Jose Unpingco

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The resource description framework (RDF) is a language for representing information about resources on the web. However, RDF can also be used to describe other data and relationships between objects in the data. Many applications in the signal/image processing (SIP) community (such as radar imaging, electromagnetics, etc.) generate large amounts of data.(More)
  • Jose Unpingco
  • 2009 DoD High Performance Computing Modernization…
  • 2009
The chief impediment to widespread usage of parallel computing is the difficulty in programming high performance computers (HPCs). Furthermore, most users work from a Windows PC, so that learning UNIX as a prerequisite to parallel programming is a further obstruction. What is needed is a smooth workflow that simplifies both the programming task and the(More)
This chapter takes a geometric view of probability theory and relates it to familiar concepts in linear algebra andgeometry.This approach connects your natural geometric intuition to the key abstractions in probability that can help guide your reasoning. This is particularly important in probability because it is easy to be misled. We need a bit of rigor(More)
Recent years have seen the development of many new parallel extensions to high level languages. However, there does not yet seem to have been a concentrated effort to quantify their performance or qualify their usability. Toward this end, we have used several parallel extensions to implement four of the high performance computing (HPC) Challenge(More)
The Resource Description Framework (RDF) language can be used to describe data and the relationships between different objects in the data. As larger amounts of data are generated, many applications in the Signal and Image Processing areas such as radar image processing, electromagnetics, etc., present users with the challenge of representing and mining the(More)
algorithms that are computationally or memory intensive were implemented in MATLAB and parallelized using MatlabMPI. The parallel algorithms were tested on selected high performance clusters. Significant performance improvements were noted in the parallel versions of two of the algorithms and in one variant of the third.
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