23 May 2013 Signal and image processing algorithm performance in a virtual and elastic computing environment
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Abstract
The U.S. Army Research Laboratory (ARL) supports the development of classification, detection, tracking, and localization algorithms using multiple sensing modalities including acoustic, seismic, E-field, magnetic field, PIR, and visual and IR imaging. Multimodal sensors collect large amounts of data in support of algorithm development. The resulting large amount of data, and their associated high-performance computing needs, increases and challenges existing computing infrastructures. Purchasing computer power as a commodity using a Cloud service offers low-cost, pay-as-you-go pricing models, scalability, and elasticity that may provide solutions to develop and optimize algorithms without having to procure additional hardware and resources. This paper provides a detailed look at using a commercial cloud service provider, such as Amazon Web Services (AWS), to develop and deploy simple signal and image processing algorithms in a cloud and run the algorithms on a large set of data archived in the ARL Multimodal Signatures Database (MMSDB). Analytical results will provide performance comparisons with existing infrastructure. A discussion on using cloud computing with government data will discuss best security practices that exist within cloud services, such as AWS.
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Kelly W. Bennett, Kelly W. Bennett, James Robertson, James Robertson, "Signal and image processing algorithm performance in a virtual and elastic computing environment", Proc. SPIE 8734, Active and Passive Signatures IV, 87340B (23 May 2013); doi: 10.1117/12.2016941; https://doi.org/10.1117/12.2016941
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