, Adaptive Grid-based DDM scheme, is proposed to improve the DDM performance in the cluster-based network environment. This new DDM scheme evaluates the input size of a simulation based on probability models. The optimum DDM performance is best approached by adapting the simulation running in a mode that is most appropriate to the size of the simulation.

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed.

This study investigated the implementation of computer-assisted education in the outcomes-based education system at Sterling Primary School in East London. A literature study was done on constructivism on which outcomes based education is based. The literature review also focused on computer-integrated education. The integration of computer-assisted education at Sterling Primary School was appraised with the help of the Evolutionary Model developed by Miller (1997). It was observed that the educators and learners at this school were trying to integrate computers into their teaching and learning process. Although this study focussed on the Intermediate Phase, it could provide a basis for further study in the field of computer-assisted education in Senior, and Further Education and Training Phases throughout the Republic of South Africa. / Dissertation (MEd (Computer-Assisted Education))--University of Pretoria, 2006. / Curriculum Studies / unrestricted

Contemporary product and production development is typically carried out with the support of computer tools where the design of products and workstations are originated and evaluated within virtual environments. Ergonomics addresses factors important to consider in the product and production development process to ensure a good fit between humans and the items being designed. Digital human modelling (DHM) tools enable simulations and analyses of ergonomics in virtual environments. Anthropometry is central when using DHM tools for product and production development to ensure that the design fits the intended proportion of the targeted population from a physical perspective. Several methods have been prescribed to consider the anthropometric diversity that exists within human populations. Still many DHM based simulations in product and production development processes are done with approaches that are poor in representing anthropometric diversity. Hence, there is a need for better tools and methods that would support DHM tool users to more effectively and efficiently consider anthropometric diversity in the design process. In this thesis current methods for anthropometric diversity considerations have been reviewed and new methods and functionality have been developed and implemented in a DHM tool. Mathematical models have been developed to consider three specific parts important to the consideration of anthropometric diversity: generation of suitable test cases, prediction of missing anthropometric data and implementation of more diverse anthropometric variables such as strength and flexibility. Results show that the proposed methods are accurate and advantageous compared to approaches often used in industry today. The mathematical models for generation of suitable test cases and prediction of missing anthropometric data have been implemented in an anthropometric software module. The module has undergone usability testing with industry DHM tools users. The developed anthropometric module is shown to answer to relevant needs of DHM tool users and fit into the work processes related to DHM simulations and ergonomics analyses utilised in industry today.

Data Distribution Management (DDM) deals with two basic problems: how to distribute data generated at the application layer among underlying nodes in a distributed system and how to retrieve data back whenever it is necessary. This thesis explores DDM in two different network environments: peer-to-peer (P2P) overlay networks and cluster-based network environments. DDM in P2P overlay networks is considered a more complete concept of building and maintaining a P2P overlay architecture than a simple data fetching scheme, and is closely related to the more commonly known associative searching or queries. DDM in the cluster-based network environment is one of the important services provided by the simulation middle-ware to support real-time distributed interactive simulations. The only common feature shared by DDM in both environments is that they are all built to provide data indexing service. Because of these fundamental differences, we have designed and developed a novel distributed data structure, Hierarchically Distributed Tree (HD Tree), to support range queries in P2P overlay networks. All the relevant problems of a distributed data structure, including the scalability, self-organizing, fault-tolerance, and load balancing have been studied. Both theoretical analysis and experimental results show that the HD Tree is able to give a complete view of system states when processing multi-dimensional range queries at different levels of selectivity and in various error-prone routing environments. On the other hand, a novel DDM scheme, Adaptive Grid-based DDM scheme, is proposed to improve the DDM performance in the cluster-based network environment. This new DDM scheme evaluates the input size of a simulation based on probability models. The optimum DDM performance is best approached by adapting the simulation running in a mode that is most appropriate to the size of the simulation.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22157号 / 工博第4661号 / 新制||工||1727(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 三村 衛, 教授 大津 宏康, 准教授 肥後 陽介 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

This master's thesis deals with the analysis, design and implementation of authentication and authorization subsystem into the environment of distributed web application. It unifies the well-known security models into the one universal security model that can be used for the development of authorization device enabling the user to secure the applications with various security models. Furthermore, it applies this integration of models into the Takeplace system.

The scientific consensus has for long been that residential segregation is best conceived of as a multidimensional phenomenon that can exist on several geographical scales (Massey & Denton, 1988; Lee et al., 2008; Reardon & O’Sullivan, 2004; Reardon et al., 2008). Despite this deepened understanding of residential segregation and how to best measure it, theoretical models of segregation processes have tended to disregard the diversity of dimensions and scales of segregation. Moreover, while residential segregation is broadly defined as the spatial separation of people of different social groups (Timberlake & Ignatov, 2014), the frequently used Schelling model is aspatial (Schelling, 1971). In contrast, the lesser-known Sakoda model incorporates a distance-decay effect and is thus explicitly spatial (Sakoda, 1971). The aim of this thesis was to evaluate two theoretical agent-based models of segregation processes—the Schelling- and Sakoda model—by measuring the segregation patterns they generate under different parameter settings across four dimensions and six spatial scales of segregation, ranging from the micro- to the macro-scale. Thus, providing an assessment of the capacity of these models to generate (grow) different forms of residential segregation. Results from simulation experiments showed that the popular Schelling model was limited in its capacity to generate different forms of segregation. In its standard configuration it could generate micro-segregation along two out of four dimensions: Evenness and Exposure. The spatially explicit Sakoda model was able to generate segregation patterns which varied substantially across all scales on the Evenness and Exposure dimensions. In addition, it was able to generate varied patterns of Concentration and Centralization under certain parameter settings. These findings contribute new insights to the possibilities afforded by these two models in modeling processes of residential segregation. If the goal for theoretical models is to generate segregation patterns which vary across all dimensions and scales of residential segregation, then the standard configuration of the Schelling model is not enough. This thesis suggest that the Sakoda model is a promising candidate for this purpose. In addition, this thesis shows the importance of using a comprehensive measurement framework in theoretical modeling of segregation processes.

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