Browsing by Subject "Layout"
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Item Open Access Distributed block formation and layout for disk-based management of large-scale graphs(Springer, 2017) Yaşar, A.; Gedik, B.; Ferhatosmanoğlu, H.We are witnessing an enormous growth in social networks as well as in the volume of data generated by them. An important portion of this data is in the form of graphs. In recent years, several graph processing and management systems emerged to handle large-scale graphs. The primary goal of these systems is to run graph algorithms and queries in an efficient and scalable manner. Unlike relational data, graphs are semi-structured in nature. Thus, storing and accessing graph data using secondary storage requires new solutions that can provide locality of access for graph processing workloads. In this work, we propose a scalable block formation and layout technique for graphs, which aims at reducing the I/O cost of disk-based graph processing algorithms. To achieve this, we designed a scalable MapReduce-style method called ICBL, which can divide the graph into a series of disk blocks that contain sub-graphs with high locality. Furthermore, ICBL can order the resulting blocks on disk to further reduce non-local accesses. We experimentally evaluated ICBL to showcase its scalability, layout quality, as well as the effectiveness of automatic parameter tuning for ICBL. We deployed the graph layouts generated by ICBL on the Neo4j open source graph database, http://www.neo4j.org/ (2015) graph database management system. Our results show that the layout generated by ICBL reduces the query running times over Neo4j more than 2 × compared to the default layout. © 2017, Springer Science+Business Media New York.Item Open Access Learning bayesian classifiers for scene classification with a visual grammar(IEEE, 2005-03) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio, G.; Tilton, J. C.A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.Item Open Access Learning bayesian classifiers for scene classification with a visual grammar(IEEE, 2005) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio, G.; Tilton J. C.A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples. © 2005 IEEE.Item Open Access Move based heuristics for the unidirectional loop network layout problem(Elsevier, 1998-07-01) Tansel, B. C.; Bilen, C.We consider the loop network layout problem in a manufacturing system where n machines must be placed in n available locations around a loop to minimize the total flow distance. The formulation of the problem results in a quadratic assignment problem which is computationally a very hard problem. We discuss the idea of positional moves and local improvement algorithms based on moves or k-way (particularly 2-way) interchanges. Even though the concept of moves has not found its way into algorithmic design in the existing literature, our computational experimentation with two-move based heuristics indicates uniformly superior performance in comparison to the well known pairwise interchange heuristic. © 1998 Elsevier Science B.V.Item Open Access A novel approach to 3-dimensional holographic television display: principles and simulations(IEEE, 1992-03) Bozdağı, Gözde; Onural, Levent; Atalar, AbdullahThe authors present a new technique for the display end of a holographic three-dimensional television system and describe the computer simulations. The technique is based on the reproduction of the desired pattern, in this case the hologram, using traveling surface waves. The proposed method is simpler and more efficient than the methods available in the literature and it solves the display resolution and refreshing rate problems completely. Simulations show that the proposed system will work as desired when implemented in real time.Item Open Access A unified graphics rendering pipeline for autostereoscopic rendering(IEEE, 2007-05) Kalaiah, A.; Çapin, Tolga K.Autostereoscopic displays require rendering a scene from multiple viewpoints. The architecture of current-generation graphics processors are still grounded in the historic evolution of monoscopic rendering. In this paper, we present a novel programmable rendering pipeline that renders to multiple viewpoints in a single pass. Our approach leverages on the computational and memory fetch coherence of rendering to multiple viewpoints to achieve significant speedup. We present an emulation of the principles of our pipeline using the current-generation GPUs and present a quantitative estimate of the benefits of our approach. We make a case for the new rendering pipeline by demonstrating its benefits for a range of applications such as autostereoscopic rendering and for shadow map computation for a scene with multiple light sources. © 2007 IEEE.