tau-Lop
tau-Lop is a new parallel performance model aimed to help in the design and optimization of parallel algorithms inside multicore clusters. It represents a parallel algorithm and predicts accurately its costs through the concept of concurrent transfers.
By now, tau-Lop has been applied to underlying algorithms in MPICH and Open MPI mainstream implementations of some MPI collectives in shared memory. Current work is in the application of the model to collective operations when deployed in networks of multicore nodes.
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Fruit&Vegetable
The Fruit&Vegetable Image Collection: The growing rise of new technologies and the widespread use of Internet has driven documents present a rich visual content on the web. To properly indexing these documents (images, videos, audio files, etc..) And build useful search engines, capable of meeting the information needs of users, methods are needed to accurately describe the visual and audio content.
Although over the years we have used a large number of diverse collections of images that have served to support multiple experiments, few collections that have an acceptable level of quality in terms of factors such as resolution, lighting, contrast, sharpening, no noise, etc. In particular, object-oriented collections, where the foreground power and tries to minimize the effect of noise and image background, we are not aware at present of the existence of any collection of open license with similar characteristics as The Fruit&Vegetable Image Collection quality.
As the name suggests, the purpose of the images are fruits, and vegetables, of different size, shape and color, but its main potential is its homogeneity in terms of technical features and parameters used (resolution, exposure time, aperture , zoom, ...) along the array, which greatly facilitates the evaluation of the descriptor in its task of characterizing the visual content of the image without influences of aspects accessories to said visual content.
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University Life
University Life Video Database: Every day, the presence of video in our lives is more evident. There are video cameras everywhere recording the activity of all of us and there where a video camera is not present a smartphone is ready to be used by someone to record what is happening and then it is uploaded to the Internet. This scenario has attracted the research community interest in developing methods and techniques able to understand at least part of the content of a video.
The presence of faces, objects, places, scenes, events or any other information is of great utility for a large number of applications that would benefit from this kind of methods. A key for the development of these methods and systems is the ability to measure their effectiveness.
In order to achieve that it is necessary to have data sets properly collected, correctly annotated and accurately catalogued which serve as testbed to measure their performance and effectiveness. This is the goal of University Life Video Database (ULVDB).
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vManager
vManager is an application for cataloging video collections according to a set of user-defined taxonomies.
It is an application for storing, cataloging and retrieval of video shots based video and textual information, but is willing to add visual information to the metadata. Video searches based on similarities of color or movement, and to enter into a video database and catalog them according to the theme or assign textual information.
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vDocXML
vDocXML: Es un modelo con su correspondiente implementación para la gestión de versiones de documentos XML. Este modelo permite representar y gestionar versionado ramificado y facilita la formulación de consultas sobre los documentos XML versionados mediante distintos estándares XML, como XQuery, XPath o XSLT.
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Iberian Ham: We have developed a real case application: A software application containing three modules was used for the analysis of MRI (Magnetic Resonance Imaging).
