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Home / programming language / Online Dotnet Projects / Rail Route Optimization System
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Rail Route Optimization System

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SKU: PROJ3154 Categories: 2015 Projects, Final Year Projects, Online Dotnet Projects Tags: academic projects, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java projects 2013-2014, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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Rail Route Optimization System

Abstract—Rail Route Optimization System. Symbolic encoding for resource allocation, task binding, and message routing during multi-objective design space exploration (DSE) has gained significant attention in recent years. To determine the message routing, existing symbolic approaches typically rely on an explicit encoding of routing hops which results in a huge number of required variables and/or constraints. As a result, these approaches fail in case of large network diameters and/or a huge number of messages or resources and even for smaller problems, the convergence of the involved optimization process suffers. To tackle this shortcomings, this work proposes three novel symbolic routing encoding strategies that all avoid to encode hops explicitly, but are based on an encoding of individual links or complete sender-receiver paths, but still cover the same design space. The result is a more compact problem representation with less constraints and, in particular, < Final Year Projects > less variables; the latter eliminates ineffective degrees of freedom from the search space and significantly enhances the optimization quality of a multi-objective optimization with even non-linear objectives. In an extensive test-suite, three major classes of wired networked embedded systems are considered: (a) hierarchical stars as in MPSoCs or automotive, (b) redundant backbone buses as common in rail systems or avionics, and (c) mesh-based architectures that often occur in NoC-based MPSoCs. For all three classes, the proposed approaches significantly outperform existing techniques in both scalability and optimization quality and, thus, considerably enlarge the field of application of a multi-objective DSE for the networked embedded system design.

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