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An adaptive optics system consists mainly of a wavefront sensor to detect optical aberrations, a control system to reconstruct the wavefront and compute a correction, and a deformable mirror to apply the correction. In this dissertation, the problem of optimal control of an adaptive optics system is investigated. A direct optimal control approach is used in the controller design. The direct optimal control methodology developed for discrete parameter systems is extended in this study to distributed parameter systems, where the Rayleigh-Ritz method is used for both spatial and temporal variables. The displacement field is written as the product of spatial functions (mode shapes for a vibrating structure, and Zernike modes for deformable mirror) and the generalized coordinates. These generalized coordinates and the control input functions (voltages) are written as simple series expansions in time in terms of selected functions and unknown coefficients. Substitution of these selected functions and their variations into Hamilton's law of varying action results in algebraic equations of motion (AEM) of the structure. These AEM are then considered as the algebraic state equations where the unknown expansion coefficients of the time series (assumed time-modes) for the generalized coordinates are recognized as the states and those of the input functions are recognized as the controls. Using the space-time assumed mode method, the usual variational optimal control problem is transformed into an equivalent algebraic problem. Optimal solutions are then obtained in a closed form and the solution is a global optimum within the time period considered. The solution procedure does not lead to any Ricatti equation or alike. The direct method proved to be simple, computationally efficient, attractive from implementation point of view, and it is general and allows a deterministic modelling of many physical problems. Applied to active vibration control of plates with piezoelectric transducers, the direct methodology exhibits results similar to those obtained through conventional methods. Active shape control of a deformable mirror using the direct approach results in high performance of the controller. The method allows direct control of Zernike modes, and highlights the relationship between the control inputs and Zernike modes through an algebraic controllability measurement index. Robustness of the controller is shown through simulation of smooth and severe random variations of the optical aberrations. In the same line of thought, a space-time finite element method is developed and applied to structural optimal control problems. Finite element method is used for both spatial and temporal discretizations. The unique feature of this method is its ability to analyse the structure-control interaction in the same mathematical framework, which allows simultaneous control and structural model design iterations. However, due to its high dimensionality, the space-time finite element method is computationally less efficient than its counterpart assumed mode. / Graduate

The rapid pace of climate and environmental changes requires some degree of adaptation, to forestall or avoid severe impacts. Adaptive capacity and water security are concepts used to guide the ways in which resource managers plan for and manage change. Yet the assessment of adaptive capacity and water security remains elusive, due to flaws in guiding concepts, paucity or inadequacy of data, and multiple difficulties in measuring the effectiveness of management prescriptions at scales relevant to decision-making. We draw on conceptual framings and empirical findings of the articles in this special issue and seek to respond to key questions with respect to metrics for the measurement, governance, information accessibility, and robustness of the knowledge produced in conjunction with ideas related to adaptive capacity and water security. Three overarching conclusions from this body of work are (a) systematic cross-comparisons of metrics, using the same models and indicators, are needed to validate the reliability of evaluation instruments for adaptive capacity and water security, (b) the robustness of metrics to applications across multiple scales of analysis can be enhanced by a “metrics plus” approach that combines well-designed quantitative metrics with in-depth qualitative methods that provide rich context and local knowledge, and (c) changes in the governance of science-policy can address deficits in public participation, foster knowledge exchange, and encourage the co-development of adaptive processes and approaches (e.g., risk-based framing) that move beyond development and use of static indicators and metrics.

This thesis focuses upon storytelling, examining processes of use and interaction as texts transform and migrate through medial boundaries. It aims to excavate new ways of considering the adapted text, and how theory may inform practice (and vice-versa) to produce an intermedial weave of both text and theoretical approach. The methodology is multidisciplinary, encompassing: adaptation studies, art, installation works, and convergent media, with analysis observing how these critical areas connect and intersect. The affordances each specific media provides is considered whilst also acknowledging that medial boundaries flex, being ‘indeterminate and flexible relative to surrounding environments’, or use.1 I examine points of connection between text, media and user, and ask ‘what that space, that necessary difference, enables’, in the manner of how we explore, view, and navigate ever-shifting adaptational frameworks.2 The text here is considered as being in motion, as it morphs into new forms and moves across textual borderlines. It is this aspect of cross-pollination, or textual blend occurring through media, that is the focus of the thesis.

In recent years, we have witnessed a massive acquisition of data and increasing need to support multi-criteria decision support (MCDS) queries efficiently. Pareto-optimal also known as skyline queries is a popular class of MCDS queries and has received a lot of attention resulting in a flurry of efficient skyline algorithms. The vast majority of such algorithms focus entirely on the input being a single data set. In this work, we provide an adaptive query evaluation technique --- AdaptiveSky that is able to reason at different levels of abstraction thereby effectively minimizing the two primary costs, namely the cost of generating join results and the cost of dominance comparisons to compute the final skyline of the join results. Our approach hinges on two key principles. First, in the input space -- we determine the abstraction levels dynamically at run time instead of assigning a static one at compile time that may or may not work for different data distributions. This is achieved by adaptively partitioning the input data as intermediate results are being generated thereby eliminating the need to access vast majority of the input tuples. Second, we incrementally build the output space, containing the final skyline, without generating a single join result. Our approach is able to reason about the final result space and selectively drill into regions in the output space that show promise in generating result tuples to avoid generation of results that do not contribute to the query result. In this effort, we propose two alternate strategies for reasoning, namely the Euclidean Distance method and the cost-benefit driven Dominance Potential method for reasoning. Our experimental evaluation demonstrates that AdaptiveSky shows superior performance over state-of-the-art techniques over benchmark data sets.

This thesis introduces different mechanisms for energy efficiency in Wireless Sensor Networks (WSNs) along with maintaining high levels of Network Performance (N.P) with reduced complexity. Firstly, a Machine-to-Machine (M2M) WSN is arranged hierarchically in a fixed infrastructure to support a routing protocol for energy-efficient data transmission among terminal nodes and sink nodes via cluster heads (CHs). A Multi-Level Clustering Multiple Sinks (MLCMS) routing protocol with the IPv6 protocol over Low Wireless Personal Area Networks (6LoWPAN) is proposed to prolong network lifetime. The simulation results show 93% and 147% enhancement in energy efficiency and system lifespan compared to M-LEACH and LEACH, respectively. By utilising 6LoWPAN in the proposed system, the number of packets delivered increases by 7%, with higher accessibility to the M2M nodes and a substantial extension of the network is enabled. Secondly, an adaptive sleep mode with MLCMS for an efficient lifetime of M2M WSN is introduced. The time period of the active and asleep modes for the CHs has been considered according to a mathematical function. The evaluations of the proposed scheme show that the lifetime of the system is doubled and the end-to-end delay is reduced by half. Thirdly, enhanced N.P is achieved through linear integer-based optimisation. A Self-Organising Cluster Head to Sink Algorithm (SOCHSA) is proposed, hosting Discrete Particle Swarm Optimisation (DPSO) and Genetic Algorithm (GA) as Evolutionary Algorithms (EAs) to solve the N.P optimisation problem. N.P is measured based on load fairness and average ratio residual network energy. DPSO and GA are compared with the Exhaustive Search (ES) algorithm to analyse their performances for each benchmark problem. Computational results prove that DPSO outperforms GA regarding complexity and convergence, thus it is best suited for a proactive IoT network. Both algorithms achieved optimum N.P evaluation values of 0.306287 and 0.307731 in the benchmark problems P1 and P2, respectively, for two and three sinks. The proposed mechanism satisfies different N.P requirements of M2M traffic by instant identification and dynamic rerouting to achieve optimum performance. Finally, a Power Model (PM) is essential to investigate the power efficiency of a system. Hence, a Power Consumption (PC) profile for SDN-WISE, based on IoT is developed. The outcomes of the study offer flexibility in managing the structure of an M2M system in IoT. They enable controlling the provided Network Quality of Service (NQoS), precisely by achieving physical layer throughput. In addition, it provides a schematic framework for the Application Quality of Service (AQoS), specifically, the IoT data stream payload size (from the PC point of view). It is composed of two essential parts, i.e., control signalling and data traffic PCs and the results show a 98% PC of the data plane in the total system power, whereas the control plane PC is only 2%, with a minimum Transmission Time Interval (TTI) (5 sec) and a maximum payload size of 92 Bytes.

Cognitive Science is at a crossroad. Since its inception, the prevailing paradigm in Cognitive Science (and associated fields such as Artificial Intelligence, Cognitive Psychology, and Linguistics), has been a formal, computer-based model of cognition - often termed the Symbol Processing System model (SPS) or cognitivism. This view, while still accepted by the majority of researchers, has been dogged by persistent and cutting criticism by various authors over many years. As well, the initial over-inflated promises made by the early practitioners within these fields have not come to fruition, and the initial enthusiasm has in many cases been reduced to frustration.Many researchers have looked to the field of connectionism as a solution, and this discipline has found a new lease of life after a serious setback in the early 1970s. The major emphasis within this area has been on feed-forward neural networks (FFNN), but this paradigm also has its detractors.In this thesis we critically evaluate both of these research programs, especially that of SPS. We propose a new model of human and animal cognition, termed Adaptive Behavioral Cognition (ABC), which integrates many current views on cognition, and provides a single-architecture, biologically-feasible theory that overcomes many of the problems associated with current models. As well as being an accurate description of the processes relevant to the new model, the term ABC is a none-too-subtle reference to the fact that we need to closely re-examine the aims and achievements of Cognitive Science and return to basic empirical findings in developing a theory of cognition.The new model synthesises, unifies and links together many previously disjoint ideas and observations, from the neural level through to neurological structures and to observed behaviour. The claims that we make of the model are that it is biologically and ++ / neurologically consistent and reasonable, and that it has properties more closely associated with the actual brain than either the computational (cognitivist) approach, or the simplistic FFNN. Further, the model is internally consistent and self-similar, and is consistent with the observed neuroanatomical structures of the cortex. It also provides for massive parallelism, yet retains a serial component through its use of temporal sequences.The ABC proposal outlined in this thesis takes the view that the processes of the brain are to learn associated and temporally connected sequences, rather than 'facts' or 'representations', and that the learned behaviours resulting from the associated temporal sequences are the means of cognition, rather than computational operations on representations.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.)--McMaster University, 2004. / Supervisor: Dr. Tim Nye. Includes bibliographical references (p. 150-158). Also available via World Wide Web.

Ultra wide band (UWB) is an emerging technology that recently has gained regulatory approval. It is a suitable solution for high speed indoor wireless communications due to its promising ability to provide high data rate at low cost and low power consumption. Another benefit of UWB is its ability to resolve individual multi-path components. This feature motivates the use of RAKE multi-path combining techniques to provide diversity and to capture as much energy as possible from the received signal. Potential future and rule limitation of UWB, lead to two important characteristics of the technology: high bit rate and low emitting power. Based on the power emission limit of UWB, the only choice for implementation is the low level modulation technology. To obtain such a high bit rate using low level modulation techniques, significant inter-symbol interference (ISI) is unavoidable. </p>Three N (N means the numbers of fingers) fingers RAKE receiver structures are proposed: the N-selective maximal ratio combiner (MRC), the N-selective MRC receiver with least-mean-square (LMS) adaptive equalizer and the N-selective MRC receiver with LMS adaptive combiner. These three receiver structures were all simulated for N=8, 16 and 32. Simulation results indicate that ISI is effectively suppressed. The 16-selective MRC RAKE receiver with LMS adaptive combiner demonstrates a good balance between performance, computation complexity and required length of the training sequence. Due to the simplicity of the algorithm and a reasonable sampling rate, this structure is feasible for practical VLSI implementations.