916 resultados para digital terrain analysis


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INTRODUCTION Managing spinal deformities in young children is challenging, particularly early-onset scoliosis (EOS). Any progressive spinal deformity particularly in early life presents significant health risks for the child and a challenge for the treating surgeon. Surgical intervention is often required if EOS has been unresponsive to conservative treatment particularly with rapidly progressive curves. An emerging treatment option particularly for EOS is fusionless scoliosis surgery. Similar to bracing this surgical option potentially harnesses growth, motion and function of the spine along with correcting spinal deformity. Dual growing rods is one such fusionless treatment, which aims to modulate growth of the vertebrae. The aim of this study was to ascertain the extent to which semi-constrained growing rods (Medtronic, Memphis, TN) with a telescopic sleeve component, reduce rotational constraint on the spine compared with standard rigid rods and hence potentially provide a more physiological mechanical environment for the growing spine. METHODS Six 40-60kg English Large White porcine spines served as a model for the paediatric human spine. Each spine was dissected into 7 level thoracolumbar multi-segment unit (MSU) spines, removing all non-ligamentous soft tissues. Appropriately sized semi-constrained growing rods and rigid rods were secured by multi-axial screws (Medtronic) prior to testing in alternating sequences for each spine. Pure nondestructive moments of +/4Nm at a constant rotation rate of 8deg/s was applied to the mounted MSU spines. Displacement of each level was captured using an Optotrak (Northern Digital Inc, Waterloo, ON). The range of motion (ROM), neutral zone (NZ) size and stiffness (Nm/deg) were calculated from the Instron load-displacement data and intervertebral ROM was calculated through a MATLAB algorithm from Optotrak data. RESULTS Irrespective of sequence order rigid rods significantly reduced the total ROM (deg) than compared to semi-constrained rods (p<0.05) and resulted in a significantly stiffer (Nm/deg) spine for both left and right axial rotation testing (p<0.05). Analysing the intervertebral motion within the instrumented levels, rigid rods showed reduced ROM (Deg) than compared to semi-constrained growing rods and the un-instrumented (UN-IN) test sequences. CONCLUSION The semi-constrained growing rods maintained rotation similar to UN-IN spines while the rigid rods showed significantly reduced axial rotation across all instrumented levels. Clinically the effect of semi-constrained growing rods evaluated in this study is that they will allow growth via the telescopic rod components while maintaining the axial rotation ability of the spine, which may also reduce the occurrence of the crankshaft phenomenon.

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Firm-customer digital connectedness for effective sensing and responding is a strategic imperative for contemporary competitive firms. This research-in-progress paper conceptualizes and operationalizes the firm-customer mobile digital connectedness of a smart-mobile customer. The empirical investigation focuses on mobile app users and the impact of mobile apps on customer expectations. Based on pilot data collected from 127 customers, we tested hypotheses pertaining to firm-customer mobile digital connectedness and customer expectations. Our test analysis using linear and non-linear postulations reveals those customers raise their expectations as they increase their digital interactions with a firm.

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This chapter addresses children’s development of digital media literacies with iPads in preschool settings. The authors argue that children living in post-industrial societies participate in ‘transmedia’ experiences that call for new understandings of media literacy that recognise children’s ability to successfully participate in complex media ecologies. The chapter outlines a model for digital media literacies that includes the application of digital materials and media concepts through the processes of media production and media analysis. This model is then used as a framework to interpret children’s media production work across the preschools in our project.

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Dealing with digital medical images is raising many new security problems with legal and ethical complexities for local archiving and distant medical services. These include image retention and fraud, distrust and invasion of privacy. This project was a significant step forward in developing a complete framework for systematically designing, analyzing, and applying digital watermarking, with a particular focus on medical image security. A formal generic watermarking model, three new attack models, and an efficient watermarking technique for medical images were developed. These outcomes contribute to standardizing future research in formal modeling and complete security and computational analysis of watermarking schemes.

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Abstract Within the field of Information Systems, a good proportion of research is concerned with the work organisation and this has, to some extent, restricted the kind of application areas given consideration. Yet, it is clear that information and communication technology deployments beyond the work organisation are acquiring increased importance in our lives. With this in mind, we offer a field study of the appropriation of an online play space known as Habbo Hotel. Habbo Hotel, as a site of media convergence, incorporates social networking and digital gaming functionality. Our research highlights the ethical problems such a dual classification of technology may bring. We focus upon a particular set of activities undertaken within and facilitated by the space – scamming. Scammers dupe members with respect to their ‘Furni’, virtual objects that have online and offline economic value. Through our analysis we show that sometimes, online activities are bracketed off from those defined as offline and that this can be related to how the technology is classified by members – as a social networking site and/or a digital game. In turn, this may affect members’ beliefs about rights and wrongs. We conclude that given increasing media convergence, the way forward is to continue the project of educating people regarding the difficulties of determining rights and wrongs, and how rights and wrongs may be acted out with respect to new technologies of play online and offline.

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Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

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This research is focused on realizing productivity benefits for the delivery of transport infrastructure in the Australian construction industry through the use of building information modeling (BIM), virtual design and construction (VDC) and integrated project delivery (IPD). Specific objectives include: (I) building an understanding of the institutional environment, business systems and support mechanisms (e.g., training and skilling) which impact on the uptake of BIM/VDC; (II) gathering data to undertake a cross-country analysis of these environments; and (III) providing strategic and practical outcomes to guide the uptake of such processes in Australia. Activities which will inform this research include a review of academic literature and industry documentation, semi-formal interviews in Australia and Sweden, and a cross-country comparative analysis to determine factors affecting uptake and associated productivity improvements. These activities will seek to highlight the gaps between current-practice and best-practice which are impacting on widespread adoption of BIM/VDC and IPD. Early findings will be discussed with intended outcomes of this research being used to: inform a national public procurement strategy; provide guidelines for new contractual frameworks; and contribute to closing skill gaps. Keywords: building information modeling (BIM); virtual design and construction (VDC); integrated project delivery (IPD); transport infrastructure; Australia; procurement

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This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

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Edited by thought leaders of the fields of urban informatics and urban interaction design, this book brings together case studies and examples from around the world to discuss the role that urban Interfaces, citizen action, and city making play in the quest to create and maintain not only secure and resilient, but productive, sustainable, and liveable urban environments. The book debates the impact of these trends on theory, policy, and practice. The chapters in this book are sourced from blind peer reviewed contributions by leading researchers working at the intersection of the social / cultural, technical / digital, and physical / spatial domains of urbanism scholarship. The book appeals not only to research colleagues and students, but also to a vast number of practitioners in the private and public sector interested in accessible accounts that clearly and rigorously analyse the affordances and possibilities of urban interfaces, mobile technology, and location-based services to engage people towards open, smart and participatory urban environments.

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This article outlines the knowledge and skills students develop when they engage in digital media production and analysis in school settings. The metaphor of ‘digital building blocks’ is used to describe the material practices, conceptual understandings and production of knowledge that lead to the development of digital media literacy. The article argues that the two established approaches to media literacy education, critical reading and media production, do not adequately explain how students develop media knowledge. It suggests there has been too little focus on material practices and how these relate to the development of conceptual understanding in media learning. The article explores empirical evidence from a four-year investigation in a primary school in Queensland, Australia using actor–network theory to explore ‘moments of translation’ as students deploy technologies and concepts to materially participate in digital culture. A generative model of media learning is presented with four categories of building blocks that isolate the specific skills and knowledge that can be taught and learnt to promote participation in digital media contexts: digital materials, conceptual understandings, media production and media analysis. The final section of the article makes initial comments on how the model might become the basis for curriculum development in schools and argues that further empirical research needs to occur to confirm the model’s utility.

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This project develops the required guidelines to assure stable and accurate operation of Power-Hardware-in-the-Loop implementations. The proposals of this research have been theoretically analyzed and practically examined using a Real-Time Digital Simulator. In this research, the interaction between software simulated power network and the physical power system has been studied. The conditions for different operating regimes have been derived and the corresponding analyses have been presented.

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With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

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Purpose This study aims to gain a clearer understanding of digital channel design. The emergence of new technologies has revolutionised the way companies interact and engage with customers. The driver for this research was the suggestion that practitioners feel they do not possess the skills to understand and exploit new digital channel opportunities. To gain a clearer understanding of digital channel design, this paper addresses the research question: What digital channels do companies from a wide range of industries and sectors use? Design/methodology/approach A content analysis of 100 international companies was conducted with multiple data sources to form a typology of digital “touchpoints”. The appropriateness of a digital channel typology for this study was for developing rigorous and useful concepts for clarifying and refining the meaning of digital channels. Findings This study identifies what digital channels companies globally currently employ and explores the related needs across industries. A total of 34 digital touchpoints and 4 typologies of digital channels were identified across 16 industries. This research helps to identify the relationship between digital channels and enabling the connections with industry. Research limitations/implications The findings contribute to the growing research area of digital channels. The typology of digital channels is a useful starting point for developing a systematic, theory-based study for enabling the development of broader, comprehensive theories of digital channels. Practical implications Typologies and touchpoints are outlined in relation to industry, company objectives and customer needs to allow businesses to seize opportunities and optimise performance through individual touchpoints. A digital channel model as a key outcome of this research guides practitioners on what touchpoint to implement through an interrelated understanding of industry, company and customer needs. Originality/value This is the first paper to explore a range of industries in relation to their use of digital channels using a unique content analysis. Contributions include clarifying and refining digital channel meaning; identifying and refining the hierarchical relations among digital channels(typologies); and establishing typology and industry relationship model.

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This study investigates the role of digital channels in community-led businesses by exploring two case study companies, Uber and Airbnb. At present, these community-led businesses are disrupting traditional industries by connecting with customers via digital channels and facilitating transactions between two parties. A deductive structured qualitative content analysis approach utilising a predetermined categorization matrix was implemented to decipher the digital channels used by both companies. The results discovered that both company’s digital channels push the customer to their core channel, allowing, customers to create their own physical, largely self-governed communities. However, little research exists which explores and analyses the role of digital channels in forming community-led businesses. Therefore, this paper aims to instigate future research and discussion in this emerging area by concluding with future research agendas.

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The 3D Water Chemistry Atlas is an intuitive, open source, Web-based system that enables the three-dimensional (3D) sub-surface visualization of ground water monitoring data, overlaid on the local geological model (formation and aquifer strata). This paper firstly describes the results of evaluating existing virtual globe technologies, which led to the decision to use the Cesium open source WebGL Virtual Globe and Map Engine as the underlying platform. Next it describes the backend database and search, filtering, browse and analysis tools that were developed to enable users to interactively explore the groundwater monitoring data and interpret it spatially and temporally relative to the local geological formations and aquifers via the Cesium interface. The result is an integrated 3D visualization system that enables environmental managers and regulators to assess groundwater conditions, identify inconsistencies in the data, manage impacts and risks and make more informed decisions about coal seam gas extraction, waste water extraction, and water reuse.