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Conserving biodiversity in human-dominated landscapes requires protecting networks of ecological reserves and managing the intervening matrix to maintain the potential for species to move among them. This dissertation provides original insights towards (1) identifying areas for protection in reserves that are critical to maintain biodiversity and (2) assessing the potential for species' movements among habitat patches in a reserve network. I develop and test methods that will facilitate conservation planning to promote viable, resilient populations through time. The first part of this dissertation tests and develops reserve selection strategies that protect either a single focal species in a dynamic landscape or multiple interacting species in a static landscape. Using a simulation model of boreal forest dynamics, I test the effectiveness of static and dynamic reserves to maintain spatial habitat requirements of a focal species, American Marten (Martes americana). Dynamic reserves improved upon static reserves but re-locating reserves was constrained by fragmentation of the matrix. Management of the spatial and temporal distribution of land-uses in the matrix will therefore be essential to retain options for re-locating reserves in the future. Additionally, to include essential consumer-resource interactions into reserve selection, a new algorithm is presented for American marten and its two primary prey species. The inclusion of their interaction had the benefit t of producing spatially aggregated reserves based on functional species requirements. The second part of this dissertation evaluates and synthesizes the network-theoretic approach to quantify connectivity among habitat patches or reserves embedded within spatially heterogeneous landscapes. I conduct a sensitivity analysis of network-theoretic connectivity analyses that derive least-cost movement behavior from the underlying cost surface which describes the relative ecological costs of dispersing through different landcover types. Landscape structure is shown to aff ect how sensitive least-cost graph connectivity assessments are to the quality (relative cost values) of landcover types. I develop a conceptual framework to classify network connectivity statistics based on the component of habitat connectivity that they quantify and the level within the network to which they can be applied. Together, the combination of reserve design and network connectivity analyses provide complementary insights to inform spatial planning decisions for conservation.

Modern and future many-core systems represent large and complex architectures. The communication fabrics in these large systems play an important role in their performance and power consumption. Current simulation methodologies for evaluating networks-on-chip (NoCs) are not keeping pace with the increased complexity of our systems; architects often want to explore many different design knobs quickly. Methodologies that trade-off some accuracy but maintain important workload trends for faster simulation times are highly beneficial at early stages of architectural exploration. We propose a synthetic traffic generation methodology that captures both application behaviour and cache coherence traffic to rapidly evaluate NoCs. This allows designers to quickly indulge in detailed performance simulations without the cost of long-running full system simulation but still capture a full range of application and coherence behaviour. Our methodology has an average (geometric) error of 10.9% relative to full system simulation, and provides 50x speedup on average over full system simulation.

The coverage and energy issues are the fundamental problems which prevent the development of wireless sensor networks. In order to accurately evaluate the monitoring quality (coverage), one needs to model the interactive of sensors, phenomenons and the environment. Furthermore, in collaborative with scheduling algorithm and computer optimization, protocols can improve the overall monitoring quality and prolong the lifetime of network. This thesis is an investigation of coverage problem and its relative applications in the wireless sensor networks. We first discuss the realistic of current boolean sensing model and propose an irregular sensing model used to determine the coverage in the area with obstacles. We then investigate a joint problem of maintaining the monitoring quality and extending the lifetime of network by using scheduling schemes. Since the scheduling problem is NP hard, genetic algorithm and Markov decision process are used to determine an achievable optimal result for the joint problem of coverage-preserving and lifetime-prolong. In order to avoid the cost of centralized or distributed scheduling algorithms, a localized coverage-preserving scheduling algorithm is proposed by exploring the construction process of Voronoi diagram. Besides exploring the coverage characteristic in a static wireless sensor network, we investigate the coverage problem when the mobile elements are introduced into network. We consider the single-hop mobile data gathering problem with the energy efficiency and data freshness concerns in a wireless sensor network where the connectivity cannot be maintained. We first investigate the upper/lower bound of the covering time for a single collector to cover the monitoring area. Through our investigation we show that for a bounded rectangle area a hexagon walk could explore the area more efficiently than a random walk when the edges of area are known. We then propose a virtual force mobile model (VFM) in which the energy consumption for data transmission is modeled as a virtual elastic force and used to guide of mobile collectors to move to optimal positions for energy saving.

Graph pattern matching is fundamental to social network analysis. Its effectiveness for identifying social communities and social positions, making recommendations and so on has been repeatedly demonstrated. However, the social network analysis raises new challenges to graph pattern matching. As real-life social graphs are typically large, it is often prohibitively expensive to conduct graph pattern matching over such large graphs, e.g., NP-complete for subgraph isomorphism, cubic time for bounded simulation, and quadratic time for simulation. These hinder the applicability of graph pattern matching on social network analysis. In response to these challenges, the thesis presents a series of effective techniques for querying large, dynamic, and distributively stored social networks. First of all, we propose a notion of query preserving graph compression, to compress large social graphs relative to a class Q of queries. We then develop both batch and incremental compression strategies for two commonly used pattern queries. Via both theoretical analysis and experimental studies, we show that (1) using compressed graphs Gr benefits graph pattern matching dramatically; and (2) the computation of Gr as well as its maintenance can be processed efficiently. Secondly, we investigate the distributed graph pattern matching problem, and explore parallel computation for graph pattern matching. We show that our techniques possess following performance guarantees: (1) each site is visited only once; (2) the total network traffic is independent of the size of G; and (3) the response time is decided by the size of largest fragment of G rather than the size of entire G. Furthermore, we show how these distributed algorithms can be implemented in the MapReduce framework. Thirdly, we study the problem of answering graph pattern matching using views since view based techniques have proven an effective technique for speeding up query evaluation. We propose a notion of pattern containment to characterise graph pattern matching using views, and introduce efficient algorithms to answer graph pattern matching using views. Moreover, we identify three problems related to graph pattern containment, and provide efficient algorithms for containment checking (approximation when the problem is intractable). Fourthly, we revise graph pattern matching by supporting a designated output node, which we treat as “query focus”. We then introduce algorithms for computing the top-k relevant matches w.r.t. the output node for both acyclic and cyclic pattern graphs, respectively, with early termination property. Furthermore, we investigate the diversified top-k matching problem, and develop an approximation algorithm with performance guarantee and a heuristic algorithm with early termination property. Finally, we introduce an expert search system, called ExpFinder, for large and dynamic social networks. ExpFinder identifies top-k experts in social networks by graph pattern matching, and copes with the sheer size of real-life social networks by integrating incremental graph pattern matching, query preserving compression and top-k matching computation. In particular, we also introduce bounded (resp. unbounded) incremental algorithms to maintain the weighted landmark vectors which are used for incremental maintenance for cached results.

Effective movement is central to survival and it is essential for all animals to react in response to changes around them. In many animals the rhythmic signals that drive locomotion are generated intrinsically by small networks of neurons in the nervous system which can be switched on and off. In this thesis I use a very simple animal, in which the behaviours and neuronal networks have been well characterised experimentally, to explore the salient features of such networks. Two days after hatching, tadpoles of the frog Xenopus laevis respond to a brief touch to the head by starting to swim. The swimming rhythm is driven by a small population of electrically coupled brainstem neurons (called dINs) on each side of the tadpole. These neurons also receive synaptic input following head skin stimulation. I build biophysical computational models of these neurons based on experimental data in order to address questions about the effects of electrical coupling, synaptic feedback excitation and initiation pathways. My aim is better understanding of how swimming activity is initiated and sustained in the tadpole. I find that the electrical coupling between the dINs causes their firing properties to be modulated. This allows two experimental observations to be reconciled: that a dIN only fires a single action potential in response to step current injections but the population fires like pacemakers during swimming. I build on this hypothesis and show that long-lasting, excitatory feedback within the population of dINs allows rhythmic pacemaker activity to be sustained in one side of the nervous system. This activity can be switched on and off at short latency in response to biologically realistic synaptic input. I further investigate models of synaptic input from a defined swim initiation pathway and show that electrical coupling causes a population of dINs to be recruited to fire either as a group or not at all. This allows the animal to convert continuously varying sensory stimuli into a discrete decision. Finally I find that it is difficult to reliably start swimming-like activity in the tadpole model using simple, short-latency, symmetrical initiation pathways but that by using more complex, asymmetrical, neuronal-pathways to each side of the body, consistent with experimental observations, the initiation of swimming is more robust. Throughout this work, I make testable predictions about the population of brainstem neurons and also describe where more experimental data is needed. In order to manage the parameters and simulations, I present prototype libraries to build and manage these biophysical model networks.

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The objective of the project was to develop models and methods suitable for computer implementation. In particular, this work has been devoted to generation scheduling of a power system with a sizeable amount. of hydro energy. Optimal operation planning of hydrothermal power systems aims at minimizing incurred production costs while supplying customer de­mand. The planning horizon may vary from one day to several years and the associated planning problems are categorized as short term, seasonal and long term operation planning. The topic of this thesis is short term operation planning. In this planning, it is necessary to use detailed models of the different parts of the power system. These include models of cas­caded reservoirs in a multi-river system as we11 as a representa­tion of the nonlinear generating characteristics of the hydro plants. he thermal generating units are modelled using linear production cost curves and by recognizing various technical constraints asso­ciated with the operation of these plants. Effects on the optimal operating strategy caused by interregional transmission capacity limitations are also accounted for by the model. These constraints are modelled to main1y affect the hydro plant operation. / <p>QC 20161207</p>