Browsing by Subject "Computer Engineering"

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    Asset allocation in frequency and in 3 spatial dimensions for electronic warfare application
    (2016-04) Crespo, Jonah Greenfield; Christopher, Lauren Ann; Dos Santos, Euzeli Cipriano, Jr.; Rizkalla, Maher; Li, Lingxi; King, Brian
    This paper describes two research areas applied to Particle Swarm Optimization (PSO) in an electronic warfare asset scenario. First, a three spatial dimension solution utilizing topographical data is implemented and tested against a two dimensional solution. A three dimensional (3D) optimization increases solution space for optimization of asset location. Topography from NASA's Digital Elevation Model is also added to the solution to provide a realistic scenario. The optimization is tested for run time, average distances between receivers, average distance between receivers and paired transmitters, and transmission power. Due to load times of maps and increased iterations, the average run times were increased from 123ms to 178ms, which remains below the 1 second target for convergence speeds. The spread distance between receivers was able to increase from 86km to 89km. The distance between receiver and its paired transmitters as well as the total received power did not change signi cannily. In the second research contribution, a user input is created and placed into an unconstrained 2D active swarm. This \human in the swarm" scenario allows a user to change keep-away boundaries during optimization. The blended human and swarm solution successfully implemented human input into a running optimization with a time delay. The results of this research show that a electronic warfare solutions with real 3D topography can be simulated with minimal computational costs over two dimensional solutions and that electronic warfare solutions can successfully optimize using human input data.
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    TOWARDS MANY-CORE PROCESSOR SIMULATION ON CLOUD COMPUTING PLATFORMS
    (2011-08-23) Schmidt, James Michael; Lee, Jaehwan (John); King, Brian; Tuceryan, Mihran
    Growth of interest and need for many-core systems have steadily increased over the recent years. Industry trends lead many-core systems to become increasingly larger and more complex. Because of these realities it is important to researchers, academia, and industry that the design of these many-core systems be straightforward and comprehensive. There is a need for a many-core simulator that can be simple to use and learn from for students, dynamic and capable of emulating large systems for researchers, and flexible with fast turnover for industry designers. At the same time, as many-core systems have been becoming popular and complex, and hence their design, the long standing field of Cloud Computing has become more prevalent and feasible to use. Such cloud computing platforms as Windows Azure allow for the easy access and use of resources that in the past were simply not available to ordinary users. Large tasks can be performed in SaaS Cloud Computing models and be accessible from a small, lightweight device using nothing more than a web browser. As a solution to the needs for designing future many-core systems, we present a Many-Core Simulator on Azure Cloud Computing Platform called M3C Simulator. This is targeted at teaching, research, and industry and as such needs to be easy to use, flexible, and powerful. The Could Computing service model meets all these needs. This thesis discusses overall design of the M3C Simulator and how it leverages Cloud Computing resources, the simple-to-use and understand Interface layout, and the software design including program flow and dynamic compilation.