992 resultados para Optimum-Path Forest classifier


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The majority of tertiary practice-led creative arts disciplines became part of the Australian university system as a result of the creation of the Unified National System of tertiary education in 1988. Over the past two decades, research has grown as the yardstick by which academic performance in the Australian university sector is recognised and rewarded. Academics in artistic disciplines, who struggled to adapt to a culture and workload expectations different from their previous, predominantly teaching based, employment, continue to see their research under-valued within the established evaluation framework. Despite a late 1990s Australian government funded inquiry, many of the inequities remain. While the Excellence in Research in Australia (ERA) exercise has acknowledged the non-text outputs of artist-academics in its evaluation of 'research outcomes', much of the process remains resolutely framed by measures that work against creative arts researchers.

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To maximize energetic savings, female bats often roost communally whilst pregnant or with non-volant dependents, whereas male bats more often roost alone; however, differences in selection of roosts by sex have not often been investigated. Better understanding of female colony locations could focus management to protect the majority of bats. New Zealand's long-tailed bat (Chalinolobus tuberculatus) roost in exotic plantation forest, where sex-specific roost selection has not been investigated, and therefore such management is not possible. We investigated sex-specific roost selection by long-tailed bats for the first time. Roosts and paired nonroosts were characterized testing predictions that males and females select roosts that differ from non-roosts, and males and females select different roosts. Females and males chose Pinus radiata roosts that differed from non-roost trees. Results suggest each sex chose roosts that maximized energetic savings. Female bats used roosts closer to water sources, that warmed earlier in the day, which allowed maintenance of high temperatures. Males appeared to choose roosts that allowed torpor use for long periods of the day. Males may be less selective with their roost locations than females, as they roosted further from water sources. This could allow persistence of male bats in marginal habitat. As all female long-tailed bats chose roosts within 150 m of waterways, management to protect bats could be focused here. To protect bats least able to escape when roosts are harvested, harvest of forest stands selected by female bats as roost sites should be planned when bats are not heavily pregnant nor have non-volant dependents.

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Clear-fell harvest of forest concerns many wildlife biologists because of loss of vital resources such as roosts or nests, and effects on population viability. However, actual impact has not been quantified. Using New Zealand long-tailed bats (Chalinolobus tuberculatus) as a model species we investigated impacts of clear-fell logging on bats in plantation forest. C. tuberculatus roost within the oldest stands in plantation forest so it was likely roost availability would decrease as harvest operations occurred. We predicted that post-harvest: (1) roosting range sizes would be smaller, (2) fewer roosts would be used, and (3) colony size would be smaller. We captured and radiotracked C. tuberculatus to day-roosts in Kinleith Forest, an exotic plantation forest, over three southern hemisphere summers (Season 1 October 2006–March 2007; Season 2 November 2007–March 2008; and Season 3 November 2008–March 2009). Individual roosting ranges (100% MCPs) post harvest were smaller than those in areas that had not been harvested, and declined in area during the 3 years. Following harvest, bats used fewer roosts than those in areas that had not been harvested. Over 3 years 20.7% of known roosts were lost: 14.5% due to forestry operations and 6.2% due to natural tree fall. Median colony size was 4.0 bats (IQR = 2.0–8.0) and declined during the study, probably because of locally high levels of roost loss. Post harvest colonies were smaller than colonies in areas that had not been harvested. Together, these results suggest the impact of clear-fell harvest on long-tailed bat populations is negative.

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Individuals' home ranges are constrained by resource distribution and density, population size, and energetic requirements. Consequently, home ranges and habitat selection may vary between individuals of different sex and reproductive conditions. Whilst home ranges of bats are well-studied in native habitats, they are often not well understood in modified landscapes, particularly exotic plantation forests. Although Chalinolobus tuberculatus (Vespertilionidae, Chiroptera) are present in plantation forests throughout New Zealand their home ranges have only been studied in native forest and forest-agricultural mosaic and no studies of habitat selection that included males had occurred in any habitat type. Therefore, we investigated C. tuberculatus home range and habitat selection within exotic plantation forest. Home range sizes did not differ between bats of different reproductive states. Bats selected home ranges with higher proportions of relatively old forest than was available. Males selected edges with open unplanted areas within their home ranges, which females avoided. We suggest males use these edges, highly profitable foraging areas with early evening peaks in invertebrate abundance, to maintain relatively low energetic demands. Females require longer periods of invertebrate activity to fulfil their needs so select older stands for foraging, where invertebrate activity is higher. These results highlight additional understanding gained when data are not pooled across sexes. Mitigation for harvest operations could include ensuring that areas suitable for foraging and roosting are located within a radius equal to the home range of this bat species.

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Environmental certification schemes have stimulated increasing interest in biodiversity and its management within exotic plantation forests. These schemes expect management to be scientifically-based, even though little is known about how often, or which, native species use exotic plantation forests. Greater knowledge of the ecology of native species within exotic plantation forests is required to advise management and reduce risks to native species, particularly those that are rare, such as the New Zealand long-tailed bat (Chalinolobus tuberculatus). Long-tailed bats use exotic plantation forests throughout New Zealand but need protection from the impacts of forest management, and particularly clear-fell harvest, that is achievable only through a better understanding of their biology. The consequences of the current reduced re-planting, and the conversion of plantation forests into pasture resulting in smaller forested areas, should not be ignored because they may be associated with reductions in long-tailed bat populations. We review the current knowledge of long-tailed bats' use of exotic plantation forests, and report for the first time which exotic plantations long-tailed bats are known to use. We make recommendations for the design of monitoring programmes to detect long-tailed bats within plantation forests, and for research into the effects of forest management, especially logging, and comment on the likely impacts of reductions in forested areas on long-tailed bats.

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Targeted monitoring of threatened species within plantations is becoming more important due to forest certification programmes’ requirement to consider protection of threatened species, and to increase knowledge of the distribution of species. To determine patterns of long-tailed bat (Chalinolobus tuberculatus) activity in different habitat structures, with the aim of improving the likelihood of detection by targeting monitoring, we monitored one stand of 26 year-old Pinus radiata over seven months between December 2007 and June 2008 in Kinleith Forest, an exotic plantation forest centred around Tokoroa, South Waikato, New Zealand. Activity was determined by acoustic recording equipment, which is able to detect and record bats’ echolocation calls. We monitored activity from sunset to sunrise along a road through the stand, along stand edges, and in the interior of the stand. Bats were recorded on 80% of the 35 nights monitored. All activity throughout the monitoring period was detected on the edge of the stand or along the road. No bats were detected within the interior of the stand. Bat activity was highest along the road through the stand (40.4% of all passes), followed by an edge with stream running alongside (35.2%), along the road within a skidsite (19.8%), and along an edge without a stream (4.6%). There was a significant positive relationship between bat pass rate (bat passes h-1) and the feeding buzz rate (feeding buzzes h-1) indicating that bat activity was associated with feeding and not just commuting. Bat feeding activity was also highest along the road through the stand (59.2% of feeding buzzes), followed by the road within the skidsite (30.6%), and along the stream-side edge (10.2%). No feeding buzzes were recorded in either the interior or along the edge without the stream. Differences in overall feeding activity were significant only between the road and edge and between edges with and without a stream. Bat activity was detected each month and always by the second night of monitoring, and in this stand was highest during April. We recommend targeted monitoring for long-tailed bats be focused on road-side and stand edge habitat, and along streams, and that monitoring take place for at least three nights to maximise probability of detection.

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We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

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The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

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We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.

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In studies of germ cell transplantation, measureing tubule diameters and counting cells from different populations using antibodies as markers are very important. Manual measurement of tubule sizes and cell counts is a tedious and sanity grinding work. In this paper, we propose a new boundary weighting based tubule detection method. We first enhance the linear features of the input image and detect the approximate centers of tubules. Next, a boundary weighting transform is applied to the polar transformed image of each tubule region and a circular shortest path is used for the boundary detection. Then, ellipse fitting is carried out for tubule selection and measurement. The algorithm has been tested on a dataset consisting of 20 images, each having about 20 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually. © 2013 IEEE.

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Hydrogeophysics is a growing discipline that holds significant promise to help elucidate details of dynamic processes in the near surface, built on the ability of geophysical methods to measure properties from which hydrological and geochemical variables can be derived. For example, bulk electrical conductivity is governed by, amongst others, interstitial water content, fluid salinity, and temperature, and can be measured using a range of geophysical methods. In many cases, electrical resistivity tomography (ERT) is well suited to characterize these properties in multiple dimensions and to monitor dynamic processes, such as water infiltration and solute transport. In recent years, ERT has been used increasingly for ecosystem research in a wide range of settings; in particular to characterize vegetation-driven changes in root-zone and near-surface water dynamics. This increased popularity is due to operational factors (e.g., improved equipment, low site impact), data considerations (e.g., excellent repeatability), and the fact that ERT operates at scales significantly larger than traditional point sensors. Current limitations to a more widespread use of the approach include the high equipment costs, and the need for site-specific petrophysical relationships between properties of interest. In this presentation we will discuss recent equipment advances and theoretical and methodological aspects involved in the accurate estimation of soil moisture from ERT results. Examples will be presented from two studies in a temperate climate (Michigan, USA) and one from a humid tropical location (Tapajos, Brazil).

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This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.