958 resultados para Engineering geological mapping
Resumo:
This paper presents a new rat animat, a rat-sized bio-inspired robot platform currently being developed for embodied cognition and neuroscience research. The rodent animat is 150mm x 80mm x 70mm and has a different drive, visual, proximity, and odometry sensors, x86 PC, and LCD interface. The rat animat has a bio-inspired rodent navigation and mapping system called RatSLAM which demonstrates the capabilities of the platform and framework. A case study is presented of the robot's ability to learn the spatial layout of a figure of eight laboratory environment, including its ability to close physical loops based on visual input and odometry. A firing field plot similar to rodent 'non-conjunctive grid cells' is shown by plotting the activity of an internal network. Having a rodent animat the size of a real rat allows exploration of embodiment issues such as how the robot's sensori-motor systems and cognitive abilities interact. The initial observations concern the limitations of the deisgn as well as its strengths. For example, the visual sensor has a narrower field of view and is located much closer to the ground than for other robots in the lab, which alters the salience of visual cues and the effectiveness of different visual filtering techniques. The small size of the robot relative to corridors and open areas impacts on the possible trajectories of the robot. These perspective and size issues affect the formation and use of the cognitive map, and hence the navigation abilities of the rat animat.
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This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.
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In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.
Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data
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In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.
Resumo:
Maps are used to represent three-dimensional space and are integral to a range of everyday experiences. They are increasingly used in mathematics, being prominent both in school curricula and as a form of assessing students understanding of mathematics ideas. In order to successfully interpret maps, students need to be able to understand that maps: represent space, have their own perspective and scale, and their own set of symbols and texts. Despite the fact that maps have an increased prevalence in society and school, there is evidence to suggest that students have difficulty interpreting maps. This study investigated 43 primary-aged students’ (aged 9-12 years) verbal and gestural behaviours as they engaged with and solved map tasks. Within a multiliteracies framework that focuses on spatial, visual, linguistic, and gestural elements, the study investigated how students interpret map tasks. Specifically, the study sought to understand students’ skills and approaches used to solving map tasks and the gestural behaviours they utilised as they engaged with map tasks. The investigation was undertaken using the Knowledge Discovery in Data (KDD) design. The design of this study capitalised on existing research data to carry out a more detailed analysis of students’ interpretation of map tasks. Video data from an existing data set was reorganised according to two distinct episodes—Task Solution and Task Explanation—and analysed within the multiliteracies framework. Content Analysis was used with these data and through anticipatory data reduction techniques, patterns of behaviour were identified in relation to each specific map task by looking at task solution, task correctness and gesture use. The findings of this study revealed that students had a relatively sound understanding of general mapping knowledge such as identifying landmarks, using keys, compass points and coordinates. However, their understanding of mathematical concepts pertinent to map tasks including location, direction, and movement were less developed. Successful students were able to interpret the map tasks and apply relevant mathematical understanding to navigate the spatial demands of the map tasks while the unsuccessful students were only able to interpret and understand basic map conventions. In terms of their gesture use, the more difficult the task, the more likely students were to exhibit gestural behaviours to solve the task. The most common form of gestural behaviour was deictic, that is a pointing gesture. Deictic gestures not only aided the students capacity to explain how they solved the map tasks but they were also a tool which assisted them to navigate and monitor their spatial movements when solving the tasks. There were a number of implications for theory, learning and teaching, and test and curriculum design arising from the study. From a theoretical perspective, the findings of the study suggest that gesturing is an important element of multimodal engagement in mapping tasks. In terms of teaching and learning, implications include the need for students to utilise gesturing techniques when first faced with new or novel map tasks. As students become more proficient in solving such tasks, they should be encouraged to move beyond a reliance on such gesture use in order to progress to more sophisticated understandings of map tasks. Additionally, teachers need to provide students with opportunities to interpret and attend to multiple modes of information when interpreting map tasks.
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There has been an increasing interest in objects within the HCI field particularly with a view to designing tangible interfaces. However, little is known about how people make sense of objects and how objects support thinking. This paper presents a study of groups of engineers using physical objects to prototype designs, and articulates the roles that physical objects play in supporting their design thinking and communications. The study finds that design thinking is heavily dependent upon physical objects, that designers are active and opportunistic in seeking out physical props and that the interpretation and use of an object depends heavily on the activity. The paper discusses the trade-offs that designers make between speed and accuracy of models, and specificity and generality in choice of representations. Implications for design of tangible interfaces are discussed.
Resumo:
This paper describes a novel probabilistic approach to incorporating odometric information into appearance-based SLAM systems, without performing metric map construction or calculating relative feature geometry. The proposed system, dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM), represents location as a probability distribution along a trajectory, and represents appearance continuously over the trajectory rather than at discrete locations. The distribution is evaluated using a Rao-Blackwellised particle filter, which weights particles based on local appearance and odometric similarity and explicitly models both the likelihood of revisiting previous locations and visiting new locations. A modified resampling scheme counters particle deprivation and allows loop closure updates to be performed in constant time regardless of map size. We compare the performance of CAT-SLAM to FAB-MAP (an appearance-only SLAM algorithm) in an outdoor environment, demonstrating a threefold increase in the number of correct loop closures detected by CAT-SLAM.
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Cell based therapies for bone regeneration are an exciting emerging technology, but the availability of osteogenic cells is limited and an ideal cell source has not been identified. Amniotic fluid-derived stem (AFS) cells and bone-marrow derived mesenchymal stem cells (MSCs) were compared to determine their osteogenic differentiation capacity in both 2D and 3D environments. In 2D culture, the AFS cells produced more mineralized matrix but delayed peaks in osteogenic markers. Cells were also cultured on 3D scaffolds constructed of poly-e-caprolactone for 15 weeks. MSCs differentiated more quickly than AFS cells on 3D scaffolds, but mineralized matrix production slowed considerably after 5 weeks. In contrast, the rate of AFS cell mineralization continued to increase out to 15 weeks, at which time AFS constructs contained 5-fold more mineralized matrix than MSC constructs. Therefore, cell source should be taken into consideration when used for cell therapy, as the MSCs would be a good choice for immediate matrix production, but the AFS cells would continue robust mineralization for an extended period of time. This study demonstrates that stem cell source can dramatically influence the magnitude and rate of osteogenic differentiation in vitro.
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Calcium Phosphate ceramic has been widely used in bone tissue engineering due to its excellent biocompatibility and biodegradability. However, low mechanical properties and biodegradability limit their potential applications. In this project, hydroxyapatite (HA) and calcium phosphate bioglass were used to produce porous tri-calcium phosphate (TCP) bio-ceramic scaffolds. It was found that porous TCP bioceramic could be obtained when 20wt percent bioglass addition and sintered in 1400 degrees celsius for 3 h. Significantly higher compressive strength (9.98 MPa) was achieved in the scaffolds as compared to those produced from tCP power (<3 MPa). The biocompatibility of the scaffold was also estimated.
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Porous yttria-stabilized zirconia (YSZ) has been regarded as a potential candidate for bone substitute due to its high mechanical strength. However, porous YSZ is biologically inert to bone tissue. It is therefore necessary to introduce bioactive coatings onto the walls of the porous structures to enhance its bioactivity. In this study, porous YSZ scaffolds were prepared using a replication technique and then coated with mesoporous bioglass due to its excellent bioactivity. The microstructures were examined using scanning electron microscopy and the mechanical strength was evaluated via compression test. The biocompatibility and bioactivity were also evaluated using bone marrow stromal cell (BMSC) proliferation test and simulated body fluid test.
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Automation technology can provide construction firms with a number of competitive advantages. Technology strategy guides a firm's approach to all technology, including automation. Engineering management educators, researchers, and construction industry professionals need improved understanding of how technology affects results, and how to better target investments to improve competitive performance. A more formal approach to the concept of technology strategy can benefit the construction manager in his efforts to remain competitive in increasingly hostile markets. This paper recommends consideration of five specific dimensions of technology strategy within the overall parameters of market conditions, firm capabilities and goals, and stage of technology evolution. Examples of the application of this framework in the formulation of technology strategy are provided for CAD applications, co-ordinated positioning technology and advanced falsework and formwork mechanisation to support construction field operations. Results from this continuing line of research can assist managers in making complex and difficult decisions regarding reengineering construction processes in using new construction technology and benefit future researchers by providing new tools for analysis. Through managing technology to best suit the existing capabilities of their firm, and addressing the market forces, engineering managers can better face the increasingly competitive environment in which they operate.
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In the past 20 years, mesoporous materials have been attracted great attention due to their significant feature of large surface area, ordered mesoporous structure, tunable pore size and volume, and well-defined surface property. They have many potential applications, such as catalysis, adsorption/separation, biomedicine, etc. [1]. Recently, the studies of the applications of mesoporous materials have been expanded into the field of biomaterials science. A new class of bioactive glass, referred to as mesoporous bioactive glass (MBG), was first developed in 2004. This material has a highly ordered mesopore channel structure with a pore size ranging from 5–20 nm [1]. Compared to non-mesopore bioactive glass (BG), MBG possesses a more optimal surface area, pore volume and improved in vitro apatite mineralization in simulated body fluids [1,2]. Vallet-Regí et al. has systematically investigated the in vitro apatite formation of different types of mesoporous materials, and they demonstrated that an apatite-like layer can be formed on the surfaces of Mobil Composition of Matters (MCM)-48, hexagonal mesoporous silica (SBA-15), phosphorous-doped MCM-41, bioglass-containing MCM-41 and ordered mesoporous MBG, allowing their use in biomedical engineering for tissue regeneration [2-4]. Chang et al. has found that MBG particles can be used for a bioactive drug-delivery system [5,6]. Our study has shown that MBG powders, when incorporated into a poly (lactide-co-glycolide) (PLGA) film, significantly enhance the apatite-mineralization ability and cell response of PLGA films. compared to BG [7]. These studies suggest that MBG is a very promising bioactive material with respect to bone regeneration. It is known that for bone defect repair, tissue engineering represents an optional method by creating three-dimensional (3D) porous scaffolds which will have more advantages than powders or granules as 3D scaffolds will provide an interconnected macroporous network to allow cell migration, nutrient delivery, bone ingrowth, and eventually vascularization [8]. For this reason, we try to apply MBG for bone tissue engineering by developing MBG scaffolds. However, one of the main disadvantages of MBG scaffolds is their low mechanical strength and high brittleness; the other issue is that they have very quick degradation, which leads to an unstable surface for bone cell growth limiting their applications. Silk fibroin, as a new family of native biomaterials, has been widely studied for bone and cartilage repair applications in the form of pure silk or its composite scaffolds [9-14]. Compared to traditional synthetic polymer materials, such as PLGA and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), the chief advantage of silk fibroin is its water-soluble nature, which eliminates the need for organic solvents, that tend to be highly cytotoxic in the process of scaffold preparation [15]. Other advantages of silk scaffolds are their excellent mechanical properties, controllable biodegradability and cytocompatibility [15-17]. However, for the purposes of bone tissue engineering, the osteoconductivity of pure silk scaffolds is suboptimal. It is expected that combining MBG with silk to produce MBG/silk composite scaffolds would greatly improve their physiochemical and osteogenic properties for bone tissue engineering application. Therefore, in this chapter, we will introduce the research development of MBG/silk scaffolds for bone tissue engineering.
Resumo:
We describe a novel two stage approach to object localization and tracking using a network of wireless cameras and a mobile robot. In the first stage, a robot travels through the camera network while updating its position in a global coordinate frame which it broadcasts to the cameras. The cameras use this information, along with image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to track the objects. We present results with a nine node indoor camera network to demonstrate that this approach is feasible and offers acceptable level of accuracy in terms of object locations.