57 research outputs found

    Migrating Traditional Web Applications to CMS-based Web Applications

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    AbstractIn recent years, Content Management Systems (CMS) have proven to be the best platforms for maintaining the large amount of digital content managed by Web applications. Thus, many organizations have experienced the necessity to base its Web applications on these CMS platforms. To do this, they start a migration process which is complex and error prone. To support this process, we propose a method based on the principles of Architecture-Driven Modernization (ADM) which automates the migration of Web applications to CMS-based Web applications. This article focuses on the implementation of two artifacts of this method: 1) the DSL ASTM_PHP, a modeling language for defining a model from PHP code (ASTM_PHP model) and 2) the model-to-model transformation rules which generate automatically a KDM model from a ASTM_PHP model. To show the feasibility of this implementation, we use a case study based on a widget implemented in PHP which lists the online users of a Web application

    A Toolkit for ADM-based Migration: Moving from PHP Code to KDM Model in the Context of CMS-based Web Applications

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    In the last few years, many organizations have based their Web applications on Content Management Systems (CMS) because of the advantages they provide to manage their huge amount of digital content. The objectives of these organizations change, for this reason they may see the necessity of migrating their CMS-based Web applications to other CMS platforms meeting better their needs. Thus, we propose a method based on Architecture-Driven Modernization (ADM) to automate this migration process. In this paper we present the toolkit supporting this ADM-based migration method. For space restrictions, we focus on the implementation of two modules of this ADM-based toolkit: i) the ASTM_PHP DSL, a modeling language which allows to model the code of a system implemented in PHP (ASTM_PHP models) and ii) the model-to-model transformation rules which allow to generate KDM models from the information captured in the ASTM_PHP models. To show its usability, we present a case study where a widget listing online users of a CMS-based Web application is migrated from Drupal to Wordpress

    Semantic Approach in Image Change Detection

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    International audienceChange detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases

    A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

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    This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion

    A Stereovision Matching Strategy for Images Captured with Fish-Eye Lenses in Forest Environments

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    We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. This is achieved by applying a pattern recognition strategy based on the combination of two classifiers: Fuzzy Clustering and Bayesian. At a second stage, a stereovision matching process is performed based on the application of four stereovision matching constraints: epipolar, similarity, uniqueness and smoothness. The epipolar constraint guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a weighted fuzzy similarity approach, obtaining a disparity map. This map is later filtered through the Hopfield Neural Network framework by considering the smoothness constraint. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies

    Cardiopoietic cell therapy for advanced ischemic heart failure: results at 39 weeks of the prospective, randomized, double blind, sham-controlled CHART-1 clinical trial

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    Cardiopoietic cells, produced through cardiogenic conditioning of patients' mesenchymal stem cells, have shown preliminary efficacy. The Congestive Heart Failure Cardiopoietic Regenerative Therapy (CHART-1) trial aimed to validate cardiopoiesis-based biotherapy in a larger heart failure cohort

    Semi-automatic rural land cover classification from high resolution remote sensing images

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    Cette thĂšse prĂ©sente un chaine d'analyse d'image qui, Ă  partir d'images numĂ©riques Ă  haute rĂ©solution et Ă  trois ou quatre canaux (50 cm, couleur et, dans certains cas, proche infrarouge), mais aussi en s'appuyant sur le parcellaire cadastral, rend une segmentation des images en parcelles agraires (champs, forĂȘts, vignes, ...) et une classification de celles-ci, avec une trĂšs haute fiabilitĂ©, et attribue Ă  chaque segment classifiĂ© une mesure qui indique la confiance que le systĂšme a en cette classification. Elle inclut une Ă©tude sur l'intĂ©rĂȘt de la texture et les espaces de couleur pour la segmentation et la classification, deux mĂ©thodes de recalage de graphes sur une image, un modĂšle de probabilitĂ© novateur et ses algorthmes de classification par rĂ©gions associĂ©es, et un Ă©stimateur trĂšs prĂ©cis de la pĂ©riode et de l'orientation.This thesis presents a complete image analisys system which, from high-resolution 3 or 4-channel digital images (50 cm, colour and optionally near infrared), and using the cadastre database, segments the images into agriculturally-homogeneous regions, (fields, forests, vines, and so on) and classifies these regions, tagging each classified region with a confidence measure which indicates the system's confidence in each classification. It includes a study of the value of texture features and transformed colour spaces for segmentation and classification, two methods for registering a graph onto an image, a novel probability model and associated per-region classification algorithms, and a high precision period and orientation estimator.PARIS5-BU Saints-PĂšres (751062109) / SudocSudocFranceF
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