950 resultados para Logistic maps


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Detection of Region of Interest (ROI) in a video leads to more efficient utilization of bandwidth. This is because any ROIs in a given frame can be encoded in higher quality than the rest of that frame, with little or no degradation of quality from the perception of the viewers. Consequently, it is not necessary to uniformly encode the whole video in high quality. One approach to determine ROIs is to use saliency detectors to locate salient regions. This paper proposes a methodology for obtaining ground truth saliency maps to measure the effectiveness of ROI detection by considering the role of user experience during the labelling process of such maps. User perceptions can be captured and incorporated into the definition of salience in a particular video, taking advantage of human visual recall within a given context. Experiments with two state-of-the-art saliency detectors validate the effectiveness of this approach to validating visual saliency in video. This paper will provide the relevant datasets associated with the experiments.

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Agents make up an important part of game worlds, ranging from the characters and monsters that live in the world to the armies that the player controls. Despite their importance, agents in current games rarely display an awareness of their environment or react appropriately, which severely detracts from the believability of the game. Some games have included agents with a basic awareness of other agents, but they are still unaware of important game events or environmental conditions. This paper presents an agent design we have developed, which combines cellular automata for environmental modeling with influence maps for agent decision-making. The agents were implemented into a 3D game environment we have developed, the EmerGEnT system, and tuned through three experiments. The result is simple, flexible game agents that are able to respond to natural phenomena (e.g. rain or fire), while pursuing a goal.

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Conventional planning and decision making, with its sectoral and territorial emphasis and flat-map based processes are no longer adequate or appropriate for the increased complexity confronting airport/city interfaces. These crowed and often contested governance spaces demand a more iterative and relational planning and decision-making approach. Emergent GIS based planning and decision-making tools provide a mechanism which integrate and visually display an array of complex data, frameworks and scenarios/expectations, often in ‘real time’ computations. In so doing, these mechanisms provide a common ground for decision making and facilitate a more ‘joined-up’ approach to airport/city planning. This paper analyses the contribution of the Airport Metropolis Planning Support System (PSS) to sub-regional planning in the Brisbane Airport case environment.

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Player experience of spatiality in first-person, single-player games is informed by the maps and navigational aids provided by the game. This project uses textual analysis to examine the way these maps and navigational aids inform the experience of spatiality in Fallout 3, BioShock and BioShock 2. Spatiality is understood as trialectic, incorporating perceived, conceived and lived space, drawing on the work of Henri Lefebvre and Edward Soja. The most prominent elements of the games’ maps and navigational aids are analysed in terms of how they inform players’ experience of the games’ spaces. In particular this project examines the in-game maps these games incorporate, the waypoint navigation and fast-travel systems in Fallout 3, and the guide arrow and environmental cues in the BioShock games.

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This paper presents a general, global approach to the problem of robot exploration, utilizing a topological data structure to guide an underlying Simultaneous Localization and Mapping (SLAM) process. A Gap Navigation Tree (GNT) is used to motivate global target selection and occluded regions of the environment (called “gaps”) are tracked probabilistically. The process of map construction and the motion of the vehicle alters both the shape and location of these regions. The use of online mapping is shown to reduce the difficulties in implementing the GNT.

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It would be a rare thing to visit an early years setting or classroom in Australia that does not display examples of young children’s artworks. This practice serves to give schools a particular ‘look’, but is no guarantee of quality art education. The Australian National Review of Visual Arts Education (NRVE) (2009) has called for changes to visual art education in schools. The planned new National Curriculum includes the arts (music, dance, drama, media and visual arts) as one of the five learning areas. Research shows that it is the classroom teacher that makes the difference, and teacher education has a large part to play in reforms to art education. This paper provides an account of one foundation unit of study (Unit 1) for first year university students enrolled in a 4-year Bachelor degree program who are preparing to teach in the early years (0–8 years). To prepare pre-service teachers to meet the needs of children in the 21st century, Unit 1 blends old and new ways of seeing art, child and pedagogy. Claims for the effectiveness of this model are supported with evidence-based research, conducted over the six years of iterations and ongoing development of Unit 1.

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Traversability maps are a global spatial representation of the relative difficulty in driving through a local region. These maps support simple optimisation of robot paths and have been very popular in path planning techniques. Despite the popularity of these maps, the methods for generating global traversability maps have been limited to using a-priori information. This paper explores the construction of large scale traversability maps for a vehicle performing a repeated activity in a bounded working environment, such as a repeated delivery task.We evaluate the use of vehicle power consumption, longitudinal slip, lateral slip and vehicle orientation to classify the traversability and incorporate this into a map generated from sparse information.

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The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])...

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Drosophila serrata is a member of the montium group, which contains more than 98 species and until recently was considered a subgroup within the melanogaster group. This Drosophila species is an emerging model system for evolutionary quantitative genetics and has been used in studies of species borders, clinal variation and sexual selection. Despite the importance of D. serrata as a model for evolutionary research, our poor understanding of its genome remains a significant limitation. Here, we provide a first-generation gene-based linkage map and a physical map for this species. Consistent with previous studies of other drosophilids we observed strong conservation of genes within chromosome arms homologous with D. melanogaster but major differences in within-arm synteny. These resources will be a useful complement to ongoing genome sequencing efforts and QTL mapping studies in this species